Despite the recognized environmental, economic, and social benefits of green buildings, their adoption in Indonesia remains limited. Although the government has established regulations for fiscal and non-fiscal green building incentives, implementation has been insufficient. This gap suggests that incentives should not be viewed merely as policy instruments but as components of broader governance systems. Therefore, this study develops an Integrated Governance Model for Green Building Incentive Implementation (IGM-GBI) to explain how governance dimensions interact to shape governance readiness for incentive implementation. A qualitative multi-stakeholder approach was employed, consisting of a desk study and focus group discussions involving representatives from national ministries, local governments, professional organizations, development partners, consultants, and the private sector. Secondary data on regulations and international incentive practices were analysed and subsequently validated through stakeholder discussions. The discussion transcripts were recorded, coded, and synthesized using thematic analysis. The findings identified four interrelated categories of governance barriers, which were subsequently synthesized into four governance dimensions supporting the model: financial capacity, institutional and regulatory readiness, implementation capacity, and stakeholder alignment. Furthermore, the model suggests that policy interventions should be prioritised according to governance readiness, recognising that different incentive mechanisms require different levels of institutional capacity, coordination, and stakeholder support. The proposed model provides a theoretical explanation of incentive implementation and practical guidance for policymakers while offering insights for other developing countries seeking to accelerate green building transitions through more effective governance.

Unmanned aerial vehicles (UAVs) are increasingly used for bridge inspection to reduce hazardous access and improve scalability. Routes are often manual or coverage-driven, and the collected imagery is not always comparable or aligned with maintenance decision needs. This study proposes a knowledge-based UAV path planning approach that converts maintenance intent into executable and repeatable inspection routes. The method integrates element-level maintenance knowledge from field inspection and maintenance records encoded as priority weights and viewing requirements with BIM derived bridge geometry to generate candidate viewpoints and element specific inspection patterns for substructure and superstructure. Viewpoints are evaluated using a maintenance-weighted utility function. A case study on a conventional concrete box-girder toll-road bridge compares the approach with three baselines manual rule-of-thumb, a coverage only planner, and a robustness oriented feasibility proxy. Results show routes that remain traceable to maintenance priorities and support cycle-to-cycle repeatability for continuous inspection planning.

ABSTRACT Modular construction is increasingly adopted in industry; however, undergraduate civil engineering students often struggle to comprehend assembly sequencing and structural connection logic when instruction relies primarily on two‐dimensional (2D) drawings and text‐based materials. These limitations hinder the development of spatial visualization and procedural cognition, skills essential for modular construction planning and coordination. To address this instructional gap, this study designed and evaluated an augmented reality (AR)–supported Building Information Modeling (BIM) learning environment named AR‐ModuConnect aimed at enhancing students' conceptual and procedural understanding of modular steel building connections. The instructional approach enables interactive three‐dimensional visualization of Intra‐Module, Inter‐Module, and module‐to‐foundation connections, supported by animated assembly sequencing. A quasi‐experimental implementation was conducted with civil engineering undergraduates to compare learning outcomes between the AR‐supported instruction and conventional methods. Learning effectiveness was assessed through measures of conceptual understanding, spatial visualization, and procedural clarity, while usability was evaluated using the System Usability Scale (SUS). Results indicate that students exposed to the AR–BIM instructional approach demonstrated significantly improved comprehension of connection systems and assembly processes. The SUS score of 69.46 suggests acceptable usability for integration into engineering curricula. These findings contribute empirical evidence to immersive technology–enhanced learning in construction education and highlight the potential of AR‐supported BIM environments for strengthening procedural learning in modular construction courses.

The construction industry faces persistent challenges of inefficiency and fragmentation. In precast concrete (PC) projects, in-situ production and yard-stock management require precise coordination of production cycles, storage layouts, and installation schedules, yet existing studies have rarely addressed their integrated optimization. This study proposes a BIM-driven scheduling optimization framework that incorporates probabilistic simulation (Oracle Crystal Ball) into BIM-based 4D modeling. The framework was applied to steel-reinforced precast concrete components in a large-scale logistics project, with the aim of minimizing project duration and improving yard-stock efficiency under site-specific constraints. Simulation results demonstrated a strong positive correlation between lead-time and project duration (+0.58), identifying lead-time control as a critical scheduling variable. A negative correlation between duration and yard-stock area (-0.52) confirmed that timely mold utilization improves spatial efficiency, while crane deployment showed minimal impact. The optimized schedule achieved 6.3 months, reducing the original 8-month plan by 10% and surpassing the client's contractual timeline by 75%. This research contributes by introducing one of the first BIM-based frameworks that integrates production, storage, and installation into a unified scheduling model, providing both practical decision support and a foundation for future objective optimization.

