A Hybrid MCDM Technique for Risk Management in Construction Projects

A Go/No-Go Decision-Making Model Based on Risk and Multi-Criteria Techniques for Project Selection

The realization of infrastructures and the deployment of processes can follow project formalism. Generally, a project goes through a design and a realization phase. Between these two phases, there is a crucial milestone: Launching the project. Making this decision is not easy at all, and constitutes a real problem-- the main reasons to this are the numerous numbers of criteria (for technical, economic, social, environmental dimensions) and risks in the sense of feared events. Criteria and risks are most of the time not considered due to lack of time (for formalization) and the difficulty to handle them. The objective of this paper is to propose a relevant approach to make the decision of launching the project or not. The proposal outlined is innovative in that it can consider indicators based on several appropriate criteria, the associated risks, and their ways of management. The fact of considering several criteria and risks increases the probability of making the good decision.

The Current Risk Management Practices and Knowledge in the Construction Industry

Construction is a critical sector of any economy in terms of value production, labor, and contributing to the gross national product. Managing risk is a relatively young area in Yemen’s construction sector, but it is gaining traction as building activity and competition rise. Construction firms mitigate risk by using a variety of risk management methods. Therefore, there is a need to assess these procedures in order to detect shortcomings. This research aims to establish the existing risk management strategies used in Yemeni building projects. Survey questionnaires were used to collect data. Respondents were drawn from Yemeni construction businesses. Risk management is not executed systematically, intentionally, or continuously, and most firms’ risk management procedures are reactive, semipermanent, informal, and unstructured, with no or few dedicated resources to address risks. This strategy is inconsistent with generally accepted risk management principles. Nonetheless, the findings suggest a general understanding of risk management and a willingness to learn from previous errors. The study of the findings suggests that risk identification approaches such as judgment and historical data are employed for risk analysis, and that the industry typically attempts to avoid or transfer risks in Yemeni building projects. The results shed light on the shortcomings of Yemen’s project management practices. To guarantee that construction projects obtain maximum value for money, project managers of big construction businesses in Yemen need a strong understanding of and training in globally accepted systematic risk management procedures. Finally, this study can help future stakeholders determine how to work together to manage risk.

Analyzing and Controlling Construction Engineering Project Gray Rhino Risks with Innovative MCDM Methods: Interference Fuzzy Analytical Network Process and Decision-Making Trial and Evaluation Laboratory

Construction engineering projects are costly and require large amounts of labor, physical, and financial resources. The failure of a construction engineering project typically brings huge losses. Previous studies have focused on the identification of risks, but insufficient attention has been given to strategic resource allocation for risk management after risk identification. Statistics show that most construction engineering project failures are caused by common risks. Common risks are called gray rhino risks. This metaphor illustrates that many risks are obvious but dangerous. This study was motivated by the challenge of efficiently managing gray rhino risks with limited inputs. The literature suggests that gray rhino risks are abundant in construction engineering projects and that there are mutual eliciting relationships between them, which make it difficult for the manager to devote enough resources to the prevention of key risks. Considerable resources are wasted on unimportant risks, resulting in key risk occurrence and failure of construction engineering projects. Therefore, this study describes an innovative multi-criteria decision making (MCDM) technique for ranking risks based on the strength of the eliciting relationships between them. This study used the fuzzy technique and created an interference fuzzy analytical network process (IF-ANP) method. By employing the IF-ANP alongside a decision-making trial and evaluation laboratory (DEMATEL) approach, the subjectivity can be effectively reduced and the accuracy improved during expert risk evaluation for construction engineering projects. IF-ANP was used to quantify eliciting relationships between risks and DEMATEL was used to rank risks based on the IF-ANP result. An empirical study was done to meticulously rank five risks that were selected from the gray rhino risks in the Chengdu–Chongqing Middle Line High-speed Railway construction engineering project. They are capital chain rupture, decision failure, policy and legal risk, economic downturn, and stakeholder conflict. The results showed that the policy and legal risk was the source of other risks, and that these other risks were symptoms rather than the disease.

A fuzzy ANP-DEMATEL model on faulty behavior risks: implications for improving safety in the workplace

The occurrence of occupational accidents and injuries has always been a major concern for industrial management. Such undesirable incidences are higher in developing countries, especially in India, than in developed countries. This research aims to identify, analyze and evaluate the faulty behavior risks (FBRs) that trigger occupational accidents and injuries. Using a data triangulation strategy, this study identified 19 FBR factors under five categories. An integrated approach comprising the fuzzy analytic network process (ANP) and the decision-making trial and evaluation laboratory (DEMATEL) method is proposed for assessing these FBRs. The five most prominent critical risk factors are the absence of continuous monitoring, defective equipment and maintenance, cognitive bias, proper signage and adverse ambient working conditions. The study postulates some implications for industrial management to mitigate occupational accidents and injuries based on the outcomes.

