Search results

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 paper, we examine the occupational distribution of individuals who hold bachelor degrees in particular fields in the United States using data from the various waves of the National Survey of College Graduates. We propose and calculate indices that describe two related aspects of the occupational distribution by major field of study: distinctiveness (how dissimilar are the occupations of a particular major when compared with all other majors) and variety (how varied are the occupations among those who hold that particular major). We discuss theoretical properties of these indices and statistical properties of their estimates. We show that the occupational variety has increased since 1993 for most major fields of study, particularly between the 1993 and 2003 waves of the survey. We explore reasons for this broadening of the occupation distribution. We find that this has not led to an increase in reported mismatch between degree and occupation.

Spatial ability is essential for engineers’ professional performance. Several studies describe it as a skill that can be enhanced using new technologies. Virtual reality (VR) is an emerging technology that is proving very useful for training different skills and improving spatial perception. In this chapter, the authors firstly present some previous works that use VR to train students, mainly in the area of engineering studies, and which demonstrate that VR can improve some aspects of the spatial perception. This study took a group of engineering students who used VR technologies to carry out learning activities designed to improve their spatial perception, which was measured with a widely used spatial ability test. The results obtained confirm that the use of VR technologies can improve students’ spatial perception. This proposal is easily transferable to other educational contexts. On the one hand, it could be implemented to improve spatial ability in other engineering studies, and on the other hand, with simple adaptation, it could be used to enhance other skills.

The current era of the fourth industrial revolution has attracted significant research on the use of digital technologies in improving construction project delivery. However, less emphasis has been placed on how these digital tools will influence the management of the construction workforce. To this end, using a review of existing works, this chapter explores the fourth industrial revolution and its associated technologies that can positively impact the management of the construction workforce when implemented. Also, the possible challenges that might truncate the successful deployment of digital technologies for effective workforce management were explored. The chapter submitted that implementing workforce management-specific digital platforms and other digital technologies designed for project delivery can aid effective workforce management within construction organisations. Technologies such as cloud computing, the Internet of Things, big data analytics, robotics and automation, and artificial intelligence, among others, offer significant benefits to the effective workforce management of construction organisations. However, several challenges, such as resistance to change due to fear of job loss, cost of investment in digital tools, organisational structure and culture, must be carefully considered as they might affect the successful use of digital tools and by extension, impact the success of workforce management in the organisations.

Universities have a long history of training students to work in industry, and in recent years the number and percentage of students, especially those trained in science and engineering, who go to work in industry has grown. Today, three-eights of all PhDs with a degree in science and engineering (S&E) work in the private sector. These placements provide a major means for universities to participate in technology transfer. Students are not only up-to-date in terms of codified knowledge; they also possess tacit knowledge that can only be transferred by face-to-face interaction. They may also have participated as research assistants or as postdocs in the development of a technology that has been licensed by the firm where they are employed. Despite the important role that alumni play in technology transfer, universities rarely track the placements of graduate students in industry. Universities do not also systematically keep information on the contributions that alums make to innovations after graduating. Moreover, few programs socialize students to think of careers in the private sector as a top choice. Instead, many programs, especially in the biomedical sciences, socialize students to aspire to research careers in academe, with industry seen as a distinct second choice. Indeed, many PhDs only take jobs in industry after failing to find an academic position after serving as a postdoc for four or five years.

This paper examines recent placements of doctoral students in industry, using the verbatim records from the Survey of Earned Doctorates (SED) for 1997–2002. An advantage of this data is that we know the name of the firm (and the location of the firm) where the individual plans to work. This permits an exploration of several interesting dimensions regarding technology transfer not explored elsewhere, such as (1) sources (in terms of universities) educating students going to industry; (2) the R&D intensity of the firms where newly trained PhDs go to work and the industrial classification of the firms; (3) the role that proximity plays in facilitating these knowledge spillovers; and (4) the degree to which universities make placements with firms licensing their technologies.

The paper also examines the amount of information that universities provide regarding the placements of their PhDs. We find that although students are ready and willing to provide information regarding work plans after graduation, universities seldom provide information on placements. We conclude with a suggestion regarding the procedures universities could follow to create and make placement data available.

For Higher Education Institutions (HEIs) to survive they need to be agile, to introduce innovations, and to effectively manage change. HEIs are guardians of the history, values and traditions that they reside within and represent. Effat University – established in Jeddah, Saudi Arabia – is a successful case in point presents a female HEI that is working continuously on being agile, while striking the balance between innovation and change within a tight framework of solid traditional values to ensure higher quality performance and valued organizational outcomes. This case study is presented in four sections. First, the conceptual framework supporting the case study is outlined. Then, description of Effat University IQRA-Based Quality Model follows. Then, the application of Effat University IQRA-Based Quality Model on Effat College of Engineering as a case study is elaborated. Summary and conclusions ensue.

This study investigates national trends in students’ science, technology, engineering, and mathematics (STEM) occupational expectations by using Program for International Student Assessment (PISA) 2000, 2003, and 2006 data. The analyses in this study revealed several noteworthy national trends in STEM occupational expectations. In many countries students’ computing or engineering (CE) occupational expectations changed between PISA 2000 and PISA 2006, while students’ health service (HS) occupational expectations remained constant. In particular, many developed countries experienced downward national trends in CE occupational expectations among top performers in science. This study also found gender differences in national trends in STEM occupational expectations. In many countries boys’ CE occupational expectations decreased between PISA 2000 and PISA 2006, while girls’ occupational expectations remained unchanged in both CE and HS fields. Finally, the gender gaps in CE occupational expectations converged in many countries, but this convergence was not due to increases in CE occupational expectations among girls, but rather decreases in expectations among boys. Because one of the policy goals in many countries is to promote engagement in STEM education and occupations among students, especially academically talented students, the current findings – national declines in CE occupational expectations among top academic performers – will most likely be viewed as problematic in several countries. Future research should use data collected over longer periods to investigate whether students’ interest in STEM education and occupations increased or decreased in a variety of countries, and whether these patterns varied by student characteristics and performance levels. Moreover, future research must focus on factors that can explain the national trends in student interest in STEM education and occupations.