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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.

In this chapter, a general introduction on artificial intelligence (AI) is given as well as an overview of the advances of AI in different engineering disciplines, including its effectiveness in driving the United Nations Sustainable Development Goals (UN SDGs). This chapter begins with some fundamental definitions and concepts on AI and machine learning (ML) followed by a classification of the different categories of ML algorithms. After that, a general overview of the impact which different engineering disciplines such as Civil, Chemical, Mechanical, Electrical and Telecommunications Engineering have on the UN SDGs is given. The application of AI and ML to enhance the processes in these different engineering disciplines is also briefly explained. This chapter concludes with a brief description of the UN SDGs and how AI can positively impact the attainment of these goals by the target year of 2030.

Earthworks are the embankments and cuttings that allow a railway to maintain a certain line, level and grade through the landscape. Earth embankments consist of an engineered bank of earth that carries the railway above the natural ground. A cutting is used to carry the railway through ground with a natural level above the line of the railway. Modern (post 1960s) earthworks are carefully engineered to perform well. However, many railways run on earthworks that were constructed over 100 years ago without the use of mechanised plant. The quality of construction of older earthworks was often poor compared with present-day engineering practice. Ageing of the earthwork structures, and the greater demands of heavier and faster trains and climatic change, means that earthworks suffer ultimate and serviceability failures that can present operational difficulties. Older earthworks that fail or do not perform well require maintenance and repair, and sometimes complete replacement. This chapter explores the main engineering considerations for modern earthworks, and the challenges associated with older earthworks including their modes of failure and upgrade and repair.

The idea of implementing supply chain management (SCM) principles for the construction industry was embraced by construction stakeholders to enhance the sector's performance. The analysis from the literature revealed that the implementation of SCM in the construction industry enhances the industry's value in terms of cost-saving, time savings, material management, risk management and others. The construction supply chain (CSC) can be managed using the pull or push system. This chapter also discusses the origin and proliferation of SCM into the construction industry. The chapter revealed that the concept of SCM has passed through five different eras: the creation era, the use of ERP, globalisation stage, specialisation stage and electronic stage. The findings from the literature revealed that we are presently in the fourth industrial revolution (4IR) era. At this stage, the SCM witnesses the adoption of technologies and principles driven by the 4IR. This chapter also revealed that the practice of SCM in the construction industry is centred around integration, collaboration, communication and the structure of the supply chain (SC). The forms and challenges hindering the adoption of these practices were also discussed extensively in this chapter.

Purpose – In this chapter, we report on the lessons of cross-disciplinary collaborative workshop between sociologists and engineering educators to synthesize what is known about legitimating and disseminating educational reform and to develop a research agenda for what needs to be known in order to spread educational reform and to overcome on-the-ground resistance to change.

Methodology/approach – This chapter is based on a case study of this workshop, describing the “white papers” prepared by participants prior to the workshop and the research agendas that emerged from discussions of them during the workshop and after.

Findings – The workshop resulted in a sophisticated research agenda as well as some modest efforts to create cross-disciplinary links to implement it. However, a one-time workshop did not overcome institutional barriers to this kind of activity.

Research limitations – Since this is a case study of a single collaboration we cannot generalize to all cross-disciplinary collaborations, although it does provide an example of what works to facilitate cross-disciplinary efforts and what obstacles remain.

Practical implications – An advantage to the workshop was the absence of institutional barriers to cross-disciplinary collaboration. Attendees were removed from their institutions, departments, disciplines, and turf battles. However, without increased institutional support for cross-disciplinary efforts, such as this one, the value of the social sciences for diffusing the innovations of science and engineering reform movements may not be realized.