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

Abstract

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.

Full text

See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/353496662
Identification and prioritization of seismic risks in urban worn-out textures
using fuzzy Delphi method
ArticleinEnvironmental Engineering and Management Journal · June 2021
DOI: 10.30638/eemj.2021.096
CITATIONS
3
READS
170
6 authors, including:
Some of the authors of this publication are also working on these related projects:
Hybrid Intelligent and Dynamic System for Adoption and Implementation of Building Information Modelling (BIM) in Construction Small and Medium-Sized Enterprises
(SMEs) View project
Blockchain Technology and Construction View project
Mohsen Oghabi
Islamic Azad University Kermanshah Branch
14 PUBLICATIONS114 CITATIONS
SEE PROFILE
Hadi Sarvari
Islamic Azad University Khorasgan (Isfahan) Branch
44 PUBLICATIONS289 CITATIONS
SEE PROFILE
Hamidreza Kashefi
37 PUBLICATIONS343 CITATIONS
SEE PROFILE
Dr Daniel W.M. Chan
The Hong Kong Polytechnic University
186 PUBLICATIONS7,417 CITATIONS
SEE PROFILE
All content following this page was uploaded by Hadi Sarvari on 27 July 2021.
The user has requested enhancement of the downloaded file.

Environmental Engineering and Management Journal June 2021, Vol. 20, No. 6, 1035-1046
http://www.eemj.icpm.tuiasi.ro/; http://www.eemj.eu
“Gheorghe Asachi” Technical University of Iasi, Romania
IDENTIFICATION AND PRIORITIZATION OF SEISMIC RISKS
IN URBAN WORN-OUT TEXTURES USING FUZZY DELPHI METHOD
Jalal Sadeghi1, Mohsen Oghabi2
∗
, Hadi Sarvari3*, Mohammad-Sediegh Sabeti1,
Hamidreza Kashefi4, Daniel Chan5
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 Mathematics Education, Farhangian University, Tehran, Iran
5Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
Abstract
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.
Keywords: Fuzzy Delphi method, Iran, seismic risk management, urban risk management, urban worn-out texture
Received: May, 2020; Revised final: October, 2020; Accepted: October, 2020; Published in final edited form: June, 2021
1. Introduction
Urban texture is a dynamic and changing
quantity that shows how cities have evolved and
expanded over the time. The texture of each city
determines the urban physical space and distance
between the urban elements (Kropf, 1996; Kong and
Qian, 2019). Urban worn-out textures are parts of the
urban context that have gradually lost their physical
and functional quality (Nakhi et al., 2016). The
recession of an area of the city will initiate a process
∗ Author to whom all correspondence should be addressed: e-mail: h.sarvari@khuisf.ac.ir; m.oghabi@iauksh.ac.ir
of wear and tear, and sooner or later, it will affect the
urban textures depending on their characteristics. The
urban worn-out texture usually involves old and
unstable buildings in textures with narrow pathways.
The residents of these buildings are of low-income and
socially-deprived class, who do not normally receive
adequate service and attention after an unfortunate
event such as earthquake. The main characteristics of
worn-out textures consists of age (Kiani et al., 2017;
Varesi et al., 2012), small size, low number of floors
(Kiani et al., 2017; Shieh et al., 2014), lack of proper
PROOF

Sadeghi et al./Environmental Engineering and Management Journal 20 (2021), 6, 1035-1046
accessibility (Lee et al., 2007; Shieh et al., 2014;
Taylor et al., 2006), deterioration (wear and tear),
vulnerability of urban infrastructure to obsolescence
and deterioration (Cirianni et al., 2012; Kongar et al.,
2017), and use of traditional and non-standard
materials in their construction processes (FEMA,
2010; Varesi et al., 2012). Typically, these buildings
lack the neccesary structural systems. These systems
are categorized based on the construction materials
(e.g., steel, concrete, masonry, wood, or iron-wood)
(FEMA, 2010; Kiani et al., 2017; Varesi et al., 2012).
In worn-out structures, infrastructure such as
electricity networks, communications structures, gas
networks, sewage and water systems etc. are often
obsolete and out of date. Earthquake causes
widespread damages to such dilapidated and old
structures, making providing service very difficult at
the time of emergencies (Cirianni et al., 2012; Kongar
et al., 2017). Such structures are also vulnerable to
secondary risks.
The existence of a large number of buildings
which have been built using traditional materials as
well as old and unreliable infrastructures increases the
possibility of fire and explosion (Mondal, 2019;
Trevlopoulos et al., 2019; Zhen-dong Zhao et al.,
2008). There is also the possibility of flooding (de
Ruiter et al., 2017). Due to their unique characteristics,
these structures play a critical role in the vulnerability
of the city to natural disasters especially when
constituting a high percentage of the total building
count, and therefore should be taken into account in
selecting an appropriate strategy to mitigate the
devastating effects of earthquakes (Huang et al., 2012;
Liu et al., 2019; Nyimbili et al., 2018; Yucesan and
Kahraman, 2019). Over 70,000 hectares of Iranian
cities include worn-out urban structures (Asgari et al.,
2015). The city of Isfahan with more than 40% of
worn-out structure was ranked first in Iran (Saghaei,
2017). In most large cities of Iran, such as Tehran
(capital of Iran), Shiraz and Kermanshah, about 5%
(Asgari et al., 2015), 15% (Varesi et al., 2012) and
12% (Mosavi et al., 2014) of the total area of the city
are made of worn-out structures, respectively. One of
the main goals of urban planning is to reduce the
vulnerability of the city to earthquakes and minimize
the human life and economic losses after such event
(Nazmfar et al., 2019).
Urban worn-out textures are at a greater risk
due to the incompatibility of their structural design
with building standard codes, lack of proper
communication network, and worn-out facilities and
equipment (Nakhi et al., 2016). Urban worn-out
textures are usually one of the most densely occupied
parts of the city and because of the quality of the
materials used in their buildings and their greater age,
a special care should be given to their vulnerability in
crisis management (Tsai and Chen, 2010). In this
context, it is important to identify the potential risk
factors and determine their corresponding probability
of occurrence. Risk assessment provides important
and essential information on prioritizing risk and
employing effective techniques to mitigate the
consequences (Garcia et al., 2014). Due to the
challenges in the urban worn-out textures, the main
objective of risk management is to eliminate
ambiguity of the situation and provide the
management team with a detailed plan to approach
this issue. In order to identify and prioritize the
potential risks, common popular methods such as
document investigation, data collection approaches
such as brainstorming, Delphi method, interview, etc.
have been used in the majority of risk management
studies. In all these methods, several descriptive and
numerical phrases are used to estimate the probability
of risks. These estimates are not accurate and need to
be examined by newer methods to increase te accuracy
of the estimates.
The purpose of this study is to identify the risks
in the urban worn-out texture followed by an
earthquake event and prioritize them according to the
Fuzzy Delphi method in order to mitigate the
destructive consequences efficiently.
2. Material and methods
2.1. Research background
2.1.1. General context
Iranian plateau is located on the Alpine-
Himalayan seismic belt. The convergent movement of
the Eurasian-Saudi tectonic plates has made Iran as
one of the most active seismic zones in the world.
From a statistical point of view, 8% of the world’s
earthquakes and 17% of the world’s largest
earthquakes have occurred in Iran (Zare and
Kamranzad, 2015). This plateau has been defined as a
young continental collision except for the Makran area
in the south-eastern coast of Iran (Byrne et al., 1992;
Masson et al., 2007). The majority of seismic activities
occur near the political borders of Iran (Walker and
Jackson, 2004). The city of Kermanshah, the capital of
Kermanshah Province, is located in the western part of
Iran and in Zagros tectonic seismic zone. The seismic
activity of this region is categorized as very high and
is one of the most earthquake-prone areas in Iran
(BHRC-PN, 2018). The city is surrounded by major
seismic faults including the Recent Testament fault
(Main recent fault), which runs northwest-southeast
and forms the northeast boundary of the Zagros
mountain range. This fault is actually a series of strike-
slip faults including Doroud Fault, Nahavand Fault,
Garon Fault, Sahneh Fault, and Pearl Fault, which
range from 33 to 35 degrees north latitude from the
southeast to the northwest. Each year a large number
of earthquakes happen in Kermanshsah province. For
example, Sare-pol Zahab earthquake of 2017 with a
magnitude of 7.3 caused many casualties and total
destruction of the city.
Given the seismic record and the existence of
important and active faults in Kermanshah province,
the issue of protecting cities and rural areas in the
province against the effects and consequences of
earthquakes seems necessary. The presence of worn-
out textures in various parts of Kermanshah city such
1036
PROOF

