Comprehensive Risk Management Using Fuzzy Fmea and McDa Techniques in Highway Construction Projects

Abstract

This paper presents a comprehensive framework to manage the main risk events of highway construction pro­jects within three stages: (1) identification of potential risks; (2) assessment and prioritisation of identified risks based on fuzzy FMEA; (3) identification of appropriate response. The main criteria analysed for prioritising potential risk events are cost, time and quality which are quantified and combined using fuzzy AHP. A new expert system is suggested for identifying an appropriate risk response strategy for a risk event based on risk factor, control number and risk alloca­tion. The best response action for a risk event is then identified with respect to the same criteria using “scope expected deviation” (SED) index. The proposed methodology is demonstrated for management of risk events in a construction project of Bijar-Zanjan highway in Iran. For the risk event of “increase in tar price”, deviation from the target values of the criteria is analysed for business-as-usual state plus two risk response actions using SED index. The results show that the response action of “changing paving construction technology from asphalt pavement to RCC pavement” can success­fully cope with the risk event of “increase in tar price” and have the minimum deviation.

Analyzing the factors affecting environmental risks of projects using a hybrid approach of DEMATEL‐ANP, artificial neural network: A case study

“…FMEA is a well‐known approach that has been implemented in many risk management projects having a variety of scopes. For more information on this approach, see Ahmadi, Behzadian, Ardeshir, and Kapelan (), Rahimi, Tavakkoli‐Moghaddam, Iranmanesh, and Vaez‐Alaei (), and Ershadi and Ershadi (). Although numerous discussions have been generated regarding issues such as project management and risk management, none has attempted to study environmental risk management as one of the most crucial issues involved in the results and performances of projects; instead, most are focused on the conceptual and organizational aspects of risk, and, in general, a comprehensive reflection is not yet made on the relationship between the environmental risk and the project (Cardona, ).…”

Sustainable Technology Innovation Path Recognition: An Evaluation of Patent Risk of International Trade

“…Many recent studies have tried to further improve this method to suit specific situations. Ahmadi et al (2017) synthetically applied fuzzy failure mode and effects analysis (FFME), fuzzy-AHP, and scope expected deviation (SED) to evaluate the risk management in a highway construction project, and the developed method overcame some shortcomings seen with the generic AHP method [51]. The AHP method can also be integrated with data envelopment analysis to rank and select data [52].…”

A Hybrid MCDM Technique for Risk Management in Construction Projects

“…Vafadarnikjoo et al [24] developed an intuitive fuzzy decision-making trial and evaluation laboratory (DEMATEL) to prioritise risks associated with construction projects by using the risk breakdown structure. Ahmadi et al [27] analysed the criteria for prioritising potential risk events and quantified it using fuzzy AHP. The best response action for a risk event is then identified with respect to the same criteria using a scope expected deviation index.…”

“…Using Equations (23) and (24), we develop an initial decision matrix M = d ij 5×9 (Table 11) along with its crisp form X = I d ij 5×9 = x ij 5×9 . Step 3: Using Equations (26) and (27), the elements of the weighted normalized decision matrix V = v ij 5×9 are calculated, and are shown in Table 13. Step 4: Next, using Equations (28) and (29), we determine the BAA, G = [g 1 g 2 .…”

“…In recent years, the number of papers related to analytic hierarchy process (AHP) and analytic network process (ANP) methods, considering supplier selection procedure, has increased substantially [25][26][27][28][29]. However, increasing the number of criteria or comparison levels causes confusion in decision makers' (DMs) judgments, resulting in incomplete decision-making and inconsistency in the evaluator's judgments, thus reducing strategy selection ability.…”