A Fuzzy Multi-Criteria Method for Sustainable Ferry Operator Selection: A Case Study

This study is motivated by the Zhuhai municipal government, which needs to select a sustainable ferry operator. Previous research has ignored the evaluation and selection of ferry operators. In addition, since ferry operator evaluation involves conflicting qualitative and quantitative criteria, and there may be uncertainty and ambiguity in the evaluation of criteria by experts, a fuzzy multi-criteria decision-making (MCDM) approach is required to address this challenge. To this end, this paper proposes an integrated MCDM framework model to evaluate and select the best ferry operator. First, a ferry operator evaluation index system with 15 sub-criteria is constructed according to literature and expert opinions; then the fuzzy analytic hierarchy process (FAHP) is used to determine the subjective weight of the criteria, and the entropy weight (EW) method is used to calculate the objective weight of the criteria. We use the linear weighting method to obtain the comprehensive weights of the criteria; finally, the fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) method is adapted to determine the best ranking of the alternatives. This paper takes the Wanshan Islands in Zhuhai as a real case study to verify the proposed FAHP-EW-FTOPSIS method. The results show that the proposed method can be effectively applied to the evaluation and selection of ferry operators. Sensitivity analysis of criteria weights demonstrates the effectiveness and robustness of the proposed framework model. Key findings based on the research provide management insights that can benefit relevant stakeholders. This is the first paper to study the evaluation and selection of ferry operators. Hence, the evaluation index system and integrated framework model proposed in this paper can make important contributions to the evaluation of ferry operators.

7-Score Function for Assessing the Strength of Association Rules Applied for Construction Risk Quantifying

There are several factors influencing the time of construction project execution. The properties of the planned structure, the details of an order, and macroeconomic factors affect the project completion time. Every construction project is unique, but the data collected from previously completed projects help to plan the new one. The association analysis is a suitable tool for uncovering the rules—showing the influence of some factors appearing simultaneously. The input data to the association analysis must be preprocessed—every feature influencing the duration of the project must be divided into ranges. The number of features and the number of ranges (for each feature) create a very complicated combinatorial problem. The authors applied a metaheuristic tabu search algorithm to find the acceptable thresholds in the association analysis, increasing the strength of the rules found. The increase in the strength of the rules can help clients to avoid unfavorable sets of features, which in the past—with high confidence—significantly delayed projects. The new 7-score method can be used in various industries. This article shows its application to reduce the risk of a road construction contract delay. Importantly, the method is not based on expert opinions, but on historical data.

Reasoning Method Based on Intervals with Symmetric Truncated Normal Density

Error parameters are inevitable in systems. In formal verification, previous reasoning methods seldom considered the probability information of errors. In this article, errors are described as symmetric truncated normal intervals consisting of the intervals and symmetric truncated normal probability density. Furthermore, we also rigorously prove lemmas and a theorem to partially simplify the calculation process of truncated normal intervals and independently verify the formulas of variance and expectation of symmetric truncated interval given by some scholars. The mathematical derivation process or verification codes are provided for most of the key formulas in this article. Hence, we propose a new reasoning method that combines the probability information of errors with the previous statistical reasoning methods. Finally, an engineering example of the reasoning verification of train acceleration is provided. After simulating the large-scale cases, it is shown that the simulation results are consistent with the theoretical reasoning results. This method needs more calculation, while it is more effective in detecting non-error’s fault factors than other error reasoning methods.

Model for the Sustainable Material Selection by Applying Integrated Dempster-Shafer Evidence Theory and Additive Ratio Assessment (ARAS) Method

The construction industry is a vital part of the modern economic system. Construction work often has significant negative impacts on the environment and sustainable economic development, such as degradation of the environment, depletion of resources, and waste generation. Therefore, environmental concerns must be taken into account when evaluating and making decisions in the construction industry. In this regard, sustainable construction is considered as the best way to avoid resource depletion and address environmental concerns. Selection of sustainable building materials is an important strategy in sustainable construction that plays an important role in the design and construction phase of buildings. The assessment of experts is one of the most important steps in the material selection process, and their subjective judgment can lead to unpredictable uncertainty. The existing methods cannot effectively demonstrate and address uncertainty. This paper proposes an integrated Dempster-Shafer (DS) theory of evidence and the ARAS method for selecting sustainable materials under uncertainty. The Dempster-Shafer Evidence Theory is a relatively new and appropriate tool for substantiating decisions when information is nonspecific, ambiguous, or conflicting. The Additive Ratio Assessment (ARAS) method has many advantages to deal with MCDM problems with non-commensurable and even conflicting criteria and to obtain the priority of alternatives based on the utility function. The proposed method converts experts’ opinions into the basic probability assignments for real alternatives, which are suitable for DS evidence theory. It uses the ARAS method to obtain final estimation results. Finally, a real case study identifying the priority of using five possible alternative building materials demonstrates the usefulness of the proposed approach in addressing the challenges of sustainable construction. Four main criteria including economic, social, environmental, and technical criteria and 25 sub-criteria were considered for the selection of sustainable materials. The specific case study using the proposed method reveals that the weight of economic, socio-cultural, environmental, and technical criteria are equal to 0.327, 0.209, 0.241, and 0.221, respectively. Based on these results, economic and environmental criteria are determined as the most important criteria. The results of applying the proposed method reveal that aluminum siding with a final score of 0.538, clay brick with a score of 0.494, and stone façade with a final score of 0.482 are determined as the best alternatives in terms of sustainability.