Reinforcement cutting and placement planning for reinforced concrete (RC) bearing structures has traditionally relied on standard stock lengths and empirical practices, leading to persistent cutting losses and material inefficiencies. This paper proposes an AI-BIM integrated hybrid optimization framework combining a Memetic Genetic Algorithm (GA) with an Adaptive Large Neighborhood Search (ALNS). Reinforcement geometry, placement information, and boundary conditions are automatically extracted from a Tekla-based IFC model, while search robustness is enhanced through a special-length-priority strategy and diameter-wise independent optimization. A case study of a 28-story RC bearing wall structure shows that reinforcement cutting waste (RCW) does not differ significantly across special-length intervals (ANOVA: F = 1.288, p = 0.277), nor is it significantly correlated with special length (p = 0.513). These results demonstrate that an automated decision-making system for site-specific rebar optimization can be established based on actual reinforcement construction data extracted from BIM models.

The involvement of various stakeholders in construction safety reflects the complexity of safety-related decision-making in construction projects. To address these complexities, evolutionary game theory is used to design mechanisms that allocate stakeholders according to their respective roles and interests. Evolutionary game theory which examines how strategic interactions evolve over time through replication dynamics and stable equilibrium states, has been used to model stakeholder behavior, strategy development, and decision-making processes under certain conditions. However, its application in the context of construction safety management remains relatively underexplored. To address this gap, this study performed a literature review to investigate how evolutionary game theory has been utilized specifically for construction safety management by following the PRISMA 2020 protocol for a systematic review. Sourcing of relevant publication is exclusively based on Scopus database due to its extensive and comprehensive index of peer-reviewed journals. The reviewed publications span from 2016 to 2025, resulting in the identification of 21 relevant studies. Five thematic areas were identified as follows disaster resilience and safety in developing country, technology for safety, workers behaviors and safety culture, incentive and penalty mechanisms, and government roles in supervision and regulation mechanism. The result of the study indicates that evolutionary game theory is not only capable of modelling stakeholder interactions but also regulatory influences, the impact of technological interventions, behavioral drivers, and disaster resilience. This study provides a current state of how evolutionary game theory is utilized for construction safety management, which serves as a base point for more in-depth study in the future.

Digital twin (DT) technology, integrated with building information modeling (BIM), enables real-time feedback and predictive analytics in construction. This study presents a BIM-enabled DT framework to optimize in situ production and yard-stock management of precast concrete (PC) components with a focus on minimizing CO2 emissions. Using Oracle Crystal Ball, scenario-based simulations revealed up to an 8.9% reduction in environmental impact. Distinct from prior research that largely emphasized cost or off-site strategies, this study uniquely addresses on-site sustainability by embedding carbon metrics into the decision-making process. The framework was validated through a large-scale logistics warehouse project that showcased its practical utility. This research contributes a replicable method for enhancing sustainability in precast construction through digital technologies.

The growing demand for reinforced concrete (RC) structures, driven by population growth, significantly contributes to carbon emissions, particularly during the construction phase. Steel rebar production, a major contributor to these emissions, faces challenges due to high material consumption and waste, often stemming from market-length rebar and conventional lap splices, impeding decarbonization efforts. This study introduces a comprehensive strategy to minimize rebar consumption and waste, advancing decarbonization in the civil and construction industry. The strategy integrates a special-length-priority minimization algorithm with lap splice position adjustments or couplers to reduce rebar consumption, waste, and carbon emissions. A case study evaluates distinct scenarios regarding rebar consumption. The study demonstrates that conventional rebar practices, such as market-length rebar and lap splices, lead to excessive consumption and waste, impeding decarbonization. Couplers significantly reduce rebar requirements, though cutting waste remains when combined with market-length rebar. Special-length-priority optimization with lap splice adjustments demonstrates greater efficiency in reducing consumption while minimizing cutting waste, proving effectiveness. The combination of special-length-priority optimization and couplers achieves the greatest reductions in rebar consumption, waste, and carbon emissions, making it the most efficient strategy for future construction projects. These findings emphasize the importance of optimizing rebar consumption in advancing decarbonization and promoting sustainable practices in the civil and construction industry.

