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Xiang, P. Construction Engineering Project. Encyclopedia. Available online: https://encyclopedia.pub/entry/24150 (accessed on 14 June 2024).
Xiang P. Construction Engineering Project. Encyclopedia. Available at: https://encyclopedia.pub/entry/24150. Accessed June 14, 2024.
Xiang, Pengcheng. "Construction Engineering Project" Encyclopedia, https://encyclopedia.pub/entry/24150 (accessed June 14, 2024).
Xiang, P. (2022, June 17). Construction Engineering Project. In Encyclopedia. https://encyclopedia.pub/entry/24150
Xiang, Pengcheng. "Construction Engineering Project." Encyclopedia. Web. 17 June, 2022.
Construction Engineering Project
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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.

MCDM IF-ANP and DEMATEL gray rhino risks

1. Introduction

Statistically, more than 92% of construction engineering projects in China are influenced by risks such as broken capital chains, decision-making errors, and changes in the economic or political environment, leading to negative consequences such as scheduling delays, budget overruns, and substandard quality. These risks are obvious but do not receive enough attention, which ultimately leads to large losses. Risks with common and dangerous characteristics are metaphorically described as gray rhino risks. Ineffective management of gray rhino risks has become one of the major reasons that prevent projects from achieving their desired goals. In construction projects, significant resources have been devoted to risk management, but gray rhino risks still occur and negatively influence project outcomes.
As construction technology and productivity continue to improve, construction projects are becoming more large-scale and complex. As a result, construction projects are characterized by longer construction cycles, more stakeholders, and more complex interactions with the external environment. Traditional risk research has several shortcomings when dealing with these evolving circumstances. First, insufficient attention has been given to handling obvious risks. Risk identification is always considered the most important stage of risk management [1], and significant effort has been invested in the identification and analysis of the risk factors in construction engineering projects. As such, the scope of risk recognition is constantly expanding [2][3][4]. However, recurrent failures to effectively address these risks have resulted in the continued occurrence of such risks, which has caused preventable project failures [5][6]. While risk identification remains a necessary task, though not a difficult one, construction engineering management must also consider strategies to effectively control risks that are already obvious in order to achieve the desired project goals. Second, few studies evaluate construction project risks from a system perspective. There are often mutualistic relationships between different construction project risks. Risks and relationships constitute a network structure of mutual influence, and evaluation of project risks from an isolated perspective will ignore the types of risks that are prone to secondary risks but do not cause much damage themselves [7]. This results in the frequent occurrence of secondary risks in engineering projects. Third, the risk evaluation process ignores the impact of complex systems on the accuracy of expert evaluation data. Existing research approaches mostly consider the fuzziness of the collected data at the computational stage and attempt to reduce its effect on the results using specific data processing methods. Few studies have reduced data ambiguity from the data collection process. Unfortunately, reducing the adverse effects of data subjectivity through a complex calculation process has limited effect. When the evaluation object is the relationship between construction project risks, a new method of data collection is required to improve the data quality.

2. Gray Rhino Risks in Construction Engineering Projects

Gray rhino risks are obvious risks that are inadequately acknowledged and ultimately lead to serious damage and great loss [8]. This metaphor illustrates that many risks are obvious but dangerous. In the field of construction engineering projects, gray rhino risks are common [9]. Xiang investigated 30 megaprojects that failed to achieve expected goals. The study found that the five most common reasons for the failures were (1) a break in the capital chain, which includes insufficient financing and cost overrun (eight projects), (2) decision-making mistakes caused by the chief company’s poor management skills and inexperience (seven projects), (3) contract changes due to business problems of the contracting award company (five projects), (4) legal risks and policy changes (five projects), and (5) stakeholder conflict (four projects) [10]. Utama et al. studied 26 papers concerning megaproject risks. They summarized 31 risks pointed out by 26 experts that may hinder the implementation of megaprojects [11]. The risks found in Xiang’s research were also recognized by these authors. Among the 26 experts, risk 1 was recognized by 22, risk 2 by 14, risk 3 by 14, risk 4 by 15, and risk 5 by 17. These risks were considered likely to hinder megaprojects from reaching their intended goals. Hence, in these two studies, the experts demonstrate an accurate understanding of the risks of megaprojects. Risk types with high recognition have a greater probability of leading to the failure of megaprojects. Whereas experts can accurately evaluate the probability and loss of various risk events, this still cannot prevent these risk events or reduce the losses after they occur.
An important reason for the phenomenon that obvious risks cause many construction engineering project failures is that the number of obvious risks is too large to allocate sufficient resources to managing each risk. Many resources are wasted on unnecessary risks, which results in insufficient investment in the prevention of key risks [12]. An effective risk ranking method for screening key risks is necessary to manage gray rhino risks in construction engineering projects. The complex eliciting relationships among gray rhino risks also make it difficult to manage them effectively. Goldratt presented the theory of constraints, which suggests that the whole concept should be regarded as a system, and the efficiency of the system can be maximized only when the relationship between each part of the system is accurately grasped and handled properly [13]. Common methods for ranking risks are based on the probability of risk occurrence and the expected loss caused by the risk [14][15]. Based on these traditional methods, more resources are put into preventing risks that have a high probability of occurrence or cause high loss. However, the occurrence of a risk event usually leads to a variety of other risks at the same time, and all risk probabilities will change [16]. Common risk ranking methods rarely consider eliciting relationships between risks. When risk prevention is carried out from an isolated perspective while ignoring the impact of other related risks, it is difficult to achieve desired holistic results [17]. Therefore, it is necessary to design a method that can consider the eliciting relationships among risks for assessing gray rhino risks in construction engineering projects.

