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A hybrid risk assessment
approach for assessing the
earthquake risks in worn-out
urban fabrics: a case study in Iran
Jalal Sadeghi
Department of Civil Engineering, Sanandaj Branch,
Islamic Azad University, Sanandaj, Iran
Mohsen Oghabi
Department of Civil Engineering, Kermanshah Branch,
Islamic Azad University, Kermanshah, Iran
Hadi Sarvari
Department of Civil Engineering, Isfahan (Khorasgan) Branch,
Islamic Azad University, Isfahan, Iran
Mohammad Sediegh Sabeti
Department of Civil Engineering, Sanandaj Branch,
Islamic Azad University, Sanandaj, Iran
Hamidreza Kashefi
Department of Mathematics Education, Farhangian University, Tehran, Iran
Daniel W.M. Chan
Department of Building and Real Estate, The Hong Kong Polytechnic University,
Hung Hom, Kowloon, Hong Kong, China, and
Aynaz Lotfata
Geography Department, Chicago State University, Illinois, USA
Abstract
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
Hybrid risk
assessment
approach
Received 15 September2021
Revised 17 November2021
28 November 2021
Accepted 1 December2021
International Journal of Disaster
Resilience in the Built
Environment
© Emerald Publishing Limited
1759-5908
DOI 10.1108/IJDRBE-09-2021-0128
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1759-5908.htm
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-outurban 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.
Keywords Iran, Risk assessment, Intuitionistic fuzzy sets, Earthquake risks, Probability impact method,
Worn-out urban fabrics, Probability impact
Paper type Research paper
1. Introduction
Investigating a city’s characteristics and potential for dealing with natural disasters and
proper planning for prevention and reduction of damages and fatalities caused by the
resulting crises requires the use of specificstrategies(
Mejri et al., 2017). One of the most
critical factors for reducing the risk of earthquakes is proper readiness. This readiness
includes predetermined plans and strategies (Kates, 1977). One of the main goals of urban
planning is reducing the vulnerability of cities against the effects of earthquakes (Salvati
et al., 2019). In this regard, worn-out urban fabrics have a higher level of risk of
earthquakes due to their unsuitable conditions. Worn-out urban fabrics are areas in a city
whose quality, physical properties and performance have declined over time (Nakhi et al.,
2016). People from lower-income societies usually occupy these areas, do not have proper
access to several urban services and are sometimes ignored during earthquakes.
Essential characteristics of worn-out urban fabrics include a low number of floors, lack of
good access points and use of traditional building materials in their construction.
Usually, these buildings lack proper construction structures (Salvati et al., 2019). Worn-
out urban fabrics are in critical conditions regarding vulnerability during crises.
Therefore, it is necessary to use proper strategies to reduce the adverse effect of
earthquakes in these areas (Liu et al., 2019).
Crisis management and addressing the vulnerabilities of worn-out urban fabrics are
essential due to the population density, types of building materials and high age of buildings
(Tsai and Chen, 2010). To reduce possible vulnerabilities during earthquakes, it is necessary
to identify existing challenges and factors affecting these challenges. Lack of proper and
accurate evaluation for identifying these uncertain factors will create challenges for any
planning and decrease the credibility of developed strategies.
Risk management is one of the essential parts of project management. One of the
common characteristics of all risk analysis literature is estimating the risk probabilities
(Chan et al., 2011). In many cases, due to ambiguities and the complexity of the problems,
experts are faced with uncertainties that challenge their estimations and decrease the
accuracy of their results. Some methods do not consider uncertainties and ambiguities or
explicitly use them in risk analysis (Yazdi and Kabir, 2017). Intuitionistic fuzzy logic is a
fuzzy set for creating multivariable logical models. It can be a valuable and powerful tool for
solving complex and uncertain problems with high accuracy (Dabiri et al., 2021). Risk
assessment provides necessary and essential information for prioritizing preventive
strategies and reducing risks (Tamošaitien_
eet al., 2021). Based on the challenges in worn-
out urban fabrics, the risk management process must focus on preventive and effective
management of all predictable risks.
The aim of the current study is to provide an accurate model for risk assessment during
earthquakes. The proposed hybrid model combines the probability-impact (P-I) model and
intuitionistic fuzzy logic and sets. For the case study of this model, the worn-out urban
fabrics of the Jalili region in Kermanshah city –Iran, are selected due to their complex and
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unique characteristics. The results of this study can be used for risk management and
preventive planning to reduce possible dangers.
2. Literature review
Many countries in the world are constantly facing the risk of earthquakes. Iran is one of the
countries with the highest risk of earthquakes in the world. Around 8% of the total
earthquakes around worldwide and approximately 17% of large-scale earthquakes have
occurred in Iran (Raeesi et al.,2017). In the meanwhile, with the presence of worn-out urban
fabrics in various regions of cities in Iran, the probability of vulnerability increases during
an earthquake.
Worn-out urban fabrics are important residential areas in many Iranian cities that
require interventions and modifications based on their natural and social characteristics
(Mili et al.,2018). Investigating the vulnerability of worn-out urban fabrics with old building
materials, including historical and cultural resource sites, as well as residential areas, is
essential (Ferreira et al.,2013). Worn-out urban fabrics are usually composed of very old
buildings and are mainly made of traditional and non-standard materials (Kiani et al.,2017;
FEMA, 2010). The main features of Worn-out urban fabrics include the age of buildings, low
number of floors, lack of proper access and obsolescence (Sadeghi et al.,2021). Typically,
these buildings do not have a proper structural system (Salvati et al., 2019). In worn-out
urban fabrics, urban infrastructures such as networks and installations of electricity, gas,
telecommunications, as well as water and wastewater networks are worn-out and very
vulnerable. These infrastructures are faced with various challenges in times of emergency
services (Cirianni et al.,2012;Dabiri et al., 2020). Due to the condition of buildings,
infrastructure and urban facilities in worn-out urban fabrics, there is a possibility of fire and
explosion, as well as flooding in case of earthquakes (Ruiter et al.,2017). Therefore, it is
necessary to provide suitable predictions regarding worn-out urban fabrics and critical
challenges during earthquakes.
