Unintentional bucket detachment resulting from quick hitch failure remains a primary reason for catastrophic incidents globally. Despite the operational necessity of rapid attachment changes, conventional systems rely on manual safety pins and designs that has not been appropriately improved for decades and remains inherently susceptible to human error. Modern safety paradigms increasingly demand the elimination of manual intervention in quick hitch and excavator operations. The research addresses these critical safety gaps by developing an innovative quick hitch featuring a dual autonomous safety locking mechanism, engineered through a robust design science research framework. The proposed solution eliminates manual pins by integrating a primary high-tensile spring-supported rear lock with a secondary front safety lock. Iterative prototype simulations of new quick hitch designs demonstrated high structural reliability, achieving a Factor of Safety of 3.0 and beyond across all components. The inclusion of high-visibility indicators ensures fail-safe visual verification from the excavator cab, ensuring regulatory alignment with ISO 13031:2023. Finally, rigorous empirical testing confirmed that buckets remained securely engaged during hydraulic failure and aggressive swing-start conditions. By replacing human-dependent processes with innovative designs, the research eliminates the root causes of unintentional bucket detachment, and enhances the operational safety of excavators and heavy machinery.

Construction sectors in fragile and least developed economies often experience persistent project delays, low productivity, and inefficient resource utilization due to interacting regulatory, labor, supply-chain, and macroeconomic constraints. Conventional policy analyses typically examine these challenges in isolation, limiting their ability to capture dynamic feedback effects and long-term system behavior. This study develops a hybrid System Dynamics–Genetic Algorithm (SD–GA) framework to analyze construction sector performance, using Sudan as an illustrative case. The methodology integrates structured expert elicitation (n = 50), importance-based factor ranking, causal loop and stock-and-flow modeling, and GA-based calibration to align simulated system behavior with expert-derived influence hierarchies. Policy scenarios combining varying levels of workforce training, regulatory streamlining, and material supply-chain improvement are simulated. Results indicate that baseline conditions lead to sustained stagnation, with average project delays exceeding 120 days. Regulatory reform alone can reduce delays by approximately 30%, while integrated, high-intensity interventions can halve delays and increase construction output by nearly 60% by Year 20. Sensitivity analysis confirms the robustness of scenario rankings and identifies regulatory efficiency and workforce development as the highest-leverage policy levers, with supply-chain reforms exhibiting nonlinear threshold effects. The proposed framework offers a potentially transferable decision-support approach, subject to contextual recalibration.

Pollution created by building renovation activities poses a hazard to human health and the proximate environment. Integrating building information modelling (BIM) with digital twin (DT) technology offers a promising means for real-time monitoring and enabling collaboration among project participants. However, it is challenging to transform the BIM models of a renovated facility into a DT platform for monitoring and reporting construction pollution on-site. This paper proposes a BIM-enhanced DT architecture for managing construction pollution in building renovation projects. The platform entails five key components: physical entity (PE), virtual model (VM), DT data (DD), services (Ss), and connections (CNs). To resolve ambiguities in information requirements, a novel Level of Information Need (LOIN) framework for BIM-enhanced DT was established. The interplay among LOIN, Level of Development (LOD), and DT functionalities defines the minimum information needed in the information containers and the metadata structure required for pollution hotspots, compliance assessment, and automated analytics. To illustrate its efficacy and practicality, the DT architecture was applied to an auditorium renovation project, where static BIM data were integrated with dynamic pollution parameters captured by IoT sensors and managed in a Common Data Environment (CDE). The results showed its capability to systematically and efficiently acquire, integrate, treat, and analyze important data.

Sustainability is vital in construction, yet limited research has explored how key dimensions interact within project management systems. Existing studies examine these dimensions separately, overlooking the systemic interdependencies that influence project outcomes. This study applies symbiosis theory to explore the relationships among six core dimensions: Economic (E), Environmental (N), Social (S), Resource Utilization (R), Health and Safety (H), and Project Governance (G). Drawing on input from 21 experts, a comprehensive multi-method framework was employed, integrating Interpretive Structural Modeling (ISM), Matrix of Cross-Impact Multiplications Applied to Classification (MICMAC) analysis, Decision Making Trial and Evaluation Laboratory (DEMATEL), and the Analytic Network Process (ANP). ISM identified G and R as primary structural drivers. MICMAC confirmed their dominant influence and classified H and N as linkage dimensions. DEMATEL revealed H as the main causal factor, with G and R operating as reactive outcomes. ANP ranked the dimensions by importance as follows: E (0.391), N (0.226), R (0.188), G (0.082), S (0.073), and H (0.039). A ± 20% sensitivity test validated the robustness of these rankings. The findings highlight the dynamic interdependencies of sustainability in construction project management, offering a holistic framework for managers and policymakers in prioritizing interventions and aligning strategies with broader objectives.

