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A combination risk-based approach to post-earthquake temporary accommodation site selection: A case study in Iran

August 2020
Iranian Journal of Fuzzy Systems 17(6):57-74

DOI:10.22111/ijfs.2020.5601

Authors:

Mohsen Oghabi

Islamic Azad University of Kermanshah

Hadi Sarvari

Birmingham City University

Mohammad Sedigh Sabeti

Islamic Azad University Sanandaj Branch

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One of the most important problems after natural disasters in every country is the preparation of temporary accom-modations for victims. The developers of preventive plans are also faced with numerous uncertainties in this crisis management topic. Furthermore, uncertainty is not defined in classical mathematical sets. Therefore, the use of in-tuitionistic fuzzy sets, which include considerations for uncertainty, can be useful in prospective planning in order to counteract possible risks. The main aim of this study is to propose a combined method using risk management and Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) for locating and prioritizing the post-earthquake temporary accommodation sites. To this end, the city of Sanandaj in Iran was selected as the case study of this method. First, brainstorming sessions with 9 crisis management experts from various organizations of Kurdistan province were used to determine 6 decision-making criteria. These criteria were based on identified risks in temporary accommodation process after an earthquake in the region, and criteria extracted from previous studies regarding temporary accommodation locations. The possible alternatives for temporary accommodation sites in this study were 13 different urban public spaces. The pairwise comparison of criteria based on the aim and pairwise comparison of alternative temporary accommodation options based on each criterion was carried out by experts and using intuitionistic fuzzy sets. Finally, the IF-AHP process was used to determine the priority of each alternative.

Location of Kurdistan Province and Sanandaj City in Iran.

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Schematic illustration of research methodology.

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The multicriteria decision-making structure (IF-AHP) of the study .

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Studies conducted related to the topic of research

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Relative risk ranking of the earthquake in the Kurdistan Provincial Demographic Centers according to Iran's 2800

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Figures - uploaded by Hadi Sarvari

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Content uploaded by Hadi Sarvari

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Iranian Journal of Fuzzy Systems

Volume 17, Number 6, (2020), pp. 57-74

A combination risk-based approach to post-earthquake temporary

accommodation site selection: A case study in Iran

M. Dabiri1, M. Oghabi2, H. Sarvari3, M. S. Sabeti4and H. R. Kasheﬁ5

1Department of Civil Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran

2Department of Civil Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

3Department of Civil Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran

4Department of Civil Engineering, Sanandaj Branch, Islamic Azad University, Kurdistan, Iran

5Department of Mathematics Education, Farhangian University, Tehran, Iran

jalilialvand123@gmail.com, m.oghabi@iauksh.ac.ir, h.sarvari@khuisf.ac.ir, mssabeti@iausdj.ac.ir, hkasheﬁ@cfu.ac.ir

Abstract

One of the most important problems after natural disasters in every country is the preparation of temporary accom-

modations for victims. The developers of preventive plans are also faced with numerous uncertainties in this crisis

management topic. Furthermore, uncertainty is not deﬁned in classical mathematical sets. Therefore, the use of in-

tuitionistic fuzzy sets, which include considerations for uncertainty, can be useful in prospective planning in order to

counteract possible risks. The main aim of this study is to propose a combined method using risk management and

Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) for locating and prioritizing the post-earthquake temporary

accommodation sites. To this end, the city of Sanandaj in Iran was selected as the case study of this method. First,

brainstorming sessions with 9 crisis management experts from various organizations of Kurdistan province were used

to determine 6 decision-making criteria. These criteria were based on identiﬁed risks in temporary accommodation

process after an earthquake in the region, and criteria extracted from previous studies regarding temporary accommo-

dation locations. The possible alternatives for temporary accommodation sites in this study were 13 diﬀerent urban

public spaces. The pairwise comparison of criteria based on the aim and pairwise comparison of alternative temporary

accommodation options based on each criterion was carried out by experts and using intuitionistic fuzzy sets. Finally,

the IF-AHP process was used to determine the priority of each alternative.

Keywords: Temporary accommodation, risk management, intuitionistic fuzzy sets, AHP, Iran.

1 Introduction

In recent decades, the eﬀects of disasters have raised rapidly around the world, and have aﬀected all sectors, in both

rich and poor countries. Millions of people are aﬀected annually by disasters, and losses were recorded at 371 billion

in 2012. This average has increased in recent years according to the United Nations International Strategy for Disaster

Reduction (UNISDR). These Natural disasters consist of ﬂoods, earthquakes, etc. [3]. The number of natural disasters

has increased sharply and has caused a great deal of damage to buildings. Many homes have been damaged and are

unusable, which threatens a large number of homeless people. Housing reconstruction programs play a determinative

role in disaster recovery, and providing temporary accommodation is an important step in these programs. This allows

the victims to have a private and safe place to return to their normal lives during the reconstruction of permanent

accommodations [13]. Temporary accommodation has always remained a major issue for the injured families after any

natural or man-made disaster [16]. Managing disaster risks can be improved by investing in methods for dealing with

changeable fundamental determinants [27]. Pervious experiences have shown that post-earthquake temporary accom-

modation process is one of the topics which always preoccupies managers in the ﬁeld of crisis management. In this

Corresponding Author: H. Sarvari

Received: September 2019; Revised: May 2020; Accepted: June 2020.

58 M. Dabiri, M. Oghabi, H. Sarvari, M. S. Sabeti, H. R. Kasheﬁ

regard, decision-making based on proven scientiﬁc theories and equations can improve the certainty and credibility of

the decisions. Therefore, the main goal of this study is to provide a combined method using risk management and

Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) for predicting suitable post-earthquake temporary accommo-

dation sites. The model proposed in this study attempts to combine current theories in the ﬁeld of crisis management

and risk management, decision-making management and intuitionistic fuzzy sets in order to ﬁll the gap in the literature

and previous studies. The use of these theories in the proposed combined model is due to several reasons; including:

(i) Providing temporary accomendations after earthquakes is one of the important processes in crisis management and

is always accompanied with a great deal of uncertainty. Therefore, in order to remove or reduce the risks of tempo-

rary accommodation process, it is necessary to use a preventive risk management plan. Identifying and evaluation of

risks and their eﬀects in various temporary accomendation programs is one of the stages of crisis management after

earthquake disasters since most of these risks can be reduced to a controlled level through use of suitable strategies

[1], (ii) Due to the existence of numerous criteria for selection of proper location for temporary accomendation sites,

use of multivariable decision-making methods is necessary. During the process of multi-variable decision-making, pair-

wise comparison of options based on decision-making criteria and determination of relative priorities of these variables

requires the use of experts opinions. In this regard, use of methods such as Delphi approach or brainstorming can be

useful, (iii) The use intuitionistic fuzzy sets seem necessary in multi-criteria decision making in which, in addition to

the degree of membership and non-membership, the degree of uncertainty is another factor also considered. This is

due to the fact that a factor called hesitation or uncertainty is not deﬁned in classical sets and even fuzzy sets. The

intuitionistic fuzzy set has shown certain advantages over fuzzy sets in managing of ambiguity and uncertainty [40].

Interval Valued Intuitionistic Fuzzy Set theory (IVIFS) is an eﬀective and simple tool for creation of MultiAttribute

Group DecisionMaking (MAGDM) models. Furthermore, this method is useful for dealing with uncertainty in the

complex decision-making support systems [22]. In the current study, a combined model was applied for identiﬁcation

and prioritization of post-earthquake temporary accomendation sites in the Sanandaj-Iran. The participants in the

Delphi panel included crisis management experts employed in 18 organizations of Kurdistan province. Some of the

identiﬁed risks in this stage included: Lack of desire among people for living in determined temporary accommodation

sites outside of the city limits; challenges related to hot and cold weather; challenge of waste disposal, snow and rain

challenge; trash disposal challenge; blocking of roads due to snow and rain and subsequent disruption in services dur-

ing winter; the outbreak of infectious diseases; challenges of wind and storm; unsittable positioning of the temporary

accommodation site and lack of attention to required criteria (including access to services, distance from fault lines and

ﬂood paths, access to gas lines, etc.); and challenges related to provision of drinking water and health products. In the

current study, the risks were identiﬁed through comprehensive literature reviews and conducting a brainstorming session

with crisis management experts of Kurdistan province. As a result, Six criteria were selected as decision criteria for

post-earthquake temporary accommodation site selection. These criteria were then used to prioritize various locations

in the Sanandaj city using IF-AHP approach.

2 Literature review and research background

2.1 Literature review on temporary accommodation after the disaster

Temporary accommodation plays a key role in the disaster process. The creation and use of temporary sites at the

location of a disaster are used to accommodate victims during an emergency situation, and reconstruction and renovation

after disaster occurrence, until permanent accommodation is provided [18]. Temporary accommodation is a vital but

controversial part of disaster recovery. Disaster-stricken families who have lost their homes need a private and safe

place to do their daily activities as soon as possible after the disaster [23]. Typically, three types of accommodations

are required after a destructive earthquake in a crowded region: (i) Emergency accommodations that often include

tent, (ii) Temporary accommodations which are usually used for 1 to 2 years, (iii) Permanent accommodations or

permanent living spaces[15]. It is generally accepted that provision of emergency and temporary accommodations

after an earthquake uses a vast amount of resources in a short time[37]. The temporary accommodation problem,

medium-term accommodations and ﬁnally permanent accommodations of victims after earthquakes is one of the largest

challenges during disaster recovery and reconstruction [25]. According to disaster relief act of Japan, the maximum

allowed duration for use of temporary accommodations is 2 years. However, some victims use these accommodations

for more than 7 years, especially after the large earthquake of 2012 on Eastern Japan [37]. Design of temporary

accommodation sites should consider various factors to ensure than these accommodations can meet the needs of the

victims. Architecture and design must be part of the solution and not part of the problem and the process must start

years before the disaster, by using suﬃcient time and a multidisciplinary team including technical and humanities experts

(various engineers), production companies, etc. and not during the disaster itself [9]. In the study of post-disaster

A combination risk-based approach to post-earthquake temporary accommodation site selection . . . 59

temporary accommodation, Principles of Presentation, Design and Construction are used for design, construction,

and preparation of temporary accommodations with suitable characteristics including Quick availability, Using of local

resources and considering local standards in terms of location and service facilities, design of Temporary accommodations

for long-term housing needs, the ability to easily remove the site and maintain the environment after the duration of

temporary accommodations, preventive planning before the event, and aadopting a developed strategy for recovery and

reconstruction. Research ﬁndings on the decline in physical performance of residents of temporary accommodation after

the Great East Japan earthquake have shown that a poor living environment in temporary accommodations may have a

negative impact on the physical performance of individuals, especially the elderly [20]. In the article ”Towards Eﬀective

Crisis Management in Egypt,” ﬁve suggestions were proposed as key steps for setting better disaster management routes

in Egypt [17] including Risk management; Damage management; Event control; Resource management; and Reducing

the impact of the crisis. A survey research on the selection of post-disaster temporary accommodation was carried out by

Anand et al. in 2015. This study examined various models for selecting post-disaster temporary rehabilitations. Each

of these methods provides selection criteria and minimum standards for handling the needs of the victims. The result

of this study shows that availability of basic services such as health centers, transportation, and livelihoods are major

concerns when site is chosen for temporary accommodations [4]. In a research entitled ”Temporary accommodation

site in earthquake crisis using AHP and Geographic Information System”, the criteria for temporary accommodation

are divided into three categories of the physical, social and environmental criteria, and have been used in the process

of ﬁnding temporary accommodation sites, using AHP and GIS, according to the weight and importance of the criteria

mentioned [12]. Another article titled ”The role of temporary accommodation buildings for post-disaster housing

reconstruction” investigated the cultural and environmental challenges of the temporary accommodation settlements,

and mentioned that the key to reducing the vulnerability of the victims is the prediction of temporary accommodation

before the occurrence of the crisis. To this end, using existing places with permanent infrastructure near the initial

location of victims has been suggested [14]. Site selection criteria for temporary accommodations after the earthquake

was carried out using Delphi Panel by Soltani et al. in 2015 to provide a list of appropriate criteria for deciding on the

choice of a temporary accommodation site. To determine the criteria, three methods were used including examining

previous studies, conducting interviews with the experts and performing a Delphi Panel. Finally, the main criteria to

select a temporary accommodation site were categorized in four sections consisting of land suitability, social and cultural

considerations, availability to services and disaster risk reduction [38]. The previous studies on crisis management and

temporary accommodation, and how they are carried out, are presented in Table 2.

