White concrete falls under the category of archi-tectural concrete and stands out for its aesthetic appear-ance and sustainability. This study presents experimental research on 15 white concrete mixtures, evaluating their performance in maritime environments. The experimen-tal research examined the effects of cement type, the com-position and dosage of metakaolin, and the addition of titanium dioxide on the properties of white concrete. Tests were carried out to evaluate the properties of fresh con-crete, as well as the mechanical and durability properties of the hardened concrete. Particular emphasis was placed on the chloride diffusion coefficient, a key parameter for concrete durability in marine conditions. The findings from this study were implemented in the construction of two coastal structures - a hotel and a beach promenade. Due to its high-quality finish, properties, sustainability, and cost-effectiveness compared to natural stone, white concrete has demonstrated significant potential for use in maritime environments.
Uncertainty in construction project management (PM) involves the perceived unpredictability of disruptions that influence project duration, costs and resource availability. This issue is particularly pronounced for micro, small and medium-sized enterprises (MSMEs), especially in regions lacking strong institutional support, digital infrastructure and facing environmental and logistical volatility. This study investigates the internal and external sources of perceived uncertainty among MSMEs in southwestern Colombia. Data from surveys of 25 construction firms were analysed to assess how frequently uncertainties occur, their magnitude, and signalling across 10 domains, both internal and external. Using bootstrapped random forest (RF) models, the most impactful features associated with higher perceived uncertainty were identified. These were complemented by classification trees (CTs) to generate interpretable decision rules. To cope with small sample sizes, a class-preserving data augmentation strategy was validated through Mann-Whitney U Tests. Results indicated that internal sources, such as organisational dynamics and resource estimation, are strongly linked to operational maturity and diversification strategies. External uncertainties, such as logistics, weather and sociopolitical factors, vary notably across different regions. Interestingly, 82.9% of firms with over 29 months of experience followed the most common path for higher perceived market uncertainty, suggesting that experience influences perception. Moreover, high uncertainty was predicted in several domains even without typical signals, implying latent variables, possibly undetected by surveys but captured by models, may be affecting perception. This research offers a practical, data-driven framework employing interpretable machine learning to model uncertainty perception in MSMEs, providing tools for early warning and better decision-making in resource-constrained contexts.
The Fourth Industrial Revolution (4IR) has transformed numerous sectors, yet the construction industry lags in adopting technologies such as artificial intelligence (AI), robotics and Internet of things (IoT). This study explores how leadership styles can drive the integration of 4IR technologies in construction, aiming to identify approaches that foster innovation and technological advancement. Using principal component analysis (PCA), the research examines data collected via a structured questionnaire from built environment professionals in Gauteng Province, South Africa. The data were analysed with Statistical Package for Social Sciences v29 to uncover key leadership patterns. The findings highlight four pivotal leadership styles: transformational, transactional, charismatic and autocratic, as essential for 4IR technology adoption. Transformational leadership encourages innovation and empowerment, transactional leadership ensures procedural compliance, charismatic leadership inspires during uncertainty and autocratic leadership offers decisive guidance. Based on these insights, a four-dimensional leadership framework was developed to assist stakeholders in balancing these styles for effective 4IR integration. The study underscores the importance of leadership training and adaptability to accelerate technological uptake and support industry progress. Despite the limitations emanating from this study, it contributes to practice by providing a practical four-dimensional leadership framework for construction stakeholders to enhance 4IR technology adoption, improving efficiency and innovation. It also provides a roadmap for further research to validate the framework and expand across different industries and regions.
Delays in transportation projects are frequently miscontrolled by contractors submitting unrealistic and inaccurate baseline schedules. Poorly developed schedules can lead to significant project overruns, making it essential for State Highway Agencies (SHAs) to evaluate and approve high-quality schedules. Despite the availability of scheduling guidelines, many SHAs lack effective evaluation processes, resulting in poor project control. This study aims to evaluate the effectiveness of industry-recognised metrics (IRMs) and the Government Accountability Office (GAO) best practices (BPs) in assessing the quality of baseline schedules in transportation projects. The study also seeks to provide practical recommendations for improving the schedule baseline review process. Baseline schedules and daily work reports (DWRs) were collected from four transportation projects managed by one SHA of the United States. The as-built schedules were developed using the DWR data, and the baseline schedules were compared to the as-built schedules using the as-planned vs. as-built delay analysis method. In addition, the baseline schedules were evaluated using IRMs and GAO BPs. The findings revealed that 24% and 41% of the performance metric thresholds were not met when evaluated using IRMs, and only 14%-71% of GAO BPs were satisfied across the projects. The analysis identified significant deficiencies in task descriptions, work breakdown structure (WBS) element identifiers, relationships between activities and float management. The results highlight flaws in current scheduling practices and emphasise the need for SHAs to adopt more robust methods for evaluating baseline schedules. This study provides actionable recommendations to enhance the review and approval processes, ultimately contributing to the timely and efficient completion of transportation projects.
