Abstract:

Risk management is a critical process for achieving construction project objectives and supporting more sustainable project delivery. However, most existing research focuses on isolated aspects of risk, lacking an integrated approach that examines how risks evolve across the entire project life cycle. This study addresses this gap by identifying and assessing key risks affecting construction projects in the United Arab Emirates (UAE), with attention to how improved risk understanding can contribute to more resilient and sustainable project outcomes. Through a literature review, fifteen critical risks involving various stakeholders were identified. A questionnaire survey was conducted to evaluate the probability and impact of these risks on project cost. The study analyzes how these risks manifest across the project life cycle and affect different stakeholders. Using a coordinate system, it visualizes risk behavior across phases, offering a dynamic view of risk exposure. Findings show that the construction phase was the riskiest, followed by the handover, design, and feasibility phases. Additionally, delayed payments by owners emerged as the most significant risk, followed by poor contractor management. The study proposes a modified probability–impact matrix to account for multi-phase risks. These findings provide valuable insights for construction firms, helping improve stakeholder risk allocation, inform contract negotiations, and enhance project delivery in the UAE context while contributing to more efficient, responsible, and sustainable project management practices.

Abstract:

This paper uses fuzzy logic to make intelligent agents that can show stupidity and context awareness in a simulation of operational environment. Focusing on dynamic interactions, the fuzzy inference system will help agents to change their behaviour according to factors that are increasing namely proximity, over-speed and environmental factors. The way the author explains is going to help develop responsive and resilient decision making automated components. These components will be able to optimize complex multi-stakeholder systems and will certainly be useful in modern supply chain systems. The study noted that AI can be used to increase logistics and inventory management flexibility and reactivity. Moreover, it was noted for both technical and practical.

Abstract: Climate change will pose greater challenges to occupational safety and health, and advanced approaches for risk and reliability modeling that can include sustainability perspectives are needed. This scoping review aims to map and synthesize the current methodologies on modeling OSH risks in the context of climate change and sustainability. We systematically searched the Scopus, Web of Science, PubMed, ERIC, and other databases following the PRISMA-ScR guidelines for studies published from 2010 to 2025. Seventeen studies met the inclusion criteria and were categorized into four thematic groups: the reliability engineering and probabilistic models, climate-related occupational risk models, hybrid and AI-based models, and the sustainability-oriented reviews. Results The study indicates a methodological shift from deterministic toward probabilistic and systems-based toward data-driven approaches, while heat stress is identified as the key climate-related hazard. Sustainability considerations are increasingly embedded within the risk frameworks, linking safety outcomes with productivity, labor capacity, and resilience. Still, the integration is uneven, and key performance metrics are underreported. The advanced computational methods, such as Bayesian networks and machine learning techniques, along with hybrid models, show promise but need further validation in a wide range of occupational settings. CONCLUSION This review stresses the need for more integrative, transparent, and harmonized modeling practices, providing support to climate-resilient and sustainable OSH management systems.

Abstract:

Coal and gas outbursts constitute a major hazard for mining safety, which is critical for the sustainable development of China’s energy industry. Rapid, accurate and reliable prediction is pivotal for preventing and controlling outburst incidents. Nevertheless, the mechanisms driving coal and gas outbursts involve highly complex influencing factors. By examining the attributes of these factors and their association to outburst intensity, four major geological and environmental indicators were identified. This study developed a machine learning-based prediction model for outburst risk. Five algorithms were evaluated: K-Nearest Neighbors (KNN), Back Propagation Neural Network (BPNN), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost). Model optimization was performed via Bayesian hyperparameter (BO) tuning. Model performance was assessed by the Receiver Operating Characteristic (ROC) curve; the optimized XGBoost model demonstrated strong predictive performance. To enhance model transparency and interpretability, the SHapley Additive exPlanations (SHAP) method was implemented. The SHAP analysis identified geological structure was the most important predictive feature, providing a practical decision-support tool for mine executives to prevent and control outburst incidents.

