Search Results (12,746)

Flood risk is increasingly shaped by the combined effects of climate change and the vulnerability of built environments, while building-level flood damage severity analysis is often constrained by limited data availability. This study develops a transparent and reproducible framework for analyzing building-level flood damage severity under climate-adjusted hazard conditions in data-constrained environments. The framework integrates administrative post-event damage records, GIS-based terrain information, a terrain-based proxy flood-depth reconstruction procedure, and a standardized Rhine Atlas/ICPR depth–damage relationship. Representative terrain-based proxy flood depths are reconstructed using building locations, terrain elevation, and settlement-level exposure assumptions. Observed damage categories are not used to assign proxy flood depths directly, but serve exclusively as empirical ordinal reference information for ordinal consistency assessment of model-derived damage severity. Climate effects are incorporated through a simplified hazard adjustment based on projected changes in extreme precipitation intensity. The framework is applied to 413 residential buildings affected by flood events in Serbia during the period 2016–2021. Results show a consistent nonlinear relationship between terrain-based proxy flood depth and ICPR-derived structural damage severity, as well as a strong influence of terrain elevation on relative hazard intensity. Climate-adjusted sensitivity scenarios indicate that even moderate increases in extreme precipitation lead to measurable increases in structural damage severity and an upward shift in model-derived damage levels. The proposed framework provides a practical approach for flood damage severity analysis in data-constrained environments, supporting improved decision-making in sustainable flood risk management and climate adaptation planning. Full article

Requirements management is fundamental to complex projects, especially in areas such as engineering, infrastructure, and defense. This article develops an integrative theoretical framework for requirements management in complex projects, grounded in a PRISMA-guided systematic literature review with a qualitative synthesis of the key dimensions of the field. In this review, 136 studies selected from an initial set of 519 records identified across multiple databases were reviewed. Five pillars were found to underpin the proposal: (i) the definition and traceability of requirements, (ii) the mitigation of uncertainties and risks, (iii) team maturity, (iv) digitalization and organizational transformation, and (v) the application of model-based systems engineering (MBSE). A literature review revealed that high-quality requirements reduce errors, improve predictability, and optimize resources, whereas digital approaches and collaborative practices strengthen the adaptive capacity of projects. Thus, in the proposed framework, these dimensions are organized into a hierarchical structure, with an emphasis on the integration of technical, organizational, and digital processes. One limitation is the lack of empirical validation, necessitating future studies on the practical application of the model in real projects, interviews with experts, and the development of operational metrics. This conceptual model is aimed at contributing to the literature and supporting more resilient, automated, and sustainability-oriented practices in complex environments. Full article

Urban plant communities are widely regarded as important nature-based solutions for climate mitigation, yet their actual carbon benefits remain uncertain: vegetation growth is accompanied by carbon emissions from construction and long-term maintenance, and existing assessments rarely integrate community succession, interspecific competition, and maintenance-related emissions within a consistent life-cycle framework. To address these limitations, this study developed a dynamic succession-based life-cycle simulation model to project the 50-year carbon source–sink transitions of 150 typical urban plant communities in Tianjin, China. The model updates plant structural attributes—diameter at breast height, crown width, and tree height—iteratively by linking individual plant growth to environmental suitability and neighborhood competition through a Plant Health Index. Simulated structural trajectories were coupled with biomass equations and carbon content coefficients to estimate aboveground carbon sequestration, while construction and maintenance emissions were quantified using life cycle assessment, enabling evaluation of modeled net carbon balance rather than gross carbon sequestration alone. Under the modeled 50-year scenario, most communities were projected to act as carbon sources during the early stage but gradually shifted toward carbon sinks as biomass accumulated; 86.1% of the communities were projected to become net carbon sinks after 50 years (a scenario-based projection under specified growth, maintenance, and emission assumptions). The highest modeled net carbon balance reached 3186.08 kg C ha⁻¹, whereas the weakest community remained a slight carbon source at −81.21 kg C ha⁻¹. Vertical structural complexity and species richness were the strongest positive predictors of modeled net carbon balance, followed by three-dimensional green quantity and canopy closure. Among maintenance processes, fertilization was the dominant emission source, followed by pesticide application and irrigation; comparative scenario analysis showed that resource-saving maintenance consistently improved projected net carbon balance relative to high-maintenance management. These results suggest that low-carbon planting design should prioritize locally adapted species, multi-layered vertical structures, and adaptive maintenance over simply maximizing planting density or minimizing inputs. The results represent scenario-based projections of aboveground vegetation carbon balance; belowground biomass, soil carbon, litter carbon, dead organic matter, and parameter uncertainty were not fully incorporated, and future studies should address these limitations to improve the robustness and transferability of the proposed framework. Full article