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

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

Prioritizing Construction Labor Productivity Improvement Strategies Using Fuzzy Multi-Criteria Decision Making and Fuzzy Cognitive Maps

Construction labor productivity (CLP) is affected by various interconnected factors, such as crew motivation and working conditions. Improved CLP can benefit a construction project in many ways, such as a shortened project life cycle and lowering project cost. However, budget, time, and resource restrictions force companies to select and implement only a limited number of CLP improvement strategies. Therefore, a research gap exists regarding methods for supporting the selection of CLP improvement strategies for a given project by quantifying the impact of strategies on CLP with respect to interrelationships among CLP factors. This paper proposes a decision support model that integrates fuzzy multi-criteria decision making with fuzzy cognitive maps to prioritize CLP improvement strategies based on their impact on CLP, causal relationships among CLP factors, and project characteristics. The proposed model was applied to determine CLP improvement strategies for concrete-pouring activities in building projects as an illustrative example. This study contributes to the body of knowledge by providing a systematic approach for selecting appropriate CLP improvement strategies based on interrelationships among the factors affecting CLP and the impact of such strategies on CLP. The results are expected to support construction practitioners with identifying effective improvement strategies to enhance CLP in their projects.

Developing a Risk Management Process for Infrastructure Projects Using IDEF0

The Mass Rapid Transit (MRT) project is a massive, large-scale construction venture with a complex interface. In order to reduce the risk of disasters and industrial accidents in the project and to save costs, a simple and flexible risk management system is necessary for projects such as MRT. A set of risk management processes was identified through a literature review and data collection, and the Integration Definition for Function Modeling (IDEF0) process was used for logical analysis. The IDEF0 diagram clearly depicts the items to be delivered at each interface, and risk is reduced by facilitating the flow of data on various risk items. The results of this research will be applied to other practical projects, with special emphasis on the project planning and design stages. Future work will verify whether the implementation of the proposed risk management process does indeed effectively reduce risks in the completed project.

A Modified CRITIC Method to Estimate the Objective Weights of Decision Criteria

In this study, we developed a modified version of the CRiteria Importance Through Inter-criteria Correlation (CRITIC) method, namely the Distance Correlation-based CRITIC (D-CRITIC) method. The usage of the method was illustrated by evaluating the weights of five smartphone criteria. The same evaluation was repeated using four other objective weighting methods, including the original CRITIC method. The results from all the methods were further analyzed based on three different tests (i.e., the distance correlation test, the Spearman rank-order correlation test, and the symmetric mean absolute percentage error test) to validate D-CRITIC. The tests revealed that D-CRITIC could produce more valid criteria weights and ranks than the original CRITIC method since D-CRITIC yielded a higher average distance correlation, a higher average Spearman rank-order correlation, and a lower symmetric mean absolute percentage error. Besides, additional sensitivity analysis indicated that D-CRITIC has the tendency to deliver more stable criteria weights and ranks with a larger decision matrix. The research has contributed an alternative objective weighting method to the area of multi-criteria decision-making through a unique extension of distance correlation. This study is also the first to propose the idea of a distance correlation test to compare the performance of different criteria weighting methods.

A Multi-Criteria Decision-Making Approach for Ideal Business Location Identification

Location has always been a primary concern for business startups to be successful. Therefore, much research has focused on the problem of identification of an ideal business site for a new business. The process of ideal business site selection is complex and depends on a number of criteria or factors. Since the ultimate goal of all businesses is to increase customer footprints and to thus increase sales, criteria including traffic accessibility, visibility, ease of access, vehicle parking, customers availability, etc. play important roles. In other words, we can say that optimal business site selection is a multi-criteria decision-making (MCDM) problem. MCDM is used to identify an optimal solution or decision out of many alternatives by utilizing a number of criteria. In mathematics, there exist a number of structured techniques for organizing and analyzing complex decisions, for instance, AHP, ANP, TOPSIS, etc. In this work, we present a hybrid of two such techniques to solve the MCDM problem for an optimal business site selection given a set of candidate sites. The proposed approach is based on the AHP (Analytic Hierarchy Process) and TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) approaches. The reason for using the proposed hybrid approach is multi-fold. The hybrid approach reduces the computational complexity and require less manual effort, thus improving the efficiency and accuracy of the proposed approach. Given a set of candidate locations for a new business, the proposed approach ranks the candidates. Thus, the candidate locations with higher ranks are identified as suitable or ideal. The approach comes up with the ranking of all of the candidate locations, thus giving business managers room to make calculated decisions. To show the effectiveness of the proposed approach, a detailed step-by-step case study is given to identify an ideal location in New York City for a new gas station. Furthermore, an experimental evaluation is also presented using a number of real New York City datasets.

Decision Tree and AHP Methods Application for Projects Assessment: A Case Study

This research is dedicated to the modelling of decision process occurring during the implementation of construction projects. Recent studies generally do not assess the robustness of the decisions regarding the possible changes during the construction project implementation. However, such an assessment might increase the reliability of the decision-making process. We addressed this gap through a new model that combines the decision-making process modelling with the AHP method and includes the analysis of model stability concerning stakeholders’ behaviour. We used the Analytic Hierarchy Process (AHP) and Decision tree methods to model the decision-making process. The proposed model was validated on a case study of multiple construction projects. The assessment was performed from individual investor’s and independent expert’s perspectives. The criteria for the assessment were selected according to the principles of sustainability. We performed the sensitivity analysis, making it possible to assess the possible changes of the decisions depending on the potential patterns of the decision-makers’ behaviour. The results of the study show that, sometimes, small fluctuations in the project factors affect the project selection indicating the possible lack of the robustness of the project decisions.

