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Incipient detection of pavement deterioration (such as crack identification) is critical to optimizing road maintenance because it enables preventative steps to be implemented to mitigate damage and possible failure. Traditional visual inspection has been largely superseded by semi-automatic/automatic procedures given significant advancements in image processing. Therefore, there is a need to develop automated tools to detect and classify cracks.

The literature review is employed to evaluate existing attempts to use Hough transform algorithm and highlight issues that should be improved. Then, developing a simple low-cost crack detection method based on the Hough transform algorithm for pavement crack detection and classification.

Analysis results reveal that model accuracy reaches 92.14% for vertical cracks, 93.03% for diagonal cracks and 95.61% for horizontal cracks. The time lapse for detecting the crack type for one image is circa 0.98 s for vertical cracks, 0.79 s for horizontal cracks and 0.83 s for diagonal cracks. Ensuing discourse serves to illustrate the inherent potential of a simple low-cost image processing method in automated pavement crack detection. Moreover, this method provides direct guidance for long-term pavement optimal maintenance decisions.

The outcome of this research can help highway agencies to detect and classify cracks accurately for a very long highway without a need for manual inspection, which can significantly minimize cost.

Hough transform algorithm was tested in terms of detect and classify a large dataset of highway images, and the accuracy reaches 92.14%, which can be considered as a very accurate percentage regarding automated cracks and distresses classification.

This paper is a response to the failure of construction firms to use sufficient attention to their working capital management (WCM) practices, resulting in operational challenges, and leading to the collapse of firms in most developing countries. Hence, this study aims to explore the empirical perspective of WCM practices among large building construction firms (LBCFs) in Ghana, to help achieve the Sustainable Development Goal 9.

The study collected primary data through structured survey questionnaires from LBCFs in Ghana. The CEOs/Directors, General Managers and Accountant/Finance of LBCFs in Ghana formed the unit of analysis based on a simple random sampling technique. Mean score, standard deviation and one-sample t-test were used to perform the empirical analysis of the study.

According to this study's empirical results, LBCFs appear to have effective WCM practices in place. This was evidenced in the surveyed responses which indicate that the sector’s WCM practices sound good based on the mean scores and statistically significant as the t-values > 1.664. Notably, LBCFs in Ghana pay their suppliers early to reduce the fear of adverse effect of late payments on their credit history, making them conservative in their approach toward financial management.

This is a pioneering paper in a developing country like Ghana, highlighting the significance of gaining an in-depth understanding of WCM practices among LBCFs. The findings of this study are expected to provide valuable information to industry players toward ensuring WCM efficiencies and can serve as a solid foundation for further empirical studies.

In recent times, both academics and industrialists have undertaken research into various areas of circular business models (CBM) in a bid to promote a green economy. Yet despite numerous studies conducted, the ensuing discourse contains scant information regarding the contributions of CBM towards the transition of green economy in the construction industry. This present study therefore aims to explore the contributions of CBM in the transition towards a green economy in the Ghanaian construction industry.

A comprehensive literature review was first conducted to identify the contributions of CBM towards the transition towards a green economy. A quantitative research strategy was then adopted to collect primary questionnaire data from professionals with knowledge of CBM and the green economy from 104 participants for the study. The data gathered was analyzed using descriptive statistics and exploratory factor analysis viz. Principal component analysis.

The contributions of CBM towards the transition towards a green economy were found to be: value contributions (i.e. lower carbon footprint, lower emission of waste by the industry, value creation for clients, innovation in construction materials and methods, reduced maintenance cost, creation of energy efficient infrastructures, improved value proposition for firms, improved sustainability of the industry and reduced pressure on finite resource.); green contributions (i.e. recycling and reuse of construction waste, promotion of green building technology, increased potential for economic growth, increased resource efficiency and creation of green building market) and longevity contribution (i.e. increased life span of buildings). It was evident that CBM make significant contributions in the transition towards green economy and as such, policymakers and other stakeholders within the construction industry must adopt these models to maximize their green credentials and accrue inherent benefits associated with transitioning towards a green economy.