Climate change poses significant challenges to urban infrastructure, particularly semi-outdoor stadiums, which are highly susceptible to climate-related hazards. The current research community has gradually recognized this issue but lacks systematic insights into the capacity and methods for stadiums to cope with climate change. This review assesses the vulnerability and climate performance of semi-outdoor stadiums and identifies adaptation strategies to enhance resilience. A systematic literature review was conducted using Web of Science and Scopus databases. Key themes included thermal comfort, wind comfort, and rain protection. Thermal comfort and CFD emerged as the most dominant research focus. This review highlighted the importance of long-term climate adaptation strategies, including the use of sustainable materials, improved ventilation, and renewable energy systems. The results also indicate a lack of research on tropical climates and that more comprehensive adaptation strategies are needed. The core contribution is a structured vulnerability framework that transforms scattered evidence into an integrated knowledge structure, identifying not only dominant themes and missing links but also cross-cutting trade-offs. These findings provide actionable insights for urban planners, architects, and policymakers aiming to enhance stadium resilience and contribute to sustainable urban development goals. Full article

This study aimed to develop a stand yield table for Acacia mangium plantations in Vietnam and to analyze their economic feasibility. Field measurements were conducted across 12 temporary sample plots established in A. mangium plantations aged 1–7 years in northeastern Vietnam, using a chronosequence sampling design. Based on this, regression analyses were performed to develop stand growth models for DBH, H, SD, BA, and V. NPV measures the absolute value of the investment at a specified discount rate (4.0%, 5.5%, and 7.0%), while IRR identifies the break-even discount rate. At rotation ages of 5–7 years, IRR values are negative—a mathematically valid result arising from non-conventional cash flow structures in which Norstrom’s sign-change criterion is not satisfied; positive IRR values (~4%) are obtained from rotation age 8 onward. A sensitivity analysis confirmed that increasing yields is the most effective strategy for increasing plantation viability. This study serves as a fundamental resource for plantation projects in Vietnam and emphasizes the necessity of management improvements to create sustainable plantation operations. Full article

The red palm weevil, Rhynchophorus ferrugineus (Olivier, 1791), is among the most destructive pests of date palm (Phoenix dactylifera L.) globally, posing a severe and escalating threat to agricultural productivity across the Arabian Peninsula. Despite its well-documented economic impact, the potential influence of climate change on its future distributional dynamics within this region remains poorly quantified. This study employed Maximum Entropy (MaxEnt) species distribution modelling to assess current and projected habitat suitability for R. ferrugineus across the Arabian Peninsula (~3.2 million km²) under two contrasting Shared Socioeconomic Pathways (SSP1-2.6 and SSP5-8.5) for the mid-century (2050) and late-century (2070). The model was calibrated using 52 spatially thinned occurrence records and six non-collinear environmental predictors selected following Variance Inflation Factor (VIF) analysis, with sampling bias corrected through a kernel density-based background weighting approach. Model performance was robust, with mean training and test AUC values of 0.921 ± 0.023 and 0.840 ± 0.052, respectively, and a mean TSS of 0.583 ± 0.046. Precipitation of the coldest quarter (Bio 19) and precipitation seasonality (Bio 15) emerged as the most influential predictors of habitat suitability, followed by elevation. Currently, approximately 727,589.8 km² (26.11%) of the peninsula is classified as suitable habitat, concentrated along the eastern Arabian Gulf coastline and the western Red Sea plain. Under SSP1-2.6, suitable habitat is projected to expand by 16.34% and 31.60% by 2050 and 2070, respectively. Under the high-emission SSP5-8.5 scenario, expansions are considerably more pronounced, reaching 34.11% by 2050 and 60.15% by 2070, with total suitable area approaching 1,158,474.8 km² (41.58%) by late-century. Habitat contraction was negligible across all scenarios, indicating a unidirectional range expansion dynamic. These findings highlight the substantial threat posed by climate-driven habitat expansion of R. ferrugineus and provide spatially explicit projections to inform proactive biosecurity planning and pest management strategies for date palm cultivation across the Arabian Peninsula. Full article