3. MCDM Methods for Construction Engineering Project Risk Evaluation

Because of its simplicity and flexibility, the MCDM method is widely used to assess problems and has been successfully implemented in many research fields [18][19]. The gray rhino risks of construction engineering projects can be characterized by several ranking criteria and managers in construction engineering projects need to convert these numerous criteria into a tangible risk prevention plan. The problem of risk ranking based on several criteria has already been identified as an MCDM problem by different researchers [20][21]. Many MCDM methods have been able to rank the factors based on their interrelationships, such as decision-making trial and evaluation laboratory (DEMATEL), distance-based approximate (DBA), complex proportional assessment (COPRAS), etc. However, the risks in construction engineering projects and their relationship network constitute a complex system, which makes it difficult for experts to directly provide clear judgments on relationships among risk factors [22]. The greater the complexity of the evaluation object, the stronger the subjectivity of expert evaluation results; hence, MCDM methods for construction engineering project risk evaluation often require additional techniques to reduce subjectivity in expert survey data [23]. Fuzzy theory has been used to reduce the influence of human cognition fuzziness. For example, Yan used the fuzzy-ANP to evaluate risks in the process of engineering construction, Yucesan and Kahraman used the fuzzy-ANP to manage risks in hydropower plant construction operation projects, and Maria and Reinhard used the fuzzy-AHP to analyze risks in energy projects [24][25]. Many fuzzy number transfer techniques such as triangle fuzzy number, trapezoid fuzzy number, fuzzy sets (type-1 and 2), and spherical fuzzy sets have been successfully used to reduce subjectivity in expert scoring [26][27]. The current work also used the fuzzy technique to deal with the complexity of construction engineering projects.
However, while fuzzy technology can remedy the subjective influence in survey data, it cannot improve the credibility of survey data itself. As research objects become more complex, further reductions in subjectivity require more complex fuzzy sets [28]. Yet the complexity of fuzzy sets used to reduce subjectivity in expert survey data has already reached the technical limits. Many studies have demonstrated that there is little difference between using different fuzzy sets; in some cases, simple fuzzy sets performed even better than complex ones [29][30]. Fuzzy technology has limited effects on the subjectivity of expert valuation when the valued object is a construction engineering risk system [31]. Other methods that can directly increase the credibility of survey data are needed to improve the accuracy of MCDM methods.

4. Methods to Improve the Accuracy of MCDM Methods

Knowledge of the sources of subjectivity is necessary to improve the credibility of expert survey data. Wang argued that risks are objective and cannot be eliminated, so managers in a construction engineering project need to anticipate risks and prepare a suitable risk prevention plan. A more appropriate prevention plan can better minimize risk loss [32]. The essence of the construction engineering project risk is the difference between the manager’s prediction of the future situation and the actual future situation. This difference can lead to the risk prevention plan not working as expected, resulting in the project failure [33]. There are two parts that comprise the human understanding of the object: the understanding of the object itself and the understanding of mental associations generated by the object [34]. To achieve a high-quality expert survey, the experts’ understanding of the research object should be consistent and their mental associations generated by the research object should be diverse [35]. These two different modes of understanding are mixed in the survey data and have different values in different studies. For cognitive studies and evaluation studies, the experts’ understanding of the evaluation object is necessary information, while the subjectivity generated by the experts’ mental associations is the noise; hence, many methods are used to reduce subjectivity [36]. However, for exploratory studies and optimization studies, innovation is hidden within the mental associations, but relatively less value is placed on the understanding of the object itself [37]. The risk assessment method should be curated to capture the essence of the risk, and lower subjectivity in the risk assessment indicates better evaluation results that accurately reflect the evaluated objects.
Most traditional MCDM methods, such as ANP, DEMATEL, and TOPSIS, require experts to directly evaluate relationships among risks [38]. The expert survey data collected by such methods always combine many experts’ understanding of mental associations with their understanding of the essence of risks, producing data that is volatile and subjective. As construction engineering project risk management emphasizes risk cognition, the experts’ understanding of construction engineering risks is more important than their mental associations [39].

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