Identification and prioritization of seismic risks in urban worn-out textures using fuzzy DELPHI method
as Jalili, Feyzabad, Bazar, Sarcheshmeh, Azadi
Square, and etc. indicates the vulnerability of the
region to seismic events. Worn-out urban textures are
a major part of the city’s urban area in Iran (Isfahan
40%, Shiraz 22%, Kermanshah 12%), which require
rehabilitation in order to maintain their functionality
and in some cases, they should be reconstructed due to
severe degradation (Nakhi et al., 2016). Masonry is
one of the main construction materials used in
different buildings of the worn-out texture of the city
such as residential buildings, historic and cultural
heritage buildings. It is important to conduct surveys
to assess the vulnerability of these buildings to
earthquake. These surveys will eventually help in
adopting appropriate strategies to deal with potential
risks (Ferreira et al., 2013). Preventive approaches
have recently attracted the attention of many experts
and specialists in the field, and many studies were
aimed at reducing earthquake risk and assess potential
disaster scenarios (Kegyes-Brassai, 2014).
Ianoş et al. (2017) have signified the need for
reconstruction and strengthening of worn-out
buildings as well as other necessary measures
regarding ancient and historical textures, schools, and
religious places. It was shown that the interplay
between urban planning and earthquake risk
management is critical in vulnerability assessment of
structures. These results can be used to formulate
strategies and programs for dealing with earthquake
impacts (Barbat et al., 2010). Seismic performance
assessment of buildings can be considered as an
important step in reducing earthquake risk, which
provides important data for the government,
authorities, and officials (Kegyes - Brassai, 2014).
Earthquake risk management is a multi-stage process
consisting of a range of data, variables, and
probabilistic factors (Vahdat et al., 2014). Multi-stage
risk management processes include risk identification,
qualitative and quantitative risk assessment, risk
planning and response, monitoring, and control. The
risk is an uncertain event, which can have a positive or
a negative impact on the project objectives (PMI,
2004). Identifying and prioritizing these risks is
essential in risk management, and the uncertainties
may have a huge effect in prioritization of these
uncertain events. One of the methods for identification
and prioritization of risks is the Fuzzy set theory which
was introduced by Zadeh in 1965. The classical sets
assign zero and one to each proposition in the fuzzy
set of each member; however, the fuzzy set of each
member actually belongs to the interval [0, 1] (Zadeh,
1965). Fuzzy set is a powerful tool in describing
phenomena affected by uncertain parameters. In this
theory, the concept of membership degrees μ: X → [0,
1] is fundamental (Bustince and Burillo, 1996).
Rashed (2003) explored the vulnerability of California
city to earthquakes and found that combining the
Analytic Hierarchy Process (AHP) and Fuzzy
methods leads to a more reliable evaluation of
vulnerability of the city to earthquakes. Combination
of the Fuzzy and AHP model were used by many
researchers for risk evaluation and prevention in
natural hazards (Huang et al., 2012; Nyimbili et al.,
2018; Yucesan and Kahraman, 2019). Tang and Wen
(2009) used an artificial intelligence (AI) system to
investigate earthquake risk in Diang city, China. Peng
(2015) has considered the importance of assessing
regional vulnerability to prevent and mitigate
earthquake effects, and used different Multi-Criteria
Decision Making (MCDM) methods to evaluate the
criteria. Finally, the TOPSIS method was shown to be
the safest and most accurate in prioritization of risks.
Imani et al (2016) developed strategies for organizing
and reducing the vulnerability of worn-out textures
(Case Study of Imamzadeh Hasan district in Tehran)
using Strength Weakness Opportunity Threat (SWOT)
model and Quantitative Strategic Planning Matrix
(QSPM) matrices. After studying the internal factors,
i.e., strengths and weaknesses, and the external
factors, i.e., opportunities and threats of the region,
Delphi method was used to complete the information.
In a study carried out by Nayeri et al. (2018) on urban
worn-out texture (case study of Abdulabad
neighbourhood of Tehran), the resistance of worn-out
texture to earthquake was studied. Fuzzy method and
AHP were used to investigate the main factors in
resistance. In addition, verbal expressions expressed
in triangular fuzzy numbers was used to eliminate the
human error. It was shown that managerial and
economic factors and participation of residents in
recreation and resuscitation process were the most
important among the studied parameters. Li et al.
(2017) identified and evaluated the risks of the historic
buildings based on AHP and entropy weight method.
Identifying the risks is the first step in the risk
management process. The purpose of risk
identification is to collect information about the details
of as many uncertain events as possible prior to their
occurrence, in order to have previous preparation to
deal with them when they occur. An effective risk
management focuses only on dealing with the risks,
i.e., it is important to identify and eliminate the non-
risk items. In this study, a number of risks were
identified in order to assess the subjectivism of
potential risks in the studied worn-out texture, based
on documentary studies and field investigations, as
well as the experts' opinions in the relevant field. The
identified risks are presented in Table 1.
2.1.2. Case study investigation
Kermanshah city is located at Kermanshah
province with GPS coordinates of 33 °: 36' to 35°: 15'
north latitude and 45°: 24' to 48°: 30' east latitude. The
worn-out texture of Jalili district in Kermanshah, Iran,
was selected as the case study in this investigation
(Fig. 1). It is confined to the Barekeh district from the
north, to Waziri and Kale Hawas district from the
south, to Faizabad district from the west, and to the
Rashidi and Waziri district from the east. Based on the
results of the 2016 population and housing census of
Kermanshah, Jalili district is one of the oldest districts
of Kermanshah with a population of 1244 people in
2019. Most of the buildings located in this district are
worn and are estimated to be over 50 years old. The
1037
PROOF