To reduce earthquake vulnerability, integration of urban planning and risk management
factors can help provide a more accurate risk and vulnerability assessment. These results
can then be used for creating strategies and plans for dealing with the aftermath of
earthquakes (Ruiter et al., 2017). Risk management process helps better understand
earthquake risks and can affect the future decision-making of managers (Dabiri et al.,2021).
Risk management is a multi-stage process using a wide range of data, parameters and
uncertain factors (Sarvari et al.,2014). Multi-stage risk management processes include risk
management planning; risk identification; qualitative and quantitative evaluation of risks;
response planning and monitoring and control (PMI, 2017).
Identification and prioritization of risks are essential in later risk management steps and
require methods dealing with uncertainty due to the uncertain nature of the risk. Fuzzy
theory is one such approach that was first introduced in an article by Zadeh (1965). Fuzzy
sets are potent tools for describing phenomena affected by uncertain parameters and
creating non-binary logical models. Unlike traditional sets, in fuzzy sets, each member’s
membership is located in the range of [0,1], which is graded and known as the degree of
membership (Zadeh, 1965). Many attempts have been made to generalize fuzzy sets, among
which are iv-fuzzy sets, L-fuzzy sets and IFSs, Vague sets. One of the most important
generalizations of fuzzy sets is Intuitionistic fuzzy sets. These sets were first introduced by
Atanassov (1986), who showed that membership of a member in the group could be
demonstrated using two parameters of the degree of membership
m
(x) and degree of non-
membership
g
(x). Intuitionistic fuzzy sets and logic are used in various scientificfields, and
Hybrid risk
assessment
approach
these sets have been extensively used to solve complex and ambiguous problems in
uncertain and probability models.
Various studies have investigated the vulnerability of cities during earthquakes. Rashed
(2003) surveyed to examine the vulnerably of the state of California against earthquakes
using the analytical hierarchy process (AHP) approach in the geographic information
system environment and introduced AHP and fuzzy approaches as reliable approaches for
measuring the vulnerability of cities against earthquakes. Tang and Wen (2009) used
artificial intelligence systems to evaluate earthquake risk in Deyang city –China. Peng
(2015) stated that evaluating the vulnerabilities of various regions is essential for preventing
and reducing the damages caused by earthquakes and proposed using different multi-
criteria decision-making approaches.
With the rapid advancement of science and technology and the resulting developments,
many decisions are affected, especially in complex and ambiguous cases, as well as due to
organizational and operational breadth (Sarvari et al., 2021;Dabiri et al.,2021). In such cases,
due to the specialization of topics and the specific complexity of the issues, as well as the
lack of knowledge, awareness and individual skills, individual decision-making cannot be
used and group interaction should be focused. There are many different ways to make group
decisions. One of these methods is the brainstorming technique, which is a group method
and a process for creating new ideas. Brainstorming is a collective and common method in
which people present their ideas to solve a specific problem or issues and then discuss them
in groups and finally reach a general agreement (Besant, 2016). The main challenges in
applying the brainstorming technique are due to real-world conditions, ambiguities and
doubts caused by human thinking, data and information. This technique is not accurate
enough in the face of these challenges, and ultimately no accurate evaluation is done.
Intuitionistic fuzzy sets theory covers many of the ambiguities and doubts that arise in
decisions.
Previous studies have shown that most researchers use common uncertain and
probability approaches for risk assessment while ignoring ambiguities and doubts during
the risk management process. Intuitionistic fuzzy logic and sets are powerful tools for
solving this problem and considering these ambiguities and uncertainties during risk
management. This will then result in higher accuracy of the risk management process. This
study uses a hybrid brainstorming and Probability–Impact model and Intuitionistic fuzzy
sets to evaluate and prioritize possible risks in worn-out urban fabrics. The earthquake risks
extracted from various literature are presented in Table 1.
3. Area of the study
Kermanshah province is located in the western part of Iran and its capital is the city of
Kermanshah. The city of Kermanshah, with a total population of 946,651, is the ninth most
populated city in Iran. This city hosts 48.5% of the total population of Kermanshah province
and is divided into eight municipal districts and 136 zones. Among the eight municipal
districts, district 3 contains the Jalili zone that was investigated in this study due to its worn-
out construction. This district is divided into 26 zones. The Jalili zone is one of the oldest
zones in Kermanshah, and its population is predicted to reach a total of 1,212 by the end of
2020. This zone covers an area of 136,336 square meters, most of which is made from worn-
out buildings of 50years or older of age.
Residential buildings in the Jalili zone often have small areas. The building materials
used in these buildings often include traditional and low-strength materials. The access
situation in this zone is unsuitable, with the width of passages inside the area being less than
8 meters. The zone also lacks open spaces such as parks and a dedicated fire station,
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emergency service, or medical center. The water and wastewater network is ancient and the
most common problem in this network is the fracturing and failure of old cast iron pipes.
The gas transfer system, the electrical network and the telecommunication cables and
facilities lack sufficient quality.
4. Research methodology
This study used the P-I and brainstorming processes while using intuitionistic fuzzy logic
and sets for analysis of the risks in worn-out urban fabrics. The identified risks in previous
studies were evaluated and analyzed using the brainstorming approach. The members of
the brainstorming team identified the risks after discussing the identified challenges and
risks based on a desktop review of previous research studies. During the review at this
stage, the experts finally confirmed all the identified risks from reviewing the literature and
Table 1.
