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Cost and time are two of the primary benchmarks in which construction projects are measured. A variety of factors, however, affect cost and time on construction projects, as identified in previous research. This has led to a need for better understanding how factors affecting cost and time overruns on public construction projects can be managed more efficiently. The purpose of this paper is to address these issues.

In this study 26 factors affecting cost and time overruns on construction projects were identified, through qualitative interviews with project managers from Danish governmental agencies and through a literature review. Through principal component analyses the 26 factors were subsequently narrowed down to four primary latent factors.

The identified four latent factors affecting cost and time overruns on public construction projects were lack of quality management, lack of project pre-planning, lack of user management and lack of project management.

Previous research has focussed on increasing knowledge by identifying and ranking factors affecting time and cost performance. This has led to the identification of an overwhelming number of factors to use for managing construction projects. The present research reduced the number of factors by clustering them into key latent factors responsible for most of the deviation in performance, narrowing the scope of construction cost and time management into a few tangible key focus areas. This supports and improves fast decision-making that is necessary in a changeable environment such as construction.

Construction material management plays a significant role in achieving successful project delivery of a construction project. However, ineffective material management is a critical issue in the construction industry, especially in developing economies, of which Sri Lanka is not an exception. Therefore, this study aims to focus on exploring the causes of ineffective material management practices in civil engineering construction projects in Sri Lanka and their impact on successful project delivery.

Furthermore, the literature findings were validated through the preliminary survey. Subsequently, a quantitative research approach was adopted to pursue the research aim. Questionnaire responses were obtained from 215 construction professionals in civil engineering projects who were selected using the judgemental and snowball sampling techniques. Collected data were analysed through Statistical Package for the Social Sciences (SPSS) V26 and Microsoft Excel 2016.

Moreover, the study revealed that material price fluctuation, shortage of material in the market, delay in material procurement, inadequate planning and delays in material delivery are the most frequent causes of ineffective material management in civil engineering projects. In addition, it was evidenced that most ineffective material management practices cause both time and cost overruns in civil engineering construction projects. Most respondents emphasized inadequate planning, inadequate qualified and experienced staff, lack of supervision and lack of leadership as the causes for both time and cost overruns.

The study was concluded by proposing strategies for effective material management. Education/training/enlightenment of staff in charge of materials management, use of software like Microsoft Project, Primavera and similar software to eliminate manual errors in material management, and providing clear specifications to suppliers were the most agreed strategies for effective material management in civil engineering construction projects.

Total risk score (TRS) is considered one of the main indicators for risk evaluation. Several studies attempted to employ different types of risk indices for the evaluation of cost overrun causes. Hence, this study aims at performing a comparative analysis to evaluate the efficiency of three different approaches for TRS calculation.

Thirty-eight unique causes of cost overrun in urban-related construction projects were identified and a survey was conducted among construction professionals in Iran. The TRS for each cost overrun cause is calculated using single-attribute (SA), double-attribute (DA), and multiple-attribute (MA) approaches, and eventually, causes were ranked. Furthermore, principal component analysis (PCA), logistic regression analysis (LRA), and K-means clustering are utilized to compare the differences in the generated TRS using different approaches.

The results revealed that the TRS generated through the MA approach demonstrated the highest efficiency in terms of generating correlation between causes and their identified latent constructs, prediction capability, and classification of the influential causes in the same group.

The originality of this study primarily stems from the adoption of statistical approaches in the evaluation of the recently introduced TRS calculation approach in comparison to traditional ones. Additionally, this study proposed a modified application of the relative importance index (RII) for risk prioritization. The results from this study are expected to fulfill the gap in previous literature toward exploring the most efficient TRS calculation approach for those researchers and practitioners who seek to utilize them as a measure to identify the influential cost overrun causes.