Sadeghi et al./Environmental Engineering and Management Journal 20 (2021), 6, 1035-1046
materials used in buildings are mostly traditional and
masonry materials, but less than 15% of buildings with
new materials are found at some places. Residential
buildings in this study were categorized based on their
total area including building with area less than 75 m2
(147 cases), 76-100 m2 (152 cases), 101-200 m2 (80
cases), and 201-500 m2 (10 cases). 97.4% of these
buildings had a total area of less than 200 square
meters (MPO, 2018). The majority of the houses in
this neighbourhood were not built according to new
construction methods such as 3D Sandwich Panel,
Prefabricated Reinforced Concrete Systems,
Insulating Concrete Formwork (ICF), Hot Rolled
Steel Structures, etc., and it should be mentioned that
the Iranian code of practice for seismic resistant
design of buildings (Standard No. 2800) has not been
observed in the construction process of nearly all of
them.
The most common building materials used are
brick, iron, wood, adobe which are traditional
construction materials. According to the recent
investigation, only 56 building blocks were
constructed by reinforced concrete and steel.
Regarding building materials this study includes 53
steel structures, 3 reinforced concrete structures, 136
iron and brick buildings, 167 brick and wood
buildings, 5 adobe buildings, and 4 building made
from other materials (MPO, 2018).
Poor accessibility is another significant issue in
these parts of the city. In fact, the only one or two
main street are acceptable as far as their width is
concerned. Next main issue is the total lack of open
areas, recreational facilities and centers, leisure parks,
and other conveniences. Equally important is also the
absence of fire stations, medical centers, clinics, and
relief centers.
--
Fig. 1. Area of the case study (Jalili, Kermanshah, Iran)
Table 1. Identified risks in urban worn-out textures from literature review
Source of risk
Risk
NO.
Risk factors Reference
Demolition and vulnerability
of residential buildings
1 Type of structural systems
FEMA (2010); Kiani et al. (2017);
Varesi et al. (2012)
2
Quality of the building
Kiani et al. (2017); Shieh et al. (2014)
3
Antiquity of buildings
Kiani et al. (2017); Varesi et al. (2012)
4
Number of floors in a building
Kiani et al. (2017); Shieh et al. (2014)
5
Non-compliance with materials standards
FEMA (2010); Varesi et al. (2012)
6
Environmental and structural conditions of the
worn-out texture neighborhood
BHRC-PN (2018); FEMA (2010)
Infrastructure vulnerability
7
Sewage and water networks and installations
Cirianni et al. (2012)
8
Gas networks and installation
Cirianni et al. (2012)
9
Electricity networks and utilities
Kongar et al. (2017)
10
Telecommunication networks and installation
Cirianni et al. (2012)
Blockages and accessibilities
11
Roadblocks (Alleyways and Streets)
Taylor et al. (2006)
12
Outdoor unavailability
Shieh et al (2014)
13
Unavailability of rescue centers
Shieh et al. (2014)
14
Unavailability of fire station
Shieh et al. (2014)
15
Unavailability of health centers
Shieh et al. (2014)
Secondary risks (Secondary
risk exposure of buildings)
16
Fire
Mondal (2019)
17
Explosion
Zhao et al. (2008)
18
Flood
Quigley and Duffy, (2020)
19
Aftershocks
Trevlopoulos et al. (2019)
1038
PROOF

Identification and prioritization of seismic risks in urban worn-out textures using fuzzy DELPHI method
2.2. Research methodology
There are different approaches for collecting
the required information for identification of the
variables involved in a given problem. The widely
used Delphi method collects information from
professional respondents who are asked to give
opinions in their area of expertise. The method is
based on reaching a consensus by taking into account
the opinions of all members of the group (Hsu and
Sandford, 2007; Khoshfetrat et al., 2020). Participants
who are included in the Delphi method form a
specialized and expert group, and are the main reason
behind its success. However, this success is dependent
upon the number of experts and their qualifications
(Powell, 2003). Based on the resources and the scope
of the problems, the number of panel experts is
changeable (Delbecq et al., 1975; Fink et al., 1984).
The larger number of panel experts, the higher the
susceptibility of the judgement (Murphy et al., 1998).
Fig. 2. Flowchart of Delphi technique in qualitative
research
The Delphi method is still evolving. One of the
advantages of the Delphi method is its ease of use;
because it does not require advanced mathematical,
execution and analysis skills, but requires a person
familiar with the Delphi method and creativity in
project design (Dabiri et al., 2020). This method has
always been faced with expert opinions with low
convergence and high implementation costs.
Important ideas and ideas may also be removed by
analysts during the Delphi process. Therefore, the
concept of combining the traditional Delphi method
and fuzzy theory was introduced by Murray et al. in
1985, in order to remove the ambiguity and
inconsistency of the Delphi method (Sarvari et al.,
2019a). In the fuzzy Delphi method, as the name
suggests the information obtained from the experts is
analyzed through a fuzzy scheme (Chen, 2012). The
fuzzy Delphi methodis the basis for decision-makers
to screen ineffective factors and to avoid the influence
of geometric mean final values. In addition to reducing
the costs and time, it allows to evaluate the fuzziness
of the decision-making process and to achieve a better
factor selection (Sanaei et al., 2011).
In order to identify the potential risks in the
worn-out urban textures, first a questionnaire was
prepared based on the studies of past earthquake
events in Iran and the world, as well as interviews with
relevant field experts. Experts were asked to amend
any other source of risk to this questionnaire if they
were not included. Reliability assessment at each stage
was based on Cronbach's alpha calculation of the
questionnaire completed by the experts. Microsoft
Excel and SPSS software were used for calculation.
The flowchart for applying the Delphi technique in
qualitative decision-making is shown in Fig. 2.
2.3. Method
2.3.1. Triangular fuzzy number
Fuzzy number is a fuzzy set with the following
three conditions:
- Being normalized
- Be convex
- Its supporting set is bounded
Triangular fuzzy number (TFN) is a fuzzy
number, which is displayed with three number (F=l,
m, u). The upper limit is denoted by u; lower limit is
denoted by l and m is the most probable value of a
fuzzy number. The membership function of a
triangular fuzzy number is given by (Habibi et al.,
2015), (Eq. 1):