Construction site fires caused by spatiotemporal overlaps between hot work (ignition sources) and combustible substances remain a critical concern. The traditional method identifies fire hazards based on the intersections among hot works and other works with combustible substances. However, the intersections between hot work and built elements containing combustible materials are ignored, which can also lead to fire accidents. In addition, the detection of such intersections relies on the computationally intensive proximity search from the ignition source to the potential combustible substances, resulting in a long-time calculation in large construction projects with the dynamic construction process. To address this limitation, this study proposes a voxel-based fire hazard identification method applicable to large 4D-BIM models, fast and accurately. By discretizing BIM into reusable LEGO voxels, both the construction activities and the building components can be mapped to the voxels, enabling a simultaneous intersection identification between ignition sources and both activities and BIM elements. In addition, voxel-based proximity searching is efficient, enabling a fast and accurate fire hazard identification. Validation tests demonstrate high accuracy with calculatable spatial error (maximum 0.57 m for 200 mm voxels) and superior efficiency (126–1368% faster than mesh-based methods). By reusing the voxelized BIM data, the speed can be enhanced by between 400% and 1975%. This method offers an efficient and reliable digital solution for proactive construction fire safety management in 4D-contexts. Full article

This study addresses the disconnect between environmental impact assessment (EIA) outputs and construction contract management, which limits the practical effectiveness of environmental decision-making in project delivery. To bridge this gap, ImpactPredict—a data-driven decision-support framework—is developed to integrate environmental impact data with environmental-based contractual risk assessment. The methodology combines: (1) severity–likelihood environmental scoring with contractual weighting to generate quantitative indicators of claim likelihood before and after mitigation; (2) developing the proposed framework using Microsoft Excel and Power BI; (3) validation using six case study energy projects in Egypt, enabling cross-case comparative analysis; and (4) statistical analysis to test the model’s sensitivity and uncertainty. The results show consistent reductions across all projects, with mitigation leading to an average 40% risk reduction across all case studies, and significant decreases in predicted claims. Linear regression analysis between initial contractual risk (CR) and residual contractual risk (RCR) produced the predictive equation R$\widehat{\mathrm{C}}$R = 4.93 + 0.351(CR). The regression coefficient and hypothesis testing (t = 3.367, p = 0.028 < 0.05) provide preliminary evidence that initial contractual risk is a statistically significant predictor of residual contractual risk. The coefficient of determination (R² = 0.758) indicates that approximately 75.8% of the variance in residual risk is explained by the initial risk conditions. In addition, low prediction error values (mean absolute error = 1.17; root mean square error = 1.28) demonstrate satisfactory predictive stability and model reliability. The sensitivity analysis indicates that the model exhibits proportional responsiveness to all input variables, with severity and likelihood identified as dominant drivers of risk magnitude, while contractual weighting governs risk translation into project performance outcomes. These findings confirm that environmental impacts can be operationalized as quantifiable contractual risk drivers. The study concludes that embedding contract-integrated environmental intelligence within accessible analytical platforms enhances decision-making, supports measurable performance improvement, and transforms EIA into a proactive risk management tool. Full article

Construction contracts are vital for governing responsibilities in large-scale infrastructure projects, but their increasing complexity often leads to interpretation difficulties, disputes, and delays. Despite advances in natural language processing (NLP), automated analysis of construction contract clauses remains limited in project management. This study proposes a text classification framework integrating transformer-based contextual embeddings (BERT, ALBERT, RoBERTa, and DistilBERT) with machine learning and deep learning models (RNN, GRU, and LSTM) to analyze FIDIC and JCT contract provisions. Two multi-class classification tasks were defined: Dataset 1 (DS1) focusing on obligations, operational actions, optional provisions, general statements, and Dataset 2 (DS2) covering cost, quality, and time dimensions. Experimental results show that deep learning models consistently outperform traditional machine learning algorithms. Specifically, LSTM combined with RoBERTa and DistilBERT achieved the highest accuracy levels of 98.06% and 98.33% for DS1. The framework may support transparent contract governance by enabling faster and more consistent identification of contractual clauses. From a sustainability perspective, the findings suggest potential process-level contributions to economic efficiency, administrative workload reduction, and decision-making support throughout the project lifecycle. Full article