Risks caused by
earthquakes in an
area based on the
literature review
Risk area No. Risks Sources
Demolition and
vulnerability of
residential buildings
1 Vulnerability caused by a type
of structure
FEMA (2010),Kiani et al. (2017)
and Salvati et al. (2019)
2 Vulnerability caused by
structure’s quality
Kiani et al. (2017),Shieh et al.
(2014) and Salvati et al. (2019)
3 Vulnerability caused due to
building’s age
Kiani et al. (2017),Tsai and Chen
(2010) and Salvati et al. (2019)
4 Vulnerability caused due to
number of floors
Kiani et al. (2017),Shieh et al.
(2014) and Salvati et al. (2019)
5 Vulnerability caused due to
substandard materials
FEMA (2010) and Tsai and
Chen (2010)
6 Vulnerability caused by
environmental conditions of
worn-out urban fabrics
FEMA (2010)
Infrastructural
vulnerabilities
7 Vulnerability of water and
wastewater infrastructures
Cirianni et al. (2012)
8 Vulnerability of gas
infrastructures
Cirianni et al. (2012)
9 Vulnerability of electrical
infrastructures and network
Sadeghi et al. (2021)
10 Vulnerability of
telecommunication
infrastructure and network
Cirianni et al. (2012)
Obstructions and lack
of access
11 Obstruction of passages (roads
and alleyways)
Salvati et al. (2019)
12 Lack of access to open space Shieh et al. (2014) and Salvati
et al. (2019)
13 Lack of accessibility for relief
forces
Shieh et al. (2014) and Salvati
et al. (2019)
14 Lack of access to fire stations Shieh et al. (2014) and Salvati
et al. (2019)
15 Lack of access to medical lefts Shieh et al. (2014) and Salvati
et al. (2019)
Secondary risks
(secondary risks for
buildings)
16 Fire hazard for buildings Sadeghi et al. (2021)
17 Explosion hazard Sadeghi et al. (2021)
18 Flooding Ruiter et al. (2017)
19 Damage to buildings due to
aftershocks
Sadeghi et al. (2021)
Hybrid risk
assessment
approach
classifying them as significant earthquake hazards in the dilapidated urban fabrics. With
the emphasis of the team experts on the comprehensiveness of the identified risks from the
literature review, finally, 19 risks were identified. The information gathered from field
studies and documents of relevant organizations (including technical data, photos, census
results, etc.) were presented to the brainstorming team members. Then, the experts during
brainstorming sessions were asked to determine the variable parameters of Intuitionistic
fuzzy sets for these risks (including the degree of accuracy, degree of inaccuracy and degree
of uncertainty). These parameters were used as the basis of Intuitionistic fuzzy set
calculations for risk analysis and prioritization. After determining the Intuitionistic fuzzy
parameters of the probability of occurrence and impact of risks, the intensity of risks was
calculated and their upper and lower limits were determined. The numerical interval of the
probability of occurrence and impact of risks and unbalanced probability scoring and effects
of risks were considered in this study and the P-I matrix was created. It is generally accepted
that the impact of a risk is calculated by the product of its level of severity and likelihood of
occurrence (Chan et al., 2011;Sarvari et al.,2014;Tamošaitien_
eet al., 2021). It is a matrix
divided into three regions of high, medium and low priority. After defuzzification of the
numbers, the resulting risks are prioritized according to their scores. Compared to the P-I
matrix, risks are classified into three categories based on their degree of risk, including high
risk (high priority), medium risk (medium priority) and low risk (low priority). This
prioritization is the basis of following risk management steps and the following decision-
making by managers and urban authorities to provide suitable solutions for the studied
region.
4.1 Hybrid brainstorming and intuitionistic fuzzy set approach
In decision-making about specialized topics, personal decisions cannot be used due to the
complexity of the case and lack of sufficient knowledge, awareness and personal skills as it
might not be adequate for solving the problem or specialized topic at hand (Chunhua et al.,
2020). Therefore, it is necessary to focus on a group approach. Various methods are used in
group decision-making. Brainstorming is one such method that Alex Fackney Osbon first
introduced in 1939 as a creative problem-solving method (Sekhar and Lidiya, 2012).
Brainstorming includes various rules and specialized techniques that result in the creation
of new ideas that might not occur under normal circumstances (Bonnardel and Didier, 2020).
The members of the brainstorming team are usually between 5 and 10 individuals (Sekhar
and Lidiya, 2012). The statistical population of the current study was included 15 experts in
various urban planning and engineering fields. The experts were included the experienced
employees of multiple companies and organizations include the electrical distribution
company, the water and wastewater organization, the telecommunication organization, the
gas company, the construction engineering council and the regional municipalities. The use
of brainstorming and Intuitionistic fuzzy sets can be divided into five stages, including
holding brainstorming sessions for risk assessment and determination of Intuitionistic fuzzy
risk parameters; data analysis using Intuitionistic fuzzy sets; calculating the severity of
risks and upper and lower boundaries; defuzzification of the calculated values and finally
prioritization and determination of high-priority risks.
An Intuitionistic fuzzy set A of reference set X is defined as follows:
A¼<x;
m
Ax
ðÞ
;
g
Ax
ðÞ
>jx2X
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The
m
A
:X![0,1] and
g
A
:X![0,1] functions with the condition of 0 #
m
A
(x)þ
g
A
(x)#1
are the membership function and real values of
m
A
(x)and
g
A
(x) in the [0,1] interval and are
known as degree of membership and degree of non-membership of X in A, respectively.