Decision Making for Project Appraisal in Uncertain Environments: A Fuzzy-Possibilistic Approach of the Expanded NPV Method

The major drawback of the classic approaches for project appraisal is the lack of the possibility to handle change requests during the project’s life cycle. This fact incorporates the concept of uncertainty in the estimation of this investment’s worth. To resolve this issue, the authors use fuzzy numbers, possibilistic moments of fuzzy numbers and the hybrid (fuzzy statistic) fuzzy estimators’ method in order to introduce a fuzzy possibilistic version of the expanded net present value method (FPeNPV). This approach consists of two factors: the fuzzy possibilistic NPV and the fuzzy option premium. For the estimation of the fuzzy NPV, some basic assumptions are taken into consideration: (1) the opportunity cost of capital, used as the present value interest factor calculated through the weighted average cost of capital (WACC), (2) the equity cost, determined through the possibilistic set-up of the capital asset pricing model CAPM, and (3) the inflation factor, also included in the estimation of the NPV. The fuzzy estimators’ method is used for the computation of the fuzzy option premium. An algorithm of nine major steps leads to the computation of the FPeNPV. This gives the administration the opportunity to adapt to potential changes in the company’s internal and external environments. In this way, the symmetry between the planning and execution phase of a project can be reinstated. The results validate the statement that fuzzy and intelligent methods remain valuable tools to express uncertainty in various scientific areas. Finally, an illustrative example aims at a thorough comprehension of this new approach of the expanded NPV method.

A New Decision-Making Approach Based on Fermatean Fuzzy Sets and WASPAS for Green Construction Supplier Evaluation

The construction industry is an important industry because of its effects on different aspects of human life experiences and circumstances. Environmental concerns have been considered in designing and planning processes of construction supply chains in the recent past. One of the most crucial problems in managing supply chains is the process of evaluation and selection of green suppliers. This process can be categorized as a multi-criteria decision-making (MCDM) problem. The aim of this study is to propose a novel and efficient methodology for evaluation of green construction suppliers with uncertain information. The framework of the proposed methodology is based on weighted aggregated sum product assessment (WASPAS) and the simple multi-attribute rating technique (SMART), and Fermatean fuzzy sets (FFSs) are used to deal with uncertainty of information. The methodology was applied to a green supplier evaluation and selection in the construction industry. Fifteen suppliers were chosen to be evaluated with respect to seven criteria including “estimated cost”, “delivery efficiency”, “product flexibility”, “reputation and management level”, “eco-design”, and “green image pollution”. Sensitivity and comparative analyses were also conducted to assess the efficiency and validity of the proposed methodology. The analyses showed that the results of the proposed methodology were stable and also congruent with those of some existing methods.

Use of a Refined Corporate Social Responsibility Model to Mitigate Information Asymmetry and Evaluate Performance

While the importance of Corporate Sociable Responsibility (CSR) has been widely acknowledged, research on how to guide a company in evaluating and improving its CSR performance is relatively under-explored. This paper adopts the predominant framework from the United Nations (UN) and proposes a refined CSR model by using a hybrid multiple criteria decision-making (MCDM) approach. The proposed approach is expected to mitigate the potential information asymmetry issue that might deteriorate the CSR performance of a company. To illustrate the hybrid approach, this study analyzes the CSR performance of four publicly listed information technology (IT) manufacturing companies with the participation of senior domain experts, by using the proposed approach. The CSR performance ranking results are consistent by using various experiments, which is similar to the annual CSR contest held by a prominent organization from Taiwan in 2019. In addition, we illustrate how to apply this refined model to gain managerial insights and pursue sustainable CSR improvement with a priority.

Determining and assessing the risks of commercial and recreational complex building projects in developing countries: a survey of experts in Iran

Purpose

As construction of commercial and recreational complex building projects (CRCBPs) is one of the most important issues in many developing countries and requires a very high cost of implementation, it is important to identify and prioritize the risks of such projects. Therefore, the purpose of this study is to identify and rank the risks of CRCBPs by studying the case of the “Hamedanian Memorial,” a CRCBP in Iran.

Design/methodology/approach

To pursue this aim, a descriptive-survey method was used. The statistical population of the study consists of 30 experienced experts (consultants, contractors and employers) of the “Hamedanian Memorial” project selected according to the Cochran formula and minimum population census. A questionnaire was used as the data collection tool, administered in all stages of risk identification and evaluation, and was devised by using library and field methods based on the literature and research background, as well as interviewing experts in the risk identification and evaluation stages. Kendall’s coefficient of agreement was used to validate the experts’ opinions in the risk identification stage. The ranking in qualitative evaluation was done based on the risk intensity and the cumulative risk index.

Findings

The results show that the risks are associated with exchange rate fluctuation, inflation fluctuation, access to skilled workers, contractors’ claims and foreign threats from international relations.

Originality/value

The results and findings of the present study can be of interest to the executives of large commercial, leisure, public and private projects in developing and developed countries; understanding risks can significantly improve the decision-making process of CRCBPs.

Developing a new BWM-based GMAFMA approach for evaluation of potential risks and failure modes in production processes

Purpose

Nowadays, the risk assessment and reliability engineering of various production processes have become an inevitable necessity. Because if these risks are not going to be evaluated and no solution is going to be taken for their prevention, managing them would be really hard and costly in case of their occurrence. The importance of this issue is much higher in producing healthcare products due to their quality's direct impact on the health of individuals and society.