Abstract: Existing experimental results are sometimes difficult to guide the design of a water mist fire extinguishing system ascribing to many factors that affect the fire extinguishing per-formance of water mist. This paper sums up the factors and combs the logical relation-ships between them based on fire extinguishing mechanism of water mist and existing literature. Direct influence factors on fire extinguishing performance are analyzed em-phatically by the model of movement, heat transfer and mass transfer of water mist in the flame zone. The results show that the velocity and diameter of water mist entering the flame zone can be determined according to the temperature difference and the height of flame without considering the action of the flame plume. The water mist will enter the flame zone from the top and the periphery of the flame when the plume effect cannot be ignored. And the maximum heat absorption power of the water mist entering through the two ways should be obtained when determining the velocity and diameter of the water mist. This research can serve as a theoretical basis for the design of a water mist fire ex-tinguishing system.

Abstract: In this paper, we presented BlockShare, a blockchain-basedsystem developed to facilitate privacy-preserving data sharing across de-centralized networks. The proposed system enables users to retain controlover their sensitive data while enabling secure, verifiable sharing with au-thorized parties. We implemented an authenticated data structure (ADS)to support decentralized verification and utilized zero-knowledge proofmechanisms to validate conditions without exposing the underlying data.Experimental analysis demonstrated that BlockShare performs efficientlyin constructing data structures, generating proofs, and verifying themwith minimal computational overhead. The platform successfully reducedprivacy risks and enhanced trust in cross-organization data exchanges.

Abstract: The present study evaluates occupational health and safety (OHS) risks at the 400/220/110/20 kV Arad Power Substation, a critical infrastructure node in Romania’s energy network, within the context of industrial development and the need to prevent energy crises. As the demand for electricity grows alongside industrial expansion, substations face increasing operational pressures, making risk management essential for ensuring workforce safety and system reliability. The assessment integrates hazard identification, risk analysis, and mitigation strategies specific to high-voltage environments, including electrical, mechanical, ergonomic, and environmental hazards. Particular attention is given to high-voltage exposure, fire hazards, equipment malfunction, and emergency response readiness. Using a combination of qualitative and quantitative approaches, the study identifies high-risk operations and proposes targeted interventions, such as improved protective equipment, training programs, maintenance protocols, and real-time monitoring systems. The findings underscore that proactive OHS measures not only safeguard personnel but also enhance operational continuity, thereby contributing to regional energy security and supporting industrial growth. By aligning health and safety management with strategic energy planning, the study demonstrates how systematic risk assessment at high-voltage substations can mitigate industrial disruptions and prevent cascading energy crises. The results provide a framework for policymakers, engineers, and OHS professionals seeking to balance workforce protection with energy infrastructure resilience.

Abstract: The widespread use of textual data sanitization techniques,such as identifier removal and synthetic data generation, has raised ques-tions about their effectiveness in preserving individual privacy. This studyintroduced a comprehensive evaluation framework designed to measureprivacy leakage in sanitized datasets at a semantic level. The frameworkoperated in two stages: linking auxiliary information to sanitized recordsusing sparse retrieval and evaluating semantic similarity between orig-inal and matched records using a language model. Experiments wereconducted on two real-world datasets, MedQA and WildChat, to assessthe privacy-utility trade-off across various sanitization methods. Resultsshowed that traditional PII removal methods retained significant privateinformation, with over 90% of original claims still inferable. Syntheticdata generation demonstrated improved privacy performance, especiallywhen enhanced with differential privacy, though often at the cost ofdownstream task utility. The evaluation also revealed that text coher-ence and the nature of auxiliary knowledge significantly influenced re-identification risks. These findings emphasized the limitations of currentsurface-level sanitization practices and highlighted the need for robust,context-aware privacy mechanisms that balance utility and protection insensitive textual data releases.