2.2 Research background

2.2.1 Crisis management

Crisis management is a set of pre-designed processes which are implemented and applied to prevent or reduce the

eﬀects, during pre-occurrence, occurrence and post-occurrence of disasters [26]. Crisis management consists of three

stages of Pre-crisis step (prevention and preparation), crisis step (reaction) and post-crisis step (learning and revision)

[10]. Crisis Management is a multidisciplinary subject that includes many sciences including social sciences, foreign

sciences, medicine, engineering, and many other disciplines which are used for investigation of major unpredictable

events [7].

2.2.2 Risk management

Risk management in a project consists of the risk management planning processes, identiﬁcation, analysis, response

planning, and risk control in a project. Increasing the likelihood and impact of positive events and reducing the

likelihood and impact of negative events is one of the objectives of the risk management in a project [35].

The process of risk management in projects is a logical chain of methods, planned and implemented by decision

makers, which control the results to maintain project implementation under certain conditions (Time, cost, and quality

parameters) [32].

Risk management is about identiﬁcation of risks that are imminent. Identifying and designing measures for reducing

risk consists of identifying possible risks, determining the occurrence probability of each risk, and estimating the extent

of risk eﬀects in communities. One of the risk management processes is Implementation of these measures and reducing

the threats [17].

60 M. Dabiri, M. Oghabi, H. Sarvari, M. S. Sabeti, H. R. Kasheﬁ

Table 1: Studies conducted related to the topic of research

No Author/s Issue Method

1 [38] provide a list of appropriate criteria for

deciding on the choice of a temporary

accommodation site

(i) Extracting 27 criteria to select accomendation site

provided by reviewing issued articles, (ii) Identiﬁca-

tion of 12 other criteria during the interview process

with experts, (iii) Approval of 21 criteria during the

Delphi process, (iv) Categorizing criteria for selecting

temporary accommodation in four sections: Land suit-

ability, Social and cultural considerations, availability

to services and reduce disaster risk

2 [17] Post-disaster temporary accommoda-

tion site: Principles of presentation,

design and constructing, the review

of a set of guidelines for designing

and construction a successful and high

quality, and at the same time, sensitiv-

ity to controversial issues in addition to

saving the costs and time

(i) Analyzing and reviewing the briefs, (ii) Matching

the executed cases with briefs, (iii) Providing the nec-

essary suggestions for designing and constructing of a

temporary accommodation site

3 [24] Temporary accommodation site locat-

ing after earthquake: one case for

Turkey

(i) Reviewing some cases of past actions in tempo-

rary accommodation, (ii) determinating of the locat-

ing metrics, (iii) Compilation of a linear program for

optimal location, (iv) Testing the compiled program

4 [31] Urban vulnerability assessment using

AHP in 19 Qazvin areas

(i) Determination of three main criteria and 20 sub-

criteria, (ii) Using questionnaire, (iii) Using AHP

method for prioritization of urban vulnerability fac-

tors

5 [5] Proposing a model for the post-

disaster design in temporary accom-

modation based on the needs of the

victims with the post-implementation

evaluation approach (Case study: Seis-

mic villages in Harris, East Azarbai-

jan)

(i) Diﬀerent methods of documentary and library, (ii)

Field studies through interviews and questionnaires

6 [12] Presenting a Method Using AHP and

Geographic Information System (Case

Study: Damavand Area)

(i) Using Geographic Information System (GIS) , (ii)

Using ARCMAP software, (iii) Propagating maps with

three factors: faults, earthquakes and soil resistance,

(iv) Using the AHP model, (v) Data was collected by

library methods and ﬁeld studies.

7 [19] Providing a multi-criteria decision-

making method for locating post-

disaster temporary accommodation in

urban areas

(i) Documentary studies, (ii) Logical studies, (iii)

Knowledge of Experts Participating in the Seminar,

(iv) SWOT approach, (v) AHP model, (vi) Design and

application (medal app)

8 [18] Exploring ways to better manage nat-

ural disasters in Egypt

(i) Identifying the various types of natural disasters

that Egypt is vulnerable to them, (ii) A general pic-

ture of the crisis management problems in Egypt, (iii)

Providing a set of briefs, a solution to the crisis man-

agement problem in Egypt, (iv) investigating a set of

considerations to ensure the correct implementation of

the briefs.

9 [20] Investigating the Length of Physical

Operation of Residents in Temporary

accommodation sites, after earthquake

in Great East Japan

(i) Implementation of physical performance tests on

residents in site and temporary accommodation con-

trol group by physiotherapists, (ii) Comparing the re-

sults of the tests performed on these two groups

10 [29] Searching the best practices for post-

disaster sustainable temporary accom-

modation that have features such as:

being cheap, fast built, maintaining en-

vironmental and social issues, paying

attention to the weather conditions of

the place and paying attention to all

needs of the victims.

(i) Documentary study of natural disasters occurring

in the world and the extraction of the statistics of

the refugees and casualties caused by the incident, (ii)

Extracting the various social and economic damages

of each of these events, (iii) Investigating the type of

structure used in previous natural disasters, and the

advantages and disadvantages each one, (iv) Investi-

gating the challenges that have occurred accommoda-

tion process in the past

A combination risk-based approach to post-earthquake temporary accommodation site selection . . . 61

Table 2: Relative risk ranking of the earthquake in the Kurdistan Provincial Demographic Centers according to Iran’s 2800

Code

No. Demographic Center Relative earthquake risk

Low Medium High Very High

1 Baneh *

2 Bijar *

3 Dehglan *

4 Divandareh *

5 Saghez *

6 Sav aabad *

7 Sanandaj *

8 Gharaveh *

9 Kamyaran *

10 Marivan *

2.2.3 Multi-criteria decision making

Description of criteria and gathering of descriptive information are two important uses of MCGDM for prioritizing

various considered options [30]. Multi-Criteria Decision Making (MCDM) is a process which allows decision-making

using numerous and sometimes contradicting criteria. MCDM topics can be divided into two main categories: (i)

Multi-Attribute Decision Making (MADM): This method is focused on selecting the best option among the predeter-

mined options, (ii) Multi-Objective Decision Making (MODM): This method includes design of alternative options for

optimization of multiple decision-making objectives.

2.2.4 Area of the study

The city studied in this study was Sanandaj, the capital of Kurdistan province in Iran (Figure 1). Kurdistan province

is located in the northwestern part of Iran. Iran is located in the alpine-Himalayan seismic belt and is one of the most

active tectonic areas in the world. Historically, this country often suﬀers from large and destructive earthquakes, and

has experienced several major earthquakes in the last few decades. More than 70 percent of Iran’s major cities are

located near seismic faults. In some cases, active faults cross the cities [21]. Regulations for the Design of Buildings

against earthquakes [8] divide Iran into four parts in terms of seismic risk which include relatively low, moderate, high,

and very high-risk areas (Table 1). According to this standard, Sanandaj is located in high risk area of earthquakes.

Figure 1: Location of Kurdistan Province and Sanandaj City in Iran.

3 Research methodology

This research is a descriptive-applied study, which used documentary methods, interview and holding a storm meeting

for data collection. In the documentary study, previous studies on the process of post-earthquake temporary accommo-

dation in Iran and the world have been investigated. Furthermore, in this section, geographic conditions, topographic

62 M. Dabiri, M. Oghabi, H. Sarvari, M. S. Sabeti, H. R. Kasheﬁ

conditions, population amount, places and the existing infrastructure of Sanandaj were studied. In this study, the

combination of risk management and intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) has been used to locate

the post-earthquake temporary accommodation site. This paper is part of a research carried out by the authors which

identiﬁed the risks involved in the temporary accommodation process in its previous sections, and has prioritized 94

identiﬁed risks in its results including Lack of desire among people for living in determined temporary accommodation

sites outside of the city limits; challenges related to hot and cold weather; challenge of waste disposal, snow and rain

challenge; trash disposal challenge; blocking of roads due to snow and rain and subsequent disruption in services during

winter; the outbreak of infectious diseases; challenges of wind and storm; unsittable positioning of the temporary accom-

modation site and lack of attention to required criteria (including access to services, distance from fault lines and ﬂood

paths, access to gas lines, etc.); and challenges related to provision of drinking water and health products. These risks

were combined in a brainstorming session with crisis management experts of Kurdistan province until 6 criteria were

selected including the existence of infrastructure, proper availability, distance from fault lines and ﬂood paths and other

natural hazards, appropriate land gradient, proximity to service centers, and capacity for receiving victims, in order to

locate and prioritize the existing locations in Sanandaj for temporary accommodation sites. In this research, the Fuzzy

Delphi method was also used to identify the risks of temporary accommodation, and the questionnaire was completed

by specialists and experts in the ﬁeld of crisis management in Kurdistan, which consists of 18 departments and organi-

zations. The challenge of people’s disclination to reside in established sites of temporary out-of-town accommodation

has been introduced as a risk in this process. Furthermore, due to the mountainous conditions of the surrounding

studied area, this study attempts to use the existing urban spatial areas that were previously considered by the crisis

management authorities to accommodate emergency situations. If these predetermined sites met the necessary condi-

tions, they were used for temporary accommodation after a short period of emergency accommodation. To summarize

the comments, in order to select the appropriate criteria for determination of the available alternatives determining the

weight against each option, and also the weight of the Alternatives against the criteria, the brainstorming session was

conducted with 9 specialists and experts in the ﬁeld of crisis management in Kurdistan province. Figure 2 shows the

schematic illustration of research methodology for this research.

3.1 Analytic Hierarchy Process (AHP)

The Analytic Hierarchy Process (AHP) was ﬁrst introduced by Saaty [33]. AHP is a measurement theory through

dual comparisons and depends on the judgment of the experts to obtain prioritization scales [34]. Some key steps in

this approach are [39]: (i) Statement of the problem, (ii) Expanding the goals of the problem or considering all the

actors, goals and their consequences, (iii) Identifying eﬀective criteria for decision making, (iv) Creating the hierarchy

structure of the problem consisting of diﬀerent levels of purpose, criteria, sub-criteria, and alternatives, (v) Comparing

each element at the corresponding level and calibrating them on a numerical scale. This requires n (n-1)/2 comparisons,

where n is the number of elements with case of comparison. The diagonal elements equal 1, or other elements are simply

intertwined with previous values, (iv) Calculating the maximum speciﬁc values consisting of compatibility index, CI,

compatibility ratio, CR and normalized values for each criterion/option, and (vii) If the maximum Eigen value, CI and

CR are satisfactory, the decision is made on the basis of normal values. Otherwise, this method will be repeated until

the values are within the desired range.

3.2 Introducing intuitionistic fuzzy sets

In the classical logic of mathematics, the value or correctness of a proposition is deﬁned with 1 as true and zero as false.

Zadeh introduced fuzzy sets for the ﬁrst time [41]. In fuzzy logic, the accuracy value is a real number which is selected

from the range of [0, 1]. Atanassov added, another real number in the range [0, 1] entitled ”Degree of non-accuracy” to

this deﬁnition in the presentation of intuitionistic fuzzy sets [6]. Therefore, two values of ν(p) and µ(p) are attributed

to the proposition p, such that: ν(p) + µ(p)≤1. An intuitionistic Fuzzy set of A in the reference set X is deﬁned as

follows:

A={hx, µA(x), νA(x)i|x∈X}

µA:X−→ [0,1]

νA:X−→ [0,1]

∀x∈X: 0 ≤µA(x) + νA(x)≤1.

The real values of µA(x) and νA(x) are the degree of membership and the degree of non-membership of x to A, which

belongs to the interval [0, 1]. Each set A’, is a special case of intuitionistic fuzzy sets and can be represented as an

A combination risk-based approach to post-earthquake temporary accommodation site selection . . . 63

Figure 2: Schematic illustration of research methodology.

intuitionistic fuzzy set A’ [28]. For each intuitionistic fuzzy set, A’ from X, we have:

A0=0{hx, µA0(x),1−µA0(x)i|x∈X},

πA0(x):1−µA0(x)−νA0(x).

