Search Results (274)

This study develops a cooperative multi-agent deep reinforcement learning (MARL) framework for simulation-based cost-overrun mitigation in smart grid construction projects under dynamic engineering uncertainty. Modern smart grid construction involves digital substations, renewable-energy-connected facilities, flexible transmission assets, intelligent monitoring systems, and geographically distributed contractors; therefore, cost escalation is driven by sequential interactions among procurement, schedule execution, equipment deployment, supervision, weather, logistics, and price volatility. The proposed framework models procurement management, construction scheduling, equipment allocation, and supervision-control units as decentralized agents embedded in a calibrated construction simulation environment. The environment is parameterized from 42 smart grid construction projects in Henan Province, China and generates disturbance scenarios involving weather efficiency loss, transportation delay, market-price volatility, labor shortage, and supply-chain interruption. A hybrid DQN–PPO mechanism represents mixed decision structures: value-based DQN modules handle discrete managerial choices such as task acceleration, supplier switching, and procurement timing, whereas PPO modules adjust continuous resource-allocation and recovery-intensity decisions. A hierarchical reward function combines local departmental objectives with project-level penalties for cost overrun, schedule delay, idle resources, recovery expenditure, safety risk, and environmental impact. The experimental protocol uses 30 paired random seeds, nonparametric bootstrap confidence intervals, Holm-adjusted Wilcoxon signed-rank tests, and comparison with deterministic optimization, rolling-horizon MPC, stochastic/robust optimization, single-agent DRL, MAPPO, MADDPG/MATD3, QMIX, and HAPPO baselines. The proposed framework achieves a mean cost-overrun rate of 6.83% and a mean schedule deviation of 16.82 days, reducing cost overrun by 18.7% and schedule deviation by 21.4% relative to rule-based construction management under the reported disturbance settings. The calibrated simulation evidence establishes a statistically evaluated decision-support framework for coordinated construction cost control and provides an artifact-level reproducibility pathway through configuration files, random-seed lists, anonymized synthetic benchmarks, and aggregated logs. Full article

Change orders in construction projects frequently lead to disputes, schedule delays, and cost overruns, particularly in the rapidly expanding construction sector of Saudi Arabia. Traditional techniques for resolving claims like litigation and arbitration are predominantly reactive in nature and do not facilitate proactive assessments of the impact of change orders before disputes materialize. A BIM-based framework is developed in this study to assess change orders in terms of time and cost with visualization functions through an integration of Autodesk Revit, Primavera and Navisworks, combined in a 5D virtual environment. The framework utilizes 3D modelling, scheduling and cost management (3D/4D/5D) supported by virtual reality (VR) visualization to create an interactive decision support platform for the project stakeholders. A real residential building case study was utilized to validate the framework, and a design modification was developed and analyzed using the BIM environment. The project cost has increased as a result of change order from SAR 411,437.26 to SAR 428,280.16, which is 4.1% of the total project cost. Also, there are deviations in the schedule which occurred from Month 4. The study results show that the suggested integrated BIM-based framework assesses the impact of change order, physically and visually, on the project time and cost that was required by the industry. The study is innovative in bringing together BIM layout, scheduling, and cost management with VR-supported visualization in a single decision support environment, allowing stakeholders to take into account the implications of change orders before they become real claims and disputes. The proposed framework allows transparent communication, collective decision making with stakeholders and early impact assessment, in contrast to existing approaches whose focus is primarily on claim resolution or improving coordination. This will improve project performance and how change orders are managed. Full article