Earned value management (EVM)–based models for estimating project actual duration (AD) and cost at completion using various methods are continuously developed to improve the accuracy and actualization of predicted values. This study primarily aimed to examine natural gradient boosting (NGBoost-2020) with the classification and regression trees (CART) base model (base learner). To the best of the authors' knowledge, this concept has never been applied to EVM AD forecasting problem. Consequently, the authors compared this method to the single K-nearest neighbor (KNN) method, the ensemble method of extreme gradient boosting (XGBoost-2016) with the CART base model and the optimal equation of EVM, the earned schedule (ES) equation with the performance factor equal to 1 (ES1). The paper also sought to determine the extent to which the World Bank's two legal factors affect countries and how the two legal causes of delay (related to institutional flaws) influence AD prediction models.

In this paper, data from 30 construction projects of various building types in Iran, Pakistan, India, Turkey, Malaysia and Nigeria (due to the high number of delayed projects and the detrimental effects of these delays in these countries) were used to develop three models. The target variable of the models was a dimensionless output, the ratio of estimated duration to completion (ETC(t)) to planned duration (PD). Furthermore, 426 tracking periods were used to build the three models, with 353 samples and 23 projects in the training set, 73 patterns (17% of the total) and six projects (21% of the total) in the testing set. Furthermore, 17 dimensionless input variables were used, including ten variables based on the main variables and performance indices of EVM and several other variables detailed in the study. The three models were subsequently created using Python and several GitHub-hosted codes.

For the testing set of the optimal model (NGBoost), the better percentage mean (better%) of the prediction error (based on projects with a lower error percentage) of the NGBoost compared to two KNN and ES1 single models, as well as the total mean absolute percentage error (MAPE) and mean lags (MeLa) (indicating model stability) were 100, 83.33, 5.62 and 3.17%, respectively. Notably, the total MAPE and MeLa for the NGBoost model testing set, which had ten EVM-based input variables, were 6.74 and 5.20%, respectively. The ensemble artificial intelligence (AI) models exhibited a much lower MAPE than ES1. Additionally, ES1 was less stable in prediction than NGBoost. The possibility of excessive and unusual MAPE and MeLa values occurred only in the two single models. However, on some data sets, ES1 outperformed AI models. NGBoost also outperformed other models, especially single models for most developing countries, and was more accurate than previously presented optimized models. In addition, sensitivity analysis was conducted on the NGBoost predicted outputs of 30 projects using the SHapley Additive exPlanations (SHAP) method. All variables demonstrated an effect on ETC(t)/PD. The results revealed that the most influential input variables in order of importance were actual time (AT) to PD, regulatory quality (RQ), earned duration (ED) to PD, schedule cost index (SCI), planned complete percentage, rule of law (RL), actual complete percentage (ACP) and ETC(t) of the ES optimal equation to PD. The probabilistic hybrid model was selected based on the outputs predicted by the NGBoost and XGBoost models and the MAPE values from three AI models. The 95% prediction interval of the NGBoost–XGBoost model revealed that 96.10 and 98.60% of the actual output values of the testing and training sets are within this interval, respectively.

Due to the use of projects performed in different countries, it was not possible to distribute the questionnaire to the managers and stakeholders of 30 projects in six developing countries. Due to the low number of EVM-based projects in various references, it was unfeasible to utilize other types of projects. Future prospects include evaluating the accuracy and stability of NGBoost for timely and non-fluctuating projects (mostly in developed countries), considering a greater number of legal/institutional variables as input, using legal/institutional/internal/inflation inputs for complex projects with extremely high uncertainty (such as bridge and road construction) and integrating these inputs and NGBoost with new technologies (such as blockchain, radio frequency identification (RFID) systems, building information modeling (BIM) and Internet of things (IoT)).

The legal/intuitive recommendations made to governments are strict control of prices, adequate supervision, removal of additional rules, removal of unfair regulations, clarification of the future trend of a law change, strict monitoring of property rights, simplification of the processes for obtaining permits and elimination of unnecessary changes particularly in developing countries and at the onset of irregular projects with limited information and numerous uncertainties. Furthermore, the managers and stakeholders of this group of projects were informed of the significance of seven construction variables (institutional/legal external risks, internal factors and inflation) at an early stage, using time series (dynamic) models to predict AD, accurate calculation of progress percentage variables, the effectiveness of building type in non-residential projects, regular updating inflation during implementation, effectiveness of employer type in the early stage of public projects in addition to the late stage of private projects, and allocating reserve duration (buffer) in order to respond to institutional/legal risks.