Design/methodology/approach

One of the most common approaches of risk assessment is the failure mode and effects analysis (FMEA), which is facing some limitations in practice. In this research, a new generalized multi-attribute failure mode analysis approach has been proposed by utilizing the best–worst method and linguistic 2-tuple representation in order to evaluate the production process of hemodialysis solution in a case of Tehran, Iran.

Findings

According to the results, entry of waste to the mixing tanker, impurity of raw materials and ingredients and fracture of the mixer screw have been identified as the most important potential failures. At last, the results of this research have been compared with the previous studies.

Originality/value

Some reinforcement attributes have been added to the traditional FMEA attributes in order to improve the results. Also, the problems of identical weights for attributes, inaccuracy in experts' opinions and the uncertainties in prioritizing the potential failures were improved. Furthermore, in addition to the need for less comparative data, the proposed approach is more accurate and comprehensive in its results.

A New Evaluation Methodology for Quality Goals Extended by D Number Theory and FAHP

Evaluation of quality goals is an important issue in process management, which essentially is a multi-attribute decision-making (MADM) problem. The process of assessment inevitably involves uncertain information. The two crucial points in an MADM problem are to obtain weight of attributes and to handle uncertain information. D number theory is a new mathematical tool to deal with uncertain information, which is an extension of evidence theory. The fuzzy analytic hierarchy process (FAHP) provides a hierarchical way to model MADM problems, and the comparison analysis among attributes is applied to obtain the weight of attributes. FAHP uses a triangle fuzzy number rather than a crisp number to represent the evaluation information, which fully considers the hesitation to give a evaluation. Inspired by the features of D number theory and FAHP, a D-FAHP method is proposed to evaluate quality goals in this paper. Within the proposed method, FAHP is used to obtain the weight of each attribute, and the integration property of D number theory is carried out to fuse information. A numerical example is presented to demonstrate the effectiveness of the proposed method. Some necessary discussions are provided to illustrate the advantages of the proposed method.

An Extension of the Failure Mode and Effect Analysis with Hesitant Fuzzy Sets to Assess the Occupational Hazards in the Construction Industry

The construction industry is considered as one of the most dangerous industries in terms of occupational safety and has a high rate of occupational incidents and risks compared to other industries. Given the importance of identifying and assessing the occupational hazards in this industry, researchers have conducted numerous studies using statistical methods, multi-criteria decision-making methods, expert-based judgments, and so on. Although, these researchers have used linguistic variables, fuzzy sets and interval-valued intuitionistic fuzzy sets to overcome challenges such as uncertainty and ambiguity in the risk assessment conducted by experts; the previous models lack in efficiency if the experts are hesitant in their assessment. This leads to the inability to assign a specific membership degree to any risk. Therefore, in this research, it is tried to provide an improved approach to the Failure Mode and Effects Analysis (FMEA) method using an Multi-Criteria Decision-Making (MCDM) method based on the hesitant fuzzy set, which can effectively cope with the hesitance of the experts in the evaluation. Also, Stepwise Weight Assessment Ratio Analysis (SWARA) method is applied for risk factor weighing in the proposed approach. This model is applied to a construction industry case study to solve a realistic occupational risk assessment. Moreover, a comparison is made between the results of this model and those obtained by the conventional FMEA and some other aggregation operators. The results indicate that the newly developed approach is useful and flexible to address complex FMEA problems and can generate logical and reliable priority rankings for failure modes.

Application of Reliability Analysis for Risk Ranking in a Levee Reconstruction Project

Levees are embankments designed for passive flood protection. In order to reduce the potential of climate-induced flooding risks, it is necessary to reconstruct or upgrade the existing levees. Flood risk management aims to reduce the probability of floods and their potential adverse effects on the population, economy, and environment. This paper presents the novel application of reliability analysis for risk ranking in the Otok Virje-Brezje levee reconstruction project in the Republic of Croatia. To identify, verify and analyse key risks, a group of 35 experts, who were directly involved in the levee reconstruction project or have extensive experience in similar projects, was selected. An Analytic network process (ANP) was used for group multi criteria decision-making. Quantitative and qualitative approaches to risk analysis were combined. Different experts from the various organisations may have diverse interests and goals. The geometric mean method was chosen to reach group consensus. The resources that will be allocated to the risk responses are proportional to the risk exposures. To analyse the reliability of the group consensus-reaching method a determination of the risk ranking probability matrix is proposed by using the Monte Carlo simulation method. Different decision-making approaches are proposed for cases in which consensus is not reached with satisfactory reliability.

Determining Weights in Multi-Criteria Decision Making Based on Negation of Probability Distribution under Uncertain Environment

Multi-criteria decision making (MCDM) refers to the decision making in the limited or infinite set of conflicting schemes. At present, the general method is to obtain the weight coefficients of each scheme based on different criteria through the expert questionnaire survey, and then use the Dempster–Shafer Evidence Theory (D-S theory) to model all schemes into a complete identification framework to generate the corresponding basic probability assignment (BPA). The scheme with the highest belief value is then chosen. In the above process, using different methods to determine the weight coefficient will have different effects on the final selection of alternatives. To reduce the uncertainty caused by subjectively determining the weight coefficients of different criteria and further improve the level of multi-criteria decision-making, this paper combines negation of probability distribution with evidence theory and proposes a weights-determining method in MCDM based on negation of probability distribution. Through the quantitative evaluation of the fuzzy degree of the criterion, the uncertainty caused by human subjective factors is reduced, and the subjective error is corrected to a certain extent.

