In infrastructure management, multi-criteria decision-making (MCDM) has emerged as a decision support tool to integrate various technical information and stakeholder values. Different MCDM techniques and tools have been developed. This paper presents a comprehensive review on the application of MCDM literature in the field of infrastructure management. Approximately 300 published papers were identified that report MCDM applications in the field of infrastructure management during 1980–2012. The reviewed papers are classified into application to the type of infrastructure (e.g. bridges and pipes), and prevalent decision or intervention (e.g. repair and rehabilitate). In addition, the papers were also classified according to MCDM methods used in the analysis. The paper provides taxonomy of those articles and identifies trends and new developments in MCDM methods. The results suggest that there is a significant growth in MCDM applications in infrastructure management applications of MCDM over the last decade...

A review of multi-criteria decision-making methods for infrastructure management

Towards a Better Modelling and Assessment of Construction Risk: Insights From a Literature Review

http://ira.lib.polyu.edu.hk/bitstream/10397/61168/1/Li_Zhengdao_2016.pdf

Schedule risks in prefabrication housing production in Hong Kong: a social network analysis

Human health and safety risks management in underground coal mines using fuzzy TOPSIS.

Group decision making based on novel fuzzy modified TOPSIS method

Project risk identification and assessment simultaneously using multi-attribute group decision making technique

Project risk management process can remarkably facilitate project’s complexity via strategic insight. In project risk management, the modelling process of the risks may not be performed sufficientl...

Project risk identification and analysis based on group decision making methodology in a fuzzy environment

Bridges are principal and vital transportation structures. If risk management is not considered in bridge construction projects, objectives cannot be delivered on time, on budget, or with suitable quality results. Risk data set sizes and experts’ judgments are not usually sufficient for analyzing significant risks in bridge construction projects; moreover, the statistical distributions for risk parameter estimates are usually unknown. Standard parametric statistical techniques cannot provide appropriate solutions for cases with small data sets or unknown distributions. This paper proposes a new hybrid approach by using a nonparametric resampling technique and interval computations for risk analysis, in particular, for bridge construction projects. Bootstrap techniques produce more accurate inferences for comparing parametric techniques and are an alternative when the underlying parametric assumptions are not considered. Increasingly, because of the complexity and uncertainty in decision making at bridge projects, it is easier or more natural to provide interval values for parts or all of decision-making judgments. Furthermore, the goal of reducing standard deviations for both risk probability and risk impact compared with the conventional approach is another conclusion of this paper. The proposed approach is applied to a case in Iran to show the validity of the approach.

Bootstrap Technique for Risk Analysis with Interval Numbers in Bridge Construction Projects

This paper aims to identify the important risks in Iranian onshore gas refinery plants and introduce new effective criteria. Risks are prioritised on the basis of the significance of impacts on typical project objectives in terms of cost, time, quality, safety and environmental sustainability. Then, this problem is defined in the Fuzzy Multicriteria Decision-Making (FMCDM) field. The Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) and Fuzzy Linear Programming Technique for Multidimensional Analysis of Preference (FLINMAP) methods are presented to rank the high risks. These two FMCDM modelling mechanisms are compared to find the better performance in project risk ranking. Moreover, a case study was conducted on the procedure of the proposed model and the results indicate that fuzzy LINMAP outperforms fuzzy TOPSIS. Therefore, the ranks of risks differ from one MCDM model to another. The selection of a proper MCDM model for risk ranking depends on the project's nature, the accuracy of data gathered on risk and experience.

S. Mohammad H. Mojtahedi

Papers

Project risk identification and assessment simultaneously using multi-attribute group decision making technique

Project risk identification and analysis based on group decision making methodology in a fuzzy environment

Bootstrap Technique for Risk Analysis with Interval Numbers in Bridge Construction Projects

A fuzzy decision-making model for risk ranking with an application to an onshore gas refinery