Ensemble methods were optimized in 70% of references. To the authors' knowledge, NGBoost from the set of ensemble methods was not used to estimate construction project duration and delays. NGBoost is an effective method for considering uncertainties in irregular projects and is often implemented in developing countries. Furthermore, AD estimation models do fail to incorporate RQ and RL from the World Bank's worldwide governance indicators (WGI) as risk-based inputs. In addition, the various WGI, EVM and inflation variables are not combined with substantial degrees of delay institutional risks as inputs. Consequently, due to the existence of critical and complex risks in different countries, it is vital to consider legal and institutional factors. This is especially recommended if an in-depth, accurate and reality-based method like SHAP is used for analysis.

Delays in utility shifting during road construction have broad ramifications. These delays not only lengthen the project's timeline but also raise expenses and cause problems with resource allocation. Thus, this study investigates the influence of delay in utility shifting for extension of time claims in road construction projects (RCPs) in Sri Lanka.

The study used a quantitative approach with three rounds of Delphi surveys to gather empirical data. Further, the probability impact assessment was used to carefully analyse the data and appraise the information gathered.

The findings initially revealed 33 causes of delays in utility shifting for extension of time claims in RCPs in Sri Lanka. Ultimately, 11 severe causes were identified based on their high probability and impact, concluding with 45 strategies that were assigned to overcoming those most severe causes of delay.

This study will contribute to the industry and theory by providing solutions to handle utility-shifting delays with the linkage of preventing time extension claims for RCPs in Sri Lanka. Further, there is a dearth of literature in the research area, both locally and globally. Thus, the findings of this research will provide a benchmark for further detailed studies in other countries as well.

This paper aims to present the result of an assessment of the potential effect of building information modelling (BIM) adoption on the reduction of construction waste (CW) from a developing country's perspective. This was done with a view to reducing the waste generated in construction projects particularly at the design and pre-contract stages.

The study adopted a post-positivism philosophical approach, which informed the use of a quantitative research design and a questionnaire as instrument for data collection. The data gathered from construction professionals in the Nigeria construction industry were analysed using an array of statistical tools such as frequency, percentage, Kruskal–Wallis H-test, Kendall's coefficient of concordance, chi-square and exploratory factors analysis.

The study revealed five major groups of factors causing CW at the design and pre-contract stages that can be avoided or minimised through BIM implementation. These are; (1) errors in design and documentation, (2) specification and quality factors, (3) estimating and site condition factors, (4) planning of work factors and (5) procurement related factors.

The findings of the study offer practical insight for industry participants on the need for BIM implementation to reduce CW by identifying the diverse areas responsible for these waste generation.

While there has been significant literature on BIM implementation, contributions on the effect of this technology in reducing waste generation particular at the design and pre-contract stages in developing countries has been almost non-existent. This study strives to fill in this gap by showcasing the major waste generating activities that can be avoided through the use of BIM.

The phenomenon of delayed payment to contractors, particularly in the construction industry, is a vital one and has implications for the health of economies of both developing and developed countries. However, the knowledge of this phenomenon seems patchy and scattered. This paper aims to provide a comprehensive overview of the knowledge on the subject matter with directions for future research.

A systematic literature review coupled with a scientometric analysis was used to identify the main strands of delayed payment to contractor research as a basis for qualitative analysis and directions for future investigations.

Current trends of delayed payment to contractor research are categorised into five broad themes, namely: causes, effects, mitigation measures, ethical and law and regulatory issues. On the basis of these themes, directions for future research are proffered.

To the best of the authors knowledge, this is the first attempt at providing a comprehensive and an integrated knowledge on delayed payment to contractor research with pointers for further investigation and policy directions.

The increasing complexity of civil engineering projects necessitates focusing on new competencies of project participants. Based on the research on team performance and design processes that are more closely linked to the relevance of the project context, it is hypothesised that empathic abilities could play an important role in the performance of civil engineering projects. Therefore, this study aims to investigate whether performance can be improved by focusing on empathic abilities during the integrated design phase.

