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Sources highlight that lack of systematic labour training components results in low performance and productivity of labour, which leads the construction industry of many countries to face various challenges. This study aims to quantify the variations in the performance and productivity levels of labour in building construction projects through the applications of effective work-based training components.

A comprehensive literature review and a series of experts’ discussions with action-oriented communication approaches were conducted to develop a set of practices related to labour training, performance assessment and productivity measurements within a framework. The developed practices were applied to around 100 labourers working on nine building construction projects through a construction supervisory training programme.

The study presents the detailed patterns of the significant changes in labour performance and productivity levels. The majority of trained labourers have grown to perform the work process with some relevant theoretical and operational knowledge and skills. The overall results spotlight the significant behavioural changes that can be observed in workforce operations by improving labour performance, which resulted in implementing effective labour-rewarding practices within a framework.

Although the study findings were limited to the Sri Lankan context, the proposed practices can be applied to the industry practices of the construction sector of other developing countries and the other developing industries in similar ways/scenarios.

The study outcomes contribute to uplifting the work qualities of labourers with life-long learning opportunities and unlocking the potential barriers for expanding the local labour supply while controlling the excessive inclination of the local firms towards foreign labour. This paper describes further implications and future scopes of the study elaborately.

The study provides generalised mechanisms and practices that transform the labour characteristics and add new attributes for strengthening the values of construction supervision practices to obtain well-improved work outputs. The study outcomes reinforce the chain relationships among the training elements, labour performance and productivity levels, leading to upgrading current planning and operational management practices, especially adding constructive mechanisms in resource levelling and productivity benchmarking practices.

The purpose of this paper is to explore the support for multi-party collaboration in project construction provided by building information modeling (BIM). Based on the perspective of value co-creation, the research results can provide support for the collaborative application and contract design of BIM platform.

In this paper, an evolutionary game model involving the owner, designer and constructor is constructed by using prospect theory and evolutionary game theory. Through simulation analysis, the evolution law of the strategy choice of each party in the collaborative application of BIM platform is discussed and the key factors affecting the strategy choice of all parties are analyzed.

The results show that there is an ideal local equilibrium point with progressive stability in the evolutionary game between the three parties: “the construction party shares information, the designer receives the information and optimizes the project and the owner does not provide incentives”; in addition, the opportunistic behaviors of the design and construction parties, as well as the probability of such behaviors being detected and the subsequent punishment have a significant impact on the evolutionary outcome.

This method can provide support for the collaborative application and contract design of BIM platform.

Prior research has emphasised the importance of the early phases of construction projects, as well as the difficulties of procuring engineering services – especially due to the uncertainties. Despite that, studies on the public procurement of engineering services are scarce. Although scholars have shown that uncertainty may affect the choice of control modes, the level of uncertainty that characterises services is not addressed by the two task characteristics: knowledge of the transformation process and output measurability. The purpose is to investigate organisational control in public procurement of engineering services.

The existing control model was adjusted in this study by conceptually adding uncertainty as a third aspect to the two task characteristics. A single case study of the Swedish Transport Administration was used. The empirical data, comprising 14 interviews with managers from the client and engineering consulting companies, were analysed using flexible pattern matching and visual mapping approaches and then illustrated using the model.

The public client did not base its choice of control modes on uncertainty, but rather on the other two task characteristics. Consequently, the service providers argued that the chosen control modes reduced their creativity, increased their financial risks and caused unclear responsibilities. This study therefore shows that uncertainty is an important factor to consider in the choice of control modes, both from a theoretical perspective and from the service providers' point of view. The developed model may therefore be useful for researchers as well as practitioners.

This study is the first attempt to add uncertainty as a task characteristic when choosing control modes. The results contribute to the scarce control literature regarding the procurement of engineering services for construction projects and the procurement of other services with high uncertainty.

This paper aims to measure the severity, frequency and importance of the factors causing delays in the United Arab Emirates (UAE) construction industry following the Covid-19 pandemic onset. The study also measures the likelihood of the effects caused by these delays.

A mixed approach of both qualitative and quantitative. Literature review was conducted to extract 40 factors of delays and 10 effects of delays. A survey was then administered to construction professionals in the UAE to collect the perceptions on the severity and frequency of factors of the causes of delays using a Likert Scale of 1–5 where 1 represented very low and 5 represented very high. Similarly, the respondents were also asked to rate the likelihood of the occurrence of the effects of the delays based on a Likert scale of 1–5 as well. Furthermore, Spearman’s rank correlation was also conducted to compute the level of agreement between the different parties; owner, consultants and contractors.