πA0(x), is called the intuitionistic index xin A0. In fact, this is the degree of xhesitancy in A0. Clearly, for each x

belonging to X, we have: 0 ≤πA0(x)≤1. In each fuzzy set of A0from Xwe have:

∀x∈X:πA0(x) = 1 −µA0(x)−[1 −µA0(x)] = 0.

The sum of the multiplication of two intuitionistic fuzzy sets is in accordance with formulas 1 and 2 [40], [11].

rtl = (µtl, vtl ), rik = (µik, vik ),

n= 1,2,3,· · · , k = 1,2,3,· · · .

degree of membership = µik,

degree of non-membership = νik,

degree of uncertainly = πik.

rik ⊕rtl = (µik +µtl −µikµtl , vikvtl ),(1)

rik ⊗rtl = (µikµtl , vik +vtl −vikvtl ).(2)

4 Intuitionistic fuzzy analytic Hierarchy

The purpose of this example is to prioritize available places in Sanandaj to determine the best post-earthquake temporary

accommodation site.

64 M. Dabiri, M. Oghabi, H. Sarvari, M. S. Sabeti, H. R. Kasheﬁ

Table 3: Examined alternatives table for locating and prioritizing the temporary accommodation site

No. Site Name Area (hectare) Useful Area (hectare) Moderate slope (percent) Available facilities Capacity (preson)

Water Electricity W.C

1 A1 81 63.2 15 Yes Yes Yes 21067

2 A2 10 10 4 Yes Yes Yes 3333

3 A3 17.6 14.3 12 No No No 4767

4 A4 0.95 0.96 1 Yes Yes Yes 320

5 A5 7.6 6.4 9 Yes Yes Yes 2133

6 A6 8.4 7.2 11 Yes Yes Yes 2400

7 A7 70 57.4 13 No No No 19133

8 A8 18.5 16.3 10 No No No 5433

9 A9 6.5 6 4 Yes Yes No 2000

10 A10 26 25.5 4 No No No 8500

11 A11 3.3 3 4 Yes Yes Yes 1000

12 A12 4.25 3.4 14 Yes Yes Yes 1133

13 A13 3.7 2.7 18 Yes Yes Yes 900

4.1 Recognizing the eﬀective criteria for multi-criteria decision making

In this study, six criteria are considered, including (i) Distance from fault lines, ﬂood paths and other natural hazards,

(ii) Appropriate ground gradient, (iii) Existence of necessary infrastructure, (iv) Availability, (v) Proximity to service

centers, and (vi) Admission Capacity. These 6 criteria were used to rank places in Sanandaj to determine the best site

of temporary accommodation. Speciﬁcations of the places examined in this research are as shown in Table 3.

4.2 Formation of decision-making structure

The overall decision-making structure in this example consists of three levels of goals, criteria and alternatives (Fogure

3). Often the set of criteria is presented with C={C1, C2, C3,· · · , Cn}and the set of Alternatives or available methods

for prioritize is presented with A={A1, A2, A3,· · · , An}.

Figure 3: The multicriteria decision-making structure (IF-AHP) of the study .

4.3 Determining the intuitionistic fuzzy linguistic variables to pairwise comparison

Since diﬀerence in linguistic variables used by diﬀerent individuals might lead to diﬀerent interpretations, in this study,

in order to create a unify procedure in evaluating experts opinions regarding the importance of each criterion compared

to others, intuitionistic fuzzy linguistic variables (µA, νA) are used. Here, µAis the degree of membership and νAis the

degree of non-membership. Table 4 shows the intuitionistic fuzzy linguistic variables used for pairwise comparisons.

A combination risk-based approach to post-earthquake temporary accommodation site selection . . . 65

Table 4: Intuitionistic fuzzy linguistic variables for determining the priority in pairwise comparison

Scale Variables

(0.00 , 1.00) Absolutely low importance

(0.05 , 0.90) Very very low importance

(0.10 , 0.80) Very low importance

(0.20 , 0.70) Low importance

(0.30 , 0.60) Slightly low importance

(0.50 , 0.50) Equally importance

(0.60 , 0.30) Slightly high importance

(0.70 0.20) High importance

(0.80 0.10) Very high importance

(0.90 0.05) Very very high importance

(1.00 0.00) Absolutely high importance

Table 5: The pairwise comparison matrix of criteria

Staying far

from faults,

streams and

other natural

hazards

Appropriate

ground gradien

Existence

of necessary

infrastructure

Availability Proximity to

service centers

Admission Ca-

pacity

Staying far from faults,

streams and other nat-

ural hazards

(0.5 , 0.5) (0.5 , 0.5) (0.2 , 0.7) (0.2 , 0.7) (0.2 , 0.7) (0.5 , 0.5)

Appropriate ground

gradient

(0.5 , 0.5) (0.5 , 0.5) (0.2 , 0.7) (0.2 , 0.7) (0.2 , 0.7) (0.2 , 0.7)

Existence of necessary

infrastructure

(0.2 , 0.7) (0.7 , 0.2) (0.5 , 0.5) (0.5 , 0.5) (0.5 , 0.5) (0.5 , 0.5)

Availability (0.7 , 0.2) (0.7 , 0.2) (0.5 , 0.5) (0.5 , 0.5) (0.7 , 0.2) (0.7 , 0.2)

Proximity to service

centers

(0.7 , 0.2) (0.7 , 0.2) (0.5 , 0.5) (0.2 , 0.7) (0.5 , 0.5) (0.7 , 0.2)

Admission Capacity (0.5 , 0.5) (0.7 , 0.2) (0.5 , 0.5) (0.2 , 0.7) (0.2 , 0.7) (0.5 , 0.5)

4.4 Determination of intuitionistic fuzzy linguistic variables for pairwise comparison of

options

Intuitionistic fuzzy linguistic variables used to determine the pairwise comparison of options for each criterion in this

study are presented in Table 4.

4.5 Formation of matrix the importance of criteria paired with each other

Pairwise comparison of criteria based on intuitionistic fuzzy linguistic variables ( µA, νA) carried out in a brainstorming

session with 9 disaster management experts and according to regional conditions and the results are presented in Table

rik = (µik, vik )

n= 1,2,3,· · · , k = 1,2,3,· · ·

Degree of membership =µik

Degree of non-membership = vik

4.6 Formation of preferential - paired relationship alternatives with the criteria

The pairwise priority relation matrices of options based on criteria, determined based on the values presented in Table

4 by experts are presented in Appendixes 1 - 6.

66 M. Dabiri, M. Oghabi, H. Sarvari, M. S. Sabeti, H. R. Kasheﬁ

Table 6: Matrix R (Relationship of the importance of the criteria paired with each other)

Staying far

from faults,

streams and

other natural

hazards

Appropriate

ground gradi-

ent

Existence

of necessary

infrastructure

Availability Proximity to

service centers

Admission Ca-

pacity

Staying far from faults,

streams and other nat-

ural hazards

(0.5 , 0.5) (0.5 , 0.5) (0.2 , 0.7) (0.2 , 0.7) (0.2 , 0.7) (0.5 , 0.5)

Appropriate ground

gradient

(0.5 , 0.5) (0.5 , 0.5) (0.2 , 0.7) (0.2 , 0.7) (0.2 , 0.7) (0.2 , 0.7)

Existence of necessary

infrastructure

(0.2 , 0.7) (0.7 , 0.2) (0.5 , 0.5) (0.5 , 0.5) (0.5 , 0.5) (0.5 , 0.5)

Availability (0.7 , 0.2) (0.7 , 0.2) (0.5 , 0.5) (0.5 , 0.5) (0.7 , 0.2) (0.7 , 0.2)

Proximity to service

centers

(0.7 , 0.2) (0.7 , 0.2) (0.5 , 0.5) (0.2 , 0.7) (0.5 , 0.5) (0.7 , 0.2)

Admission Capacity (0.5 , 0.5) (0.7 , 0.2) (0.5 , 0.5) (0.2 , 0.7) (0.2 , 0.7) (0.5 , 0.5)

4.7 Investigating the compatibility of matrix, the importance of criterion paired- intu-

itionistic with each other

4.7.1 Formation of matrix R (Relationship of the importance of the criteria paired with each other)

[40]

. The pairwise comparison of the criteria was calculated based on the results of the brainstorm panel and intuitionistic

fuzzy verbal variables (Table 4). The results have been shown in Table 6.

4.7.2 Forming matrix R

In order to determine the memberships and non-membership degrees in this matrix, the following equations can be

used [40]

µik =

k−i−1

qΠk−1

t=i+1µit µtk

k−i−1

qΠk−1

t=i+1µit µtk +k−i−1

qΠk−1

t=i+1(1 −µit )(1 −µtk)

k > i + 1,(3)

vik =

k−i−1

qΠk−1

t=i+1vit vtk

k−i−1

qΠk−1

t=i+1vit vtk +k−i−1

qΠk−1

t=i+1(1 −vit )(1 −vtk)

k > i + 1.(4)

In order to carry out the pairwise comparison of the criteria, intuitionistic fuzzy linguistic variables were calculated

based on formulas (3) and (4). The results of Matrix Rare presented in Table 7.

4.7.3 Calculating the distance between matrix Rand matrix R [40]

d(R−R) = 1

2(n−1)(n−2)

i=1

k=1

|µik −µtl|+|vik −vtl |+|πik −πtl|,(5)

(R−R) = 1

2(6 −1)(6 −2)(0.6463 + 0.6463 + 1.2926) = 0.0646

After calculating this gap, we will have: d(R−R)=0.0646

Since the distance between R

.and Ris less than 0.10, it is possible to move to the next step without revising the

pairwise comparison matrix created in this step [36], [40].

A combination risk-based approach to post-earthquake temporary accommodation site selection . . . 67

Table 7: Matrix R

Matrix RStaying far

from faults,

streams and

other natural

hazards

Appropriate

ground gradi-

ent

Existence

of necessary

infrastructure

Availability Proximity to

service centers

Admission Ca-

pacity

Staying far from faults,

streams and other nat-

ural hazards

(0.5 , 0.5) (0.5 , 0.5) (0.2 , 0.7) (0.2 , 0.7) (0.249 , 0.595) (0.276 , 0.538)

Appropriate ground

gradient

(0.5 , 0.5) (0.5 , 0.5) (0.2 , 0.7) (0.2 , 0.7) (0.276 , 0.529) (0.305 , 0.481)

Existence of necessary

infrastructure

(0.7 , 0.2) (0.7 , 0.2) (0.5 , 0.5) (0.5 , 0.5) (0.7 , 0.2) (0.7 , 0.2)

Availability (0.7 , 0.2) (0.7 , 0.2) (0.5 , 0.5) (0.5 0.5) (0.7 , 0.2) (0.849 , 0.059)

Proximity to service

centers

(0.595 , 0.249) (0.529 , 0.276) (0.2 , 0.7) (0.2 , 0.7) (0.5 0.5) (0.7 , 0.2)

Admission Capacity (0.538 , 0.276) (0.481 , 0.305) (0.2 , 0.7) (0.059 , 0.845) (0.2 , 0.7) (0.5 0.5)

4.8 Calculating weight of criteria and Alternatives in an intuitionistic fuzzy environ-

ment

The following formula can be used to calculate the weight of criteria and Alternatives [40] Therefore, the weight of

criteria were calculated based on formula (6) and the results are presented in Table 8.

Table 8: The weight of criteria with each other

1∗2∗3∗4∗5∗6∗wj

C1C2C3C4C5C6

µAνAµAνAµAνAµAνAµAνAµAνAµAνA

Staying far

from faults,

streams and

other natural

hazards

C10.5 0.5 0.5 0.5 0.2 0.7 0.2 0.7 0.249 0.595 0.276 0.538 0.098 0.849

Appropriate

ground gradi-

ent

C20.5 0.5 0.5 0.5 0.2 0.7 0.2 0.7 0.277 0.529 0.305 0.481 0.101 0.842

Existence

of necessary

infrastructure

C30.7 0.2 0.7 0.2 0.5 0.5 0.5 0.5 0.7 0.2 0.7 0.2 0.193 0.743

Availability C40.7 0.2 0.7 0.2 0.5 0.5 0.5 0.5 0.7 0.2 0.845 0.059 0.201 0.734

Proximity

to service

centers

C50.595 0.249 0.529 0.276 0.2 0.7 0.2 0.7 0.5 0.5 0.7 0.2 0.139 0.794

Admission

Capacity

C60.538 0.276 0.481 0.305 0.2 0.7 0.059 0.845 0.2 0.7 0.5 0.5 0.101 0.836

1∗: Staying far from faults, streams and other natural hazards.