<<
−
−
<<
−
−
=
otherwise
uxm
mu xu
mxl
m
x
xu
f
0
1
1
)(
(1)
Triangular fuzzy number F= (l, m, u) is
displayed geometrically in Fig. 3.
Fig. 3. The geometrical image of the triangular fuzzy
number (Habibi et al., 2015)
The fuzzy Delphi method consists of the
following essential steps (Habibi et al., 2015): (i)
Identify and select the appropriate spectrum to fuzzify
the linguistic expressions of the responders, (ii) Fuzzy
1039
PROOF

Sadeghi et al./Environmental Engineering and Management Journal 20 (2021), 6, 1035-1046
aggregation of fuzzification values, (iii)
Defuzzification of values, (iv) Selecting of threshold
and screening criteria. In the algorithm of
implementation of fuzzy Delphi method, the triangular
fuzzy numbers are in 5-point Likert scale of
measurement according to Table 2 and Fig. 4.
Table 2. Triangular fuzzy number of five-point Likert scale
Triangular fuzzy
number (l, m, u)
Fuzzy
number
Linguistic Variable
(0,0,0.25) 1
Very Unimportant
(VU)
(0,0.25,0.5) 2 Unimportant (U)
(0.25,0.5,0.75) 3
Moderately Important
(MI)
(0.5,0.75,1)
4
Important (I)
(0.75,1,1) 5 Very Important (VI)
Fig. 4. Triangular fuzzy numbers equivalent to the five-
point Likert spectrum (Habibi et al., 2015)
In this study, the fuzzy average method was
used to aggregate the experts' opinions. Each expert's
viewpoint can be presented by a triangular fuzzy
number (l, m, u) (Eq. 2), and the fuzzy average can be
calculated by the following expression (Eq. 3) (Habibi
et al., 2015):
),,(
iiii
umlF =
(2)
n
u
n
m
n
l
f
ave
∑∑∑
=,,
(3)
where n is the total number of experts. The
defuzzification of values obtained is based on the
following equations Eqs.(4-5):
),,( umlF =
(4)
3uml
X++
=
(5)
Table 3 shows the defuzzification of triangular
fuzzy numbers for a five-point scale of measurement
calculated using (Eq. 5).
2.3.2. Lawshe method
The Lawshe method (Lawshe, 1975) was used
to validate the content of the questionnaire. The
number of participants involved in the validation of
the method was 10 experts from different fields to
provide a more accurate judgment. Quantifying panel
member votes is done by calculating the content
validity ratio (CVR) (Lawshe, 1975). The following
formula (Eq. 6) is used for this purpose:
2
2
n
n
ne
CVR −
=
(6)
where: ne is the number of group members who
consider the questionnaire necessary and n is the total
number of group members.
Note that, the minimum acceptable CVR for
the 10-member panel is 0.62. To determine the mean
value of panel members' judgments, the following
transformations were performed in the questionnaire:
(i) Replacement with number 3 if the parameter is
considerred as necessary, (ii) Replacement with
number 2 if the parameter is considerred as useful but
unnecessary, (iii) Replacement with number 1 if the
parameter is considerred as unnecessary. The results
for the average score of panel judgement and CVR
value for each question and the results of acceptance
and rejection of questions are given in Table 4.
According to the results, all the potential risks
identified in the survey questionnaire were approved
and confirmed by the experts. The statistical
population of this study consisted of 15 experts in
various technical and engineering fields. These
experts are among the most experienced and highly
qualified industrial practitioners in their fields selected
from the public and private sectors and governmental
organizations. Table 5 shows the demographic
characteristics of the experts who attended the Delphi
process. The Fuzzy Delphi questionnaire includes the
19 risk factor related.
3. Results and discussion
In Fuzzy Delphi technique the analysis of
experts’ opinions is done in several phases. If in two
successive phases the average experts’ opinions seems
reasonable the process stops. Rejection or acceptance
of criterion is done through a specific threshold. This
threshold is normally 0.7, but based on the type of
research and also the viewpoints of experts it can be
different. If the criterion is higher than the threshold it
is accepted, and if not it is rejected (Cheng and Lin,
2002; Habibi et al., 2015).
Table 3. Defuzzification numbers for a five-point Likert scale
Very Important (VI)
Important (I)
Moderately Important (MI)
Unimportant (U)
Very Unimportant (VU)
0.92
0.75
0.5
0.25
0.083
1040
PROOF