A New Divergence Measure of Pythagorean Fuzzy Sets Based on Belief Function and Its Application in Medical Diagnosis

As the extension of the fuzzy sets (FSs) theory, the intuitionistic fuzzy sets (IFSs) play an important role in handling the uncertainty under the uncertain environments. The Pythagoreanfuzzy sets (PFSs) proposed by Yager in 2013 can deal with more uncertain situations than intuitionistic fuzzy sets because of its larger range of describing the membership grades. How to measure the distance of Pythagorean fuzzy sets is still an open issue. Jensen–Shannon divergence is a useful distance measure in the probability distribution space. In order to efficiently deal with uncertainty in practical applications, this paper proposes a new divergence measure of Pythagorean fuzzy sets, which is based on the belief function in Dempster–Shafer evidence theory, and is called PFSDM distance. It describes the Pythagorean fuzzy sets in the form of basic probability assignments (BPAs) and calculates the divergence of BPAs to get the divergence of PFSs, which is the step in establishing a link between the PFSs and BPAs. Since the proposed method combines the characters of belief function and divergence, it has a more powerful resolution than other existing methods. Additionally, an improved algorithm using PFSDM distance is proposed in medical diagnosis, which can avoid producing counter-intuitive results especially when a data conflict exists. The proposed method and the magnified algorithm are both demonstrated to be rational and practical in applications.

A Principal-Agent Theory Perspective on PPP Risk Allocation

This study proposes a framework for the allocation of risk in public private partnerships (PPP) projects. Its contribution lies in the recognition and incorporation of risks introduced by project stakeholders, and as articulated by the principal-agent theory (PAT). The framework assesses risks and routes these risks to those parties best equipped to mitigate their impact on the project. This allocation of risk is facilitated by a thirteen-step process. The practical benefit of this study lies in outlining a clear, systematic method for allocating risk efficiently to both the government and private enterprise parties of the project. In so doing, risk mitigation can be expected to improve project performance, optimize stakeholder goals, and enhance sustainability objectives, including improved operational life-cycle efficiency and elevated social and community benefits.

An Evidential Model for Environmental Risk Assessment in Projects Using Dempster–Shafer Theory of Evidence

One of the goals of sustainable development is to achieve economic and social growth according to environmental criteria. Nowadays, impact assessment is an efficient decision making method in planning and management with environmental perspectives. Environmental risk assessment is a tool to reduce the impacts and consequences of various activities on the environment in order to achieve sustainable development. One of the commonly used environmental risk assessment methods is the probability–impact matrix method, which is known as a quantitative method for risk assessment of projects. In this method, numerical estimates of probability and impact of risk occurrence are very difficult, and these factors are associated with uncertainty. When uncertainty exists, data integration is of great importance, for which the fuzzy inference system and evidence theory are known as effective methods. Unavailability of experts’ opinion and the exponential growth of the number of required fuzzy rules associated with the risk factors are two drawbacks of fuzzy inference. Dempster–Shafer’s theory of evidence is one of the popular theories used in intelligent systems for modeling and reasoning under uncertainty and inaccuracy. In this paper, an evidential model for project environmental risk assessment is proposed based on the Dempster–Shafer theory, which is capable of taking into account the uncertainties. The proposed model is used to assess the environmental risks of Maroon oil pipelines in Isfahan. In addition, the proposed model is used in the case of tunneling risk assessment taken from the subject literature. To evaluate the validity of the proposed evidential model, the results are compared in two case studies, with the results of the conventional risk assessment method and the fuzzy inference system method. The comparative results show that the proposed model has a high potential for project risk assessment under an uncertain environment.

Risk Assessment of Additional Works in Railway Construction Investments Using the Bayes Network

The implementation of railway infrastructure construction projects including sustainable development goals is a complex process characterized by a significant extension of individual investment stages. The need for additional works has a big impact on construction railway projects, representing a risk which is the result of many different factors. During the execution of works, both the design assumptions and the conditions of the project’s implementation can be changed. An attempt to eliminate potential risks is a key element of construction projects. The article proposes a proprietary management method for the risk of additional works in railway projects. A methodology for creating risk management strategies using a standard algorithm that includes risk identification, risk analysis, and risk assessment is presented. The original elements of the work include risk identification followed by analysis using Bayesian networks. Using the example of a scenario of events, it is shown that a well-programmed network can be used to implement risk mitigation methods. Using the network, it is possible to compare different ways to reduce risk, check the effect of reducing the risk factors, and determine a satisfactory level of effects, e.g., increased financial resources as a result of additional works.

The Redesigning of Tires and the Recycling Process to Maintain an Efficient Circular Economy

The redesigning of tires is addressed from two points of view, namely the structure of the materials and the constructive shape of these products. The necessity for research is justified by the fact that even during the product design stage, there may be situations that can irreversibly affect the separation of product components (rubber and insertion), and because it is strictly necessary to achieve the reuse and recycling of waste components. The proposed redesign refers to the inscription of the types of materials that are present in each area on the lateral surface of the tire. Thus, the new redesign has positive effects both economically and socially. To highlight these, a cost-benefit analysis (CBA) has been applied and the net present value (NPV) as well as the internal rate of return (IRR) have been determined for the classic scenario and for the two proposed scenarios. Testing the financial sustainability of the proposed solution was done through sensitivity analysis. An analysis of the new tire design from the point of view of the circular economy was also presented. The results obtained have highlighted the effectiveness of the proposed solution from a technical, economic, social, and protection of the environment point of view.