2∗: Appropriate ground gradient.

3∗: Existence of necessary infrastructure.

4∗: Availability.

5∗: Proximity to service centers.

6∗: Admission Capacity.

ωi= ( Pn

k=1 µik

i=1 Pn

k=1(1 −vik ),1−Pn

k=1(1 −vik )

i=1 Pn

k=1 µik

).(6)

For example, in order to calculate the weight for C1 (Distance from fault lines, ﬂood paths and other natural hazards)

we have:

68 M. Dabiri, M. Oghabi, H. Sarvari, M. S. Sabeti, H. R. Kasheﬁ

ω1= ( P6

k=1 µik

i=1 P6

k=1(1 −vik ),1−P6

k=1(1 −vik )

i=1 P6

k=1 µik

)

ω1= ( 1.9254

4.0746 + 4.0182 + 2.2+2.0552 + 3.2763 + 4.022 ,

1−2.4665

3.5335 + 3.4094 + 1.8+1.6588 + 2.6254 + 3.3266 )

ω1= ( 1.9254

19.6463,1−2.4665

16.3537)

ω1= (0.0980,0.8492).

In order to calculate the weight of alternatives according to each criterion, fuzzy numbers presented in Apendixes 1-6

as well as equation (6) are used. For example, in order to calculate the alternatives weights in regards to C1 (Distance

from fault lines, ﬂood paths and other natural hazards), the intuitionistic fuzzy numbers of table 4 and equation (6)

are used. These results are presented in Table 9.

Table 9: The weight of alternatives in regard to criterion (distance from fault lines, ﬂood paths and other natural hazards)

Criterion C1Pn

k=1 µik Pn

k=1(1 −vik )Pn

i=1 Pn

k=1(1 −vik )Pn

i=1 Pn

k=1 µik ωi

k=1 µik

i=1 Pn

k=1(1 −vik )1−

k=1(1 −vik )

i=1 Pn

k=1 µik

A1 5.3 8.1 88.8 80.2 0.0822 0.8990

A2 5.3 8.1 88.8 80.2 0.0822 0.8990

A3 7.3 8.1 88.8 80.2 0.0822 0.8990

A4 5.5 8.1 88.8 80.2 0.0822 0.8990

A5 5.5 6.6 88.8 80.2 0.0653 0.9177

A6 5.5 5.9 88.8 80.2 0.0597 0.9264

A7 5.5 5.9 88.8 80.2 0.0597 0.9264

A8 5.3 5.9 88.8 80.2 0.0597 0.9264

A9 5.3 8.1 88.8 80.2 0.0822 0.8990

A10 7.3 6 88.8 80.2 0.0619 0.9252

A11 5.5 6 88.8 80.2 0.0619 0.9252

A12 5.5 6 88.8 80.2 0.0619 0.9252

A13 5.5 6 88.8 80.2 0.0619 0.9252

Table 10 shows the results of pairwise comparison calculations for all alternatives for each criterion.

4.9 Final weights of research criteria and alternatives

Equation (7) is used to calculate the ﬁnal weight of each alternative based on intuitionistic fuzzy set [40]:

wi=⊕6

j=1(wj⊗wij ) (7)

To calculate equation (7) requires the use of equations (1) and (2) [40], [11]. An example of calculations for alternative

1 is shown below:

w1=⊕6

j=1(wj⊗w1j)

= (0.0980,0.8492) ⊗(0.822,0.899) ⊕(0.1009,0.8416) ⊗(0.211,0.9659)

⊕(0.1934,0.7432) ⊗(0.0813,0.9033) ⊕(0.2008,0.7345) ⊗(0.0866,0.8965)

⊕(0.1386,0.7936) ⊗(0.0836,0.8953) ⊕(0.1007,0.8365) ⊗(0.1036,0.871)

= (0.654,0.8896).

Table 11 shows the ﬁnal weignts of alternatives and criteria.

A combination risk-based approach to post-earthquake temporary accommodation site selection . . . 69

Table 10: The weight calculation for each alternative based on criteria

Alternative 1∗2∗3∗4∗5∗6∗

C1C2C3C4C5C6

A1 0.0822 0.899 0.0211 0.9659 0.0813 0.9033 0.0866 0.8965 0.0836 0.8953 0.1036 0.871

A2 0.0822 0.899 0.1036 0.8762 0.0813 0.9033 0.0681 0.9245 0.0836 0.8953 0.0634 0.9208

A3 0.0822 0.899 0.0581 0.9267 0.0601 0.9207 0.0785 0.9135 0.0836 0.8953 0.0691 0.9144

A4 0.0822 0.899 0.0939 0.8907 0.0813 0.9033 0.0681 0.9245 0.0635 0.9193 0.0351 0.9489

A5 0.0653 0.9177 0.0581 0.9267 0.0813 0.9033 0.0681 0.9245 0.0836 0.8953 0.0634 0.9208

A6 0.0597 0.9264 0.0717 0.9154 0.0813 0.9033 0.0704 0.9233 0.0635 0.9193 0.0691 0.9144

A7 0.0597 0.9264 0.0581 0.9267 0.0445 0.938 0.0855 0.8989 0.0424 0.9382 0.0991 0.8761

A8 0.0597 0.9264 0.0615 0.9217 0.0445 0.938 0.0704 0.9233 0.0635 0.9193 0.0691 0.9144

A9 0.0822 0.899 0.1002 0.8812 0.0579 0.9219 0.0704 0.9233 0.0836 0.8953 0.0691 0.9144

A10 0.0619 0.9252 0.1002 0.8812 0.0412 0.9418 0.0704 0.9233 0.0424 0.9382 0.0895 0.8838

A11 0.0619 0.9252 0.1002 0.8812 0.0813 0.9033 0.0704 0.9233 0.0635 0.9193 0.0521 0.9298

A12 0.0619 0.9252 0.0535 0.9305 0.0813 0.9033 0.0704 0.9233 0.0635 0.9193 0.0691 0.9144

A13 0.0619 0.9252 0.0211 0.9659 0.0813 0.9033 0.0704 0.9233 0.0635 0.9193 0.0351 0.9489

1∗: Staying far from faults, streams and other natural hazards.

2∗: Appropriate ground gradient.

3∗: Existence of necessary infrastructure.

4∗: Availability.

5∗: Proximity to service centers.

6∗: Admission Capacity.

Table 11: The weight calculation for each alternative based on criteria

1∗2∗3∗4∗5∗6∗wj

C1C2C3C4C5C6

µAνAµAνAµAνAµAνAµAνAµAνAµAνA

0.0980 0.8492 0.1009 0.8416 0.1934 0.7432 0.2008 0.7345 0.1386 0.7936 0.1007 0.8365

A1 0.0822 0.899 0.0211 0.9659 0.0813 0.9033 0.0866 0.8965 0.0836 0.8953 0.1036 0.871 0.0654 0.8896

A2 0.0822 0.899 0.1036 0.8762 0.0813 0.9033 0.0681 0.9245 0.0836 0.8953 0.0634 0.9208 0.0659 0.8909

A3 0.0822 0.899 0.0581 0.9267 0.0601 0.9207 0.0785 0.9135 0.0836 0.8953 0.0691 0.9144 0.0599 0.8988

A4 0.0822 0.899 0.0939 0.8907 0.0813 0.9033 0.0681 0.9245 0.0635 0.9193 0.0351 0.9489 0.0592 0.9016

A5 0.0653 0.9177 0.0581 0.9267 0.0813 0.9033 0.0681 0.9245 0.0836 0.8953 0.0634 0.9208 0.0596 0.9007

A6 0.0597 0.9264 0.0717 0.9154 0.0813 0.9033 0.0704 0.9233 0.0635 0.9193 0.0691 0.9144 0.0586 0.9036

A7 0.0597 0.9264 0.0581 0.9267 0.0445 0.938 0.0855 0.8989 0.0424 0.9382 0.0991 0.8761 0.0532 0.9054

A8 0.0597 0.9264 0.0615 0.9217 0.0445 0.938 0.0704 0.9233 0.0635 0.9193 0.0691 0.9144 0.0505 0.9128

A9 0.0822 0.899 0.1002 0.8812 0.0579 0.9219 0.0704 0.9233 0.0836 0.8953 0.0691 0.9144 0.0620 0.8948

A10 0.0619 0.9252 0.1002 0.8812 0.0412 0.9418 0.0704 0.9233 0.0424 0.9382 0.0895 0.8838 0.0531 0.9066

A11 0.0619 0.9252 0.1002 0.8812 0.0813 0.9033 0.0704 0.9233 0.0635 0.9193 0.0521 0.9298 0.0600 0.9008

A12 0.0619 0.9252 0.0535 0.9305 0.0813 0.9033 0.0704 0.9233 0.0635 0.9193 0.0691 0.9144 0.0570 0.9056

A13 0.0619 0.9252 0.0211 0.9659 0.0813 0.9033 0.0704 0.9233 0.0635 0.9193 0.0351 0.9489 0.0503 0.9160

1∗: Staying far from faults, streams and other natural hazards.

2∗: Appropriate ground gradient.

3∗: Existence of necessary infrastructure.

4∗: Availability.

5∗: Proximity to service centers.

6∗: Admission Capacity.

4.10 Prioritize temporary accommodation site based on intuitionistic fuzzy analytic

Hierarchy process

After the defuzziﬁcation of the ﬁnal weights of alternatives in the process of selecting the intuitionistic fuzzy hierarchical

multi criteria, alternatives or locations are prioritized based on the ﬁnal defuzziﬁcation weights for each location. In

this study, the geometrical average calculated using equation (8) is used for defuzziﬁcation of variables [2]:

(8) MG(µ, v) = pµ(1 −v).

70 M. Dabiri, M. Oghabi, H. Sarvari, M. S. Sabeti, H. R. Kasheﬁ

Table 12: Prioritize alternatives for selecting temporary post-earthquake accommodation

Prioritized arrangement Option Defuzziﬁcation weights

1 A1 0.0850

2 A2 0.0848

3 A9 0.0808

4 A3 0.0779

5 A11 0.0772

6 A5 0.0769

7 A4 0.0763

8 A6 0.0752

9 A12 0.0733

10 A7 0.0710

11 A10 0.0704

12 A8 0.0663

13 A13 0.0650

For example, the ﬁnal defuzziﬁed weight of A1 alternative is calculated as follows:

MA1(µ, v) = p0.0654(1 −0.8896) = 0.085.

Table 12 shows the defuzziﬁed weight of all options in the order of their priority.

5 Conclusions

Pairwise comparison of the criteria and alternatives based on each criterion is one of the main stipulations and steps of

the multi-criteria decision-making process. One of the important features of risk-based preventive planning for the post-

earthquake temporary accommodation process is the presence of multiple uncertainties. In fact, due to the nature of the

risk, a number of uncertainties will be added in the decision-making process. Given the fact that uncertainty factor is not

considered in the classical studies, using intuitionistic fuzzy sets can be very useful in multi-criteria planning and decision

making. In expert’s opinions, the result is more accurate in calculations and optimal selection, when taking into account

the three elements of degree of membership, the degree of non-membership, and degree of doubt and uncertainty. In this

study, the combination risk-based approach was applied to post-earthquake temporary accommodation site selection in

Sanandaj-Iran. The proposed model combines risk management and Intuitionistic Fuzzy Analytic Hierarchy Process

(IF-AHP). Some of the practical features of this approach include: (i) Ability to measure the compatibility between the

pairwise comparison of the criteria and therefore the judgment of the experts opinions regarding the importance of each

criterion with the intended goal; (ii) Participation of all organizations and departments involved in preventive decision-

making of crisis management (This is useful not only due to the use of various specializations and disciplines but also

due to improved cooperation between various crisis management elements); and (iii) Planning to respond to risks while

choosing a suitable location for the post-earthquake temporary accommodation (Given that the criteria for selecting a

location are determined by the expert in charge of the temporary accommodation process and based on the risks). The

decision-making included six criteria. These criteria were selected using the brainstorming method and were based on

opinions of crisis management experts of Kurdistan Province organizations. The criteria were determined by combining

the identiﬁed risks of the temporary accommodation process after the earthquake in the region and the location criteria.