The results revealed that the top five factors of delays include: award the project for the lowest bidder, delay in progress payment, change orders by the owner, poor subcontractor performance and inadequate planning and scheduling by the contractor. The findings of this study emphasize the financial challenges and economic crisis brought upon the construction industry due to the pandemic. Furthermore, the pandemic also shifted the perceptions of construction professionals, who are now more aware of the delays caused by awarding the project to the lowest bidder who would not have the required qualifications to conduct efficient planning and scheduling that are relevant in the case of extraordinary events such as Covid-19. Moreover, a high level of agreement between the consultants and contractors was observed, with a Spearman’s rank correlation of 0.804. Additionally, the most likely effects of delays concluded from this study were time overrun/extension and poor quality of work.

Literature review is very rich in the field of construction projects delays. However, there is very limited research on the impact of Covid-19 in the context of construction projects delays, and insights from construction professionals regarding this matter are particularly lacking in literature. Therefore, this paper bridges the gap in literature by providing perceptions of construction professionals on the impact of Covid-19 on the factors causing delays in the UAE construction industry. The findings of this research are expected to be an invaluable resource for future to help the construction industry heal faster when encountering similar epidemics or extraordinary events.

The variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the quality of the owner's estimation for predicting precisely the contract cost at the pre-tendering phase and avoiding future issues that arise through the construction phase.

This paper integrated artificial neural networks (ANN), deep neural networks (DNN) and time series (TS) techniques to estimate the ratio of a low bid to the OEC (R) for different size contracts and three types of contracts (building, electric and mechanic) accurately based on 94 contracts from King Saud University. The ANN and DNN models were evaluated using mean absolute percentage error (MAPE), mean sum square error (MSSE) and root mean sums square error (RMSSE).

The main finding is that the ANN provides high accuracy with MAPE, MSSE and RMSSE a 2.94%, 0.0015 and 0.039, respectively. The DNN's precision was high, with an RMSSE of 0.15 on average.

The owner and consultant are expected to use the study's findings to create more accuracy of the owner's estimate and decrease the difference between the owner's estimate and the lowest submitted offer for better decision-making.

This study fills the knowledge gap by developing an ANN model to handle missing TS data and forecasting the difference between a low bid and an OEC at the pre-tendering phase.

This study aimed to address a gap in subcontractor management by focusing on previously unexplored complexities surrounding subcontractor management in developing countries. While past studies concentrated on selection and relationships, this study delved into how effective subcontractor management impacts project success.

This study used the Bayesian Network analysis approach, through a meticulously developed questionnaire survey refined through a piloting stage involving experienced industry professionals. The survey was ultimately distributed among participants based in Accra, Ghana, resulting in a response rate of approximately 63%.

The research identified diverse components contributing to subcontractor disruptions, highlighted the necessity of a clear regulatory framework, emphasized the impact of financial and leadership assessments on performance, and underscored the crucial role of main contractors in Integrated Project and Labour Cost Management with Subcontractor Oversight and Coordination.

Previous studies have not considered the challenges subcontractors face in projects. This investigation bridges this gap from multiple perspectives, using Bayesian network analysis to enhance subcontractor management, thereby contributing to the successful completion of construction projects.

The construction industry has struggled to deliver schemes on time to budget and right-first-time (RFT). There have been many studies into nonconformance and rework through quantitative research over the years to understand why the industry continues to see similar issues of failure. Some scholars have reported rework figures as high as 12.6% of total contract value, highlighting major concerns of the sustainability of construction projects. Separately, however, there have been few studies that explore and detail the views of industry professions who are caught in the middle of quality issues, to understand their perceptions of where the industry is failing. As such, this paper interrogates qualitative data (open-ended questions) on the topic of nonconformance and rework in construction to understand what industry professionals believe are the causes and suggested improvement areas.

A qualitative approach is adopted for this research. An industry survey consisting of seven open-ended questions is presented to two professional working groups within a Tier 1 contractor, and outputs are analysed using statistic software (NVivo 12) to identify prominent themes for discussion. Inductive analysis is undertaken to gain further insight into responses to yield recurrent areas for continuous improvement.

Qualitative analysis of the survey reveals a persistent prioritisation of cost and programme over quality management in construction project. Furthermore, feedback from construction professionals present a number of improvement areas that must be addressed to improve quality. These include increased training and competency investment, overhauling quality behaviours, providing greater quality leadership direction and reshaping the way clients govern schemes.