Development of a Model for Predicting Probabilistic Life-Cycle Cost for the Early Stage of Public-Office Construction

Decisions made in the early stages of construction projects significantly influence the costs incurred in subsequent stages. Therefore, such decisions must be based on the life-cycle cost (LCC), which includes the maintenance, repair, and replacement (MRR) costs in addition to construction costs. Furthermore, as uncertainty is inherent during the early stages, it must be considered in making predictions of the LCC more probabilistic. This study proposes a probabilistic LCC prediction model developed by applying the Monte Carlo simulation (MCS) to an LCC prediction model based on case-based reasoning (CBR) to support the decision-making process in the early stages of construction projects. The model was developed in two phases: first, two LCC prediction models were constructed using CBR and multiple-regression analysis. Through k-fold validation, one model with superior prediction performance was selected; second, a probabilistic LCC model was developed by applying the MCS to the selected model. The probabilistic LCC prediction model proposed in this study can generate probabilistic prediction results that consider the uncertainty of information available at the early stages of a project. Thus, it can enhance reliability in actual situations and be more useful for clients who support both construction and MRR costs, such as those in the public sector.

Investment Opportunities Identification Based on Macroeconomic Development, the Multiple Criteria Decision Approach

The aim of this work is to develop a “learning model” which outranks countries according to their confrontation of historical macroeconomic indicators for a given period of time with the spreads at the end of that time and to formulate a forward-looking investment strategy regarding government bonds for the following time period. The mechanism of identifying investment opportunities among government bonds is based on the multiple criteria decision making technique, and we look to the Promethee II method as a symmetry approach to country ordering. The spread is defined as the difference between the yield to maturity of the 10-year government bond of a country and the Germany government bond with the same maturity. In this paper, an optimization approach based on three models is developed to find the weights of importance for macroeconomic characteristics, together with a sensitivity analysis on changes in these characteristics. The method was applied to 17 European countries characterized by 16 macroeconomic characteristics. The originality of this paper lies in the two-stage approach to the investment strategy construction based on criteria weights optimization with stability intervals for their values.

An Improved Multi-Source Data Fusion Method Based on the Belief Entropy and Divergence Measure

Dempster–Shafer (DS) evidence theory is widely applied in multi-source data fusion technology. However, classical DS combination rule fails to deal with the situation when evidence is highly in conflict. To address this problem, a novel multi-source data fusion method is proposed in this paper. The main steps of the proposed method are presented as follows. Firstly, the credibility weight of each piece of evidence is obtained after transforming the belief Jenson–Shannon divergence into belief similarities. Next, the belief entropy of each piece of evidence is calculated and the information volume weights of evidence are generated. Then, both credibility weights and information volume weights of evidence are unified to generate the final weight of each piece of evidence before the weighted average evidence is calculated. Then, the classical DS combination rule is used multiple times on the modified evidence to generate the fusing results. A numerical example compares the fusing result of the proposed method with that of other existing combination rules. Further, a practical application of fault diagnosis is presented to illustrate the plausibility and efficiency of the proposed method. The experimental result shows that the targeted type of fault is recognized most accurately by the proposed method in comparing with other combination rules.

Estimating Conditional Value at Risk in the Tehran Stock Exchange Based on the Extreme Value Theory Using GARCH Models

This paper attempted to calculate the market risk in the Tehran Stock Exchange by estimating the Conditional Value at Risk. Since the Conditional Value at Risk is a tail-related measure, Extreme Value Theory has been utilized to estimate the risk more accurately. Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models were used to model the volatility-clustering feature, and to estimate the parameters of the model, the Maximum Likelihood method was applied. The results of the study showed that in the estimation of model parameters, assuming T-student distribution function gave better results than the Normal distribution function. The Monte Carlo simulation method was used for backtesting the Conditional Value at Risk model, and in the end, the performance of different models, in the estimation of this measure, was compared.

The Evaluation of the Contractor’s Risk in Implementing the Investment Projects in Construction by Using the Verbal Analysis Methods

The growth of the company’s investment potential is closely associated with the evaluation of the attendant risks of the process, various influencing factors, and the expected results. Therefore, the analysis of a number of qualitative and quantitative criteria of the projects and risks, as well as the potential profit-making opportunities in the investment decision making is required. This paper analyzes a decision-making strategy based on qualitative estimates obtained by investigating the risks posed, the management methods used, and the application of the proposed methods for assessing the contractor’s risk in construction companies.

Civil Engineering and Symmetry

A topic of utmost importance in civil engineering is finding optimal solutions throughout the life cycle of buildings and infrastructural objects, including their design, manufacturing, use, and maintenance. Operational research, management science, and optimisation methods provide a consistent and applicable groundwork for engineering decision-making. These topics have received the interest of researchers, and, after a rigorous peer-review process, eight papers have been published in the current special issue. The articles in this issue demonstrate how solutions in civil engineering, which bring economic, social and environmental benefits, are obtained through a variety of methodologies and tools. Usually, decision-makers need to take into account not just a single criterion, but several different criteria and, therefore, multi-criteria decision-making (MCDM) approaches have been suggested for application in five of the published papers; the rest of the papers apply other research methods. The methods and application case studies are shortly described further in the editorial.