Rebar layout is crucial for structural integrity, cost efficiency, and accurate estimation in construction. This paper presents advanced algorithms for optimizing rebar cutting lengths and automating structural drawings. Utilizing structural design outputs, the approach integrates special length rebar concepts and shape codes for precise bend deductions. Algorithms were developed to calculate rebar lengths for structural elements like columns and beams, addressing longitudinal continuous, discontinuous, and confinement rebars. This resulted in a 10.56% reduction in column rebar quantities and a 2.22% reduction for beams compared to the original design. Automated rebar layout algorithms also established equations for determining rebar coordinates and arrangements, facilitating efficient structural drawing generation. This reduces manual drafting, streamlines rebar workflows, and enhances project efficiency. The findings highlight the potential of computational techniques in rebar layout optimization, enabling cost-effective, sustainable construction practices while improving accuracy and efficiency.

This study aims to address inefficiencies and errors in current manual rebar practices within the architecture, engineering, and construction (AEC) industry, which contribute to significant rebar waste. The Design-Bid-Build (DBB) project delivery method further exacerbates these issues by hindering effective stakeholder collaboration. By contrast, integrated project delivery and building information modeling (BIM) have shown potential for enhanced coordination and waste reduction. Yet, the continued reliance on manual rebar modeling in BIM highlights a pressing need for intelligent automation. This study presents a novel BIM-based algorithm that leverages Dynamo for rebar layout optimization and Navisworks for automatic clash detection, integrating rebar optimization prior to BIM model creation to minimize data exchange errors. The methodology was validated on a continuous-column rebar arrangement, supported by a standardized rebar summary sheet to streamline the optimization and clash-detection process. Key findings indicate that this approach significantly reduces working hours while achieving high modeling accuracy compared to traditional manual methods. These results highlight the potential of the proposed framework to transform rebar layout practices, enabling smart, efficient, and sustainable construction through automated clash-free rebar layout generation.

Rebar procurement inefficiencies, such as inaccurate quantity estimation and misaligned delivery schedules, often lead to excessive waste, supply shortages, and project delays. While existing optimization methods reduce cutting waste, their effectiveness diminishes without integration into supply chain management (SCM). This study presents an integrated framework to optimize rebar processing and supply chain management (SCM) by leveraging Building Information Modeling (BIM) and data-driven optimization strategies. A 24-floor case study validated the approach, optimizing continuous main rebars into special lengths and combining discontinuous lengths into cutting patterns based on special lengths. Rebar orders were organized into 12 batches, each meeting a 15-ton minimum and requiring order placement at least two months in advance. An activity database integrated rebar optimization with the construction schedule, facilitating SCM analysis. BIM automation streamlined procurement by generating Bar Bending Schedules (BBSs) and synchronizing rebar tracking with real-time updates, improving coordination, efficiency, and project outcomes, particularly in high-rise building projects.

The integration of Building Information Modeling (BIM) and Digital Twin (DT) technologies offers new opportunities for enhancing reinforcement design and on-site constructability. This study addresses a current gap in DT applications by introducing an intelligent framework that simultaneously automates rebar layout generation and reduces rebar cutting waste (RCW), two challenges often overlooked during the construction execution phase. The system employs heuristic algorithms to generate constructability-aware rebar configurations and leverages Industry Foundation Classes (IFC) schema-based data models for interoperability. The framework is implemented using Autodesk Revit and Dynamo for rebar modeling and layout generation, Microsoft Project for schedule integration, and Autodesk Navisworks for clash detection. Real-time scheduling synchronization is achieved through IFC schema-based BIM models linked to construction timelines, while embedded clash detection and constructability feedback loops allow for iterative refinement and improved installation feasibility. A case study on a high-rise commercial building demonstrates substantial material savings, improved constructability, and reduced layout time, validating the practical advantages of BIM–DT integration for RC construction.

As cities densify, deep underground infrastructure construction such as mass rapid transit (MRT) systems increasingly demand smarter, digitalized, and more sustainable approaches. RC diaphragm walls, essential to these systems, present challenges due to complex rebar configurations, spatial constraints, and high material usage and waste, factors that contribute significantly to carbon emissions. This study presents an AI-assisted rebar optimization framework to improve constructability and reduce waste in MRT-related diaphragm wall construction. The framework integrates the BIM concept with a custom greedy hybrid Python-based metaheuristic algorithm based on the WOA, enabling optimization through special-length rebar allocation and strategic coupler placement. Unlike conventional approaches reliant on stock-length rebars and lap splicing, this approach incorporates constructability constraints and reinforcement continuity into the optimization process. Applied to a high-density MRT project in Singapore, it demonstrated reductions of 19.76% in rebar usage, 84.57% in cutting waste, 17.4% in carbon emissions, and 14.57% in construction cost. By aligning digital intelligence with practical construction requirements, the proposed framework supports smart city goals through resource-efficient practices, construction innovation, and urban infrastructure decarbonization.

