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It is widely accepted that one criterion for determining if a construction project is successful is whether it is completed within the expected budget. There have been advancements in the management of building projects throughout time but cost overruns remain a key concern in the construction sector internationally, particularly in emerging economies such as Ghana. This study aims to answer the question, “What are the critical success factors (CSFs) that can assist reduce cost overruns in public sector infrastructure projects in the Ghanaian construction industry?”

This study used a quantitative survey method. The questionnaire was pre-tested by interviewing 15 contractors to ascertain the validity of the content. Factor analysis and multiple regression were adopted to analyze the data.

This study discovered that the critical factors that can reduce cost overruns in construction projects in Ghana are directly linked to five themes: early contractor involvement in the project planning stage, adequate funding, good project team relations, competent managers/supervisors and project participant incentives/bonuses. This study identifies indestructible, empirically measurable important success criteria for reducing cost overruns in public building projects in Ghana.

When well thought through from the project initiation stage to completion, these critical successes can also be used to deal with damaging economic effects such as allocative inefficiency of scarce resources, further delays, contractual disputes, claims and litigation, project failure and total abandonment.

The uniqueness of this research resides in the fact that it is, to the best of the authors’ knowledge, a first-of-its-kind investigation of the CSFs for reducing cost overruns in public building projects in developing countries.

The current estimation practice in construction projects greatly needs upgrading, as there has been no improvement in the cost overrun issue over the past 70 years. The purpose of this research was to develop a new multiple regression analysis (MRA)-based model to forecast the final cost of road projects at the pre-design stage using data from 43 projects in New Zealand (NZ).

The research used the case study of 43 completed road projects in NZ. Document analysis was conducted to collect data, and statistical tests were used for model development and analysis.

Eight models were developed, and all models achieved the required F statistics and met the regression assumptions. The models’ mean absolute percentage error (MAPE) was between 21.25% and 22.77%. The model with the lowest MAPE comprised the road length and width, number of bridges, pavement area, cut and fill area, preliminary cost and cost indices change.

The model is based on road projects in NZ. However, it was designed to be able to adapt to other contexts. The findings suggest that the model can be used to improve traditional conceptual estimating methods. Past project data is often stored by the project team but rarely used for analysing and forecasting purposes. This research emphasises that past data can be effectively used to predict the project cost at the pre-design stage with limited information.

No research was conducted to adopt cost modelling techniques into the conceptual estimation practice in the NZ construction industry.

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 Government’s investment in infrastructure projects is considerably high, especially in bridge construction projects. Government authorities must establish an initial forecasted budget to have transparency in transactions. Early cost estimating is challenging for Quantity Surveyors due to incomplete project details at the initial stage and the unavailability of standard cost estimating techniques for bridge projects. To mitigate the difficulties in the traditional preliminary cost estimating methods, there is a requirement to develop a new initial cost estimating model which is accurate, user friendly and straightforward. The research was carried out in Sri Lanka, and this paper aims to develop the artificial neural network (ANN) model for an early cost estimate of concrete bridge systems.

The construction cost data of 30 concrete bridge projects which are in Sri Lanka constructed within the past ten years were trained and tested to develop an ANN cost model. Backpropagation technique was used to identify the number of hidden layers, iteration and momentum for optimum neural network architectures.

An ANN cost model was developed, furnishing the best result since it succeeded with around 90% validation accuracy. It created a cost estimation model for the public sector as an accurate, heuristic, flexible and efficient technique.

The research contributes to the current body of knowledge by providing the most accurate early-stage cost estimate for the concrete bridge systems in Sri Lanka. In addition, the research findings would be helpful for stakeholders and policymakers to propose policy recommendations that positively influence the prediction of the most accurate cost estimate for concrete bridge construction projects in Sri Lanka and other developing countries.

The building sector is vital to a nation’s economy, as it has a major influence on economic activity and growth, job creations and the advancement of infrastructure. Intricate challenges that are inherent in crises such as the COVID-19 outbreak lead to material scarcities, project delays, labor shortages, escalated expenses, funding challenges, regulatory obstacles and dwindling investment funds, all of which culminate in costs that are in excess of those budgeted. While numerous studies have explored the ramifications of COVID-19 on project budgets, there is little, if any, data available on forecasting the magnitude of this impact.

This investigation seeks to bridge this knowledge deficiency by devising a predictive tool grounded in an ordinal logistic regression method. An online survey was designed and disseminated to gauge the views of construction field experts about the diverse contributors to excessive costs during the viral outbreak, and a predictive tool, crafted from the survey participants’ feedback.

Findings showed that smaller-scale enterprises and contractor-centric establishments faced greater adversities than medium-to-large ones and consultancy-or-owner-type entities.

The insights from this research shed light on the amplified risk of higher project costs amid health crises or analogous events, underlining the imperative need for fortified risk management approaches to bolster project outcomes. By factoring in demographics, this research offers policymakers a refined lens through which to customize interventions and promote balanced and enduring advancement in the construction industry.

Urban rail projects are typically large-scale transport infrastructure projects (megaprojects) which have many potential risks that can influence the strategic goals of owners. However, there is a paucity of studies which explore the impact of risks on both “urban rail” project time and cost together considering quantitative assessments. Therefore, this paper focuses on investigating critical risks and quantifying such risk impacts on urban railway project schedule and cost in practice.