Modelling Construction Site Cost Index Based on Neural Network Ensembles

Construction site overhead costs are key components of cost estimation in construction projects. The estimates are expected to be accurate, but there is a growing demand to shorten the time necessary to deliver cost estimates. The balancing (symmetry) between time of calculation and satisfaction of reliable estimation was the reason for developing a new model for cost estimation in construction. This paper reports some results from the authors’ broad research on the modelling processes in engineering related to estimation of construction costs using artificial intelligence tools. The aim of this work was to develop a model capable of predicting a construction site cost index that would benefit from combining several artificial neural networks into an ensemble. Combining selected neural networks and forming the ensemble-based models compromised their strengths and weaknesses. With the use of data including training patterns collected on the basis of studies of completed construction projects, the authors investigated various types of neural networks in order to select the members of the ensemble. Finally, three models that were assessed in terms of performance and prediction quality were proposed. The results revealed that the developed models based on ensemble averaging and stacked generalisation met the expectations of knowledge generalisation and accuracy of prediction of site overhead cost index. The proposed models offer predictions of cost in an accepted error range and prove to deliver better predictions than those based on single neural networks. The developed tools can be used in the decision-making process regarding construction cost estimation.

An Optimization Model for Construction Stage and Zone Plans of Rockfill Dams Based on the Enhanced Whale Optimization Algorithm

Rockfill dams are among the most complex, significant, and costly infrastructure projects of great national importance. A key issue in their design is the construction stage and zone optimization. However, a detailed flow shop construction scheme that considers the opinions of decision makers cannot be obtained using the current rock-fill dam construction stage and zone optimization methods, and the robustness and efficiency of existing construction stage and zone optimization approaches are not sufficient. This research presents a construction stage and zone optimization model based on a data-driven analytical hierarchy process extended by D numbers (D-AHP) and an enhanced whale optimization algorithm (EWOA). The flow shop construction scheme is optimized by presenting an automatic flow shop construction scheme multi-criteria decision making (MCDM) method, which integrates the data-driven D-AHP with an improved construction simulation of a high rockfill dam (CSHRD). The EWOA, which uses Levy flight to improve the robustness and efficiency of the whale optimization algorithm (WOA), is adopted for optimization. This proposed model is implemented to optimize the construction stages and zones while obtaining a preferable flow shop construction scheme. The effectiveness and advantages of the model are proven by an example of a large-scale rockfill dam.

Application of Duration Measure in Quantifying the Sensitivity of Project Returns to Changes in Discount Rates

In this research, the concept of Duration with a new application in project management has been defined. The Duration of each project provides the project manager with a combined measure containing concepts of return, cost and time of the project. Further in this article, the changes in project return, based on different assumptions such as discount rate, have been examined. To examine the effect of the changes in these factors, the Monte Carlo simulation has been used. The relationship between these factors is nonlinear which reflects the great importance of investment on appropriate risk management systems. The data from a set of construction projects have been used in order to verify the results of this study. Similar relationships can be expected to exist in other industries as well.

Risk Level Evaluation on Construction Project Lifecycle Using Fuzzy Comprehensive Evaluation and TOPSIS

A risk is a predominant problem in the developing countries construction projects. Although numerous studies have been concerned on risk, there is a limited study on a mechanism to identify the typical risks and effects level. This paper presents an approach for evaluating the risks in case of schedule delays at the various lifecycles of construction projects. The methodology applied is an integrated model of the technique for order preference by similarity to ideal solution (TOPSIS) and fuzzy comprehensive evaluation (FCE). In this study, common criteria, sub-criteria, and attributes are constructed to make a decision concerning the influence level of risk of delay at the construction project lifecycle. The results showed that the construction stage (44%) is highly influenced, the second highly influenced stage is post construction (37%), and the least risked stage is pre-construction (35%). The construction projects in Ethiopia have faced an average delay risk of 38% at a high and very high-risk level. This work is expected to serve as a tool to assist managers in the management and control of schedule delays to mitigate their risks.

Risk Assessment in PPP Projects by Applying Different MCDM Methods and Comparative Results Analysis

Recently, risk assessment has become one of the most challenging issues in the areas of construction and public-private partnerships (PPPs). To address risk assessment issues, various decision-making techniques have been proposed, each with its own specific disadvantages and advantages. This paper investigates step-wise weight assessment ratio analysis (SWARA), complex proportional assessment (COPRAS), fuzzy analytic network process (FANP), fuzzy analytic hierarchy process (FAHP), fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS), simple additive weighting (SAW) and evaluation based on distance from average solution (EDAS) in order to define how various multi-attribute decision-making (MADM) methods compare when used for risk assessment in PPP projects. For this study, 5 risk assessment criteria and 10 types of risk used in Iranian highway PPP projects were selected. Four suitability and applicability tests were used to measure agreement between the rankings derived from the MADM methods. Final results show that all techniques had approximately the same rankings of risk assessment, with the SWARA, COPRAS, and EDAS methods performing slightly better. The findings of this study will help the parties in PPP and construction projects to select the best risk assessment method.