In this study, 13 alternative locations were assessed for temporary accommodation after the earthquake. According to

the results of the IF-AHP approach, the top ﬁve options among the 13 alternatives for the post-earthquake temporary

accommodation site in the Sanandaj-Iran include: (i) The alternative A1 is known to be the most suitable site for the

post-earthquake temporary accommodation; (ii) The alternative A2 was recognized as the second-best alternative; (iii)

The alternative A9 is the third-best suitable place for the temporary accommodation site in Sanandaj; and (iv) The

alternative A3 and A11 were ranked fourth and ﬁfth, respectively. It is worth mentioning that this prioritization is

carried out based on the stated criteria and their weights as calculated through agreement between crisis management

experts of Kurdistan province during this study and might be diﬀerent for other regions or in studies using diﬀerent

criteria. This is due to the fact that each region has its speciﬁc characteristics including climate, city structure, city

type, medical centers, etc. Therefore, while the authors believe that the presented method to be applicable and highly

recommend its application, it is recommended that the terms and conditions of the study area be carefully considered

when selecting criteria for selecting a temporary accommodation site.

A combination risk-based approach to post-earthquake temporary accommodation site selection . . . 71

Acknowledgement

The authors wish to express their appreciation for several excellent suggestions for improvements in this paper made

by the referees.

References

[1] M. Abdel-basset, M. Gunasekaran, M. Mohamed, N. Chilamkurti, A framework for risk assessment, management

and evaluation: Economic tool for quantifying risks in supply chain, Future Generation Computer Systems, 90

(2019), 489-502.

[2] L. Abdullah, S. Iaafar, I. Taib, Intuitionistic fuzzy analytic Hierarchy process approach in ranking of human capital

indicators, Journal of Applied Sciences, 13 (2013), 423-429.

[3] A. Aﬁfy, B. Farhat, D. Alhabbal, Temporary houses from emergency to sustainability, 2nd International Conference

on Architecture, Structure and Civil Engineering (ICASCE’16), (2016), London (UK), 126-129.

[4] A. Anand, A. Jethoo, G. Sharma, Selection of temporary rehabilitation location after disaster: A review, European

Scientiﬁc Journal, (2015), 161-169.

[5] M. Aseﬁ, S. Farrokhi, Proposing a model for the design of post-disaster temporary housing based on the needs of

the injured with post-implementation evaluation approach (Case study: Earthquake-stricken villages in heris of east

Azerbaijan), Journal of Research and Rural Planning, 7(2018), 81-101.

[6] K. T. Atanassov, Intuitionistic fuzzy sets, Studies in Fuzziness and Soft Computing. Physica, Heidelberg, 1999.

[7] L. Bakos, Crisis management between public relations and the Holonic multi-agent approach, Procedia-Social and

Behavioral Sciences, 238 (2018), 527-534.

[8] Bhrc. Iranian Code of Practice for Seismic Resistant Resign of Buildings (Standard No. 2800). Tehran: Building

and Housing Research Center, Permanent committee for revising the Iranian code of practice for seismic resistant

design of buildings, (2014), 129-174.

[9] P. Bris, F. Bendito, Impact of Japanese post-disaster temporary housing areas(THAs) design on mental and social

health, International Journal of Environmental Research and Public Health, 16 (2019), 47-57.

[10] W. T. Coombs, D. Laufer, Global crisis management-current research and future directions, Journal of International

Management, 24 (2018), 199-203.

[11] P. Ejegwa, S. Akowe, P. Otene, J. Ikyule, An overview on intuitionistic fuzzy sets, International Journal of Scientiﬁc

and Technology Research, 3(2014), 142-145.

[12] N. Esmailpour Zanjani, A. Fallahi, S. Motavaﬁ, Locating temporary settlements in earthquake crisis using AHP

and GIS (Case study: Damavand region), Journal of Civil Engineering and Urbanism, 4(2014), 581-585.

[13] D. Felix, J. M. Branco, A. Feio, Temporary housing after disasters: A state of the art survey, Habitat International,

40 (2013), 136-141.

[14] D. Felix, D. Monteiro, J. M. Branco, R. Bologna, A. Feio, The role of temporary accommodation buildings for

post-disaster housing reconstruction, Journal of Housing and the Built Environment, 30 (2015), 683-699.

[15] A. J. Forouzandeh, M. Hosseini, M. Sadeghzadeh, Guidelines for design of temporary shelters after earthquakes

based on community participation, In 14th World Conference on Earthquake Engineering, Beijing, China, Oct,

(2008), 12-17.

[16] F. Hadaﬁ, A. Fallahi, Temporary housing respond to disasters in developing countries-case study: Iran-Ardabil and

Lorestan province earthquakes, World Academy of Science, Engineering and Technology, 66 (2010), 1536-1542.

[17] A. Hany Abulnour, The post-disaster temporary dwelling: Fundamentals of provision, design and construction,

Hbrc Journal, 10 (2014), 10-24.

[18] A. Hany Abulnour, Towards eﬃcient disaster management in Egypt, HBRC Journal, 10 (2014), 117-126.

72 M. Dabiri, M. Oghabi, H. Sarvari, M. S. Sabeti, H. R. Kasheﬁ

[19] S. A. Hosseini, A. De la Fuente, O. Pons, Multicriteria decision-making method for sustainable site location of post-

disaster temporary housing in urban areas, Journal of Construction Engineering and Management, 142(9) (2016),

13 pages.

[20] T. Ishii, S. Ochi, M. Tsubokura, S. Kato, T. Tetsuda, J. Kato, Y. Nishikawa, T. Morita, M. Kami, Y. Iwamoto,

Physical performance deterioration of temporary housing residents after the great east Japan earthquake, Preventive

Medicine Reports, 2(2015), 916-919.

[21] Y. Izadkhah, H. K. Amini, An evaluation of disaster preparedness in four major earthquakes in Iran, Journal of

Seismology and Earthquake Engineering, 12 (2010), 61-75.

[22] Z. Jia, Y. Zhang, Interval-valued intuitionistic fuzzy multiple attribute group decision making with uncertain weights,

Mathematical Problems in Engineering, (2019), 9.

[23] C. Johnson, Impacts of prefabricated temporary housing after disasters: 1999 earthquakes in Turkey, Habitat

International, 31 (2007), 36-52.

[24] F. Kilci, B. Y. Kara, B. Bozkaya, Locating temporary shelter areas after an earthquake: A case for Turkey, European

Journal of Operational Research, 243 (2015), 323-332.

[25] Q. Li, K. Umaier, O. Koide, Research on post-Wenchuan earthquake recovery and reconstruction implementation:

A case study of housing reconstruction of Dujiangyan city, Progress in Disaster Science, 4(2019), 100041.

[26] E. Nikbakhsh, R. Zanjirani Farahani, Humanitarian logistics planning in disaster relief operations, Logistics Op-

erations and Managemen, (2001), 291-332

[27] S. Nomura, A. J. Parsons, M. Hirabayashi, R. Kinoshita, Y. Liao, S. Hodgson, Social determinants of mid-to

long-term disaster impacts on health: A systematic review, International Journal of Disaster Risk Reduction, 16

(2016), 53-67.

[28] E. A. Pasha, A. Fatemi, Intuitionistic fuzzy sets clustering (IFSC) with an application in psychology, Journal of

Mathematics and Applications, 28 (2006), 113-120.

[29] D. V. Perrucci, B. A. Vazquez, C. B. Aktas, Sustainable temporary housing: Global trends and outlook, Procedia

Engineering, 145 (2016), 327-332.

[30] Y. Qin, Q. Qi, P. J. Scott, X. Jiang, Multi-criteria group decision making based on archimedean power partitioned

muirhead mean operators of q-rung orthopair fuzzy numbers, PloS ONE, 14 (2019), e0221759.

[31] A. Rezaei, S. Tahsili, Urban vulnerability assessment using AHP, Advances in Civil Engineering, (2018), 20.

[32] L. H. Rodrigues-da-silva, J. A. Crispim, The project risk management process, a preliminary study, Procedia

Technology, 16 (2014), 943-949.

[33] T. L. Saaty, A scaling method for priorities in hierarchical structures, Journal of Mathematical Psychology, 15

(1977), 234-281.

[34] T. L. Saaty, Decision making with the analytic hierarchy process, International Journal of Services Sciences, 1

(2008), 83-98.

[35] H. Sarvari, M. Rakhshanifar, J. Tamosaitiene, D. W. Chan, M. Beer, A risk based approach to evaluating the impacts

of Zayanderood Drought on sustainable development indicators of riverside Urban in Isfahan-Iran, Sustainability,

11 (2019), 67-97.

[36] R. W. Satty, The analytic hierarchy processwhat it is and how it is used, Mathematical Modelling, 9(1987), 161-176.

[37] T. Seike, T. Isobe, Y. Hosaka, Y. Kim, S. Watanabe, M. Shimura, Design and supply system for emergency

temporary housing by various construction methods from the perspective of environmental impact assessment: The

case for the great east Japan earthquake, Energy and Buildings, 203 (2019), 109425.

[38] A. Soltani, A. Ardalan, A. D. Boloorani, A. Haghdoost, M. J. Hosseinzadeh-attar, Criteria for site selection of

temporary shelters after earthquakes: A delphi panel, PLoS Currents, 7(2015), 11 pages.

A combination risk-based approach to post-earthquake temporary accommodation site selection . . . 73

[39] O. S. Vaidya, S. Kumar, Analytic hierarchy process: An overview of applications, European Journal of Operational

Research, 169 (2006), 1-29.

[40] Z. Xu, H. Liao, Intuitionistic fuzzy analytic hierarchy process, IEEE Transactions on Fuzzy Systems, 22 (2013),

749-761.

[41] L. A. Zadeh, Fuzzy sets, Information and Control, 8(1965), 338-353.

6 Apendix

Table 13: Preferential - paired relationship Alternatives with criterion C1 (staying far from faults, streams and other

natural hazards)

A1A2A3A4A5A6A7A8A9A10 A11 A12 A13

Alternatives µAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνA

A1 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.3 0.5 0.5 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.3

A2 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.3 0.5 0.5 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.3

A3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.3 0.5 0.5 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.3

A4 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.3 0.5 0.5 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.3

A5 0.3 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.5 0.5 0.6 0.3 0.6 0.3 0.6 0.3 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

A6 0.3 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

A7 0.3 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

A8 0.3 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

A9 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.3 0.5 0.5 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.3

A10 0.3 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

A11 0.3 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

A12 0.3 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

A13 0.3 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

Table 14: Preferential - paired relationship Alternatives with criterion C2 (Appropriate ground gradient)

A1A2A3A4A5A6A7A8A9A10 A11 A12 A13

Alternatives µAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνA

A1 0.5 0.5 0.05 0.9 0.1 0.8 0.05 0.9 0.1 0.8 0.1 0.8 0.1 0.8 0.1 0.8 0.05 0.9 0.05 0.9 0.05 0.9 0.1 0.8 0.5 0.5

A2 0.9 0.05 0.5 0.5 0.8 0.1 0.5 0.5 0.8 0.1 0.8 0.1 0.8 0.1 0.8 0.1 0.5 0.5 0.5 0.5 0.5 0.5 0.8 0.1 0.9 0.05

A3 0.8 0.1 0.1 0.8 0.5 0.5 0.1 0.8 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.1 0.8 0.1 0.8 0.1 0.8 0.5 0.5 0.8 0.1

A4 0.05 0.5 0.5 0.5 0.8 0.8 0.5 0.5 0.8 0.1 0.8 0.1 0.8 0.1 0.8 0.1 0.5 0.5 0.5 0.5 0.5 0.5 0.8 0.1 0.9 0.05

A5 0.8 0.1 0.1 0.8 0.5 0.5 0.1 0.8 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.1 0.8 0.1 0.8 0.1 0.8 0.5 0.5 0.8 0.1