A combination of qualitative and quantitative research methods comprising semi-interviews with five experts and a questionnaire survey of 132 professional respondents is used. The data were modeled using Monte Carlo Simulation to predict the probability of project schedule and cost.

The results show that 30 risk variables are categorized into seven main groups which have significant impacts on both project time and cost. Outstanding five risk variables were highlighted as follows: (1) project site clearance and land compensation; (2) design changes; (3) physical project resources; (4) contractors’ competencies and (5) project finance. Such findings were supported by Monte Carlo simulation which predicted in the worst case that the project may suffer 11.03 months’ delays and have cost overrun with a contingency of US$287.68 million.

This study expands our knowledge about time and cost contingency of urban metro railway implementation across developing economies and particularly within the context of Vietnam. Policymakers will not only gain an understanding about risk structure but will also recognize the significant impacts of critical risk through risk impact modeling and simulation. Such an approach provides insights into risk treatment priorities for planners so that they can proactively establish suitable strategies for risk mitigation in practice.

An increase in outsourcing of consultancy services has been observed during procurement and delivery of public-sector infrastructure projects. However, the incidence of project failure has continued unabated despite this shift by public-sector entities. Also, there appears to be limited literature focussed on seeking to provide the rationale governing the decision to outsource or insource consultancy services by public-sector organisations. The purpose of this study was to appraise the performance of public-sector projects in which consultancy services have been outsourced or insourced. These are the gaps which this study was undertaken to fill.

A grounded theory methodology (GTM) research design was adopted based on the nature of evidence sought and gathered from a Provincial Department of Public Works and Infrastructure (PDPWI) in South Africa. Data was obtained from a mixture of semi-structured interviews and project-specific documents spanning a five-year period and was analysed according to the procedures associated with GTM. Accordingly, open coding, axial coding and pattern matching were carried out at several intervals to develop categories and themes.

The findings of the study showed the absence of a structured approach within the PDPWI for facilitating decisions pertaining to outsourcing or insourcing consultancy services within construction projects. Furthermore, the study established that both approaches yielded similar results across all performance facets of cost, time and quality. In addition, a detailed insight into the steps required for the successful application of GTM in built environment research has been provided in the study.

Limited studies have been undertaken to compare the impact of either outsourced or insourced services on the organisational and project performance. This was the gap to which the study reported in this paper was undertaken to contribute.

The study aims to explore the role of procurement of good governance (PGG) on value for money (VfM) achievement in public construction projects. By investigating various dimensions of PGG, including transparency, accountability, competition and integrity, this study provides insights into how these factors contribute to the successful achievement of VfM outcomes in public construction projects.

The data were sourced from 203 construction project practitioners from 24 selected procuring entities in Tanzania using a census approach and a cross-sectional questionnaire survey. Confirmatory factor analysis (CFA) and structural equation modelling (SEM) were used for data analysis.

The findings of the study revealed a positive and significant impact of various dimensions of PGG on VfM. Specifically, transparency, accountability, competition and integrity were positively and significantly related to VfM, with p-values <0.001. Based on the study findings, we conclude that PGG is an important predictor of VfM achievement in public construction projects.

The study offers policy implications for streamlined PGG and VfM achievement in public construction projects. It is suggested that procuring entities can enhance VfM by enforcing compliance with the principles of PGG throughout the entirety of construction projects. In this case, streamlined legislative frameworks and control mechanisms are crucial components that could enhance PGG and the achievement of VfM.

This study contributes to the academic literature on the strategic role of PGG in enhancing VfM achievement. This is one of the research domains, which has not been adequately researched, particularly in Tanzania’s context. In addition, the study provides valuable insights to policymakers, practitioners and stakeholders involved in public construction projects to improve project outcomes and resource allocation.

Completing Right-of-Way (ROW) acquisition process on schedule is critical to avoid delays and cost overruns on transportation projects. However, transportation agencies face challenges in accurately forecasting ROW acquisition timelines in the early stage of projects due to complex nature of acquisition process and limited design information. There is a need of improving accuracy of estimating ROW acquisition duration during the early phase of project development and quantitatively identifying risk factors affecting the duration.

The quantitative research methodology used to develop the forecasting model includes an ensemble algorithm based on decision tree and adaptive boosting techniques. A dataset of Georgia Department of Transportation projects held from 2010 to 2019 is utilized to demonstrate building the forecasting model. Furthermore, sensitivity analysis is performed to identify critical drivers of ROW acquisition durations.

The forecasting model developed in this research achieves a high accuracy to predict ROW durations by explaining 74% of the variance in ROW acquisition durations using project features, which is outperforming single regression tree, multiple linear regression and support vector machine. Moreover, number of parcels, average cost estimation per parcel, length of projects, number of condemnations, number of relocations and type of work are found to be influential factors as drivers of ROW acquisition duration.

This research contributes to the state of knowledge in estimating ROW acquisition timeline through (1) developing a novel machine learning model to accurately estimate ROW acquisition timelines, and (2) identifying drivers (i.e. risk factors) of ROW acquisition durations. The findings of this research will provide transportation agencies with insights on how to improve practices in scheduling ROW acquisition process.

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.