Identification and prioritization of seismic risks in urban worn-out textures using fuzzy DELPHI method
Table 4. CVR value, numerical average of judgment and results of accepting and rejecting questions
Source of risk
NO.
Question
Experts’ opinions
CVR
Numerical
mean of
judgments
Minimum
acceptable
CVR for 10
experts
Accept
query
efficiency
Unnecessary
Abstain
Necessary
Demolition and
vulnerability of
residential
buildings
1
Type of structural systems
0
1
9
0.8
2.9
0.62
accept
2
Quality of the building
1
9
0.8
2.8
0.62
accept
3
Antiquity of buildings
0
0
10
1
3
0.62
accept
4
Number of floors in a
building
0 1 9 0.8 2.9 0.62 accept
5
Non-compliance with
materials standards
0 0 10 1 3 0.62 accept
6
Environmental and structural
conditions of the worn-out
texture neighborhood
0 0 10 1 3 0.62 accept
Infrastructure
vulnerability
7
Sewage and water networks
and installations
0 0 10 1 3 0.62 accept
8
Gas networks and installation
0
0
10
1
3
0.62
accept
9
Electricity network and
utilities
0 0 10 1 3 0.62 accept
10
Telecommunication networks
and installation
0 1 9 0.8 2.9 0.62 accept
Blockages and
accessibilities
11
Roadblocks (Alleyways and
Streets)
0 0 10 1 3 0.62 accept
12
Outdoor unavailability
0
1
9
0.8
2.9
0.62
accept
13
unavailability of rescue
centers
0 1 9 0.8 2.9 0.62 accept
14
Unavailability of fire station
0
0
10
1
3
0.62
accept
15
Unavailability of health
centers
0 1 9 0.8 2.9 0.62 accept
Secondary risks
(Secondary risk
exposure of
buildings)
16
Fire
0
0
10
1
3
0.62
accept
17
Explosion
0
1
9
0.8
2.9
0.62
accept
18
Flood
0
1
9
0.8
2.9
0.62
accept
19
Aftershocks
0
1
9
0.8
2.9
0.62
accept
Table 5. Personal characteristics of Delphi panel of experts
Frequency (%)
Respond
Background
7 (47)
Bachelor
Education
level
5 (33)
Master
3 (20)
PhD
3 (20)
Below 10 years
Working
experience
7(47)
11 - 20 years
5 (33)
Over 21 years
7 (47)
Public
Working
Sector
6 (40)
Private
2 (13)
Academic
4 (27)
Senior manager
Position
2 (13.3)
Project coordinator
3 (20)
Civil engineer
2 (13.3)
Financial manager
2 (13.3)
Project manager
2 (13.3)
Faculty member
3.1. First phase of the fuzzy Delphi method
The fuzzy Delphi questionnaire was designed
according to the previous studies. The questionnaire
consists of 4 sources of risks and 19 questions. Fuzzy
Delphi Analysis of collected data was performed with
Microsoft Excel software program. Fuzzy average
method is used for aggregation of experts’ opinions
Eqs. (1- 2). Defuzzification of opinions is done using
Eqs. (3-4). The threshold is set to 0.25. The average
experts’ opinions after first survey are presented in
Table 6. Given that in the first step of the Fuzzy Delphi
method, none of the responses are less than the
threshold (0.25), thus none of them were removed in
the continuation of the Fuzzy Delphi process (Cheng
and Lin, 2002; Habibi et al., 2015 ).
3.2. Second phase of fuzzy Delphi method
In this phase, the results of the first phase and
the extent of their disagreement with the views of
other experts were given to the members of the group
along with a new questionnaire and they were asked to
comment on it. Polls stopped if the difference between
the two polls was below 0.1 (Cheng and Lin, 2002).
The analysis results of the second phase and the
difference between the first and second survey are
presented in Table 7. As it can be observed, the
average defuzzification difference in the two steps was
less than 0.1, and thus the convergence was achieved,
implying that a third phase was not necessary (Cheng
and Lin, 2002).
3.3. Prioritization of the risks of urban worn-out
textures
To prioritize the risk factors considered in the
questionnaire, the defuzzification averages obtained
from the second phase of the fuzzy Delphi method
(Table 7) were compared to the Defuzzification
numbers of the five-point Likert scale shown in Table
3. For example, type of structural system, where
the average is 0.817 and is classified VI. As shown in
Table 8, risks are classified based on their significance
(Habibi et al., 2015).
1041
PROOF

Sadeghi et al./Environmental Engineering and Management Journal 20 (2021), 6, 1035-1046
Table 6. Average experts' opinions after the first phase survey of Delphi method
Source of Risk
Risk No.
Risk factors
Triangular fuzzy mean with
experts' opinions
Average
defuzzification after
first phase survey
u
m
l
Demolition and
Vulnerability of
Residential
Buildings
1
Type of structural systems
0.967
0.883
0.633
0.828
2
Quality of the building
0.933
0.817
0.567
0.772
3
Antiquity of buildings
1.000
0.967
0.717
0.894
4
Number of floors in a building
0.967
0.850
0.600
0.806
5
Non-compliance with materials standards
1.000
0.983
0.733
0.906
6
Environmental and structural conditions of the
worn-out texture neighborhood
0.950 0.883 0.633 0.822
Infrastructure
vulnerability and
urban Installation
7
Sewage and water networks and installations
0.983
0.850
0.600
0.811
8
Gas networks and installation
0.983
0.950
0.700
0.878
9
Electricity network and utilities
0.967
0.800
0.550
0.722
10
Telecommunication networks and installation
0.850
0.650
0.400
0.633
Blockages and
accessibilities
11
Roadblocks (Alleyways and Streets)
1.000
1.000
0.750
0.917
12
Outdoor unavailability
0.933
0.817
0.583
0.778
13
Unavailability of rescue centers
0.983
0.883
0.633
0.833
14
Unavailability of fire station
0.983
0.967
0.717
0.889
15
Unavailability of health centers
0.983
0.933
0.683
0.867
Secondary risks
16
Fire
1.000
0.967
0.717
0.894
17
Explosion
0.900
0.750
0.517
0.722
18
Flood
0.783
0.583
0.350
0.572
19
Aftershocks
0.867
0.767
0.533
0.722
Table 7. Average expert’s opinions after the second phase survey of Delphi method
Source of
Risk Risk
NO.
Risk factors
Triangular fuzzy
Average with
experts' opinions
Defuzzification
Average of
specialists in
the 2th stage of
Delphi method
Defuzzification
Average of
specialists in the
1th stage of
Delphi method
Difference of
Defuzzification
Average of
specialists in the
1th and 2th stage
of Delphi method
u
m l
Demolition
and
vulnerability
of residential
buildings
1 Type of structural systems
0.967 0.867 0.617
0.817 0.828 -0.011
2
Quality of the building
0.950
0.833
0.583
0.789
0.772
0.017
3
Antiquity of buildings
1.000
0.967
0.717
0.894
0.894
0.000
4
Number of floors in a
building
0.983 0.867 0.617
0.822 0.806 0.017
5
Non-compliance with
materials standards
1.000 0.983 0.733
0.906 0.906 0.000
6
Environmental and
structural conditions of the
worn-out texture
neighborhood
0.950 0.867 0.617
0.811 0.822 -0.011
Infrastructure
and urban
Installation
vulnerability
7
Sewage and water
networks and installations
0.100 0.850 0.600
0.817 0.811 0.006
8
Gas networks and
installation
0.100 0.950 0.700
0.883 0.878 0.005
9
Electricity network and
utilities
0.983 0.817 0.567
0.789 0.772 0.017
10
Telecommunication
networks and installation
0.883 0.667 0.417
0.656 0.633 0.023
Blockages
and
accessibilities
11
Roadblocks (Alleyways
and Streets)
1.000 1.000 0.750
0.917 0.917 0.000
12
Outdoor unavailability
0.933
0.800
0.567
0.767
0.778
-0.011
13
Unavailability of rescue
centers
0.983 0.867 0.617
0.822 0.833 -0.011
14
Unavailability of fire
station
0.983 0.967 0.717
0.889 0.889 0.000
15
Unavailability of health
centers
0.983 0.933 0.683
0.867 0.867 0.000
Secondary
risks
16
Fire
1.000
0.967
0.717
0.894
0.894
0.000
17
Explosion
0.900
0.733
0.500
0.711
0.722
-0.011
18
Flood
0.783
0.600
0.367
0.583
0.572
0.011
19
Aftershocks
0.850
0.733
0.500
0.694
0.722
-0.028
1042
PROOF