The construction industry, recognized as one of the most hazardous sectors globally, continues to face escalating challenges, particularly in Indonesia. This sector experiences a yearly increase in workplace accidents, which significantly disrupts economic stability at both micro and macro levels. These incidents lead to substantial economic losses, reduced productivity, and increased medical and compensation costs. To address these risks, the adoption of Learning from Incidents (LFI) has emerged as a critical approach. LFI is a structured process that involves analysing and learning from past incidents to prevent future occurrences, offering a proven methodology to enhance workplace safety. However, despite its potential, the implementation of LFI in Indonesia encounters persistent obstacles. These include a weak safety culture, inadequate reporting systems, and insufficient enforcement of safety standards. Such challenges hinder the effectiveness of LFI and limit its capacity to drive meaningful improvements in construction safety. This study seeks to bridge the gap between the importance and current performance of LFI implementation in the Indonesian construction industry. The research methodology integrates a literature review, expert validation, and Importance-Performance Analysis. Through the literature review and expert validation, critical indicators for LFI implementation were identified, while the Importance-Performance Analysis assessed the alignment of expectations with actual performance as perceived by construction practitioners. Input from three construction safety experts and industry practitioners formed the basis of the analysis. The findings reveal that while Investigation Participation met or exceeded expectations, several other LFI implementation indicators–including Contextual Safety Engineering and Dissemination Reach–require substantial improvement. This consensus highlights significant discrepancies between intended outcomes and actual practices, underscoring the need for targeted strategies to enhance LFI processes. Addressing these gaps can better align LFI implementation with safety objectives, ultimately fostering a safer and more sustainable construction industry in Indonesia.

The demand for infrastructure continues to grow, including diaphragm walls, which are used in many projects. However, large-scale constructions often rely on conventional lap splicing composed of market-length rebar for rebar connections, leading to significant waste due to the required long overlapping lengths. This reliance stems from a limited understanding of special-length rebar and mechanical couplers, which have been shown to reduce rebar waste and consumption effectively. Furthermore, the broader advantages of couplers, particularly in terms of cost savings and improved productivity, remain largely unrealized, as engineers may delay implementing them due to perceived planning complexities. This study aims to quantify the impact of minimizing rebar waste through the integration of special-length rebar and couplers, focusing on their impact on cost reduction and productivity improvements in large-scale diaphragm wall projects. The findings indicate reductions in waste by 95.41

Reducing the construction time for large logistics buildings can result in reduced construction management costs and economic gains from early operation. A large logistics building with a heavy load and long spans was constructed as a precast concrete (PC) structure, which required the use of a sizable crane to lift heavy PC members. A basic analytical approach was employed to resolve potential errors in the planning of PC member erection and to build a systematic erection plan. Calculation techniques for the trajectory distance that use the crane location were applied to select an erection plan that minimizes crane work. A crane trajectory distance calculation algorithm for sustainable PC member erection in large logistics buildings with heavy loads and long spans has been developed. The developed model aids in creating simulation and optimization models to ensure the minimal usage of cranes in the future and in determining the cost, construction time, CO2 emissions, and energy use for each erection plan.

If PC components are produced on site under the same conditions, the quality can be secured at least equal to that of factory production. In-situ production can reduce environmental loads by 14.58% or more than factory production, and if the number of PC components produced in-situ is increased, the cost can be reduced by up to 39.4% compared to factory production. Most of the existing studies focus on optimizing the layout of logistics centers, and relatively little attention is paid to the layout of PC parts for in-situ production. PC component yard layout planning for in-situ production can effectively reduce carbon dioxide emissions and improve construction efficiency. Therefore, the purpose of this study is to develop an environmental impact minimization model for in-situ production of PC components. As a result of applying the developed model, the optimization of the improved dung beetle optimization algorithm was verified to be efficient by improving the neighboring correlation by 22.79% and reducing carbon dioxide emissions by 18.33% compared to the dung beetle optimization algorithm. The proposed environmental impact minimization model can support the construction, reconstruction, and functional upgrade of logistics centers, contributing to low carbon dioxide in the logistics industry.