A6 0.8 0.1 0.1 0.8 0.5 0.5 0.1 0.8 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.8 0.1

A7 0.8 0.1 0.1 0.8 0.5 0.5 0.1 0.8 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.1 0.8 0.1 0.8 0.1 0.8 0.5 0.5 0.8 0.1

A8 0.8 0.1 0.1 0.8 0.5 0.5 0.1 0.8 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.1 0.8 0.1 0.8 0.1 0.8 0.8 0.1 0.8 0.1

A9 0.9 0.05 0.5 0.5 0.8 0.1 0.5 0.5 0.8 0.1 0.5 0.5 0.8 0.1 0.8 0.1 0.5 0.5 0.5 0.5 0.5 0.5 0.8 0.1 0.9 0.05

A10 0.9 0.05 0.5 0.5 0.8 0.1 0.5 0.5 0.8 0.1 0.5 0.5 0.8 0.1 0.8 0.1 0.5 0.5 0.5 0.5 0.5 0.5 0.8 0.1 0.9 0.05

A11 0.9 0.05 0.5 0.5 0.8 0.1 0.5 0.5 0.8 0.1 0.5 0.5 0.8 0.1 0.8 0.1 0.5 0.5 0.5 0.5 0.5 0.5 0.8 0.1 0.9 0.05

A12 0.8 0.1 0.1 0.8 0.5 0.5 0.1 0.8 0.5 0.5 0.5 0.5 0.5 0.5 0.1 0.8 0.1 0.8 0.1 0.8 0.1 0.8 0.5 0.5 0.8 0.1

A13 0.5 0.5 0.05 0.9 0.1 0.8 0.05 0.9 0.1 0.8 0.1 0.8 0.1 0.8 0.1 0.8 0.05 0.9 0.05 0.9 0.05 0.9 0.1 0.8 0.5 0.5

Table 15: Preferential - paired relationship Alternatives with criterion C3 (Existence of necessary infrastructure)

A1A2A3A4A5A6A7A8A9A10 A11 A12 A13

Alternatives µAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνA

A1 0.5 0.5 0.5 0.5 0.6 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.7 0.2 0.7 0.2 0.6 0.3 0.7 0.2 0.5 0.5 0.5 0.5 0.5 0.5

A2 0.5 0.5 0.5 0.5 0.6 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.7 0.2 0.7 0.2 0.6 0.3 0.7 0.2 0.5 0.5 0.5 0.5 0.5 0.5

A3 0.5 0.5 0.3 0.6 0.5 0.5 0.3 0.6 0.3 0.6 0.3 0.6 0.6 0.3 0.6 0.3 0.5 0.5 0.6 0.3 0.3 0.6 0.3 0.6 0.3 0.6

A4 0.5 0.5 0.5 0.5 0.6 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.7 0.2 0.7 0.2 0.6 0.3 0.7 0.2 0.5 0.5 0.5 0.5 0.5 0.5

A5 0.5 0.5 0.5 0.5 0.6 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.7 0.2 0.7 0.2 0.6 0.3 0.7 0.2 0.5 0.5 0.5 0.5 0.5 0.5

A6 0.5 0.5 0.5 0.5 0.6 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.7 0.2 0.7 0.2 0.6 0.3 0.7 0.2 0.5 0.5 0.5 0.5 0.5 0.5

A7 0.2 0.7 0.2 0.7 0.3 0.6 0.2 0.7 0.2 0.7 0.2 0.7 0.5 0.5 0.5 0.5 0.6 0.3 0.5 0.5 0.2 0.7 0.2 0.7 0.2 0.7

A8 0.2 0.7 0.2 0.7 0.3 0.6 0.2 0.7 0.2 0.7 0.2 0.7 0.5 0.5 0.5 0.5 0.6 0.3 0.5 0.5 0.2 0.7 0.2 0.7 0.2 0.7

A9 0.3 0.6 0.3 0.6 0.5 0.5 0.3 0.6 0.3 0.6 0.3 0.6 0.6 0.3 0.6 0.3 0.5 0.5 0.6 0.3 0.3 0.6 0.3 0.6 0.3 0.6

A10 0.2 0.7 0.2 0.7 0.3 0.6 0.2 0.7 0.2 0.7 0.2 0.7 0.5 0.5 0.5 0.5 0.3 0.6 0.5 0.5 0.2 0.7 0.2 0.7 0.2 0.7

A11 0.5 0.5 0.5 0.5 0.6 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.7 0.2 0.7 0.2 0.6 0.3 0.7 0.2 0.5 0.5 0.5 0.5 0.5 0.5

A12 0.5 0.5 0.5 0.5 0.6 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.7 0.2 0.7 0.2 0.6 0.3 0.7 0.2 0.5 0.5 0.5 0.5 0.5 0.5

A13 0.5 0.5 0.5 0.5 0.6 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.7 0.2 0.7 0.2 0.6 0.3 0.7 0.2 0.5 0.5 0.5 0.5 0.5 0.5

74 M. Dabiri, M. Oghabi, H. Sarvari, M. S. Sabeti, H. R. Kasheﬁ

Table 16: Preferential - paired relationship Alternatives with criterion C4 (Availability)

A1A2A3A4A5A6A7A8A9A10 A11 A12 A13

Alternatives µAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνA

A1 0.5 0.5 0.6 0.3 0.5 0.5 0.6 0.3 0.6 0.3 0.6 0.3 0.5 0.5 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.3

A2 0.3 0.6 0.5 0.5 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

A3 0.5 0.5 0.6 0.3 0.5 0.5 0.6 0.3 0.6 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

A4 0.3 0.6 0.5 0.5 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

A5 0.3 0.6 0.5 0.5 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

A6 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

A7 0.5 0.5 0.6 0.3 0.5 0.5 0.6 0.3 0.5 0.5 0.6 0.3 0.5 0.5 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.3

A8 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

A9 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

A10 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

A11 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

A12 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

A13 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

Table 17: Preferential - paired relationship Alternatives with criterion C5 (Proximity to service centers)

A1A2A3A4A5A6A7A8A9A10 A11 A12 A13

Alternatives µAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνA

A1 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.3 0.5 0.5 0.6 0.3 0.7 0.2 0.6 0.3 0.5 0.5 0.7 0.2 0.6 0.3 0.6 0.3 0.6 0.3

A2 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.3 0.5 0.5 0.6 0.3 0.7 0.2 0.6 0.3 0.5 0.5 0.7 0.2 0.6 0.3 0.6 0.3 0.6 0.3

A3 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.3 0.5 0.5 0.6 0.3 0.7 0.2 0.6 0.3 0.5 0.5 0.7 0.2 0.6 0.3 0.6 0.3 0.6 0.3

A4 0.3 0.6 0.3 0.6 0.3 0.6 0.5 0.5 0.3 0.6 0.5 0.5 0.6 0.3 0.5 0.5 0.3 0.6 0.6 0.3 0.5 0.5 0.5 0.5 0.5 0.5

A5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.3 0.5 0.5 0.6 0.3 0.7 0.2 0.6 0.3 0.5 0.5 0.7 0.2 0.6 0.3 0.6 0.3 0.6 0.3

A6 0.3 0.6 0.3 0.6 0.3 0.6 0.5 0.5 0.3 0.6 0.5 0.5 0.6 0.3 0.5 0.5 0.3 0.6 0.6 0.3 0.5 0.5 0.5 0.5 0.5 0.5

A7 0.2 0.7 0.2 0.7 0.2 0.7 0.3 0.6 0.2 0.7 0.3 0.6 0.5 0.5 0.3 0.6 0.2 0.7 0.5 0.5 0.3 0.6 0.3 0.6 0.3 0.6

A8 0.3 0.6 0.3 0.6 0.3 0.6 0.5 0.5 0.3 0.6 0.5 0.5 0.6 0.3 0.5 0.5 0.3 0.6 0.6 0.3 0.5 0.5 0.5 0.5 0.5 0.5

A9 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.3 0.5 0.5 0.6 0.3 0.7 0.2 0.6 0.3 0.5 0.5 0.7 0.2 0.6 0.3 0.6 0.3 0.6 0.3

A10 0.2 0.7 0.2 0.7 0.2 0.7 0.3 0.6 0.2 0.7 0.3 0.6 0.5 0.5 0.3 0.6 0.2 0.7 0.5 0.5 0.3 0.6 0.3 0.6 0.3 0.6

A11 0.3 0.6 0.3 0.6 0.3 0.6 0.5 0.5 0.3 0.6 0.5 0.5 0.6 0.3 0.5 0.5 0.3 0.6 0.6 0.3 0.5 0.5 0.5 0.5 0.5 0.5

A12 0.3 0.6 0.3 0.6 0.3 0.6 0.5 0.5 0.3 0.6 0.5 0.5 0.6 0.3 0.5 0.5 0.3 0.6 0.6 0.3 0.5 0.5 0.5 0.5 0.5 0.5

A13 0.3 0.6 0.3 0.6 0.3 0.6 0.5 0.5 0.3 0.6 0.5 0.5 0.6 0.3 0.5 0.5 0.3 0.6 0.6 0.3 0.5 0.5 0.5 0.5 0.5 0.5

Table 18: Preferential - paired relationship Alternatives with criterion C6 (Admission capacity)

A1A2A3A4A5A6A7A8A9A10 A11 A12 A13

Alternatives µAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνAµAνA

A1 0.5 0.5 0.8 0.1 0.8 0.1 0.9 0.05 0.8 0.1 0.8 0.1 0.5 0.5 0.8 0.1 0.8 0.1 0.7 0.2 0.05 0.9 0.8 0.1 0.9 0.05

A2 0.1 0.8 0.5 0.5 0.5 0.5 0.7 0.2 0.5 0.5 0.5 0.5 0.1 0.8 0.5 0.5 0.5 0.5 0.3 0.6 0.2 0.7 0.5 0.5 0.7 0.2

A3 0.1 0.8 0.5 0.5 0.5 0.5 0.7 0.2 0.5 0.5 0.5 0.5 0.1 0.8 0.5 0.5 0.5 0.5 0.3 0.6 0.7 0.2 0.5 0.5 0.7 0.2

A4 0.05 0.9 0.2 0.7 0.2 0.7 0.5 0.5 0.2 0.7 0.2 0.7 0.05 0.9 0.2 0.7 0.2 0.7 0.1 0.8 0.5 0.5 0.2 0.7 0.5 0.5

A5 0.1 0.8 0.5 0.5 0.5 0.5 0.7 0.2 0.5 0.5 0.5 0.5 0.1 0.8 0.5 0.5 0.5 0.5 0.3 0.6 0.2 0.7 0.5 0.5 0.7 0.2

A6 0.1 0.8 0.5 0.5 0.5 0.5 0.7 0.2 0.5 0.5 0.5 0.5 0.1 0.8 0.5 0.5 0.5 0.5 0.3 0.6 0.7 0.2 0.5 0.5 0.7 0.2

A7 0.5 0.5 0.8 0.1 0.8 0.1 0.9 0.05 0.8 0.1 0.8 0.1 0.5 0.5 0.8 0.1 0.8 0.1 0.3 0.6 0.05 0.9 0.8 0.1 0.9 0.05

A8 0.1 0.8 0.5 0.5 0.5 0.5 0.7 0.2 0.5 0.5 0.5 0.5 0.1 0.8 0.5 0.5 0.5 0.5 0.3 0.6 0.7 0.2 0.5 0.5 0.7 0.2

A9 0.1 0.8 0.5 0.5 0.5 0.5 0.7 0.2 0.5 0.5 0.5 0.5 0.1 0.8 0.5 0.5 0.5 0.5 0.3 0.6 0.7 0.2 0.5 0.5 0.7 0.2

A10 0.2 0.7 0.6 0.3 0.6 0.3 0.8 0.1 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.3 0.6 0.3 0.5 0.5 0.8 0.1 0.6 0.3 0.8 0.1