Identification and prioritization of seismic risks in urban worn-out textures using fuzzy DELPHI method
Table 8. Prioritization of the risks of urban worn-out textures
Risk Priority No. Risk factors
Risk Score
The degree of risk
relevance
1
Road blocks (Alleyways and Streets)
0.917
VI
2
Non-compliance with materials standards
0.906
VI
3
Antiquity of buildings
0.894
VI
4
Fire
0.894
VI
5
Unavailability of fire station
0.889
VI
6
Gas networks and installation
0.883
VI
7
Unavailability of health centers
0.867
VI
8
Number of floors in a building
0.822
VI
9
Unavailability of rescue centers
0.822
VI
10
Type of structural systems
0.817
VI
11
Sewage and water networks and installations
0.817
VI
12
Environmental and structural
0.811
VI
13
Quality of the building
0.789
VI
14
Electricity network and utilities
0.789
VI
15
Outdoor unavailability
0.767
VI
16
Explosion
0.711
I
17
Aftershocks
0.694
I
18
Telecommunication networks and installation
0.656
I
19
Flood
0.583
I
According to Table 8, the risk of blockages
with 0.917 defuzzification number due to the narrow
internal passages of the studied worn-out texture has
the highest risk potential in this area. This risk has a
direct impact on the accessibility of the
neighbourhood. Due to narrow pathways in the
studied area, the rescue operation becomes
challenging which increases the vulnerability of the
area to earthquakes. Furthermore, due to the
unavailability of fire stations as well as the lack of
health centres, the risk of the aforementioned items is
determined as “very important”.
Most of the buildings in the area are made of
traditional and weak materials such as adobe, adobe
and brick, brick and wood, brick and iron, which are
over 50 years old. Most of the buildings suffer high
degree of degradation and they have not been
retrofitted or renewed over the years, making them
less resistant to earthquakes. In addition, the materials
used in the construction of the buildings do not comply
with the available government standards. The risks of
Non-compliance with materials standards and
Antiquity of buildings, with score of 0.906 and 0.894,
respectively, confirms this issue.
The unavailability of fire station with a score
of 0.889 is ranked in the top 5 among the considered
risks in this study. This greatly increased their
importance in buildings where 96% of them are single
and double floor, lack earthquake resistant structural
systems or are not built in accordance with technical
and engineering principles and specifications. In this
prioritization, the flood risk with a score of 0.583 has
the lowest score in Table 8, but it is still characterized
as important risk factor.
These risks are of vital importance both in
crisis management plans and in worn-out texture
renovation so that earthquake hazard and vulnerability
are significantly decreased (Narimisa and Basri, 2019;
Sarvari et al., 2019b).
3.4. Prioritization of the source of risk in the urban
worn-out context
This prioritization is based on the
defuzzification average of the total number of
questions (i.e. risk factors) in each domain. The results
are presented in Table 9. In this prioritization, the
’Blockages and accessibilities’’ risk source was
ranked first with a score of 0.852. This indicates the
importance of this area of risk in the worn-out texture.
The area of risk of demolition and vulnerability of
residential buildings was rated with a score of 0.840.
This area is also very important in terms of financial
loss and casualties. Areas of infrastructures
vulnerability and urban installations and secondary
risk areas ranked third and fourth respectively with
scores of 0.786 and 0.721.
Table 9. Prioritization of the source of risk of urban worn-
out textures
Priority Source of Risk
Defuzzification
average in experts'
opinions
1
Blockages and
accessibilities
0.852
2
Demolition and
Vulnerability of Residential
Buildings
0.840
3
Infrastructure and urban
Installation vulnerability
0.786
4
Secondary risk
0.721
4. Conclusions
Risk management consists of identification and
prioritization of important risks. In most international
standards such as Project Management Institute
(PMI), Association for Project Management (APM),
International Analysis and Management (ISO), etc.,
1043
PROOF