Cost overrun remains a pervasive issue in building construction projects, yet most predictive studies operate within a single data domain, ignoring the systemic interactions across project, schedule, resource, quality, and safety dimensions. This study quantifies the incremental predictive value of integrating these five construction data domains and identifies the cross-domain interaction patterns that explain prediction accuracy. As a simulation-based methodological study, an integrated dataset of 100,000 records was synthesised with theory-grounded causal structures derived from the construction management literature; no real project data were used. Gradient Boosting (GB), Random Forest (RF), and Linear Regression were evaluated on an 80/20 hold-out test split, with robustness verified through alternative domain orderings and hyperparameter sensitivity. SHAP analysis, including exact interaction values, was used to interpret feature importance and cross-domain synergies. The full five-domain GB model achieved R² ≈ 0.97 and MAPE ≈ 6%, a 220% relative R² improvement over the Project-domain baseline (R² rising from 0.305 to 0.975), robust across three ordering schemes. Schedule and Quality contributed the largest marginal gains (ΔR² = +0.312 and +0.255), whereas Resource integration yielded approximately one-thirty-first of Schedule’s return. Because the dataset is synthetic, the results are interpreted as a methodological demonstration rather than empirical evidence from real projects; they provide a reusable framework for prioritising data-integration investment and show that, within the simulated causal structure, cross-domain interactions—particularly Schedule × Risk and Project Type × Change Cost—carry predictive information that single-domain analyses cannot recover. Validation on real, partially integrated datasets is identified as essential future work. Full article

Demand-side management (DSM) is a security-critical function in residential smart grids. The same communication and sensing infrastructure that enables fine-grained load flexibility also exposes schedulers to corrupted measurements, price manipulation, and delayed control signals. Conventional DSM formulations generally treat cyber and communication impairments as external disturbances, which are addressed only after the schedule has already been calculated. This study proposes and evaluates Cyber-Resilient and QoS-Aware Demand-Side Management (CQ-DSM) as a hierarchical optimization framework that embeds cyber-risk likelihood and communication quality-of-service (QoS) directly into the scheduling objective. Local home energy management systems (HEMSs) solve mixed-integer linear programs at the appliance level, and central aggregators broadcast compact coordination signals based on real-time prices, measured QoS, and a sliding-window GRU-feature MLP risk estimator. The key intuition is to convert uncertainty about trust and actuation reliability into scheduling prices: high cyber risk discourages exposed loads during vulnerable periods, whereas poor QoS increases the value of locally preserving thermal flexibility. Under the simulation conditions (NYISO August pricing, P = 50 prosumers, Seed 42), CQ-DSM reduces overall system costs by 5.75% and imbalance procurement costs relative to an attack-unaware baseline under normal operation, limits the FDI-induced cost increase to 0.46% versus 0.83% (44% reduction in cost overrun), and reduces thermal-violation penalties by 81% under degraded QoS. The ablation results are consistent with cyber-risk pricing and QoS-aware fallback being complementary rather than redundant under the scenarios tested. Full article

Decarbonising the built environment has increased the importance of prefabricated construction, yet its cost and resource efficiency are still constrained by fragmented supply chain collaboration. This study examines how lifecycle supply chain collaboration affects cost control performance in prefabricated construction. Based on supply chain management theory and expert consultation, a conceptual model was developed and tested through structural equation modelling using 517 valid responses from stakeholders in China’s prefabricated construction supply chain. The results show that management factors across all four project phases (decision and design, component production, transportation, and construction and installation) significantly improve cost control performance, with design standardisation, production scheduling, transport logistics, quality assurance, and workforce proficiency as key drivers. Process coordination exerts a significant mediating effect, while environmental factors significantly moderate the relationships. In practical terms, the findings indicate that stakeholders should prioritise design standardisation at the early stage, strengthen coordination across production, transport, and installation activities, and enhance quality control and workforce training to reduce avoidable cost overruns and resource waste. Beyond their theoretical contribution to research on supply chain collaboration in prefabricated construction, these results offer concrete direction for practitioners seeking to improve cost efficiency and make better use of resources within industrialised building systems. Full article

This study examines upstream non-quality drivers and their root causes in development-to-production transition processes, with a focus on New Product Introduction (NPI) environments characterized by high technological and organizational complexity. Accelerated innovation and compressed development cycles increase exposure to early-stage organizational and process-related deficiencies that may later materialize as quality failures and cost overruns. While extensive research addresses downstream quality failures and the Cost of Poor Quality (COPQ), fewer studies focus on structured prioritization of upstream drivers at early NPI stages, where empirical failure-based data remain limited. Building on a previously developed Quality Deviation Index (QDI), this study applies the framework empirically within a complex NPI context to support structured prioritization of upstream organizational and process drivers. The analytical approach integrates root cause classification, frequency-based prioritization, and QDI-derived ranking to organize observed patterns among drivers, without introducing new quality metrics or inferential models. The findings illustrate how QDI-based prioritization can be applied to identify highly ranked upstream drivers within the examined context, thereby supporting early-stage organizational awareness and structured decision consideration prior to the manifestation of downstream quality failures. Sustainability-related aspects, such as waste generation and rework, are discussed strictly as interpretive downstream implications of improved early-stage prioritization rather than as empirically measured outcomes. Overall, the study provides a context-bound empirical illustration of how structured prioritization mechanisms may be applied in early-stage NPI environments characterized by high uncertainty and limited failure data, without implying statistical generalization beyond the studied setting. Full article