Aligning with industry trends towards digitalisation, sustainability, and the use of high-strength materials, this study aims to enhance the operational efficiency and environmental performance of high-strength rebar (HSS) construction. This study aims to address critical challenges in the construction industry, such as the need for digital integration, sustainable practices, and efficient use of high-strength materials by developing a comprehensive, data-driven framework for digital transformation focussed on optimising HSS operations. The core objective of this framework is to optimise HSS management by providing real-time, actionable insights. The study employs a closed-loop, hybrid methodology that integrates remote sensing for data capture from construction sites, utility theory to model complex decision-making under uncertainty across quality, safety, and environmental factors, and a data-driven optimisation model. By applying this utility-driven approach in a case study, the research demonstrates significant improvements, including a 15% increase in schedule adherence, a 3% reduction in material waste (MW), and measurable gains in operational efficiency, safety, and resource optimisation. Ultimately, this research provides a practical, utility-driven approach that offers clear metrics for progress tracking and resource optimisation, representing a significant advancement in lean and digital construction management.
This study investigates the interactions among leadership capability (LCAP), organisation capability (OCAP), emphasising the mediating role of digital mindset (DIND) and the moderating effect of digital technology capability (DCAP) in the Malaysian construction industry. Data from 341 managerial leaders, analysed through confirmatory composite analysis and the bootstrap method, indicate that a strong LCAP significantly enhances the leadership styles. The DIND mediates the relationship between LCAP and OCAP, confirming that enhanced digital leadership capability (DLCAP) is crucial for transforming OCAP towards effective organisational innovation. Furthermore, DCAP negatively moderates the relationship between leadership and organisational capability. The current lack of automation demands more effective management of organizational challenges, highlighting a key area for future technological advancement. These findings extend the discourse on digital literacy by offering practical insights for construction stakeholders striving to incorporate the Construction Industry Competency Standard (CICS) through advancements in digital capabilities. Moreover, provides an understanding of DLCAP on the synergy between digital technology, leadership styles and organisational innovation, offering valuable perspectives for policymakers and construction leaders aiming to foster continuous digital transformation within the construction industry.
In recent years, automation in construction scheduling has gained popularity due to advancements in digital construction, yet it has not achieved widespread adoption. Significant challenges remain in developing adaptive schedules that effectively manage unforeseen events and construction delays. This study addresses a critical research gap by evaluating the automation levels of individual construction planning processes, an area previously underexplored. Employing a systematic literature review, this study investigates the state of the art in automated, dynamic and adaptive scheduling techniques. The review examined proposed planning procedures, assessing the extent of automation in key aspects of construction scheduling, including task sequencing, resource allocation and task duration estimation, with a focus on building information modelling (BIM) integration. The analysis reveals limited adoption of automated scheduling, BIM technologies and adaptive scheduling methods. Future research should explore advanced automation approaches, enhance BIM integration and develop adaptive scheduling solutions to improve efficiency and responsiveness in construction management.
This study presents a systematic review on the effect of superplasticiser additives on the properties of fresh and hardened concrete. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2000 guidelines were followed for the collection and analysis of experimental studies obtained from databases such as Scopus, Science-Direct and Web of Science (WoS), and evaluates the published literature (2014-2025) on the influence of different superplasticiser types and doses - including polycarboxylate ethers, naphthalene and lignosulphonates - across both self-compacting and conventional high-performance concretes (HPC). The results show that the incorporation of these additives enhances workability, compressive strength and durability, especially when combined with mineral additions such as silica fume and metakaolin. Optimised dosing of superplasticisers can increase compressive strength by 20%-40% and significantly reduce water-to-cement ratios without loss of flowability. It is concluded that the use of PCE superplasticisers has driven the development of HPC, optimising their application in the construction industry However, limitations include insufficient statistical comparison across additive types, and heterogeneity in reporting performance metrics. Recommendations are made for standardised reporting, deeper exploration of environmental impacts and long-term durability assessment.