Each fuzzy set A’is a special case of Intuitionistic fuzzy sets and can be written as an
Intuitionistic fuzzy set of A={<x,
m
A
(x), 1 –
m
A
(x)>jx
e
X}. For every Intuitionistic fuzzy
set A of X,
p
A
(x)=1–[
m
A
(x)þ
g
A
(x)] is known as the intuitionistic parameter of x in A
which is the degree of uncertainty of x in A. Obviously, for every x in X, we have 0 #
p
A
(x)#
1 (1989).
5. Results and discussion
5.1 Using a proposed method for risk assessment of the studied region
During nine brainstorming sessions, experts evaluated and reviewed the information and
data regarding the risks, and a consensus was reached regarding the final list of risks based
on previous literature. In the next step, experts evaluated and determined Intuitionistic
fuzzy parameters of the probability of occurrence and impact of risks on goals (financial and
human losses) and selected the values for degree of accuracy, degree of inaccuracy and
degree of uncertainty for every risk. The results obtained from this step are presented in
Table 2. They show the Intuitionistic fuzzy parameters of 46 main risks evaluated in this
study and their probability and impact on financial and human losses. The degree of
accuracy of risks indicates the likelihood of their occurrence and effects on losses, while a
degree of inaccuracy is the inverse concept. The Intuitionistic parameter of the degree of
uncertainty suggests the amount of uncertainty for each risk assessment. For example, the
probability of a particular risk during an earthquake may be high while its impact on losses
is low or vice versa. For example, Table 2 indicates that the probability of obstruction in
paths with widths of less than 5 meters of 0.9, its impact on financial losses is 0.15 and its
effects on human losses is 0.85.
5.2 Prioritization of risks using intuitionistic fuzzy sets and probability-impact model
In this stage, the identified risks were evaluated and prioritized using Intuitionistic fuzzy
sets. In qualitative risk assessment, to combine the probability and impact dimensions and
their estimations, each linguistic variable was assigned a numerical equivalent. Then the
risk assessment matrix is formed based on the unbalanced scoring system, and the
prioritization limits are determined in the matrix. After the intuitionistic fuzzy calculations
of risks and defuzzification of their impacts, the prioritization of the risks is carried out. All
numerical comparisons for the probability of occurrence and impact of risks and their
numerical range and the risk assessment matrix are specific to this study but can be used in
other studies. The following section explains these steps.
5.2.1 Interpretation of linguistic variables of risk dimensions (probability and impact).
The linguistic variables include five-level (i.e. very low, low, medium, high and very high).
Numerical values are used for the interpretation of variables Furthermore, to increase the
accuracy of comparisons, a numerical range was used for the probability of occurrence and
the impact of risks instead of a number (Table 3).
5.2.2 Probability-impact risk assessment matrix. This matrix is one of the most common
methods for showing the combination of probability and impact. The severity of risks is
their probability multiplied by their effect, as presented in this matrix. This study uses a 5
5 matrix which includes three zones of red (high priority), orange (medium priority) and
green (low priority). Figure 1 shows the probability and impact scores based on the
unbalanced scoring system. This scoring is presented in Table 3. According to Figure 1,
Hybrid risk
assessment
approach
Main risk area Risks Type Code Probability of occurrence and impact
Degree of
accuracy
Degree of
inaccuracy
Degree of
uncertainty
Demolition and
vulnerability of
residential buildings
Vulnerability
caused by a type of
structure
Metal skeleton R
1
Probability of occurrence 0.75 0.15 0.10
Financial impact 0.70 0.20 0.10
Human loss 0.80 0.15 0.05
Concrete skeleton R
2
Probability of occurrence 0.5 0.35 0.15
Financial impact 0.65 0.30 0.05
Human loss 0.70 0.25 0.05
Other (brick and steel,
brick and wood,
mudbrick and wood, etc.)
R
3
Probability of occurrence 0.90 0.07 0.03
Financial impact 0.95 0.02 0.03
Human loss 0.90 0.05 0.05
Vulnerability
caused by
structure’s quality
New buildings R
4
Probability of occurrence 0.55 0.30 0.15
Financial impact 0.15 0.70 0.15
Human loss 0.05 0.90 0.05
Acceptable R
5
Probability of occurrence 0.65 0.25 0.10
Financial impact 0.25 0.60 0.15
Human loss 0.10 0.80 0.1
Requiring renovation R
6
Probability of occurrence 0.80 0.15 0.05
Financial impact 0.60 0.30 0.10
Human loss 0.25 0.65 0.10
Requiring reconstruction R
7
Probability of occurrence 0.95 0 0.05
Financial impact 0.85 0.10 0.05
Human loss 0.90 0.05 0.05
Demolition and
vulnerability of
residential buildings
Vulnerability
caused by
building’s age
1–10 years R
8
Probability of occurrence 0.15 0.75 0.10
Financial impact 0.20 0.70 0.10
Human loss 0.05 0.90 0.05
11–30 years R
9
Probability of occurrence 0.55 0.35 0.10
Financial impact 0.95 0.30 0.05
Human loss 0.70 0.25 0.05
31–50 years R
10
Probability of occurrence 0.85 0.10 0.05
Financial impact 0.80 0.15 0.05
Human loss 0.70 0.20 0.05
Older than 51 years R
11
Probability of occurrence 0.95 0 0.05
Financial impact 0.95 0.05 0
Human loss 0.90 0.05 0.05
Vulnerability
caused due to
number of floors
1–2floors R
12
Probability of occurrence 0.90 0.03 0.07
Financial impact 0.95 0 0.05
Human loss 0.90 0.05 0.05
(continued)
Table 2.