Road infrastructure projects are frequently affected by cost overruns driven by volatility in critical construction inputs and by the uneven association between external market shocks and material price movements. However, existing studies still provide limited evidence on how comparative forecasting, temporal price-signal diagnostics and probabilistic simulation can be integrated into a contingency-oriented decision framework. This study examines how construction input price forecasting and probabilistic simulation can inform contingency estimation in a road infrastructure case study. The empirical application is based on a Peruvian bridge project and combines benchmark-oriented forecasting using Bi-GRU and Random Walk models, descriptive temporal diagnostics based on lead–lag assessment and rolling-correlation analysis, and Monte Carlo simulation. Monthly series for structural steel, construction steel, cement, and diesel were transformed into log-returns and evaluated under a strict chronological design, while oil, the exchange rate, and the consumer price index were incorporated as exogenous variables. The Random Walk model produced lower forecasting errors for most inputs, achieving lower RMSE values in seven of the eight input-period comparisons; Bi-GRU outperformed it only for diesel in the test subset, with a 7.24% lower RMSE. From a project cost-risk perspective, the P95 contingency was estimated at 3.92% under Bi-GRU and 3.96% under Random Walk, indicating a similar upper-percentile contingency envelope under both forecasting specifications. The findings support contingency as a confidence-based budgeting decision rather than a fixed percentage. Full article

This study evaluates the adoption and efficacy of the 5th Dimension Building Information Modelling (5D-BIM) as a cost dimension for mega rail projects, extending the discussion beyond just technological implementation to consider broader policy and practical implications. The purpose of this article is to understand the governance context of 5D-BIM implementation for rail and transport projects and evaluate the effectiveness of the 5D-BIM framework as currently applied by conducting semi-structured interviews with key stakeholders. Drawing on semi-structured interviews with 22 stakeholders across government, industry, and technology providers, the research examines current 5D-BIM practices. While the primary focus of the research is 5D BIM implementations within the state of Victoria, Australia, which is currently experiencing a surge in rail projects, interviews were also conducted with additional stakeholders from international rail projects for context. The findings reveal fragmented adoption, varying levels of organisational maturity, and significant policy and implementation gaps, particularly in the role of government as the primary client of transport infrastructure. The results of the interviews emphasise the centrality of government and regulatory context in driving the adoption and implementation of 5D-BIM as the primary client of transportation infrastructure and identify actionable recommendations for policymakers and practitioners towards a more integrated approach to 5D-BIM in mega rail projects. While 5D-BIM demonstrates clear benefits in enhancing cost estimation, coordination, and decision-making, its effectiveness is constrained by the absence of clear standards, limited BIM literacy, and inconsistent regulatory guidance. This study provides one of the first empirical validations of the 5D-BIM governance framework, demonstrating that its success is driven less by technological capability and more by policy alignment, standardisation, and institutional leadership. Full article