The scientific and practitioner literature suggests that building large and complex nuclear reactors is frequently associated with major cost increases that undermine project completion and discourage investors. Small modular reactors (SMRs) target a distinct market segment by shifting from traditional economies of scale to economies achieved through multiple units, typically up to 300 MWe. However, modularisation, design simplification and co-siting economies-key SMR features-are often insufficiently represented by conventional topdown cost estimation models. These models are generally calibrated on large pressurised water reactors (PWRs) and tend to overestimate SMR costs by emphasising the loss oi economies of scale. To address this limitation, this paper introduces a bottom-up cost estimation approach that explicitly incorporates SMR-specific design and construction characteristics. The method uses itemised cost equations for each cost item defined by the Energy Economic Data Base (EEDB) Code of Accounts developed by the US Department of Energy. The resulting model has an estimated accuracy of -30%/+50% and is applied to two SMR concepts: IRIS (335 MWe per unit) and NuScale (77 MWe per unit). Using a large PWR as reference (100% overnight capital cost, OCC), the Nth-of-a-kind (NOAK) twin-unit IRIS plant is estimated at 94% OCC, while a 12-module NuScale plant is estimated at 105% OCC. In contrast, topdown scaling yields 163% OCC for IRIS and 294% OCC for NuScale. The results suggest that NOAK SMRs can be cost-competitive with large NOAK PWRs when assessed through bottom-up modelling.
The problems faced by the engineering procurement construction (EPC) industry in Indonesia are related to this research, including the presence of more competitive competitors, ineffective planning and project implementation, and the lack of tools in systematic decision-making (DM). This study attempts to use big data analytic capability (BDAC) to improve the quality of DM to enhance performance and improve competitiveness, insightful and predictive information. This study also aims to close the gap between hierarchical DM and the use of BDAC to increase the chances of winning projects and reducing losses in project implementation. This study was conducted on companies engaged in the EPC industry in Indonesia under the Association of Indonesian National Design and Build Companies (AINDBC). The results of this study indicate that BDAC, as a basis for DM, has a positive influence on firm performance (FP) through the process of selecting formulation and implementation strategies and more efficient use of project budgets. The limitation is that the construct of FP cannot be explained by the formulation strategy, but it can be explained by the implementation of strategy (IS). The difference in the results of this study and previous studies is due to differences in product and market characteristics, which are strategic components of the relationship between marketing strategy and FP. The EPC company can increase speed, accuracy and precision of the strategic DM process by using BDAC. Managers or practitioners, who are pioneers in developing and implementing BDACs, are required to increase the quality of DM.
The Saudi Arabian construction industry is one of the largest in the region. Previous research suggests that blockchain technology (BCT) can enhance the process performance and management. However, studies also highlight significant barriers to BCT application in construction. Despite this, there is limited research on BCT in the Saudi construction sector. This study aims to identify the barriers and drivers in adopting BCT in the Saudi Arabian construction industry. It also assesses the level of knowledge among construction professionals and proposes a comprehensive framework for BCT implementation to enhance project management in the Saudi construction sector. A quantitative research approach was used, involving a self-reported structured questionnaire survey targeting workers and experts in the construction sector in Eastern Saudi Arabia. The main findings indicate that most participants lack sufficient knowledge of BCT. The identified barriers were ranked as human, industrial, technical, organisational and legal. Additionally, participants identified the key drivers for BCT adoption as smart contracts, transparency and traceability, innovation potential, regulatory compliance, data security, decentralisation, stakeholder engagement, cost-saving and interoperability. This study proposed a framework for implementing BCT in the Saudi construction sector. These findings are important for policy implications and pave the way for further research.
Repetitive movements and awkward postures in construction work often lead to muscle fatigue and work-related musculoskeletal disorders (WMSDs), contributing to reduced productivity and heightened occupational risks. As Industry 5.0 emphasises human-centric technologies, this study evaluates two back support exoskeleton designs - HAPO SD (springbased) and BISKO (elastic textile) - in a repetitive lifting task. Using an exploratory single-case study methodology, six trials were conducted over 18 days, with each trial including three sets of repetitive lifting using a 24 kg kettlebell under three conditions: (1) no exoskeleton, (2) HAPO SD, and (3) BISKO. Performance was measured through repetition count, time per repetition, and user experience. Results showed that the HAPO SD system achieved a 120%-162% increase in repetitions compared with no exoskeleton and outperformed BISKO by 7%-58%. Both systems demonstrated reduced time per repetition and lower standard deviations versus the control condition, indicating enhanced endurance stability. HAPO SD provided superior lifting support, while BISKO offered better ease of use and adjustability. Although the single-case design and controlled environment of this study warrant further investigation in actual construction settings and with larger sample sizes, the findings suggest that exoskeleton adoption could improve worker productivity and reduce WMSD risk.