Intuitionistic fuzzy
parameters of risks
IJDRBE
Main risk area Risks Type Code Probability of occurrence and impact
Degree of
accuracy
Degree of
inaccuracy
Degree of
uncertainty
3–5floors R
13
Probability of occurrence 0.30 0.65 0.05
Financial impact 0.40 0.55 0.05
Human loss 0.20 0.70 0.05
Vulnerability
caused due to
substandard
materials
–R
14
Probability of occurrence 0.80 0.15 0.05
Financial impact 0.90 0.05 0.05
Human loss 0.70 0.25 0.05
Vulnerability
caused by
environmental
conditions of worn-
out urban fabrics
–R
15
Probability of occurrence 0.10 0.45 0.10
Financial impact 0.30 0.65 0.05
Human loss 0.15 0.75 0.10
Obstructions and lack of
access
Obstruction of
passages (roads
and alleyways)
Width of fewer than 5
meters
R
16
Probability of occurrence 0.90 0.05 0.05
Financial impact 0.15 0.80 0.05
Human loss 0.85 0.10 0.05
Width of 5–8 meters R
17
Probability of occurrence 0.80 0.15 0.05
Financial impact 0.10 0.80 0.10
Human loss 0.75 0.15 0.10
Width of higher than 8
meters
R
18
Probability of occurrence 0.20 0.75 0.05
Financial impact 0.05 0.90 0.05
Human loss 0.10 0.82 0.05
Lack of access to
open space
–R
19
Probability of occurrence 0.85 0.10 0.05
Financial impact 0.05 0.90 0.05
Human loss 0.70 0.20 0.10
Lack of
accessibility for
relief forces
–R
20
Probability of occurrence 0.90 0.05 0.05
Financial impact 0.05 0.85 0.10
Human loss 0.85 0.10 0.05
Lack of access to
fire stations
–R
21
Probability of occurrence 0.85 0.10 0.05
Financial impact 0.80 0.15 0.05
Human loss 0.90 0.05 0.05
Lack of access to
medical centers
–R
22
Probability of occurrence 0.80 0.15 0.05
Financial impact 0.80 0.15 0.05
Human loss 0.05 0.85 0.10
(continued)
Table 2.
Hybrid risk
assessment
approach
Main risk area Risks Type Code Probability of occurrence and impact
Degree of
accuracy
Degree of
inaccuracy
Degree of
uncertainty
Secondary risks
(secondary risks for
buildings)
Fire hazard –R
23
Probability of occurrence 0.50 0.40 0.10
Financial impact 0.40 0.55 0.05
Human loss 0.35 0.55 0.10
Explosion hazard –R
24
Probability of occurrence 0.10 0.85 0.05
Financial impact 0.15 0.80 0.05
Human loss 0.10 0.80 0.10
Flooding –R
25
Probability of occurrence 0.05 0.85 0.10
Financial impact 0.05 0.90 0.05
Human loss 0 0.95 0.05
Damage to
buildings due to
aftershocks
–R
26
Probability of occurrence 0.20 0.70 0.10
Financial impact 0.15 0.80 0.05
Human loss 0.05 0.90 0.05
Urban infrastructural
and utility vulnerability
Vulnerability of
water and
wastewater
infrastructures
Type of materials used
in water distribution
networks
R
27
Probability of occurrence 0.75 0.10 0.15
Financial impact 0.90 0.05 0.05
Human loss 0.05 0.90 0.05
Quality of water
distribution network
R
28
Probability of occurrence 0.60 0.35 0.05
Financial impact 0.50 0.40 0.10
Human loss 0.03 0.90 0.07
The age of water
distribution network
R
29
Probability of occurrence 0.95 0.03 0.02
Financial impact 0.80 0.10 0.10
Human loss 0.05 0.90 0.05
Fracture of water
distribution pipes
R
30
Probability of occurrence 0.60 0.30 0.10
Financial impact 0.50 0.45 0.05
Human loss 0.02 0.95 0.03
Type of materials used
in wastewater networks
R
31
Probability of occurrence 0.90 0.05 0.05
Financial impact 0.85 0.05 0.10
Human loss 0.03 0.90 0.07
Quality of wastewater
network
R
32
Probability of occurrence 0.95 0.03 0.02
Financial impact 0.90 0.05 0.05
Human loss 0.02 0.95 0.03
The age of wastewater
network
R
33
Probability of occurrence 0.95 0.03 0.02
Financial impact 0.90 0.05 0.05
Human loss 0.02 0.95 0.05
Fracture of wastewater
pipes
R
34
Probability of occurrence 0.90 0.05 0.05
Financial impact 0.85 0.10 0.05
Human loss 0.02 0.95 0.05
(continued)
Table 2.
IJDRBE
Main risk area Risks Type Code Probability of occurrence and impact
Degree of
accuracy
Degree of
inaccuracy
Degree of
uncertainty
Vulnerability of gas
infrastructures
Type of materials used R
35
Probability of occurrence 0.15 0.80 0.05
Financial impact 0.20 0.70 0.10
Human loss 0.03 0.90 0.07
The quality of network
and utilities
R
36
Probability of occurrence 0.05 0.90 0.05
Financial impact 0.10 0.85 0.05
Human loss 0.02 0.90 0.08
The age of network and
utilities
R
37
Probability of occurrence 0.10 0.85 0.05
Financial impact 0.15 0.80 0.05
Human loss 0.02 0.95 0.03
Fracture of gas transfer
pipes
R
38
Probability of occurrence 0.05 0.90 0.05
Financial impact 0.1 0.85 0.05
Human loss 0.10 0.95 0.04
Vulnerability of
electrical
infrastructures and
network
Type of materials used R
39
Probability of occurrence 0.60 0.30 0.10
Financial impact 0.95 0.03 0.02
Human loss 0.10 0.85 0.05
The quality of network
and utilities
R
40
Probability of occurrence 0.65 0.25 0.10
Financial impact 0.85 0.10 0.05
Human loss 0.10 0.85 0.05
The age of network and
utilities
R
41
Probability of occurrence 0.70 0.25 0.05
Financial impact 0.93 0.05 0.02
Human loss 0.15 0.75 0.10
Vulnerability of
aboveground and
underground networks
R
42
Probability of occurrence 0.60 0.30 0.10
Financial impact 0.95 0.05 0
Human loss 0.10 0.85 0.05
Vulnerability of
telecommunication
infrastructure and
network
Type of materials used
in networks and utilities
R
43
Probability of occurrence 0.70 0.25 0.05
Financial impact 0.96 0 0.04
Human loss 0 0.95 0.05
The quality of
telecommunication
networks and utilities
R
44
Probability of occurrence 0.05 0.45 0.05
Financial impact 0.95 0 0.05
Human loss 0 0.95 0.05
The age of
telecommunication
networks and utilities
R
45
Probability of occurrence 0.65 0.25 0.10
Financial impact 0.96 0 0.04
Human loss 0.05 0.95 0
Damage to central cables R
46
Probability of occurrence 0.30 0.65 0.05
Financial impact 0.20 0.70 0.10
Human loss 0 0.95 0.05
Table 2.