Urban greening is increasingly promoted as a pathway to sustainable and climate-resilient cities; however, its implementation in metropolitan municipalities is often constrained by persistent challenges in project costs and financial management. This qualitative study examines the institutional, fiscal, and financial factors shaping the costing and financing of urban greening initiatives in Gauteng’s metropolitan municipalities: Johannesburg, Tshwane and Ekurhuleni. Semi-structured interviews were conducted with municipal officials, project managers, and environmental planners involved in green infrastructure planning and delivery, complemented by document analysis of municipal planning and financial reports. Thematic analysis reveals six interrelated challenges: institutional fragmentation, constrained fiscal capacity, weak cost estimation and project appraisal practices, limited access to innovative financing instruments, insufficient technical and monitoring capacity, and misalignment between environmental ambitions and financial accountability frameworks. The findings show that greening initiatives are often treated as discretionary expenditures rather than long-term investments, leading to underbudgeting, cost overruns, and reliance on short-term grants. The study argues that improving the financial sustainability of urban greening requires integrated green budgeting, strengthened project appraisal, life-cycle costing and investment appraisal, targeted capacity building, and reforms to municipal fiscal and audit frameworks. This paper contributes to debates on sustainable urban finance and offers policy-relevant insights for metropolitan municipalities in the Global South. Full article

Megaprojects involve complex, multi-tier supply chains characterised by high interdependence, diverse stakeholders, and significant uncertainty. Despite their strategic and economic importance, many megaprojects continue to experience persistent cost overruns and schedule delays, suggesting that performance challenges stem largely from difficulties in managing supply chain complexity rather than technical issues alone. This study adopts a systematic literature review of peer-reviewed publications from 2015 to 2025, sourced from Scopus and Web of Science. Using PRISMA-guided screening and eligibility procedures, 94 relevant articles were analysed to examine the drivers of supply chain complexity, their performance implications, and resilience strategies applicable to megaproject contexts. The review identifies various supply chain complexity drivers that intensify coordination challenges, reduce supply chain visibility, and increase disruption risks, contributing to inefficiencies, delays, and cost escalation. Proactive and reactive resilience strategies, such as multi-sourcing, collaboration, flexibility, redundancy, and contingency planning, are found to strengthen adaptive capacity and recovery. The study concludes that integrating complexity management with resilience-oriented practices provides a critical pathway for improving megaproject supply chain performance and offers a conceptual foundation for future empirical validation. Full article

Rework continues to be a critical issue in construction projects, contributing to cost escalation, schedule delays, and compromised quality. While earlier studies have identified isolated causes such as design deficiencies, communication failures, and inadequate workmanship, the structural relationships among these factors have not been sufficiently examined. This study investigates the interdependencies among major rework causation domains using Structural Equation Modelling (SEM) based on survey responses from 200 construction professionals. A total of 43 observed variables, identified through an extensive literature review, were grouped into four latent constructs: contractor-related, owner-related, design-related, and resource/workforce-related factors. Confirmatory Factor Analysis (CFA) was conducted to validate the measurement model, followed by structural path analysis to examine causal linkages. The findings reveal that design-related and owner-related factors exert the most significant direct and indirect influence on rework, followed by contractor- and workforce-related factors. The proposed model demonstrates satisfactory goodness-of-fit indices, confirming its reliability and applicability. Compared to conventional ranking and fuzzy-based approaches, SEM provides a more systematic and comprehensive understanding of rework dynamics. The findings provide practical guidance for project managers and decision-makers by identifying the most critical drivers of rework, enabling targeted mitigation strategies and improved resource allocation to enhance overall construction project performance. Full article

The European Union aims to achieve climate neutrality by 2050, with biogas and biomethane expected to play an increasingly important role in the decarbonisation of the energy system. This study investigates the economic and social determinants shaping the development of biogas production in European countries and identifies an optimal investment strategy for new biogas plants under varying environmental conditions. An expert–mathematical method was applied to assess and hierarchise twenty economic and social factors influencing biogas production, based on evaluations provided by 71 experts from eleven European countries. Subsequently, individual choice criteria derived from game theory were used to determine the optimal strategy for biogas plant construction under conditions of uncertainty. The results indicate that six determinants—EU-level production support mechanisms, investment costs, national support instruments, process efficiency improvements, community involvement, and agricultural raw material prices—account for 52.9% of the total impact on biogas development potential. Among the analysed investment options, large-scale biogas plants with an installed capacity of 3 MW were identified as the optimal strategy, offering the lowest unit production costs and the lowest risk of cost overruns across diverse economic and social environments. These findings provide policy-relevant insights for supporting efficient and socially acceptable biogas deployment in Europe. Full article