This paper presents a novel and comprehensive study that explores the contributions of CBM towards engendering a green economy. The study’s results provide construction industry stakeholders and policymakers with clear insight into the contributions of CBM towards the transition into a green economy. In practice, this study provides much needed guidance to support construction practitioners to transition towards a green economy in alignment with the United Nations' Sustainable Development Goals (SDGs).

The purpose of this paper was to first identify and then model the impact of critical success factors (CSFs) of public–private partnership (PPP) power projects.

Review of empirical literature came out with 20 CSFs which were ranked by experts and industry practitioners through a two-round Delphi questionnaire survey.

These CSFs were grouped into CSF groups (CSFGs) using component analysis, and they served as the input variables for fuzzy analysis. The six components were collaboration and transparency, guarantee and permits, socio-political support, expected profitability, technical feasibility and risk allocation (RA). Overall success index of PPP power projects in Ghana was 5.966 and showed there is high impact of CSFGs on project success. Fuzzy analysis also confirmed RA as the most significant CSFG.

The model developed can serve as a multi-dimension CSF framework that can be used as a success attainment tool for PPP power projects. For policy developers and stakeholders, the model serves as a pointer to issues which the government/public sector must focus on to attract huge investments from the private sector in the power sector.

In comparison to other countries, power generation in Sub-Sahara Africa is poor. Public–private partnership (PPP) model has become increasingly popular for addressing infrastructural challenges, especially in the power sector. The purpose of this study is to evaluate and classify the factors that influence public sector involvement in Ghanaian PPP power projects.

Using purposive and snowball sampling techniques, questionnaires were used to gather responses from experts in the PPP power sector in a two round Delphi survey. Analytical tools adopted were descriptive statistics, mean score ranking, Cronbach’s alpha and factor analysis.

The most significant factors that influence public sector involvement in PPP power projects were: achieving improved value for money; access to additional capital; increased certainty of projects; greater efficiency of project delivery services; and improved ability to deliver new infrastructure. From factor analysis, the four components were: value for money and efficiency; capital and skills; innovation and technology; and project delivery.

Government bodies in the power sector will benefit from the findings, as it would aid them develop policies that would strengthen regulatory structures as well as institutions.

This study provides a comprehensive analysis of the transition from Building Information Modelling (BIM) to digital twins (DT) in the construction industry. Specifically, the research explores the current state (themes and trends) and future directions of this emerging research domain.

A multi-stage approach was employed that combines scientometric and systematic review approaches. The scientometric analysis involves quantitative assessment of scientific publications retrieved from the Web of Science database – using software tools like VOSviewer and HistCite. The systematic review involved a rigorous synthesis and evaluation of the existing literature to identify research gaps, themes, clusters and future directions. Clusters obtained from the scientometric analysis of the co-occurrence network were then used as a subject base for a systematic study.

Emergent findings reveal a rapidly growing interest in BIM-DT integration, with over 90% of publications since 2020. The United Kingdom, China and Italy are the leading contributing countries. Five prominent research clusters identified are: (1) Construction 4.0 technologies; (2) smart cities and urban environments; (3) heritage BIM and laser scanning; (4) asset and facility management; and (5) energy and sustainability. The study highlights the potential of BIM-DT integration for enhancing project delivery, asset management and sustainability practices in the built environment. Moreover, the project’s life cycle operation phase has garnered the most attention from researchers in this field compared to other phases.

This unique study is comprehensive in its approach by combining scientometric and systematic methods to provide a quantitative and qualitative evaluation of the BIM-DT research landscape. Unlike previous reviews that focused solely on facility management, this study’s scope covers the entire construction sector. By identifying research gaps, challenges and future directions, this study establishes a solid foundation for researchers exploring this emerging field and envisions the future landscape of BIM-DT integration in the built environment.

This study aims to identify the critical success factors of public private partnership (PPP) power projects in Ghana and further evaluates the most significant critical success factors (CSFs) influencing both the public and private sector participation in PPP power projects.

Ranking-type Delphi survey in two rounds was conducted to establish a comprehensive list of critical success factors of PPP power projects. Using purposive and snowball sampling techniques, experts were targeted for the Delphi survey. Mean score ranking, Cronbach’s alpha coefficient and Kendall’s concordance were used for analysis.