Hybrid risk
assessment
approach
risks with scores higher than 0.288 are high priority and risks with scores lower than 0.096
are low priority, with scoresbetween these two values considered a medium priority.
5.2.3 Determination of upper and lower boundaries and risk severity (probability-impact
scores). Based on the Intuitionistic fuzzy parameters, the upper and lower boundaries and
severities of risks in worn-out urban fabrics investigated in this study were determined
using Excel software. These results are presented in Table 4. The following equations are
used for boundary calculations:
L¼
m
Ax
ðÞ
D
U¼1
g
Ax
ðÞ
D
ð
UL¼
p
Ax
ðÞ
D
Where
m
A
(x) is the degree of accuracy,
g
A
(x) is the degree of inaccuracy,
p
A
(x) is the degree
of uncertainty, L is the lower boundary of damage risk, U is the upper boundary of damage
risk and D is the impact of the damage caused.
5.2.4 Defuzzification of risk severities. A threshold is defined for defuzzification of risk
severities. This threshold is the difference between upper and lower boundaries of risk
severities. The following equations are used for defuzzification:
If U L>M;UþL
ðÞ
=2
If U L<M;U
Where U is the upper boundary of damage, L is the lower boundary, M is the median and U-
L is the difference between upper and lower boundaries. The median of U-L value is
calculated using Excel software. This value was equal to 0.02925 for financial damage and
0.008750 for human damage (casualties).
Table 3.
The numerical range
for probability
linguistic variables
Scale (linguistic
variables)
Numerical range for
probability (%)
Probability
score
Numerical range for
impact (%) Impact score
Very low 1–10 0.1 1–6 0.6
Low 11–30 0.3 7–12 0.12
Medium 31–50 0.5 13–24 0.24
High 51–70 0.7 25–48 0.48
Very high 71–99 0.99 49–96 0.96
Figure 1.
The probability and
impact (P-I) matrix
used for this study
ThreatProbability
0.95040.47520.23760.11880.05940.99
0.6720.3360.1680.0840.0420.70
0.480.240.120.060.0300.50
0.2880.1440.0720.0360.0180.30
0.0960.0480.0240.0120.0060.10
0.960.480.240.120.06
Impact
High priorityMedium priorityLow priority
IJDRBE
Table 4.
The results of
calculations for
intuitionistic fuzzy
parameters for risks
associated with
worn-out urban
fabrics in the Jalili
zone
Code Lf Uf Uf-Lf Lc Uc Uc-Lc
R
1
0.525 0.595 0.07 0.6 0.68 0.08
R
2
0.325 0.4225 0.0975 0.35 0.455 0.105
R
3
0.855 0.8835 0.0285 0.81 0.837 0.027
R
4
0.0825 0.105 0.0225 0.0275 0.035 0.0075
R
5
0.1625 0.1875 0.025 0.065 0.075 0.01
R
6
0.48 0.51 0.03 0.2 0.2125 0.0125
R
7
0.8075 0.85 0.0425 0.855 0.9 0.045
R
8
0.03 0.05 0.02 0.0075 0.0125 0.005
R
9
0.3575 0.4225 0.065 0.385 0.455 0.07
R
10
0.68 0.72 0.04 0.595 0.63 0.035
R
11
0.925 0.95 0.0475 0.855 0.9 0.045
R
12
0.855 0.9215 0.0665 0.81 0.873 0.063
R
13
0.12 0.14 0.02 0.06 0.07 0.01
R
14
0.72 0.765 0.045 0.56 0.595 0.035
R
15
0.135 0.165 0.03 0.0675 0.0825 0.015
R
16
0.135 0.1425 0.0075 0.765 0.8075 0.0425
R
17
0.08 0.085 0.005 0.6 0.6375 0.0375
R
18
0.01 0.0125 0.0025 0.02 0.025 0.005
R
19
0.425 0.45 0.025 0.595 0.63 0.035
R
20
0.045 0.0475 0.0025 0.765 0.8075 0.0425
R
21
0.68 0.72 0.04 0.765 0.81 0.045
R
22
0.04 0.0425 0.0025 0.72 0.765 0.045
R
23
0.2 0.24 0.04 0.175 0.21 0.035
R
24
0.015 0.0225 0.0075 0.01 0.015 0.005
R
25
0.0025 0.0075 0.005 0 0 0
R
26
0.03 0.045 0.015 0.01 0.015 0.005
R
27
0.675 0.81 0.135 0.0375 0.045 0.0075
R
28
0.3 0.325 0.025 0.018 0.0195 0.0015
R
29
0.76 0.776 0.016 0.0475 0.0485 0.001
R
30
0.3 0.35 0.05 0.012 0.014 0.002
R
31
0.765 0.8075 0.0425 0.027 0.0285 0.0015
R
32
0.855 0.873 0.018 0.019 0.0194 0.0004
R
33
0.855 0.873 0.018 0.019 0.0194 0.0004
R
34
0.765 0.8075 0.0425 0.045 0.0475 0.0025
R
35
0.03 0.04 0.01 0.0045 0.006 0.0015
R
36
0.005 0.01 0.005 0.001 0.002 0.001
R
37
0.015 0.0225 0.0075 0.002 0.003 0.001
R
38
0.005 0.01 0.005 0.0005 0.001 0.0005
R
39
0.57 0.665 0.095 0.06 0.07 0.01
R
40
0.5525 0.6375 0.085 0.065 0.075 0.01
R
41
0.651 0.6975 0.0465 0.105 0.1125 0.0075
R
42
0.57 0.665 0.095 0.06 0.07 0.01
R
43
0.672 0.72 0.048 0 0 0
R
44
0.475 0.5225 0.0475 0 0 0
R
45
0.624 0.72 0.096 0 0 0
R
46
0.06 0.07 0.01 0 0 0
Notes: Lf: lower boundary of financial damage; Lc lower boundary of casualties, Uf: upper boundary of
financial damage; Uc upper boundary of casualties, Df: impact of financial damage; Dc impact of casualties
Hybrid risk
assessment
approach
Mf ¼0:02925
Mc ¼0:00875
Finally, the defuzzification equations and Microsoft Excel software were used to defuzzify
risk severities.
