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With numerous and ambiguous sets of information and often conflicting requirements, construction management is a complex process involving much uncertainty. Decision makers may be challenged with satisfying multiple criteria using vague information. Fuzzy multi-criteria decision-making (FMCDM) provides an innovative approach for addressing complex problems featuring diverse decision makers’ interests, conflicting objectives and numerous but uncertain bits of information. FMCDM has therefore been widely applied in construction management. With the increase in information complexity, extensions of fuzzy set (FS) theory have been generated and adopted to improve its capacity to address this complexity. Examples include hesitant FSs (HFSs), intuitionistic FSs (IFSs) and type-2 FSs (T2FSs). This chapter introduces commonly used FMCDM methods, examines their applications in construction management and discusses trends in future research and application. The chapter first introduces the MCDM process as well as FS theory and its three main extensions, namely, HFSs, IFSs and T2FSs. The chapter then explores the linkage between FS theory and its extensions and MCDM approaches. In total, 17 FMCDM methods are reviewed and two FMCDM methods (i.e. T2FS-TOPSIS and T2FS-PROMETHEE) are further improved based on the literature. These 19 FMCDM methods with their corresponding applications in construction management are discussed in a systematic manner. This review and development of FS theory and its extensions should help both researchers and practitioners better understand and handle information uncertainty in complex decision problems.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

Most decision-making problems in construction are complex and difficult to solve, as they involve multiple criteria and multiple decision makers in addition to subjective uncertainties, imprecisions and vagueness surrounding the decision-making process. In many instances, the decision-making process is based on linguistic terms rather than numerical values. Hence, structured fuzzy consensus-reaching processes and fuzzy aggregation methods are instrumental in multi-criteria group decision-making (MCGDM) problems for capturing the point of view of a group of experts. This chapter outlines different fuzzy consensus-reaching processes and fuzzy aggregation methods. It presents the background of the basic theory and formulation of these processes and methods, as well as numerical examples that illustrate their theory and formulation. Application areas of fuzzy consensus reaching and fuzzy aggregation in the construction domain are identified, and an overview of previously developed frameworks for fuzzy consensus reaching and fuzzy aggregation is provided. Finally, areas for future work are presented that highlight emerging trends and the imminent needs of fuzzy consensus reaching and fuzzy aggregation in the construction domain.

Managing complex construction projects is a challenging task because it involves multiple factors and decision-making processes. A systematic evaluation of these complex factors is imperative for achieving project success. As most of these factors are qualitative or intangible in nature, decision makers often rely on subjective judgements when comparing and evaluating them. The hybrid techniques that integrate fuzzy set theory and the analytic hierarchy process (AHP) are able to deal with such problems. This chapter discusses various hybrid techniques of the fuzzy AHP and presents an application of these techniques to the evaluation of transportation project complexity, which is essential for prioritising resource allocation and assessing project performance. Project complexity can be quantified and visualised effectively with the application of the fuzzy AHP. This chapter enhances the understanding of construction project complexity and fuzzy hybrid computing in construction engineering and management. Future research should address the calibration of fuzzy membership functions in pairwise comparisons for each individual decision maker and develop computational tools for solving optimisation problems in the constrained fuzzy AHP. In the area of construction project complexity, future research should investigate how scarce resources are allocated to better manage complex projects and how appropriate resource allocation improves their performance.

Constructing and evaluating behavioral science models is a complex process. Decisions must be made about which variables to include, which variables are related to each other, the functional forms of the relationships, and so on. The last 10 years have seen a substantial extension of the range of statistical tools available for use in the construction process. The progress in tool development has been accompanied by the publication of handbooks that introduce the methods in general terms (Arminger et al., 1995; Tinsley & Brown, 2000a). Each chapter in these handbooks cites a wide range of books and articles on specific analysis topics.

This chapter discusses the involvement of procurement in smart city development. Procurement plays a vital role in the development of strategies in planning, and execution in the construction industry. This is further introduced into smart city to find the best possible methods of bringing the ideas behind smart city into realisation. The common procurement methods are explained along with their involvement in smart city. Common drawbacks to smart cities procurement and measures to challenges in the procurement of smart city are also explicitly explained in the chapter. Through an identified and accepted procurement method into smart city, the processes involved in executing smart city will be the ones with directions and proper planning.

For a public project leader charged with a construction project, it can be crucial to meet the budget, while maximizing the qualities within a construction project. A method to achieve this is to use the “opposite design build” procurement method, in which the price is fixed and the contractors compete on adding a wide range of qualities to the project. However, such procurement approaches are rare, and it is difficult to find models on how to implement such an approach.

This study firstly looks at the literature on design-build, quality-only as the main selection criterion and to some degree on constructors’ bidding behaviour. Secondly, it explains a model for a design-build, quality-only procurement designed within the public tendering legislations. Thirdly, it investigates the outcome of the model applied to a specific case in Norway.

There seems to be a research gap within literature and cases on design-build, quality-only selection with a fixed price. The developed model allowed for negotiations, which led to more comparable and improved bids. In the investigated case, and the client was able to implement more qualities in the project than expected within the budget.

The researcher was himself partly involved in the process as an advisor.

The developed method is relatively simple and might readily be applied by any client to maximise a project’s qualities within a given fixed price.

The long-term value should be to widen the range of useful procurement methods.

This chapter offers the first full translation from Russian to English of the Balance of the National Economy of the USSR, 1924–26’s first chapter. Involving 12 authors and composed of 21 chapters, the Balance is a collective work published in June 1926 in Moscow by the Soviet Central Statistical Administration under the scientific supervision of its former director, Pavel Illich Popov (1872–1950). In this first chapter, titled ‘Studying the Balance of the National Economy: An Introduction’, Popov set the theoretical foundations of what might be considered as the first modern national accounting system and paved the way to multisector macroeconometric modelling.