From the list of 37 critical success factors, 9 CSFs were deemed to be extremely significant. The five topmost CSFs were as follows: shared authority, trust and communication between public and private sectors; necessity of power project; debt guarantee to enable private partner to raise funds from the local or international financial markets; appropriate risk allocation and risk sharing; and thorough and realistic assessment of cost, projections and benefits.

The CSFs identified and prioritized in this study have the propensity to trigger policy development towards the PPP power sector in Ghana and developing countries that shares similar context. This is because the study has wide implications for financing, politics, procurement, regulations, legal and capacity building.

This study aims to investigate the effects of moral hazard on quality and satisfaction of public–private–partnership (PPP) construction projects in Ghana.

After undertaking a literature review, questionnaires were used to elicit responses from respondents. Population consisted of quantity surveyors, project managers, procurement officers, consultants, public agency officers involved in PPP projects, private partners and contractors. A total of 211 questionnaires were received from 250 distributed. Purposive and snowballing sampling techniques were adopted. Analytical tools were Cronbach’s alpha for testing reliability, regression, mean score ranking and relative importance index.

Reduced mutual trust and respect, poor clarity of project objectives; consequence on decision-making; less effective construction process; and increased construction risks were the significant effects of moral hazard on satisfaction of PPP construction projects. Value-based effects; manufacturer-based effects; product-based effects; user-based effects; and transcendent-based effects were the significant effects of moral hazard on quality of PPP construction projects.

Construction stakeholders involved in delivering PPP projects ought to take note of the findings and recommendations arising. Further studies should explore the effects on other project performance indicators apart from satisfaction and quality.

This paper extends knowledge in the area of exploring the effects of moral hazard on PPP project satisfaction and quality. The findings are beneficial to both academia and industry practitioners.

In recent decades, interest in digital transformation (DX) within the architecture, engineering, and construction (AEC) industry has significantly increased. Despite the existence of several literature reviews on DX research, there remains a notable lack of systematic quantitative and visual investigations into the structure and evolution of this field. This study aims to address this gap by uncovering the current state, key topics, keywords, and emerging areas in DX research specific to the AEC sector.

Employing a holistic review approach, this study undertook a thorough and systematic analysis of the literature concerning DX in the AEC industry. Utilizing a bibliometric analysis, 3,656 papers were retrieved from the Web of Science spanning the years 1990–2023. A scientometric analysis was then applied to these publications to discern patterns in publication years, geographical distribution, journals, authors, citations, and keywords.

The findings identify China, the USA, and England as the leading contributors in the field of DX in AEC sector. Prominent keywords include “building information modeling”, “design”, “system”, “framework”, “adoption”, “model”, “safety”, “internet of things”, and “innovation”. Emerging areas of interest are “deep learning”, “embodied energy”, and “machine learning”. A cluster analysis of keywords reveals key research themes such as “deep learning”, “smart buildings”, “virtual reality”, “augmented reality”, “smart contracts”, “sustainable development”, “building information modeling”, “big data”, and “3D printing”.

This study is among the earliest to provide a comprehensive scientometric mapping of the DX field. The findings presented here have significant implications for both industry practitioners and the scientific community, offering a thorough overview of the current state, prominent keywords, topics, and emerging areas within DX in the AEC industry. Additionally, this research serves as an invaluable reference and guideline for scholars interested in this subject.

Firms in the architectural, engineering, construction and operations (AECO) sector continue undertaking projects in a disruptive environment due to the coronavirus disease 2019 (COVID-19) pandemic. This study aims to explore environmental turbulence in the AECO project delivery space and suggest mechanisms for enhancing resilience against future pandemics.

The study adopts the quantitative approach by administering 110 survey questionnaires to participants comprising project managers, site engineers, quantity surveyors, contractors and subcontractors.

The study identifies 24 COVID-19 disruptions linked to environmental turbulence categorised as scheduling, performance and productivity, project budget, supply chain, resource allocation and technological and regulatory. The study suggested resilient mechanisms for surviving in future pandemics.

This study enhances the understanding of environmental turbulence from the perspective of COVID-19 disruptions in AECO project delivery, while the implementation of the resilient mechanisms improves capability of AECO firms against future pandemics.