Sadeghi et al./Environmental Engineering and Management Journal 20 (2021), 6, 1035-1046
several numerical and descriptive phrases are used for
identification and assessment of risks. These phrases
are estimative by nature and the accuracy of the
estimation is vital in future risk management in
decision-making. Fuzzy sets, as a vague set, are a
reliable tool in solving problems and result in high
level of accuracy through creating multiple-value
logical models.
In this research, these sets are used in risk
analysis. Due to limited resources in the majority of
cities all around the world, it is necessary to prioritize
the sources of risks based on their importance. This
study presents the results of identification and
prioritization of seismic risks in worn-out textures of
Jalili neighbourhood located in Kermanshah city,
Kermanshah, Iran. The risk identification process
indicated 19 potential risks, which were prioritized
based on the experts’ opinions using fuzzy Delphi
method. The 5-point Likert spectrum and the
triangular fuzzy numbers corresponding to each of the
19 risk areas were used to prioritize the risks. In this
prioritization, the risk of road blockages, Non-
compliance with materials standards, and Antiquity of
buildings with a score of 0.917, 0.906, and 0.894,
respectively, were ranked as top three significant risks.
In this ranking, flood risk with defuzzification number
of 0.583 has the lowest risk potential but is still
characterized of high importance.
Prioritization of the different areas of risk
indicates the high importance of accessibility of the
area during and after an earthquake event. Since a
large portion of worn-out textures throughout Iran
share similar characteristics, identifying and
prioritizing the risks in the worn-out texture of the case
study can provide useful information and valuable
insights for city managers and government authorities
to make better informed decisions when encountering
the potential hazards in the area.
It is concluded that the fuzzy Delphi method is
effective in determination and prioritization of the
risks in urban worn-out textures subjected to seismic
hazards. New risk analysis with Fuzzy method was
conducted to increase its validity, but future research
studies can be envisaged to increase the accuracy of
these estimates using other novel statistical
approaches and more advanced analytical methods.
References
Asgari N., Zamanzadeh S., Chavoshi K., (2015), Financing
methods of housing renovation in urban distressed areas
(The Case of Tehran), Journal of Urban Economic and
Management, 9, 87-103.
Barbat A.H., Carreño M.L., Pujades L.G., Lantada N.,
Cardona O.D., Marulanda M.C., (2010), Seismic
vulnerability and risk evaluation methods for urban
areas. A review with application to a pilot area,
Structure and Infrastructure Engineering, 6, 17-38.
Bustince H., Burillo P., (1996), Vague sets are intuitionistic
fuzzy sets, Fuzzy Sets and Systems, 79, 403-405.
Byrne D.E., Sykes L.R., Davis D.M., (1992), Great thrust
earthquakes and aseismic slip along the plate boundary
of the Makran Subduction Zone, Journal of
Geophysical Research, 97, 449-478.
BHRC-PN, (2018), Iranian code of practice for seismic
resistant design of buildings, No. 2800, 253, Iranian
Building &Housing Research Center.
Chen P., (2012), Data mining applications in E-Government
information security, Procedia Engineering, 29, 235-
240.
Cheng C.H., Lin Y., (2002), Evaluating the best main battle
tank using fuzzy decision theory with linguistic criteria
evaluation, European Journal of Operational Research,
142, 174-186.
Cirianni F., Fontea F., Leonardia G., Scopellitia F., (2012),
Analysis of lifeline transportation vulnerability,
Procedia - Social and Behavioral Siencey, 53, 29-38.
Dabiri M., Oghabi M., Sarvari H., Sabeti S., Kashefi H.R.,
(2020), A combination risk-based approach to post-
earthquake temporary accommodation site selection: A
case study in Iran, Iranian Journal of Fuzzy Systems,
17, 54-74.
Delbecq A.L., Van de Ven A.H., Gustafson D.H., (1975),
Group Techniques for Program Planning: A Guide to
Nominal Group and Delphi Processes, Scott,
Foresman.
FEMA, (2010), Earthquake-Resistant Design Concepts.
Report number: FEMA P-749, Report of Federal
Emergency Management Agency of the US
Department of Homeland Security By the National
Institute of Building Sciences Building Seismic Safety
Council, Washington, DC, On line at:
https://www.fema.gov/sites/default/files/2020-
07/fema_earthquake-resistant-design-concepts_p-
749.pdf.
Ferreira T.M., Vicente R., Mendes da Silva J.A.R., Varum
H., Costa A., (2013), Seismic vulnerability assessment
of historical urban centres: case study of the old city
centre in Seixal, Portugal, Bulletin of Earthquake
Engineering, 11, 1753-1773.
Fink A., Kosecoff J., Chassin M., Brook R.H., (1984),
Consensus methods: characteristics and guidelines for
use, American Journal of Public Health, 74, 979-983.
Garcia C., Frigerio S., Daehne A., Corsini A., Sterlacchini
S., (2014), The Relevance of Early-Warning Systems
and Evacuations Plans for Risk Management, In:
Mountain Risks: From Prediction to Management and
Governance, Springer, Dordrecht, 341-364.
Habibi A., Jahantigh F.F., Sarafrazi A., (2015), Fuzzy
Delphi technique for forecasting and screening items,
Asian Journal of Research in Business Economics and
Management, 5, 130-143.
Hsu C.C., Sandford B.A., (2007), The Delphi technique:
making sense of consensus, Practical Assessment,
Research, and Evaluation, 12, 1-8.
Huang S., Li X., Wang Y., (2012), A new model of geo-
environmental impact assessment of mining: a
multiple-criteria assessment method integrating Fuzzy-
AHP with fuzzy synthetic ranking, Environmental
Earth Sciences, 66, 275-284.
Ianoş I., Merciu G.L., Merciu C., Pomeroy G., (2017),
Mapping accessibility in the historic urban center of
Bucharest for earthquake hazard response, Natural
Hazards and Earth System Sciences Discussions, 13, 1-
24.
Imani B., Kanooni R., Biniaz M., Alimohammadi A.,
(2016), Urban decay vulnerability mitigation strategies
against earthquake case study: Imamzadeh Hasan
neighborhood in Tehran, Bagh-e Nazar, (in Persian),
13, 75-90.
1044
PROOF

