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Through its advanced computational capabilities, cyber–physical systems (CPS) proffer solutions to some of the cultural challenges plaguing the effective delivery of facilities management (FM) mandates. This study aims to explore the drivers for the uptake of CPS for FM functions using a qualitative approach – the Delphi technique.

Using the Delphi technique, the study selected experts through a well-defined process entailing a pre-determined set of criteria. The experts gave their opinions in two iterations which were subjected to statistical analyses such as the measure of central tendency and interquartile deviation in ascertaining consensus among the experts and the Mann–Whitney U test in establishing if there is a difference in the opinions given by the experts.

The study’s findings show that six of the identified drivers of the uptake of CPS for FM were attributed to be of very high significance, while 12 were of high significance. Furthermore, it was revealed that there is no significant statistical difference in the opinions given by experts in professional practice and academia.

The study’s outcome provides the requisite insight into the propelling measures for the uptake of CPS for FM by organisations and, by extension, aiding digital transformation for effective FM delivery.

To the best of the authors’ knowledge, evidence from the literature suggests that no study has showcased the drivers of the incorporation of CPS for FM. Hence, this study fills this gap in knowledge by unravelling the significant propelling measures of the integration of CPS for FM functions.

The quest for improved facilities management (FM) delivery is receiving immense focus through the incorporation of innovative technologies such as cyber-physical systems (CPS). The system’s high computational capabilities can aid in the abatement of some of the challenges plaguing FM functions. However, the requisite ingredients for the uptake of the system for FM have still not gained scholarly attention. Because performance measurement is a vital index in determining the outcome of FM methods, this study aims to investigate the influence of performance measurement indicators that are influential to the uptake of CPS for delivering FM functions.

A qualitative technique was adopted using the Delphi technique. The panel of experts for the study was selected through a well-defined process based on stipulated criteria. The experts gave their opinions in two rounds before consensus was attained on the identified performance measurement indicators, whereas methods of data analysis were measures of central tendency, inter-quartile deviation and Mann–Whitney U test.

Results from this study showed that 11 of the performance indicators were of very high significance in the determination of the uptake of CPS for FM functions, whereas 5 of the indicators were proven to be of high significance. Furthermore, there was no statistical difference in the opinions of the experts based on their affiliation with academic institutions and professional practice.

The findings of this study contribute practically by aiding policymakers, facility managers and relevant stakeholders with the vital knowledge of delivery mandates for efficient FM services that can spur the uptake of digital technologies such as CPS.

This study contributes to the body of knowledge as it unveils a roadmap of the expected performance output and its accompanying evaluation that would drive the adoption of a promising technology such as CPS in the delivery of FM tasks.

The South African construction industry (SACI) is a huge employer of labour as the industry depends mostly on human resources to carry out its activities. However, the industry has also been noted for its poor workforce management and lack of attention to human resource management (HRM) issues. Therefore, this study aims to assess the challenges facing the effective management of the construction workforce in South Africa with a view to improving HRM activities in the industry and improve overall service delivery through an effectively managed workforce.

A pragmatic philosophical view using a mixed-method research design was adopted. The study’s qualitative strand was achieved through a Delphi, whilst the quantitative strand was achieved through a questionnaire survey. In addition, descriptive and inferential statistics such as mean item score, Kruskal-Wallis H-test, exploratory and confirmatory factor analysis were adopted for data analysis.

With high reliability, construct validity and model fit indices, the study found that for effective HRM to be attained within construction organisations, careful attention must be given to issues surrounding the nature of the industry, unhealthy working environment, employee-related issues, diversity and working condition in the industry.

Whilst several studies have been conducted on HRM, there is a lack of studies on the major challenges facing effective HRM in the SACI. The findings also offer future research studies a good theoretical platform to build upon.

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

It is essential for developed nations to have adequate and functional infrastructure to sustain economic growth and well-being. Despite efforts to reduce the chances of infrastructure problems, several scholars have expressed concern about infrastructure standards deteriorating at an alarming rate and the need to ensure their sustainability. To achieve sustainable infrastructure development (SID), Sahely et al. (2005) proposed an uncomplicated strategy based on fundamental cooperation between infrastructure and ecological, economic, and social frameworks. Sustainable infrastructure is defined as the development and dependable management of a safe built environment based on efficient resource utilization and environmental standards. SID aims to mitigate or eliminate ecological problems and challenges while maximizing the potential social and economic benefits. United Nations Economic and Social Commission for Asia and the Pacific (UN-ESCAP, 2007) defines SID as infrastructure compatible with continued financial and environmental sustainability. Sustainable road development infrastructure (SRID) is a procedure for constructing infrastructure that incorporates all the essential sustainable development (SD) parameters and is complicated by the interdependence of multiple factors. Stakeholders are essential for the successful execution of infrastructure projects, and a comprehensive evaluation of stakeholder interests and requirements is necessary to achieve SRID goals while meeting the needs of all parties involved. To achieve SRID, it is necessary to understand the relationship between road infrastructure development (RID) processes and SD standards and to implement criteria and indicators that accurately depict the long-term viability of a development process. Al Sanad (2015) identified several factors that may impede the implementation of SD in the infrastructure sector. Shafii et al. (2006) identified insufficient knowledge of SID, lack of SID training, a perception of SID as expensive, acquisition concerns, administrative issues, expert capacities, and motivational factors for manufacturing local materials as potential obstacles. Serpell (2013) identified four categories of SID challenges, including knowledge, economic and financial, organizational, and go. Darko and Chan (2016) identified the most prevalent barriers as a lack of data, training, research, information, and expertise, high cost, government interest, premium and demand, and the absence of SID standards of practice. Azis et al. (2012) identified improved project efficiency, waste reduction in the construction industry, and energy efficiency as advantages of SID. Ametepey and Aigbavboa (2014) identified energy savings, environmental protection, contribution to a higher standard of living and a healthy work environment, resource preservation for future generations, reduction in lifecycle costs, promotion of sustainable economic development, and stakeholder satisfaction as the top benefits of sustainable construction (SC). Du Plessis (2007) identified technological factors, Al Sanad (2015) identified educational programmes, Serpell et al. (2013) identified transformation, economic, and stakeholder engagement as drivers of SC, and Hankinson and Breytenbach (2012) identified enhanced SC awareness. This chapter reviewed the literature on international infrastructure and sustainability development, discussing factors, and benefits promoting SID.