A11 0.05 0.9 0.7 0.2 0.2 0.7 0.5 0.5 0.7 0.2 0.2 0.7 0.05 0.9 0.7 0.2 0.2 0.7 0.1 0.8 0.5 0.5 0.2 0.7 0.5 0.5

A12 0.1 0.8 0.5 0.5 0.5 0.5 0.7 0.2 0.5 0.5 0.5 0.5 0.1 0.8 0.5 0.5 0.5 0.5 0.3 0.6 0.7 0.2 0.5 0.5 0.7 0.2

A13 0.05 0.9 0.2 0.7 0.2 0.7 0.5 0.5 0.2 0.7 0.2 0.7 0.05 0.9 0.2 0.7 0.2 0.7 0.1 0.8 0.5 0.5 0.2 0.7 0.5 0.5

Developing a hybrid risk assessment method for prioritizing the critical risks of temporary accommodation sites after destructive earthquakes

Article

Oct 2022
HABITAT INT

One of the most critical challenges in preventive planning and disaster management is the multitudinous uncertainties involved in decision-making. Previous studies showed the usefulness of intuitionistic fuzzy sets for considering uncertainties in decision-making process. Hence, the current study aims to present a combined model using Intuitionistic Fuzzy Sets and Risk Failure Mode and Effects Analysis (IF-RFMEA) to determine and prioritize the critical risks of temporary accommodation sites after destructive earthquakes in Iran and bridge the existing research gaps in the literature. To this end, 49 common temporary accommodation risks after earthquakes were identified via a desktop literature survey. Then, the fuzzy Delphi technique was applied to determine the top 20 critical risks with the highest priorities according to experts for evaluation using the proposed method. The Delphi panel members included 18 experts based in Iran with relevant hands-on experience in crisis management and risk management. Finally, 20 identified critical risks were evaluated using three criteria of the probability of occurrence, level of effect, and detection value using the IF-RFMEA technique. According to the analytical results, infectious disease challenges, mental and psychological disorders among survivors, and unemployment and closing of businesses, were the most critical risks after earthquakes in the region. The proposed method of analysis can diminish uncertainties and adopt the main criteria of the probability of occurrence, level of effect, and detection value to improve risk assessment results and analysis in relation to the critical risks of temporary accommodation sites after destructive earthquakes.

Identification and Prioritization of Critical Risk Factors of Commercial and Recreational Complex Building Projects: A Delphi Study Using the TOPSIS Method

Article

Full-text available

Aug 2021

Construction development of Commercial and Recreational Complex Building Projects (CRCBPs) is one of the community needs of many developing countries. Since the implementation of these projects is usually very costly, identifying and evaluating their Critical Risk Factors (CRFs) are of significant importance. Therefore, the current study aims to identify and prioritize CRFs of CRCBPs in the Iranian context. A descriptive-survey method was used in this research; the statistical population, selected based on the purposive sampling method, includes 30 construction experts with hands-on experience in CRCBPs. A questionnaire related to the risk identification stage was developed based on a detailed study of the research literature and also using the Delphi survey method; 82 various risks were finally identified. In order to confirm the opinions of experts in identifying the potential risks, Kendall’s coefficient of concordance was used. In the first stage of data analysis, qualitative evaluation was performed by calculating the severity of risk effect and determining the cumulative risk index, based on which 25 CRFs of CRCBPs were identified for more accurate evaluation. At this stage, the identified CRFs were evaluated based on multi-criteria decision-making techniques and using the TOPSIS technique. Results show that the ten CRFs of CRCBPs are external threats from international relations, exchange rate changes, bank interest rate fluctuations, traffic licenses, access to skilled labor, changes in regional regulations, the condition of adjacent buildings, fluctuations and changes in inflation, failure to select a suitable and qualified consultant, and employer’s previous experiences and records. Obviously, the current study’s results and findings can be considered by CRCBPs in both the private and public sectors for proper effective risk identification, evaluation, and mitigation.

A qualitative investigation of revitalisation efforts to foster residents’ attachment in dilapidated neighbourhoods: Is identity a matter?

Article

Full-text available

Jul 2024

Abstract Historic neighborhoods in the Global South face challenges like urban core shrinkage and gentrification, impacting residents' socio-cultural dynamics. This qualitative study aims to identify critical aspects contributing to the deterioration of historic fabrics during the revitalisation process and explore their influence on residents' attachment to the Aghazaman neighbourhood in Sanandaj City, Iran. Data was collected from 17 experts using semi-structured interviews. The analysis followed Braun and Clarke's six-step thematic approach: familiarization with the data, generating initial codes, searching for themes, reviewing themes, defining and naming themes, and producing the report. This process identified five themes from 54 codes: (1) strengthening identity, motivation, and ownership; (2) building inclusive and vibrant communities; (3) ensuring socioeconomic adaptation and residents' empowerment; (4) boosting functional adaptation for standard living; and (5) enhancing infrastructure for connected living with the environment. These themes illustrate the factors influencing residents' attachment during the revitalisation process and demonstrate how identity formation can enhance attachment to dilapidated neighbourhoods, thereby strengthening revitalisation efforts. The study contributes to an integral framework for addressing the aspects involved in nurturing residents' attachment, which is a key factor in facilitating sustained revitalisation efforts. From an empirical perspective, there is a need for revitalisation efforts to integrate cultural values, nurture neighbourhood attachment, and preserve identity. While holistic community engagement approaches are recognised, the study also emphasises the need for identity-driven interventions to address shortcomings in enhancing attachment and community well-being, even in community-driven initiatives.

Determining the Essential Criteria for Choosing Appropriate Methods for Maintenance and Repair of Iraqi Healthcare Building Facilities

Article

Full-text available

Jun 2023

Today, building maintenance and repair (M&R) is a neglected aspect of the construction business throughout a building's entire life cycle. Selecting appropriate M&R strategies is crucial, particularly for emerging economies like Iraq with severely constrained resources. This study seeks to identify the primary selection criteria for M&R methods of healthcare building facilities (HBFs) in Iraq. A comprehensive desktop literature analysis was undertaken to extract and determine the essential selection criteria for the most suited M&R approaches to buildings in general. Then, two rounds of the Delphi survey were conducted to consolidate the specific selection criteria to suit the circumstances of Iraq and HBFs. A total of 21 sub-criteria were identified and divided into six main groups. The main criteria and the associated sub-criteria were then analyzed and ranked using the fuzzy analytic hierarchy process (FAHP) technique. The ranking of the various main criteria revealed that the "cost" criterion was ranked first in terms of importance, followed by the "human resources" and "quality" criteria. The fourth, fifth, and sixth main criteria are "reliability/flexibility", "safety/risk/environment", and "facilities/technology", respectively. The overall ranking of the sub-criteria placed "optimization and cost reduction" in the first position and "extending the life of the equipment and preserving their initial quality" in the bottom place. It is anticipated that the key findings and effective recommendations of this study will considerably contribute to the improvement of building maintenance and repair management practices in developing nations while enhancing different stakeholders' understanding of the most important selection criteria for M&R methods, particularly with regard to healthcare building facilities in Iraq.

A multi-criteria decision-making is the ANP network analysis process for identification relief centers in urban areas: A case of study Tabriz city, Iran

Preprint

Full-text available

Feb 2023

Cities' growing populations and densities create an atmosphere that is conducive to changes in their structure. Accordingly, factors that increase the number of vulnerable groups, such as women, children, the elderly, and people with disabilities, include urban population growth, high-rise buildings, apartment living, industrial concentration, the growth of impoverished neighbourhoods, and informal settlements. Given the earthquake-prone regions of northwest Iran and the frequency of earthquakes in this region in recent years, it is essential to comprehend the characteristics of the natural environment in order to determine the optimal place for the development of urban structures and relief centres. The purpose of this study is to locate potential sites for the development of temporary housing and relief operations. This study, whose methodology is survey and analysis using the ANP model, has discussed the location of places for the establishment of rescue centres and temporary housing for the population according to the physical, environmental, and social criteria, which immediately after the earthquake, the possibility of rescuing the lives of those who witnessed the accident, and the creation of a temporary shelter. The findings indicate that west of Tabriz is the optimum place for the construction of rescue facilities and temporary people settlement based on the distance from natural crisis elements.

A hybrid risk assessment approach for assessing the earthquake risks in worn-out urban fabrics: a case study in Iran

Article

Full-text available

Dec 2021

Purpose To reduce financial and human losses, managing risks associated with earthquakes is essential in practice. However, in using common risk management methods, experts are often faced with ambiguities that can create profound challenges for risk management. Therefore, it is necessary to develop a logical and straightforward risk assessment model to provide scientific and accurate answers to complex problems. This study aims to recommend an innovative combined method based on the probability-impact (P-I) approach and intuitionistic fuzzy set theory to identify and prioritize the essential earthquake risks associated with worn-out urban fabrics in the context of Iran. Design/methodology/approach The opinions of 15 experts in the fields of civil engineering and urban construction were gathered during brainstorming sessions. These brainstorming sessions were conducted to determine the probability of risks and the effect of identified risks. After calculating the severity of risks using the P-I approach and converting them to intuitionistic fuzzy sets, the risks were measured and prioritized based on their individual scores. Findings The study results indicated that risk of damage due to buildings’ age and flooding risk had the highest and lowest priorities in causes of financial damage, respectively. Furthermore, the risk of damage due to building quality (demolition) and building age was the most important. The risk of flooding and damage to communication networks has the lowest importance among causes of fatalities in worn-out urban fabrics. Originality/value The study findings and recommendations can be served as a policy and consultative instrument for the relevant stakeholders in the area of urban management.

Identification and prioritization of seismic risks in urban worn-out textures using fuzzy Delphi method

Article

Full-text available

Jun 2021

Earthquake is a random natural phenomenon, which can occur at any time and location in a given seismic zone with any magnitude. The earthquake vulnerability in buildings and urban infrastructures is a key issue for crisis management. Therefore, an assessment model should be developed to identify and prioritize the significant seismic risks involved. In risk management, several numerical and descriptive phrases are used for risk identification and assessment. These phrases are estimative by nature and the accuracy of the estimations is vital in future decision-making in risk management. Fuzzy sets are a reliable tool in solving such problems and result in high level of accuracy through creating multiple-value logical models. The purpose of this study is to identify and prioritize the major risks associated with earthquakes in urban worn-out textures through the Delphi survey technique and fuzzy sets approach. The experts' opinions were collected using a fuzzy Delphi questionnaire with a five-point Likert scale of measurement method. Participants in the Delphi panel consist of 15 experts in the field of engineering. Important risks were determined and prioritized in the two phases of fuzzy Delphi method. According to the results, among the 19 identified major risks, road blockage and flood with defuzzification values of 0.917 and 0.583, respectively, have the highest and lowest risk potential respectively in Jalili Neighborhood's worn-out textures. It is expected that, because of the simplicity and the high accuracy for identification of the most vulnerable parts, this study provides scientific and useful guidance to urban managers and planners in decision-making and adopting the most appropriate strategies for mitigating damages and potential risks of earthquakes in urban worn-out textures.

Identifying and prioritizing the selection criteria of appropriate repair and maintenance methods for commercial buildings

Article

Full-text available

Jul 2021

All buildings require Repair and Maintenance (R&M) in their life cycle period. However, if R&M activities are not carried out properly, deterioration will occur, service life of buildings will be reduced, and maintenance costs will increase. Hence, selecting the appropriate R&M methods is pivotal, especially for developing countries, such as Iran, which are featured by highly constrained resources. The present study aims to identify and prioritize the main criteria for selecting the suitable R&M methods for Commercial Buildings (CBs), which is considered as a profound challenge for the Architecture , Engineering and Construction/Facility Management (AEC/FM) industry. A total of 20 senior experts in the AEC/FM industry and CBs in Iran were invited to participate in a Delphi survey to solicit their perceptions and opinions on the selection criteria. The total number of individual criteria identified is 16, which are further divided into five categories: human resources, flexibility and technical capability, risks, cost of maintenance, together with facilities and technology. Then, the Fuzzy Analytic Hierarchy Process (FAHP) technique was applied to prioritize the identified criteria. Among the 5 main selection criteria, the cost of maintenance is the most important criterion for selecting appropriate R&M methods for CBs whereas the criterion of human resources (HR) was recognized as the least important.