5.2.5 Prioritization of individual risks. In this stage, individual risks were prioritized
based on their scores and compared using the risk evaluation matrix. First, risks were sorted
in descending order according to their scores. Then, the P-I matrix was compared to
determine their priority (high, medium, or low). In this matrix, red indicates risks with high
priority, orange shows risks with medium priority and green reveals risks with low priority.
Table 5 shows the severity of risks and their impact on financial and human losses,
respectively.
5.3 Discussion of the analytical results
5.3.1 Effect of risks on financial losses. The results presented in Table 5 show that among 46
risks, 27 risks had high priority (numbers 1 to 27), 6 risks had medium priority (risks 28 to
33) and 13 risks had low priority (risks 34 to 46). Evaluating the risks with high priority
shows that vulnerability caused by building’s age (51years and older) with a total score of
0.9262 had the highest and the vulnerability caused by fracture of water pipes with a score
of 0.325 had the lowest scores in this group. Among these 27 high-priority risks, 20 risks had
scores higher than 0.5. Only 7 had scores below 0.5 with vulnerability caused by building’s
age (51 years and older), vulnerability caused by several floors (1–2floors) and vulnerability
caused by a type of structure (other) having first to third highest priorities, respectively.
There was a total of six risks with medium priority, among which fire hazard with a total
score of 0.22 had the highest score and vulnerability caused by building quality (new
buildings) with a score of 0.105 had the lowest score in this category. Risks with low priority
included 13 risks, among which the Obstruction of passages (width of 5–8 meters) had the
highest score with 0.085 and flooding of paths and buildings had the lowest score with
0.0075. The highest financial losses are observed for the top 10 risks in the high priority
category, with the most apparent damage observed to residential buildings and water and
wastewater networks.
5.3.2 Effect of risks on human losses. The prioritization and risk assessment results
indicate that among 46 risks, 15 risks had high priority and were very impactful; 3 risks had
medium priority and 28 risks had low priority, which related to urban facilities and
secondary risks. The high score of the first 15 risks indicates that the situation of the studied
region is very critical in regard to human losses and casualties in case of an earthquake. A
brief explanation of investigated risks is as follows: risks of damage to residential buildings
and lack of access and obstruction of passages and roads have the highest scores and the
most significant effect on casualties. These risks, therefore, have high priority. Risks of
worn-out buildings and buildings with the age of higher than 51 years are in the first and
second place in Table 5 with a score of 0.8775 and the risk of buildings with 1–2floors is in
the third place with a total score of 0.8415. Given the fact that the majority of buildings in the
Jalili zone use other types of building materials (mudbricks and wood, brick and wood, brick
and steel and other traditional building materials), they are significantly vulnerable to
earthquakes. Moreover, these buildings lead to high human losses. Therefore, this risk is in
the fourth place of the table with a score of 0.8235. Risks of lack of access to fire stations,
emergency services and medical centers also have high priorities in this table, is placed in
5th, 7th and 8th positions, respectively. The most common problem mentioned is the
obstruction of alleyways and roads due to their small width, which will cause issues for
IJDRBE
Table 5.