Used engine oil (UEO) is a hazardous waste stream that poses significant environmental risks when improperly managed. However, its high heating value makes it a promising candidate for energy recovery. In Kuwait, rising vehicle ownership has led to increasing quantities of UEO, while the power sector remains heavily dependent on conventional fossil fuels. Although extensive research has examined UEO treatment methods and combustion characteristics, limited attention has been given to its integration into utility-scale power-generation systems. This study presents a national-scale techno-economic and environmental assessment of using UEO as a supplementary fuel for electricity generation in Kuwait. East Doha Power Station was selected as a representative case study to evaluate fuel-substitution potential and the practicality of integrating UEO into existing power-generation infrastructure. Historical vehicle-registration data were used to estimate UEO generation, and future availability was projected through 2035 based on vehicle-growth trends. The corresponding thermal energy potential, equivalent electricity generation, fuel-displacement capacity, economic benefits, and environmental impacts were subsequently evaluated. The results indicate that annual UEO generation is projected to increase from approximately 181,800 tonnes/year in 2024 to 303,300 tonnes/year in 2035. This quantity corresponds to about 12,126 TJ/year of recoverable thermal energy and an equivalent electricity-generation potential of approximately 1.1 TWh/year (4000 TJ/year), assuming a power-plant efficiency of 33%. The recovered UEO could displace approximately 311,000 tonnes/year of heavy oil or 287,000 tonnes/year of crude oil, with estimated net annual fuel-cost savings of approximately 28–30 million KD. Based on literature-reported emission factors, UEO utilization could reduce combustion-related CO₂ emissions by up to 19.0% and NO_x emissions by up to 45.5% compared with heavy oil. Sensitivity analysis further confirmed the robustness of the findings under a range of recovery and operating conditions. To the best of the authors’ knowledge, this study represents the first comprehensive national-scale assessment of the potential use of UEO for utility-scale power generation in Kuwait. The findings indicate that UEO has the potential to serve as a strategic secondary energy resource that supports waste reduction, fuel conservation, economic savings, and circular-economy objectives. However, practical implementation will require appropriate collection and treatment infrastructure together with further technical validation, pilot-scale demonstration, and regulatory evaluation. Full article

Maritime accident analysis increasingly uses machine learning to support safety management, but many existing studies focus on single-output prediction, such as accident-occurrence probability, severity class, near-miss frequency, or one specific consequence. This study proposes a data-driven decision-support framework based on a Multi-Input Multi-Output Artificial Neural Network (MIMO-ANN) for the simultaneous prediction of multiple maritime accident consequences. A dataset of 582 recorded accident cases is constructed by integrating SafePASS project records with consequence, severity, and structural-damage information from the literature. The dataset includes 15 input variables covering ship characteristics, operational context, environmental conditions, accident type, and geographical zone and 15 consequence outputs covering structural damage, casualties, emergency-response indicators, total loss, and secondary consequence/escalation mechanisms. The ANN is trained using the Scaled Conjugate Gradient (SCG) algorithm and evaluated under different network configurations and data-partitioning strategies. The best-performing model uses 30 hidden neurons with a 60/20/20 split, achieving a correlation coefficient (R) equal to 0.9249 and a mean squared error (MSE) equal to 0.0240 for testing, and a R equal to 0.9278 and a MSE equal to 0.0231 for validation. Ten-fold cross-validation further confirms internal predictive stability, with mean testing R equal to 0.8803 ± 0.0827 and MSE equal to 0.0445 ± 0.0478. Permutation-based sensitivity analysis shows that accident type, zone, flag, natural light, environment, and visibility are key drivers of predicted consequences, whereas vessel-specific parameters have a secondary, context-dependent influence. The framework should be interpreted as predicting the relative likelihood, severity, or magnitude of accident consequences in recorded or scenario-defined accident cases, not the probability of accident occurrence. Future work should address dataset imbalance, include near-miss and nonserious records, incorporate richer AIS and metocean data, integrate exposure data, and validate the framework using independent accident datasets. Full article

Construction contract durations are fixed during procurement, yet delivery often changes after risks materialize and formal extensions of time are approved. Although delays, extension-of-time claims, change orders, and risk-based duration estimation are well studied, less is known about how contract-amendment records can be converted into duration multipliers for planning. This paper develops a quantitative, document-based Risk-Calibrated Duration Multiplier framework linking initially contracted duration, approved extensions, and documented risk causes. The framework was applied to 197 signed works contracts from 60 projects within a broader portfolio of 63 EU-funded water and wastewater infrastructure projects, predominantly administered under FIDIC Red and Yellow Book conditions. The analysis combined duration multipliers, impact-weighted attribution of multi-risk amendments, risk-time coefficients, bootstrap uncertainty assessment, concentration indicators, benchmark regression models, and reconstruction validation. For completed contracts, the mean multiplier was 1.372, with P50, P80, and P90 values of 1.233, 1.635, and 1.886. Public-law procedural and design risk categories accounted for 60.9% of the total extension premium. The results show that contract-amendment records can be transformed into statistically interpretable planning parameters and used as a portfolio learning and contract-governance tool for more realistic infrastructure contract planning. Full article