An Analysis of Intuitionistic Fuzzy Sets in Risk-Based Inspection: A Case Study of Hydrogen Crack Damage in Steel Tanks Under Gas Pressure

Article

Dec 2024

AI-Based model for site-selecting earthquake emergency shelters

Article

Full-text available

Nov 2024

The objective of this research is to develop an AI-based model that provides a map of potential emergency shelters for seismic-prone regions. While the predominant approach for locating emergency shelters involves expert-answered questionnaires, their accuracy has often been criticized globally. To address this, machine learning can enhance the speed and accuracy of shelter site selection; the results can also be generalized to other regions. Support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), gaussian processes classifier (GPC), and artificial neural network (ANN) methods are used to develop the model here. These algorithms are trained using maps of emergency shelters in San Francisco, enabling the resulting model to automatically provide potential emergency shelter maps not only for the studied case but also for cities with similar criteria. Except for LR, the other algorithms achieved F1 scores between 0.7 and 1.0 in selecting emergency shelter sites. The model developed in this research can serve as a reliable tool for disaster management planners in managing emergency shelters for people affected by earthquakes.

Locating Temporary Settlements in Earthquake Crisis Using AHP and GIS (Case Study: Damavand Region)

Article

Full-text available

Jan 2014

Planning before crisis occurrence is a key problem that managers are dealing with these days, especially in the field of crisis management. The aim of this research is optimum locating of temporary accommodation for those injured after earthquake using model AHP and also by applying geographical information system GIS in Damavand Area. It is worth mentioning that due to the presence of the main faults of Mosha, this county is a seismic one in Tehran Province. The research method used is the descriptive-analytical method. In the first method, a brief review of theoretical definitions of the research was provided and then the AHP model was analyzed. After studying factors that are effective on the environment, and population of Damavand, we dealt with weighting to parameters using analytical hierarchical process AHP. Finally, with the results we gained and by combining layers in software GIS we conducted a fine locating for setting up temporary accommodation resulting from an earthquake.

A Risk Based Approach to Evaluating the Impacts of Zayanderood Drought on Sustainable Development Indicators of Riverside Urban in Isfahan-Iran

Article

Full-text available

Nov 2019

In recent years, the Zayanderood River in Isfahan-Iran has been encountered by hydrological imbalance and drought. Literature review shows that long-term climate change, drought, and disruption of the river's water supply has led to depletion of underground aquifers and, consequently, gradual subsidence of the river and serious damage to old buildings and structures along the riverbank. This fact would be followed up by adverse environmental, social, and economic effect that could threaten the sustainable development of urban space. Therefore, it is necessary to use efficient risk identification and assessment approaches toward a more effective risk management. The goal of this study is to identify and prioritize the risks of river drought with regards to all three sustainable development areas including environmental, social, and economic. The research methodology was a mixed field method that included a set of questionnaires and interviews. To evaluate collected data, the analytic network process (ANP) method was used. Eighteen important risks were identified. Based on the results, decrease in the groundwater level, climate change, and gradual soil degradation were ranked first, second, and third, respectively. As this study examined the impacts of river drought on all three areas of sustainable development simultaneously and comprehensively, it is expected that the results will fill the existing theoretical and practical gap affecting improvements in assessment and management of sustainable development risks.

Impact of Japanese Post-Disaster Temporary Housing Areas’ (THAs) Design on Mental and Social Health

Article

Full-text available

Nov 2019
Int J Environ Res Publ Health

The phenomenon named kodokushi, meaning death alone without the care or company of anyone inside temporary housing, appeared after the Kobe earthquake in Japan in 1995 with some 250 cases. This paper analyzes the evolution of Japanese temporary houses—to attempt to prevent the problem of kodokushi—from the point of view of management, how services and activities are organized, and design. We will use case studies as our methodological tool, analyzing the responses in 1995 Kobe (50,000 THs), 2004 Chūetsu (3000 THs), 2011 Tōhoku (50,000 THs), and 2016 Kumamoto (4000 THs). This article shows how the Japanese THAs follow a single design that has undergone very little variation in the last 25 years, a design which promotes the social isolation of their residents, making recovery—from the psychological perspective—and helping the most vulnerable members of society, more difficult. In small scale disasters (Chūetsu) applying organization and management measures was able to correct the problems caused by design and there were no cases of kodokushi: in large-scale disasters (Tōhoku), however, the difficulties to implement the same measures resulted in the reappearance of new cases at rates similar to Kobe’s. Our main conclusion is that the design of Japanese THAs must be reconsidered and changed to respond to the real needs of the most vulnerable groups.

Multi-criteria group decision making based on Archimedean power partitioned Muirhead mean operators of q-rung orthopair fuzzy numbers

Article

Full-text available

Sep 2019
PLOS ONE

Two critical tasks in multi-criteria group decision making (MCGDM) are to describe criterion values and to aggregate the described information to generate a ranking of alternatives. A flexible and superior tool for the first task is q-rung orthopair fuzzy number (qROFN) and an effective tool for the second task is aggregation operator. So far, nearly thirty different aggregation operators of qROFNs have been presented. Each operator has its distinctive characteristics and can work well for specific purpose. However, there is not yet an operator which can provide desirable generality and flexibility in aggregating criterion values, dealing with the heterogeneous interrelationships among criteria, and reducing the influence of extreme criterion values. To provide such an aggregation operator, Muirhead mean operator, power average operator, partitioned average operator, and Archimedean T-norm and T-conorm operations are concurrently introduced into q-rung orthopair fuzzy sets, and an Archimedean power partitioned Muirhead mean operator of qROFNs and its weighted form are presented and a MCGDM method based on the weighted operator is proposed in this paper. The generalised expressions of the two operators are firstly defined. Their properties are explored and proved and their specific expressions are constructed. On the basis of the specific expressions, a method for solving the MCGDM problems based on qROFNs is then designed. Finally, the feasibility and effectiveness of the method is demonstrated via a numerical example, a set of experiments, and qualitative and quantitative comparisons.

Research on post-Wenchuan earthquake recovery and reconstruction implementation: A case study of housing reconstruction of Dujiangyan City

Article

Full-text available

Aug 2019

This paper is a systematic study on the reconstruction planning and policy implementation of the Wenchuan earthquake. It mainly summarizes the whole process of reconstruction from the perspective of post-disaster housing reconstruction. From both macro and micro scales, it carries out an in-depth analysis of the emergency response and reconstruction mechanisms adopted by the central and local governments after the earthquake occurred. The content mainly includes housing disaster assessment, housing reconstruction process management, and housing reconstruction particular policies, which are the key factors affecting post-disaster reconstruction benefits. To better explain the great success of the Wenchuan earthquake recovery and reconstruction policy for housing reconstruction, this paper conducts a detailed investigation of the reconstruction planning, policies, implementation and results of the housing reconstruction in Dujiangyan central city. The study found that the diversification of the Dujiangyan housing reconstruction development model has mostly met the different needs of the victims. The participation of multiple entities such as the market and social institutions has accelerated the economic recovery of post-disaster reconstruction and promoted the early completion of housing work; The unique system introduced during the reconstruction process not only protected the interests of the victims, but also effectively solved the complex property rights problem in rural areas, achieved the goal of saving land and integrating resources, and replaced a large number of land resources for the government. Keywords: Wenchuan earthquake, Post-disaster housing reconstruction, Reconstruction mode and policy, Reconstruction management

Interval-Valued Intuitionistic Fuzzy Multiple Attribute Group Decision Making with Uncertain Weights

Article

Full-text available

Mar 2019
MATH PROBL ENG

The theory of interval-valued intuitionistic fuzzy sets (IVIFSs) has been an impactful and convenient tool in the construction of advanced multiple attribute group decision making (MAGDM) models to counter the uncertainty in the developing complex decision support system. To satisfy much more demands from fuzzy decision making problems, we propose a method to solve the MAGDM problem in which all the information supplied by the decision makers is expressed as interval-valued intuitionistic fuzzy decision matrices where each of the elements is characterized by an interval-valued intuitionistic fuzzy number, and the information about the weights of both decision makers and attributes may be completely unknown or partially known. Firstly, we introduce a consensus-based method to quantify the weights of all decision makers based on all interval-valued intuitionistic fuzzy decision matrices. Secondly, we utilize the interval-valued intuitionistic fuzzy weighted arithmetic (IVIFWA) operator to aggregate all interval-valued intuitionistic fuzzy decision matrices into the collective one. Thirdly, we establish an optimization model to determine the weights of attributes depending on the collective decision matrix and the given attribute weight information. Fourthly, we adopt the weighted correlation coefficient of IVIFSs to rank all the alternatives from the perspective of TOPSIS via the collective decision matrix and the obtained weights of attributes. Finally, some examples are used to illustrate the validity and feasibility of our proposed approach by comparison with some existing models.

Crisis Management Between Public Relations and the Holonic Multi-Agent Approach

Article

Full-text available

Jan 2018

Levente-Attila Bakos

Urban Vulnerability Assessment Using AHP

Article

Full-text available

Feb 2018

Purpose. Physical expansion of urban areas and cities is of great importance nowadays. Irreparable damages will thus be caused by lack of proper planning against natural disasters. Crisis management will therefore guide through prevention, preparedness, disaster relief, and recovery by planning an appropriate program. Methodology. Principal processes of crisis management against earthquake in Iran were evaluated and discussed. Multicriteria earthquake crisis management was then proposed by means of Analytic Hierarchy Process (AHP). Vulnerability of 19 urban areas in Qazvin city was studied and analyzed as a case study. Three main criteria were considered as “physical dimensions and physical vulnerability texture,” “the amount of urban texture responsibility to aid after crisis,” and “possibility of city reversibility after the crisis.” These criteria were divided into 20 subcriteria which were prioritized by a questionnaire survey. Findings: “High population density,” “urban texture of old and repairable buildings,” “lack of relief and medical services,” “a few organic texture areas,” “sidewalks with less than 6 meters width in the region,” and “lack of open spaces in the area” were concluded to be the most important reasons causing high vulnerability of urban texture in Qazvin city.

Design and Supply System for Emergency Temporary Housing by Various Construction Methods from the Perspective of Environmental Impact Assessment: The Case for the Great East Japan Earthquake

Article

Sep 2019
ENERG BUILDINGS

Recently in Japan, a large number of emergency temporary housing (ETH) units have been supplied as great disasters occurred. In general, victims cannot use supplied ETH units for more than 2 years, as specified in the Disaster Relief Act. Nonetheless, some victims have used ETH for over 7 years, particularly after the Great East Japan earthquake (2011). ETH units were adopted with various construction methods such that ETH could be readily available after the earthquake. Today, there are various ETH supply systems, although it is generally accepted that supplying ETH units consumes large amounts of resources in a short time. The goal of this study is to clarify and quantify Greenhouse Gas (GHG) emissions from supplying ETH units using a life cycle assessment that considers the construction methods and usage life. The results indicate that for a usage time shorter than 2 years, GHG emissions are the lowest for steel frames with reused materials. For usage times longer than 5 years, GHG emissions are lowest for ETH units with high thermal performance. In summary, ETH usage time should be considered when choosing a suitable construction method for reducing GHG emission.

A framework for risk assessment, management and evaluation: Economic tool for quantifying risks in supply chain

Article

Aug 2018
FUTURE GENER COMP SY

Nowadays risks become a critical part in our life since they are involved in everything we do and participate. Some people do not want to do anything which associated with risk and others flourish on risk. In both types of people, they must relieve their risk through utilizing safety measures such as flame retardant suits and helmets for race car drivers, and safety ropes for rock climbers. All risks can be minimized to a manageable level by employing the proper mitigation strategy. In supply chain, the decisionmaking process contains risks which can be influential on the company's progress in introducing a new product, expanding in various markets, and outsourcing manufacturing operations. Companies will be likely to perform well via considering risks in their decisions and employing the proper mitigation strategy for responding to the unexpected events The subjectivity, uncertainty and vagueness which exist in reality are the key factors to make risks difficult to handle Hence, risk analysis, mitigation and control provide recommendations for making suitable decisions. In order to quantify risks in supply chain, an integrated method with a neutrosophic analytical hierarchy process (N-AHP) and neutrosophic technique has been demonstrated for this purpose. It is aimed for matching similarity to the ideal solution (N-TOPSIS) by order preference. The neutrosophic values in our research can deal effectively and efficiently with vague, uncertain and in incomplete information which has a significant impact on risk management. For illustrating the suggested methodology, a real case study is illustrated.

A combination risk-based approach to post-earthquake temporary accommodation site selection: A case study in Iran

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