The proposed work investigates the role of building information modelling (BIM) in facilitating the regulatory landscape of extended producer responsibility (EPR) in the Indian construction sector (CS). An exploratory study was conducted among 319 professionals from the CS, BIM and waste management sectors. The analysis was performed using partial least squares structural equation modelling. The findings revealed that waste minimisation and planning functions (β = 0.271, p < .05), digital records and automated compliance documentation features (β = 0.763, p < .05) of BIM significantly contribute to EPR. The descriptive statistics revealed that Autodesk Revit and Autodesk BIM 360 are widely used BIM software, mainly in 3D modelling. More than 50% of the professionals urge the need for tailored EPR software for better management of CS waste streams. The study emphasises that market demand and the regulatory policy landscape are driving factors for the adoption of EPR in CS. Further, the industry readiness in the adoption of BIM in EPR can be achieved through capacity building, digital compliance frameworks and stakeholder collaboration. Establishing the BIM-linked EPR can help in minimising resource wastage and can promote the circular economy in the CS, thereby supporting the Sustainable Development Goals.

This study investigates the barriers that hinder the adoption of sustainable construction practices and examines their impact on the environmental, social, and economic dimensions of the Triple Bottom Line (TBL). A systematic literature review following the PRISMA protocol identified key categories of barriers, which informed the development of a survey instrument. Data were collected from 132 construction professionals with experience in sustainable projects and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) within a reflective–reflective hierarchical model. The results demonstrate that barriers exert a significant and negative influence on overall sustainability performance. Governmental, financial, knowledge and information, workforce, and market barriers were all found to adversely affect the Triple Bottom Line (TBL) pillars. Workforce- and market-related barriers showed the strongest contributions within the higher-order barriers construct, while social and economic dimensions were the most prominent components of the TBL construct. The findings highlight the systemic and interdependent nature of sustainability barriers, indicating that isolated interventions are insufficient. The study provides empirical evidence to support integrated policy, financial, educational, and market-based strategies aimed at enabling a comprehensive transition toward sustainable construction.

Utilizing recycled aggregates and calcined clay (CC) advances circular, sustainable, carbon‑neutral construction. This study investigates low-carbon mortar alternatives using carbonated and non-carbonated recycled fines (CRCF, NCRCF) and CC under CO2 curing. Along with the measurement of compressive strength development and life cycle assessment (LCA) through cradle to gate, characterization methods including scanning electron microscopy, energy dispersive X-ray spectroscopy, X-ray diffraction, Fourier transform infrared spectroscopy and thermogravimetry analysis were employed to estimate the degree of hydration and carbonation phases quantitatively. The findings revealed that carbon neutral mix with 6% CRCF and 15% CC sequesters the highest amount of carbon dioxide up to 39.26%. Whereas, the highest hydration degree, strength contribution is illustrated by the mix comprising of 9% CRCF and 15% CC. Further, it is observed that mixes containing CC demonstrated the lowest chloride ion penetration and permeable porosity in the mix. The LCA illustrated 12% lesser GWP with mixes enriching CC and CRCF. Overall, this study enriches the recycled concrete fines and CC as a low-carbon substitute for the traditional building materials by giving the researchers an in-depth understanding of the macro- and micro-characteristics of sustainable mixes for nurturing a circular economy and recommends the replacement of virgin fine aggregates with 9% CRCF particles for achieving reduced carbon emissions.

The assembly and installation phases of Modular Integrated Construction (MiC) involve closely sequenced and interdependent activities in which disruptions can rapidly affect project performance, yet these phases remain underexamined. This study develops a risk profile for MiC during these stages. Using a structured process combining literature review, expert validation, and interviews with twelve professionals, thirty-five risk factors relevant to MiC installation in Vietnam were consolidated, including eleven newly identified risks reflecting context-specific challenges. Each risk factor is mapped to its underlying sources, its influence on time, cost, and quality, and its severity assessed using a probability–impact approach. The results indicate that many high-level risks are associated with communication and coordination deficiencies, weaknesses in monitoring and management, plan- and process-related sources, equipment-related sources, and change- and information-related sources. Weather disruptions, crane malfunctions, contractor inexperience, modular connection errors, and approval-related delays were among the most severe risks. To support systematic risk management, the study proposes an integrated coding structure that organizes risk sources, affected objectives, severity levels, and response measures. The resulting risk profile clarifies how risks emerge during MiC installation and provides a structured basis for consistent risk management. The findings offer an evidence-based reference for contractors, consultants, and project owners.

The study examines the predictability of cost, time and quality in public construction projects delivered through design–build (DB) contracts, employing a mixed-methods approach that combines quantitative analysis with interviews. Based on data from over 100 completed DB contracts, the study assesses schedule adherence, cost deviations and the DB model’s impact on project quality. The research finds that infrastructure contracts demonstrate a schedule predictability with nearly 75% completed on or before the agreed deadline. However, there is substantial variance in the data set, with actual completion times ranging from one year early to more than two years beyond the deadline. The data from building contracts showed significantly less overall variation but were more prone to modest delays, with two out of three contracts completed one to three months late. Regarding costs, the average increase from the original contract amount was 14%, with a median of 10%. Approximately half of the contracts had change volumes of 10% or less, and around 80% fell within a 20% range. Regarding quality, the findings suggest that DB contracts often encourage delivery to minimum standards; therefore, their use should be approached with caution in projects requiring high degree of quality predictability but with poorly defined requirements.