The study examined the government's motives in adopting public-private procurement (PPP) and its relationship with the private investor's service commitment to infrastructure delivery. The study adopted a quantitative research method with a survey conducted using a structured questionnaire targeted at 384 respondents selected through random sampling. The data were analysed using mean, percentile and Pearson correlation statistical tools. The study identified 17 key motives responsible for the government adopting PPP in socio-economic infrastructure delivery. These motives were factored into three main groups: public infrastructure maintenance, financial and economic benefits; improved discipline in the contractual relationship and predictable path in infrastructure delivery and integration of innovative approach in enhancing infrastructure delivery. Therefore, the relationships between the three factored government's motives and investor's commitments, including finance, management, technical and operational services in PPP, were tested using the Pearson correlation statistical tool. The results showed that the motive for public infrastructure maintenance, financial and economic benefits of the government in PPP showed a significant relationship with the four services commitment of private partners. Also, the government's motive for improved discipline in the contractual relationship and predictable path in infrastructure delivery showed significant relationships with technical, finance and management commitments of investors. Lastly, the government's motive for the integration of innovative approaches in enhancing infrastructure delivery showed a significant relationship with only technical commitment. The outcome indicates that factors measuring government motive and investor's services commitment are mutually dependent and critical in ensuring a sustained relationship among stakeholders in a PPP arrangement for infrastructure procurement.
This research aimed to study the impact of risk factors on the success of irrigation projects in Thailand. Data were collected using questionnaires at irrigation agencies in various projects. A structural equation model (SEM) was then developed, and the risks of various factors affecting the irrigation project were analysed. The study results found that the risk factors affecting the construction process were as follows: The study results found that the risk factors affecting the construction process were as follows: In terms of work control, the presence of multiple chains of command led to delays in decision-making. This was followed by delays caused by the employer’s material approval process, the lack of clear and detailed construction project planning by the employer, and delays resulting from the performance of subcontractors. The factors mentioned above resulted in the highest score of all 56 factors. The findings of this study showed factors that affected the project, causing project managers, related agencies, or stakeholders to know and find a way for problem solution. The results of this study have found that the SEM values of risk management affecting the construction irrigation project have passed the criteria and have caused the relationship. Those values consist of significance chi-square ( p -value = 0.799), chi-square relation value at 0.413, normal fit index (NFI) value at 0.999, goodness of fit index (GFI) value at 0.999, CFI value at 0.999, standardised root mean square residual (RMR) value at 0.029, and root mean square error of approximation (RMSEA) value at 0.001.
Electronic performance monitoring (EPM) in workforce productivity lacks quantitative indexes, such as labour rating factors (LRFs). This study introduces an integrated method using smartphones and work sampling (WS) to measure productivity based on LRF. An experiment with 10 welders in a pipe shop demonstrated the method’s effectiveness. This research aims to fill this gap using the design science research (DSR) methodology to introduce an integrated method based on electronic devices (smartphones) and human observation WS to measure productivity based on LRF. To demonstrate and evaluate this method, an experiment was carried out with industrial workers while welding steel pipes in low-carbon alloy using tungsten inert gas (TIG) and flux-cored arc welding (FCAW) methods. The results indicate the feasibility of this integrated method based on the complementarity of the WS and the EPM approach tested. The LRF using WS was determined to be 55.52% while the EPM factor was 57.78%. Also, welders are directly engaged in the welding process 75.55% of the time. Considering a standard productive state average of 50%, EPM results can represent an accuracy of 84%–96% of the LRF. The electronic method based only on the workers’ location has the limitation of not identifying idleness within the production zone (PZ); as a result, some calibration is provided by the WS method. This research contributes a low-cost, accessible approach for continuous productivity improvement. The integrated method allows for both quantitative measurement and qualitative diagnosis of productivity factors, bridging the gap between traditional and modern monitoring techniques.
The accurate estimation of project costs is pivotal for the ongoing financial success of construction companies. Despite the clear definition of direct costs in tendering information, indirect costs (also known as project overheads and preliminaries) are often overlooked or underestimated. These costs are influenced by various factors, including company resource availability, site and project characteristics, contractual conditions and procurement methods. The pricing of preliminaries is a complex task, and the varying nature of these costs, coupled with the lack of transparency in current pricing practices, can lead to significant discrepancies in tender pricing. The challenge is to identify what should be included in preliminaries and arrive at a value in practice. This research has explored how contractors’ estimating departments address the complexities of pricing preliminaries for building and civil infrastructure works, particularly considering stringent contractual requirements and post-pandemic construction market disruptions. Through a literature review and an online survey of 30 senior estimators from major contractors, addressing 18 questions, the research sought to understand current practices, differing approaches and metrics employed in pricing preliminaries during the tendering stage. The multifaceted nature of preliminary studies was examined, offering a structured analysis of their categorisation, estimation methods, associated challenges and the impact of project delivery methods. The findings reveal that each contractor processes the pricing of preliminaries using certain tendering gateways and a variety of different metrics. This indicates that systematic risk and pricing models for contractors may lack a justifiable basis, with existing pricing models facing acceptance challenges across different contractors.