Semi-structured in-depth interviews with experts were conducted to explore the relevance of empathic abilities and their interaction with performance in a real-life infrastructure project. The project team’s empathy level was measured by means of a survey using Davis’ Interpersonal Reactivity Index method. Finally, differences between expected and measured levels of empathy were analysed.

The results provide insights into how empathic abilities interact with performance. The measurement indicates that, on average, professionals in the civil engineering industry score relatively low on empathy. In addition, differences were identified between the expected distribution and the measured empathy levels of the team, implying a potential for improvement, in particular by increasing the empathic abilities of the project management and increasing gender diversity.

To the best of the authors’ knowledge, this study is the first to investigate a relationship between empathy and the performance of civil engineering projects. The results provide initial insights into the empathic ability of civil engineering project teams and the potential of empathy to improve performance. Furthermore, from an empathy perspective, this study advocates increasing the gender diversity of project teams to improve performance.

A rise in productivity is associated with higher profits, competitiveness and the sustainability of an industry and a nation. Recent studies highlight inadequate labour supervision and training facilities as the main causes of productivity-related challenges among construction enterprises. This study aims to evaluate the construction supervisors' capabilities in applying the required elements of work practices for enhancing the performance and productivity of construction operations using a case study.

A new construction supervisory training programme was developed through comprehensive sequential processes, and 64 construction supervisors underwent training . Marking guides with different levels of descriptions/standards were developed through consultations with experts and literature reviews, and the supervisors' capabilities were assessed under 64 competency elements of 12 competency units.

The findings show a clear cross-section of all the required competencies of construction supervisors with various levels of standards/descriptions, leading to a new generalised guideline that helps to comprehend what degrees of skills can be taken into account in supervision attributes. Statistical tests and expert reviews were used to ensure the generalisability of the research applications and the reliability of the results.

Despite the study findings being limited to the Sri Lankan construction industry, its applicability could create considerable impacts on the current/future practices of the construction sector in developing countries as well as other developing industries.

The study adds new characteristics and values to construction supervision practices that can be remarkable in encouraging construction supervision to drive the sustainability of construction practices. The study findings are significant in decision-making/planning procedures related to technical comprehension, industry training, scientific documentation, adherence to workforce employment constraints and job outputs. This paper describes the further extensive implications and future scopes of the study elaborately.

This study addresses the knowledge gap in the industry related to the development of protocols and application methodologies necessary to track their performance. The study opens a new window that inflows knowledge attributes to the industry sector along with the necessary comparison of the relevant competency elements to predict/comprehend what levels of capabilities can be theoretically considered and practically applied in supervision characteristics.

Studies highlight that poor labour supervision and inadequate labour training facilities are the primary factors that result in labour skill shortages and productivity-related challenges among construction firms. This study aims to assess the construction supervisors’ abilities in providing work-based training elements and evaluating labour skills in construction.

A construction supervisory training programme was newly designed with a set of labour training exercises using comprehensive approaches. A total of 64 construction supervisors were trained to deliver the labour training components for more than 250 labourers working on 23 construction projects in Sri Lanka. The supervisors’ competencies were assessed using a detailed marking guide developed through expert discussions and literature reviews.

The results show the detailed cross-section of a wide range of competencies of the construction supervisors in providing labour training elements with the levels of standards/descriptions. The generalisability of the study applications and the reliability of the results were ensured using statistical tests and expert reviews. The findings further describe the impacts of the well-improved competencies of construction supervisors on labour working patterns and work outputs.

Though the study findings were limited to the Sri Lankan construction sector, the study applications can have a considerable impact on the current/future practices of the construction sector in developing countries as well as other developing industries.

The study outcomes may contribute to a rapid increase in the number of construction supervisors becoming certified assessors of National Vocational Qualifications up to certain levels. This paper describes the further extensive implications and future scopes of the study elaborately.

The study adds new characteristics and values to construction supervision practices that can be remarkable in achieving higher levels of performance and productivity in labour operations. Importantly, the study contributes to adorning the job role of construction supervisors with the title of “labour training expert”.