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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 purpose of this paper is to propose a new framework for time–cost trade-off. The new framework provides the optimum time–cost value taking into account the float loss impact.

The stochastic framework uses Monte Carlo Simulation to calculate the effect of float loss on risk. This is later translated into an added cost to the trade-off problem. Five examples, from literature, are solved using the proposed framework to test the applicability of the developed framework.

The results confirmed the research hypothesis that the new optimum solution will be at a higher duration and cost but at a lower risk compared to traditional methods. The probabilities of finishing the project on time using the developed framework in all five cases were better than those using the classical deterministic optimization technique.

The objective of time–cost trade-off is to determine the optimum project duration corresponding to the minimum total cost. Time–cost trade-off techniques result in reducing the available float for noncritical activities and thus increasing the schedule risks. Existing deterministic optimization technique does not consider the impact of the float loss within the noncritical activities when the project duration is being crashed. The new framework allows project managers to exercise new trade-offs between time, cost and risk which will ultimately improve the chances of achieving project objectives.

The purpose of this study is to identify and assess new risks in construction projects that use 3D printing.

A mixed approach of both qualitative and quantitative methods was used. Literature review was conducted to extract 30 risks of 3D printing in construction. A survey was then developed to assess the probability and impact of these risks. In total, 37 respondents, who have experience and/or knowledge of 3D printing, completed the survey. The risk priority was calculated using a fuzzy logic approach. The main benefit of the proposed model is being able to use numerical and linguistic data in the risk assessment model.

The results show that the main risks, in terms of priority, are lack of codes and regulations for 3D printing in construction, delay in government approvals, shortage in labour skilled in 3D printed construction, lack of knowledge and information of 3D printed design concepts and changes in 3D construction codes and regulations.

This paper fills an identified gap in the literature related to 3D printing in construction and provides insights into the key risks affecting this disruptive technology.

This paper aims to identify and assess the key criteria for selecting green suppliers in the United Arab Emirates (UAE) construction industry.

A total of 20 criteria were identified and shortlisted through an extensive literature review. These criteria were grouped into four categories: technical and commercial bid, company characteristics, environmental and socioeconomic. A questionnaire was then developed and distributed to construction professionals in the UAE. A total of 39 professionals responded to the survey including contractors, consultants, owners and suppliers. The respondents performed pairwise comparisons among the selection criteria. Data was then analyzed using the Expert Choice Software.

The research findings highlighted that the technical and commercial bid category was ranked as the most important with a weight of 0.338, followed by socioeconomic, company characteristics and environmental categories weighing 0.239, 0.225 and 0.199, respectively. The UAE construction professionals also ranked health and safety, material’s quality and tender price as the top three most important criteria when selecting a sustainable supplier.

This research addresses the lack of literature toward green supplier selection in the UAE. In addition, it assists contractors in selecting the appropriate supplier and promotes sustainable practices in the construction industry.

Material suppliers play an important role in the successful delivery of construction projects. Selecting the appropriate supplier is of paramount importance to project success. Several methods can be used to evaluate and select the best-fit suppliers. However, the selection criteria in such methods are primarily based on traditional construction projects rather than sustainable construction projects. Recently, there is an increase in the number of sustainable construction projects in the UAE. Therefore, identifying and assessing the key criteria for selecting green suppliers is needed. This paper fills the gap in literature as to selecting green suppliers in construction projects.

This paper aims to propose a new model for project selection using the combined approach of analytic hierarchy process (AHP) and linear programming (LP). The key selection criteria for construction projects are also identified and assessed based on the perception of construction professionals.

Fifteen project selection criteria are identified based on review of related literature. A questionnaire is developed and distributed among construction professionals in the United Arab Emirates to assess the importance of these criteria. The questionnaire was designed to enable pair-wise comparisons using the AHP methodology. Based on the responses of 42 professionals, the weight of each criterion was calculated using Expert Choice software. Selected criteria are then used to compare available projects. AHP is applied first to prioritize the competing projects, in line with owner strategic goals and objectives. Next, the priority ratios (weights) obtained from the AHP model are used as the coefficients of the decision variables in a LP model to allocate the available budget in an optimal way to maximize the owner’s benefit.

Results indicate that project-specific criteria are the most important with an overall weight of 0.48. The top three criteria are profit, financial standing and risk with corresponding weights of 0.161, 0.114 and 0.1, respectively. The proposed model provided more benefit to the owner than using the traditional project selection method.

There are several methods for project selection. However, the optimization techniques do not consider qualitative factors. Other techniques consider both qualitative and quantitative factors but do not consider budget limitations to optimize the selection decision. The model proposed here combines both approaches and takes into consideration unique criteria that are specific to the construction industry.