There are limitations to this paper that require noting. Firstly, the survey was conducted within one principal contractor with varying levels of knowledge across multiple sectors. Secondly, the case study was from one major highways scheme; therefore, the generalisability of the findings is limited. It is suggested that a similar exercise is undertaken in other sectors to uncover similar improvement avenues.

The implications of this study calls for quality to be re-evaluated at project, company, sector and government levels to overhaul how quality is delivered. Furthermore, the paper identifies critical learning outcomes for the construction sector to take forward, including the need to reassess projects to ensure they are appropriately equip with competent personnel under a vetted, progressive training programme, share collaborative behaviours that value quality delivery on an equal standing to safety, programme and cost and tackle the inappropriate resource dilemmas projects finding themselves in through clear tendering and accurate planning. In addition, before making erratic decisions, projects must assess the risk profiling of proceed without approved design details and include the client in the decision-making process. Moreover, the findings call for a greater collaborative environment between the construction team and quality management department, rather than being seen as obstructive (i.e. compliance based policing). All of these must be driven by leadership to overhaul the way quality is managed on schemes. The findings demonstrate the importance and impact from open-ended survey response data studies to enhance quantitative outcomes and help provide strengthened proposals of improvement.

This paper addresses the highly sensitive area of quality failure outcomes and interrogates them via an industry survey within a major UK contractor for feedback. Unique insights are gained into how industry professionals perceive quality in construction. From previous research, this has been largely missing and offers a valuable addition in understanding the “quality status quo” from those delivering schemes.

This study collected the bibliographic data of 2034 journal articles published in 2000–2021 from Web of Science (WoS) core collection database and adopted two bibliometric analysis methods, namely historiography and keyword co-occurrence, to identify the evolution trend of construction risk management (CRM) research topics.

CRM has been a key issue in construction management research, producing a big number of publications. This study aims to undertake a review of the global CRM research published from 2000 to 2021 and identify the evolution of the research topics relating to CRM.

This study found that risk analysis methods have shifted from simply ranking risks in terms of their relative importance or significance toward examining the interrelationships among risks, and that the objects of CRM research have shifted from generic construction projects toward specified types of construction projects (e.g. small projects, underground construction projects, green buildings and prefabricated projects). In addition, researchers tend to pay more attention to an individual risk category (e.g. political risk, safety risk and social risk) and integrate CRM into cost, time, quality, safety and environment management functions with the increasing adoption of various information and communication technologies.

This study focused on the journal articles in English in WoS core collection database only, thus excluding the publications in other languages, not indexed by WoS and conference proceedings. In addition, the historiography focused on the top documents in terms of document strength and thus ignored the role of the documents whose strengths were a little lower than the threshold.

This review study is more inclusive than any prior reviews on CRM and overcomes the drawbacks of mere reliance on either bibliometric analysis results or subjective opinions. Revealing the evolution process of the CRM knowledge domain, this study provides an in-depth understanding of the CRM research and benefits industry practitioners and researchers.

Prefabricated technology is gradually being applied to the construction of subway stations due to its characteristic of mechanization. However, the prefabricated subway station in China is in the initial stage of development, which is prone to construction safety issues. This study aims to evaluate the construction safety risks of prefabricated subway stations in China and formulate corresponding countermeasures to ensure construction safety.

A construction safety risk evaluation index system for the prefabricated subway station was established through literature research and the Delphi method. Furthermore, based on the structure entropy weight method, matter-element theory and evidence theory, a hybrid evaluation model is developed to evaluate the construction safety risks of prefabricated subway stations. The basic probability assignment (BPA) function is obtained using the matter-element theory, the index weight is calculated using the structure entropy weight method to modify the BPA function and the risk evaluation level is determined using the evidence theory. Finally, the reliability and applicability of the evaluation model are verified with a case study of a prefabricated subway station project in China.

The results indicate that the level of construction safety risks in the prefabricated subway station project is relatively low. Man risk, machine risk and method risk are the key factors affecting the overall risk of the project. The evaluation results of the first-level indexes are discussed, and targeted countermeasures are proposed. Therefore, management personnel can deeply understand the construction safety risks of prefabricated subway stations.

This research fills the research gap in the field of construction safety risk assessment of prefabricated subway stations. The methods for construction safety risk assessment are summarized to establish a reliable hybrid evaluation model, laying the foundation for future research. Moreover, the construction safety risk evaluation index system for prefabricated subway stations is proposed, which can be adopted to guide construction safety management.

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.