This study provides a structured operational-to-financial roadmap for soil organic carbon (SOC) sequestration in mineral soils as a specific carbon-farming pathway. It integrates SOC management; Monitoring, Reporting, and Verification (MRV) execution; financial recognition; and farmer adoption barriers. A comparison of carbon farming pathways is first presented to investigate their strengths and limitations, highlighting the specific importance of SOC management in mineral soils. For high-integrity carbon accounting, SOC gains should be assessed not only for quantity, but also for additionality, permanence, uncertainty, leakage, lifecycle emissions, and transparent verification. Credible MRV frameworks operationalize this logic: monitoring quantifies SOC changes, reporting ensures transparency, and verification provides independent assurance for carbon credit issuance and financial recognition. However, MRV execution faces several challenges, including high spatial variability of SOC, slow accumulation rates, methodological uncertainty, and high costs that limit scalability and reduce trust among stakeholders. Financial incentives are available from both public and private sources, supporting long-term soil carbon stabilization, verified carbon removals, and corporate insetting projects. Yet, adoption remains constrained by uncertain payments, poor transparency, contract and permanence concerns, as well as learning and operational costs for farmers. Addressing these bottlenecks is essential for transforming mineral-soil SOC sequestration into a scalable, high-integrity climate and economic opportunity. Full article

The transition from antibiotic-based (AB) to antibiotic-free (ABF) broiler production represents a major shift in poultry management, with potential implications for flock health, welfare, and processing outcomes. This study evaluated its impact on condemnation percentage (%condemnation) using Bayesian structural time-series (BSTS) analysis. Data from a Thai integrator comprised 105,899 truckload-level records (2015–2020) across 260 contract farms. The AB period (2015–2017) served as the baseline, and the ABF period (2018–2020) was assessed using counterfactual projections. Time-series decomposition and change-point analysis revealed an increasing trend in %condemnation during the early phase of ABF implementation, followed by a decline in 2020, with five structural shifts detected. The BSTS model estimated an absolute effect of +1.10% (95% CI: −1.50 to 3.80; p = 0.207) and a relative effect of +95% (95% CI: −38% to 657%), indicating no statistically significant causal impact. The transient increase may reflect short-term adaptation challenges, whereas subsequent stabilization may be associated with adaptation to ABF production and other concurrent management changes. Overall, the transition from AB to ABF production did not significantly affect %condemnation. Adaptive management measures were implemented as a company-wide policy but were not directly evaluated within the BSTS framework. Full article

The restoration of karst rocky desertification is reflected not only in the reduction in severely degraded areas but also in the stability of the restoration process and the potential risk of future reversal. Taking southeastern Yunnan, China, as the study area, this study constructed a six-period rocky desertification grade sequence for the years 2000, 2005, 2010, 2015, 2020, and 2024 using Landsat imagery, CLCD land-cover data, and DEM-derived slope constraints. Area change analysis, grade-transition matrices, Sen–MK trend analysis, coefficient of variation (CV), Markov–PLUS scenario simulation, scenario-sensitivity analysis, and PLUS driver contribution assessment were integrated into a process-oriented diagnostic framework to examine rocky desertification recovery from three dimensions: grade-structure adjustment, trend-variability stability, and potential future reversal risk. The results indicate that rocky desertification in southeastern Yunnan generally weakened from 2000 to 2024. The proportion of moderate-and-above rocky desertification decreased from 50.32% to 28.58%, while non-rocky desertification and potential rocky desertification expanded substantially. Grade transitions were dominated by gradual conversions among adjacent classes, with the most evident improvement occurring during 2010–2015, when the proportion of improvement transitions reached 44.16%. The trend-variability analysis indicated that while improvement dominated the study area overall, the northern, northwestern, central mountainous, and parts of the southwestern areas still exhibited relatively strong variability and localized deterioration risk. Hindcast validation showed relatively high map-level consistency between simulated and historical patterns, with an overall accuracy of 0.9354 and a Kappa coefficient of 0.9153. The three-scenario comparison further showed that the proportion of moderate-and-above rocky desertification varied from 28.07% to 29.41% in 2030 and from 26.85% to 29.06% in 2035 under different transition-probability assumptions. Specifically, the ecological restoration enhancement scenario reduced projected moderate-and-above rocky desertification, whereas the degradation pressure scenario increased it relative to the baseline scenario. These findings indicate that rocky desertification recovery in southeastern Yunnan is not a continuous or linear process, but is characterized by stage-specific adjustment, spatial differentiation, and local variability. Therefore, future rocky desertification control should focus not only on reducing high-severity areas, but also on maintaining restoration stability, identifying variability-sensitive transitional zones, and strengthening differentiated management in areas where terrain constraints, land-cover proximity, and historical variability jointly increase reversal risk. Full article