Identification and prioritization of seismic risks in urban worn-out textures using fuzzy DELPHI method
Kegyes-Brassai O., (2014), Earthquake hazard analysis and
building vulnerability assessment to determine the
seismic risk of existing buildings in an urban area, PhD
Thesis, Széchenyi István University, Győr, Hungary.
Khoshfetrat R., Sarvari H., Chan D.W.M., Rakhshanifar M.,
(2020), Critical risk factors for implementing building
information modelling (BIM): a Delphi-based survey,
International Journal of Construction Management, 1-
10, https://doi.org/10.1080/15623599.2020.1788759.
Kong L., Qian J., (2019), Knowledge circulation in urban
geography/urban studies, 1990–2010: Testing the
discourse of Anglo-American hegemony through
publication and citation patterns, Urban Studies, 56, 44-
80.
Kropf K., (1996), Urban tissue and the character of towns,
Urban Design International, 1, 247-263.
MPO, (2018), Management and Planning Organization of
Iran, Deputy of Statistics and Information: Population
an Housing Census Results, (2016), Kermanshah City,
(Separated by Municipalities and Neighborhoods and
Population Estimates by year 2021.
Kongar I., Giovinazzi S., Rossetto T., (2017), Seismic
performance of buried electrical cables: evidence-based
repair rates and fragility functions, Bull Earthquake
Engineering, 15, 3151-3181.
Kiani A., Moradi K.A., Pour tangestani M., (2017), The
evaluation of the vulnerability of the urban fabric of
Alishahr to earthquake using GIS software, Institucion
Universitaria Salazar Y Herrera (IUSH), 1, 2473-2477.
Lee Y.L., Ho M.C., Huang T.C., Tai C.A., (2007), Urban
Disaster Prevention Shelter Vulnerability Evalution
Considering Road Network haracteristics, 2nd Int.
Conf. on Urban Disaster Reduction, 21-29.
Lawshe C.H., (1975), A quantitative approach to content
validity, Personnel Psychology, 28, 563-575.
Li M., Han L., Lei B., (2017), Fire risk assessment of the
historic buildings based on AHP and entropy weight
method, Journal of Xi'an University of Science and
Technology, 37, 537-543.
Liu Y., Li Z., Wei B., Li X., Fu B., (2019), Seismic
vulnerability assessment at urban scale using data
mining and GIScience technology: application to
Urumqi (China), Geomatics, Natural Hazards and Risk,
10, 958-985.
Masson F., Anvari M., Djamour Y., Walpersdorf A.,
Tavakoli F., Daignières M., Nankali H., Van Gorp S.,
(2007), Large-scale velocity field and strain tensor in
Iran inferred from GPS measurements: new insight for
the present-day deformation pattern within NE Iran,
Geophysical Journal International, 170, 436-440.
Murray T.J., Pipino L.L., van Gigch J.P., (1985), A pilot
study of fuzzy set modification of Delphi, Human
Systems Management, 5, 76-80.
Murphy M.K., Black N.A., Lamping D.L., McKee C.M.,
Sanderson C.F., Askham J., Marteau T., (1998),
Consensus development methods, and their use in
clinical guideline development, Health Technology
Assessment, 2, 1-88.
Mosavi D.L., Shams M., Ghanbari N., (2014), The Analysis
of developmental opportunities in old urban texture
(Case study: down town Kermanshsh), Geographical
Quarterly Journal Enviromental Management, 25, 111-
128.
Mondal D.R.., (2019), High risk of post-earthquake fire
hazard in Daka, Bangladesh, Fire, 2, 24,
https://doi.org/10.3390/fire2020024.
Nakhi A.A., Ahmari N., Rezaei S., (2016), Renovation and
rehabilitation strategies for worn-out texture of ab-
anbar-no district in Sari using swot technique, Open
Journal of Geology, 6, 270-283.
Nayeri M., Shieh E., Rezaei M., Saeidi Rezvani N., (2018),
Managing neighborhood resilience in earthquake
encountered in urban exhausted tissues with FAHP
method (case study L Abdolabad, Tehran) (in Persian),
Quarterly of Geography (Regional Planning), 8, 21-38.
Nazmfar H., Saredeh A., Eshgi A., Feizizadeh B., (2019),
Vulnerability evaluation of urban buildings to various
earthquake intensities: A case study of the municipal
zone 9 of Tehran, Human and Ecological Risk
Assessment: An International Journal, 25, 455-474.
Nyimbili P.H., Erden T., Karaman H., (2018), Integration of
GIS, AHP and TOPSIS for earthquake hazard analysis,
Natural Hazards, 92, 1523-1546.
Narimisa M.R., Basri N.E.A., (2019), Earthquake crisis
management; increasing sustainability and decreasing
urban seismic vulnerability with passive defense
approach, Religacion Revista de Ciencias Sociales
Humanidades, 4, 619-624.
Peng Y., (2015), Regional earthquake vulnerability
assessment using a combination of MCDM methods,
Annals of Operations Research, 234, 95-110.
Project Management Institute, (2004), A Guide to the
Project Management Body of Knowledge, 3rd Edition,
Project Management Institute, 1-390.
Powell C., (2003), The Delphi technique: myths and
realities, Blackwell Publishing Ltd., Journal of
Nursing, 41, 376-382.
Quigley M., Duffy B., (2020), Effects of earthquakes on
flood hazards: A case study from Christchurch, New
Zealand, Geosciences, 10, 114, doi:
10.3390/geosciences10030114.
Rashed T., (2003), Measuring the environmental context of
social vulnerability to urban earthquake hazards: An
integrative remote sensing and GIS approach, PhD
Thesis, University of California, Santa Barbara.
de Ruiter M.C., Ward P.J., Daniell J.E., Aerts J.C.J.H.,
(2017), Revieve Article: A comparison of flood an
earthquake vulnerability assessment indicators, Natural
Hazards and Earth Systems Sciences, 45, 1231-1251.
Saghaei M., (2017), Identification and prioritization of
urban deteriorated texture in order to reduce the
earthquake induced vulnerability, case study: Isfahan
region 5, Geographical Data, 27, 171-182.
Sanaei A., Ghazifard A., Sobhanmanesh F., (2011), Factors
affecting the development of identification technology
by radio frequency in electronic supply chain
management, (in Persian), Journal of New Marketing
Research, 1, 41-70.
Sarvari H., Valipour A., Yahya N., Noor N., Beer M.,
Banaitiene N., (2019a), Approaches to risk
identification in public–private partnership projects:
Malaysian private partners’ overview, Administrative
Sciences, 9, 17,
https://doi.org/10.3390/admsci9010017.
Sarvari H., Rakhshanifar M., Tamošaitienė J., Chan
D.W.M., Beer M., (2019b), A risk based approach to
evaluating the impacts of Zayanderood drought on
sustainable development indicators of riverside urban
in Isfahan-Iran, Sustainability, 11, 6797,
https://doi.org/10.3390/su11236797.
Shieh E., Habibi K., Torabi K., Masoumi H., (2014),
Erthquake risk in urban street network: An example
from region 6 of Tehran, Iran, International Journal of
Disaster Resilience in the Built Enviroment, 5, 413-426.
Tang A., Wen A., (2009), An intelligent simulation system
for earthquake disaster assessment, Computers &
Geosciences, 35, 871-879.
1045
PROOF

Sadeghi et al./Environmental Engineering and Management Journal 20 (2021), 6, 1035-1046
Tsai C.H., Chen C.W., (2010), An earthquake disaster
management mechanism based on risk assessment
information for the tourism industry-a case study from
the island of Taiwan, Tourism Management, 31, 470-
481.
Taylor M., Sekhar S., D’Este D., (2006), Application of
accessibility based methods for vulnerability analysis
of strategic road networks, Network Spatial Economy,
6, 267-291.
Trevlopoulos K., Gueguen P., Helmstetter A., Cotton F.,
(2019), Forecasting time – variable earthquake risk for
reinforced concrete building during aftershock
sequences based on operational earthquake forecasting
and resonance period elongation, ECCOMAS
Proceedia, 2690-2707, doi:
10.7712/120119.7103.19705.
Vahdat K., Smith N.J., Amiri G., (2014), Developing a
Knowledge Based Expert System (KBES) for seismic
Risk Management, Second International Conference on
Vulnerability and Risk Analysis and Management
(ICVRAM) and the Sixth International Symposium on
Uncertainty, Modeling, and Analysis (ISUMA), July
13-16, 2014, Liverpool, UK,
https://doi.org/10.1061/9780784413609.175.
Varesi H., Akbari M.A., (2012), Study of earthquake
resistance in urban residetial buldings Case study:
Hamedan city, Haft Hesar Journal of Enviromental
Studies, 1, 45-60.
Walker R., Jackson J., (2004), Active tectonics and late
Cenozoic strain distribution in central and eastern Iran,
Tectonics, 23, 1-24.
Varesi H., Taghvaei M., Rezaei N., (2012), Organizing the
worn out urban fabric (Case study: Shiraz), Spatial
Planning Quarterly (Geography), 2, 129-156.
Yucesan M., Kahraman G., (2019), Risk evaluation and
prevention in hydropower plant operations: A model
based on Pythagorean fuzzy AHP, Energy Policy, 126,
343-351.
Zhao Z.-d.., Li Ti-q., Yu S.-z., Cui D., (2008), Research on
Earthquake Disaster Chain Models and Hazard
Assessment, The 14th World Conf. on Earthquake
Engineering, 12-17.
Zadeh L.A., (1965), Fuzzy sets, Information and Control, 8,
338-353.
Zare M., Kamranzad F., (2015), A study on the seismicity of
Iran, Journal of Spatial Analysis Environmental
Hazards, 1, 39-58.
1046
PROOF
View publication statsView publication stats