A project’s success or failure can be attributed to various management factors, including consistency or discrepancy in decision-making. The degree of inconsistency, lack of expertise, comprehension, or ignorance in some cases are just a few of the many variables that might impact the process’s consistency and result in poor decision-making. Techniques have been developed to aid in consistent decision-making, but the selection process became extensive and disallowed wide adoption of such techniques in construction and project management practice. As such, a project-to-contractor assignment problem is one of the important areas in which a slight mistake leads to notable losses. Therefore, there is a need for better decision-support tool development. This study introduces Analytical Hierarchy Process (AHP)-based development and application of a management science technique along with game-theoretic modelling to develop a decision-support tool, successful project management (SPM), to assess and lessen the impact of human inconsistency on construction projects to contractor assignment and management decision-making. It is demonstrated that contractor preferences can be used to assign projects to contractors. The developed method significantly reduces managers’ effort to make informed decisions for successful project completion. Case studies are presented to demonstrate the improvement in AHP and efficiency of the proposed method, along with precise results and comparisons for the effort one would need to complete the process successfully. Sample problems demonstrate the use of traditional AHP and then the application of the proposed technique to highlight the improvement. As demonstrated, time savings to arrive at consistent decisions are multi-fold.
This paper presents an artificial neural network (ANN) model designed to predict the optimal delivery methods for construction projects based on organisational characteristics. Effective organisational characteristics were identified through a combination of the Delphi method and data collected via questionnaire surveys. The study sample consisted of 354 construction experts selected using a random sampling method. The validity and reliability of the research were confirmed through the formcontent validity and the Cronbach’s alpha test, respectively. The ANN model, implemented using RapidMiner software, demonstrated a prediction accuracy of 76.42%. The results revealed that financial, managerial, contextual, optimisation, and manpower variables significantly impact the prediction of the delivery method. Compared to other data mining models, such as the decision tree, random forest, and support vector machine (SVM), the ANN model showed a superior accuracy. This research highlights the contribution of organisational characteristics in forecasting the delivery methods of construction projects and offers a novel approach to improving project delivery decisions. While the findings are based on data from the Mazandaran province in Iran, the methodology and insights can be adapted and applied to other regions with similar organisational characteristics, suggesting a potential for generalisation.
Decades of decline in skilled trade labour require an urgency of addressing this trend. This study investigates Michigan’s skilled construction labourer (SCLs) shortage through three overarching questions: what factors influence and cause this shortage; how are stakeholders responding; and what innovative solutions could reverse this trajectory. A review of the literature revealed recruitment difficulties due economic volatility, skill deficiencies, and perceived lack of advancement. Based on these factors and others, two surveys were conducted to study opinions of construction managers and skilled trade labourers mostly in Southeast Michigan by analysing demographics across different categories using analysis of variance (ANOVA). The results not only corroborated the importance of vocational training but also added to the body of knowledge details that were not previously investigated. The results indicate that exposure to vocational education at high school increased the likelihood of seeking vocational training before joining the industry from 2.6% to 29%, and internships played a major role in training union workers. However, project managers (PMs) preferred recruiting workers from internships and on-the-job training over trade schools and unions. The young workers had mixed expectations of remaining in the industry until retirement compared to those over 40 years of age. The methodology utilised in this study can help the construction industry in the United States and elsewhere to identify areas of improvement to attract more workers into the field, and the findings of this study are particularly useful to construction companies in Michigan in determining where they need to focus their efforts to improve training and recruitment.
In recent years, the online education system (OES) has been actively used at all educational levels for various reasons, becoming a lasting element of education. Identifying critical success factors for effective online architectural education (OAE) is essential for educational sustainability. This study aimed to pinpoint these success factors and identify an appropriate digital platform to uphold education quality. A mixed-methods approach, incorporating both qualitative and quantitative techniques, was utilised. A systematic literature review (SLR) identified 69 success criteria (SCs). A survey was conducted with 232 architecture students in Turkey, and the data were analysed quantitatively. This research comprehensively quantified transition process-related SCs impacting OAE, categorising them by scope and impact and uncovering their sub-dimensions. The study also calculated effect sizes and determined the significance levels of these factors. It explored the relationship between these factors influencing the success of OAE and digital platforms, while examining the correlation between course types and these platforms.