The formulation of construction material unit price policies in areas with limited accessibility is a critical issue in ensuring effective and accountable government infrastructure planning. In such regions, construction costs are often highly volatile and difficult to predict, primarily due to transportation constraints, logistical inefficiencies, and geographical challenges. These conditions frequently result in budget overruns and inconsistencies between planned and actual project expenditures. Therefore, a rational and context-sensitive policy framework is required to support accurate cost estimation and sustainable infrastructure development. This study aims to develop a policy-oriented model for determining construction material unit prices in areas with limited accessibility based on influencing factors. A quantitative research approach was employed through a questionnaire survey involving 235 respondents, consisting of contractors, government representatives, consultants, and academics with experience in infrastructure development in remote or access-constrained regions. The collected data were analysed using Partial Least Squares–Structural Equation Modelling (PLS-SEM) to identify and validate the dominant factors affecting construction material unit prices. The results of the PLS-SEM analysis identified 33 influential factors that significantly contribute to the unpredictability of construction material unit prices in limited-accessibility areas. These factors encompass logistical costs, material price dynamics, government policies, geographical conditions, and local cultural aspects. The proposed model demonstrates that government policy plays a central role, both directly and indirectly through local cultural mediation, in influencing project performance and cost reliability. The findings of this study provide a structured and empirically grounded framework that can be utilized by local governments as a policy reference in establishing construction material unit prices for remote and access-constrained areas. By incorporating the identified influencing factors into unit price formulation, cost prediction accuracy can be improved, thereby supporting more effective budget allocation and ensuring that infrastructure quality is maintained without compromise due to unanticipated cost escalation. These improvements contribute to more sustainable infrastructure development by enhancing resource efficiency, minimizing cost overruns, and supporting equitable infrastructure provision in remote areas. Full article

Risk management is critical to maintain consistency between estimated and actual costs in construction investment projects, especially those that incorporate tourism and heritage components. This study aims to quantify the impact of risk factors on construction investment costs and to estimate an updated maximum project budget at a defined confidence level using an integrated expert-based and probabilistic approach. The approach combines a Frequency–Impact matrix, weighted scaling, and PERT/Monte Carlo simulation, thereby transforming expert judgments into comparable numerical parameters suitable for predictive modeling. The methodology is applied to the rehabilitation of the Esmeralda Hotel project in Cuba, a heritage asset characterized by high cultural value and technical complexity. The results quantify the effects of prioritized risk factors, compute their impact coefficients, and re-estimate the project’s upper budget limit at a 95% confidence level. The findings show that risk drivers associated with higher-complexity construction processes concentrate the main vulnerabilities and explain most of the increase in total cost. In addition, the analysis indicates that contingency margins established by regulation are insufficient to absorb the project’s observed variability. The proposed model supports proactive budget control by anticipating cost deviations, improving resource allocation, and strengthening decision-making under high uncertainty. Its flexible structure enables adaptation to different project types and serves as a practical decision-support tool for investors, designers, and project managers seeking greater financial accuracy and reduced risk of cost overruns. Full article

Complexity in engineered systems presents one of the most persistent challenges in modern development since it is driving cost overruns, schedule delays, and outright project failures. Yet while architectural complexity has been studied, the structural complexity embedded within requirements specifications remains poorly understood and inadequately quantified. This gap is consequential: requirements fundamentally drive system design, and complexity introduced at this stage propagates through architecture, implementation, and integration. To address this gap, we build on Natural Language Processing methods that extract structural networks from textual requirements. Using these extracted structures, we conduct a controlled experiment employing molecular integration tasks as structurally isomorphic proxies for requirements integration—leveraging the topological equivalence between molecular graphs and requirement networks while eliminating confounding factors such as domain expertise and semantic ambiguity. Our results demonstrate that spectral measures predict integration effort with correlations exceeding 0.95, while structural metrics achieve correlations above 0.89. Notably, density-based metrics show no significant predictive validity. These findings indicate that eigenvalue-derived measures capture cognitive and effort dimensions that simpler connectivity metrics cannot. As a result, this research bridges a critical methodological gap between architectural complexity analysis and requirements engineering practice, providing a validated foundation for applying these metrics to requirements engineering, where similar structural complexity patterns may predict integration effort. Full article