Abstract: This paper presents an outline of the problems facing the Polish energy sector. It high-lights the significant role of wind energy in the National Power System, while limiting the possibility of installing new wind farms. It is suggested that repowering and ex-tending the operational life of wind turbines will be an important solution to this problem. The possibility of using data from existing turbines to inform operational strategies was analyzed. Historical data was obtained for selected wind turbines and statistically analyzed. The main goal of the study was to develop regression models for wind conditions and electricity production. The best fit between the actual distribu-tions of the analyzed variables and selected theoretical distributions was determined. It was demonstrated that in the analyzed case, the Log-Normal distribution provided a better fit than the Weibull distribution, preferred by the energy industry.

Abstract: Unexploded ordnance (UXO) poses a significant hazard in military training areas. This paper assesses the effectiveness of aerial drone-mounted magnetometry for detecting buried UXO located outside the designated impact zones of the National Training Center (CENAD) of San Gregorio (Zaragoza, Spain), considered the largest maneuver area in Europe. To this end, a high-resolution aeromagnetic survey was conducted using a GEM GSMP-35U proton magnetometer mounted on a hexacopter drone. Data were collected at flight altitudes of 7 m and 2 m above ground level along a grid with 1-m line spacing. For its validation, eleven UXOs were deliberately buried at known coordinates to evaluate the system’s sensitivity and spatial resolution under operational conditions. The results demonstrate the capability of aerial drone-based magnetometry to detect small magnetic anomalies (with amplitudes between 2 and 18 nT) associated with buried UXO in complex environments characterised by high ferromagnetic noise, achieving signal-to-noise ratios greater than 5 (SNR > 5) at 2-m altitude and a geolocation accuracy of approximately 0.5 m. These findings support the use of unmanned aerial magnetometry as a viable tool for identifying hazardous remnants in military training ranges and operational scenarios, enabling coverage of 0.53 ha in less than one hour of effective flight time.

Abstract: In complex energy storage operating scenarios, batteries seldom undergo complete charge–discharge cycles required for periodic capacity calibration. Methods based on accelerated aging experiments can indicate possible aging paths; however, due to uncertainties like changing operating conditions, environmental variations, and manufacturing inconsistencies, the degradation information obtained from such experiments may not be applicable to the entire lifecycle. To address this, we develop a stage-wise state-of-health (SOH) prediction approach that combines offline training with online updating. During the offline training phase, multiple single-cell experiments were conducted under various combinations of depth of discharge (DOD) and C-_rate. Multi-dimensional health features (HFs) were extracted and an accelerated aging probability p_AA was defined. Based on the correlation statistics between HF, k_HF, SOH, and p_AA, all cells in the dataset were divided into general early, middle, and late aging stages. For each stage, cells were further classified by their longevity (long, medium, short), and multiple models were trained offline for each category. The results show that models trained on cells following similar aging paths achieve significantly better performance than a model trained on all data combined. Meanwhile, HF optimization was performed via a three-step process: an initial screening based on expert knowledge, a second screening using Spearman correlation coefficients, and an automatic feature importance ranking using a random forest regression (RFR) model. The proposed method offers the following innovations: (1) The stagewise multi-model strategy significantly improves SOH prediction accuracy across the entire lifecycle, maintaining the mean absolute percentage error (MAPE) within 1%. (2) The improved model provides uncertainty quantification, issuing a warning signal at least 50 cycles before the onset of accelerated aging, thereby enabling early detection of accelerating degradation. (3) Analysis of feature importance from the model outputs allows indirect identification of the primary aging mechanisms at different stages. (4) The model is robust against missing or low-quality HFs—if certain features cannot be obtained or are of poor quality, the prediction process does not fail.