Risk prioritization
based on human and
financial losses based
on the P-I matrix
Code
Prioritization
(defuzzied financial
risk values) Rank (financial losses)
Prioritization
(defuzzied human
risk values) Rank (human losses)
R
1
0.5600 20 0.6400 9
R
2
0.3738 25 0.4025 15
R
3
0.8835 3 0.8235 4
R
4
0.1050 33 0.0350 28
R
5
0.1875 29 0.0700 20
R
6
0.4950 22 0.2062 16
R
7
0.8288 6 0.8775 1
R
8
0.0500 36 0.0125 37
R
9
0.3900 24 0.4200 14
R
10
0.7000 12 0.6125 11
R
11
0.9262 1 0.8775 2
R
12
0.8820 2 0.8415 3
R
13
0.1400 32 0.0650 22
R
14
0.7425 10 0.5775 13
R
15
0.1500 30 0.0750 19
R
16
0.1425 31 0.7862 6
R
17
0.0850 34 0.6178 10
R
18
0.0125 43 0.0250 30
R
19
0.4500 23 0.6125 12
R
20
0.0475 37 0.7862 7
R
21
0.7000 13 0.7875 5
R
22
0.0425 39 0.7425 8
R
23
0.2200 28 0.1925 17
R
24
0.0225 41 0.0150 34
R
25
0.0075 46 0 42
R
26
0.0450 38 0.0150 35
R
27
0.7425 11 0.0450 27
R
28
0.3250 26 0.0485 25
R
29
0.7760 9 0.0195 31
R
30
0.3250 27 0.0140 36
R
31
0.7862 7 0.0285 29
R
32
0.8730 4 0.0194 32
R
33
0.8730 5 0.0194 33
R
34
0.7862 8 0.0475 26
R
35
0.0400 40 0.0060 38
R
36
0.0100 44 0.0020 40
R
37
0.0225 42 0.0030 39
R
38
0.0100 45 0.0010 41
R
39
0.6175 17 0.0650 23
R
40
0.5950 19 0.0700 21
R
41
0.6742 15 0.1125 18
R
42
0.6175 18 0.0650 24
R
43
0.6960 14 0 43
R
44
0.4988 21 0 44
R
45
0.6720 16 0 45
R
46
0.0700 35 0 46
Hybrid risk
assessment
approach
relief and rescue efforts. The vulnerability caused by roads with a width of fewer than 5
meters with a total score of 0.7862 and the vulnerability caused by roads with a width of 5–8
meters with a score of 0.6187 is in the 6th and 10th positions, respectively; making them
high priority risks. Three risks with medium priority in this table include the vulnerability
caused by buildings requiring renovation with 0.2062, fire hazard with a score of 0.1925 and
vulnerability causedby the age of electrical utilities and network with a total score of 0.1125.
Regarding low-priority risks, vulnerability caused by environmental conditions and location
had the highest score with 0.075 and flooding. Five vulnerabilities related to
telecommunication networks with a score of 0 were in the last places in this table. These
results show that these risks have almost zero effect on the number of human losses and
casualties.
6. Conclusions and research implications
Risk Identification is one of the most critical steps in the risk management process. Any
mistakes when identifying risks or the source and impacts of risks can reduce the accuracy
of risk management. Therefore, the use of scientific and systematic processes is essential for
risk identification. The aim of this study was to recommend an innovative combined method
based on the P-I approach and intuitionistic fuzzy set theory to identify and prioritize the
earthquake risks associated with worn-out urban fabrics. For this purpose, the main
earthquake risks in the worn-out urban fabrics were extracted by reviewing the research
literature, then it was confirmed by 15 experts during brainstorming sessions. Finally, 19
critical earthquake risks in the worn-out urban fabrics were identified. The researcher-made
questionnaire was developed based on 19 identified risks based on a five-point Likert
measurement scale. Then the probability of risks and the effect of identified risks were
solicited and determined. The P-I approach and brainstorming and Intuitionistic fuzzy sets
were used to investigate the impact of risks on objectives (financial and human losses and
damages) during an earthquake in worn-out urban fabrics. Analyzing the top 10 priority
risks regarding financial losses indicated that the unfavorable situation of buildings and
water and wastewater facilities in this zone and their vulnerability to earthquakes is due to
the age of the structures. Prioritization in terms of human losses (casualties) also indicates
the high casualties in this zone due to the destruction of buildings. In addition, prioritization
based on human losses indicates that obstructions of passages and roads, and lack of access
to emergency service, fire stations and medical centers for timely relief and rescue during
earthquakes.
In terms of the theoretical implications, this study helps to better manage safety in worn-
out urban fabrics by identifying and assessing the critical earthquake risks in these areas by
a novel quantitative hybrid method –which, according to the authors’knowledge, has not
been studied before. In terms of the practical implications related to the results of this study,
to reduce earthquake risks in worn-out urban fabrics, it is recommended to determine the
relevant risk response strategies in advance and also to set standards and protocols by
organizations. A structured definition of risk and safety management in worn-out urban
fabrics dramatically increases the chance of improving disaster resilience of these areas in
the built environment. In particular, it demonstrates that the renovation of dilapidated
buildings, reinforcement and improvement of buildings and electrical and mechanical
facilities and expansion of access roads, as well as proper risk management planning of
processes, allows urban managers to help improve their effectiveness and productivity in
disaster management. To succeed in disaster management, urban managers need proper
organization, improvement of environmental and building conditions. Hence, some of the
possible future research directions for deepening the identified findings are highlighted as
IJDRBE
follows: What are the specific managerial and environmental capabilities that allow urban
managers to perform better in the field of disaster management in worn-out urban fabrics?
What are the risk reduction strategies that can be adopted for worn-out urban fabrics?
Moreover, as suggested by Sarvari et al. (2019) and Khosravi et al. (2020), it will also be
valuable to compare the critical earthquake risks of worn-out urban fabrics with other urban
spaces to identify any similarities and differences. Finally, the hybrid approach proposed in
this study can not only be adopted for other urban worn-out fabrics in Iran and other
developing countries for generalization but can also be applied in other studies in the field of
risk management.
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Zadeh, L.A. (1965), “Fuzzy sets”,Information and Control, Vol. 8 No. 3, pp. 338-353.
Further reading
Atanassov, K.T. (1989), “More on intuitionistic fuzzy sets”,Fuzzy Sets and Systems, Vol. 33 No. 1,
pp. 37-45.
Tai, C.A. and Lee, Y.L. (2013), “Urban disaster prevention shelter vulnerability evaluation considering
road network characteristics”,Journal of Civil Engineering and Architecture, Vol. 7 No. 5, p. 609.
Corresponding author
Hadi Sarvari can be contacted at: h.sarvari@khuisf.ac.ir
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Hybrid risk
assessment
approach