Abstract: Workplace safety and health remain a major global challenge, with work-related accidents and diseases still causing millions of deaths each year despite decades of regulatory, technical and organizational advances. In parallel, the digitalization of Industry 4.0/5.0 is generating unprecedented volumes of safety-relevant data and new opportunities to move from reactive analysis to proactive, data-driven prevention. This review maps how artificial intelligence (AI), with a specific focus on natural language processing (NLP) and large language models (LLMs), is being applied to occupational risk prevention across sectors. A structured search of the Web of Science Core Collection (2013– October 2025), combined OSH-related terms with AI, NLP and LLM terms. After screening and full-text assessment, 126 studies were discussed. Early work relied on text mining and traditional machine learning to classify accident types and causes, extract risk factors and support incident analysis from free-text narratives. More recent contributions use deep learning to predict injury severity, potential serious injuries and fatalities (PSIF) and field risk control program (FRCP) levels, and to fuse textual data with process, environmental and sensor information in multi-source risk models. The latest wave of studies deploys LLMs, retrieval-augmented generation and vision–language architectures to generate task-specific safety guidance, support accident investigation, map occupations and job tasks, and monitor personal protective equipment (PPE) compliance. Together, these developments show that AI-, NLP- and LLM-based systems can exploit unstructured OSH information to provide more granular, timely and predictive safety insights. However, the field is still constrained by data quality and bias, limited external validation, opacity, hallucinations and emerging regulatory and ethical requirements. In conclusion, AI and LLMs should be positioned as human-in-the-loop decision-support tools and outlines a research agenda centered on high-quality OSH datasets, hybrid models integrating domain knowledge and AI, and rigorous evaluation of fairness, robustness, explainability and governance.

Abstract: Early detection of bubble generation from tube arrays in systems such as fast reactor steam generators, Pressurized Water Reactor (PWR) cores, and Liquefied Natural Gas (LNG) regasification units is critical for safety. While various methods have been proposed, they face challenges such as high spatial resolution requirements, rapid response times, and varying strengths and weaknesses, suggesting the need for a combined approach. This study integrates the ultrasonic testing (UT) with Machine Learning (ML) to identify the presence, location, and direction of bubbles within a complex tube array that cause signal attenuation. A Convolutional Neural Network (CNN) successfully achieved 100% identification accuracy. Furthermore, a novel method was developed that uses an autoencoder as a feature extractor, combined with a One-Class Support Vector Machine (SVM) and k-means. This approach achieved high accuracy and a correct decision basis. It also demonstrated strong generalization, successfully detecting anomalies without requiring labels for anomalous data, enabling robust bubble identification.

Abstract: The acceptance or rejection of a measurement is determined based on its associated measurement uncertainty. This decision-making process inherently carries the risk of errors, including the possible rejection of compliant measurements or the acceptance of non-conforming ones. This study introduces a mathematical model for the spatial evaluation of global risk to both producers and consumers, grounded in Bayes' theorem and with the application of a decision rule incorporating a guard band. The proposed model is well-suited for risk assessment within the framework of multivariate linear regression. The model's applicability was demonstrated through an example involving the flatness of the workbench table surface of the CMM. The least-risk direction on the workbench was identified, and risks were calculated under varying selections of the reference planes and differing measurement uncertainties anticipated in future measurement processes. Model evaluation was conducted using performance metrics derived from confusion matrices. The spaces of the most used metrics over the domain limited by the dimensions of the CMM workbench were constructed. Using the tested metrics, the optimal widths of the guard band were determined, which ensures the smallest values of the global producer's and consumer's risk.

Abstract: Corporate bankruptcy prediction has become increasingly critical amid economic uncertainty. This study proposes a novel two-stage machine learning approach to enhance bankruptcy prediction accuracy, applied to Tokyo Stock Exchange-listed companies. First, models were trained using 173 financial indicators. Second, a wrapper-based feature selection process was employed to reduce dimensionality and eliminate noise, thereby identifying an optimal seven-feature set. Two ensemble learning methods, Random Forest and LightGBM, were used. Random Forest correctly predicted 566 bankruptcies using the reduced feature set (88 more than when using all features) compared with 451 by LightGBM (31 more than when using all features). The study also addresses challenges posed by imbalanced data by employing resampling techniques (SMOTE, SMOTE-ENN, and KMeans). Additionally, the need for industry-specific modeling is recognized by constructing models for the six industry sectors. These findings highlight the importance of feature selection and ensemble learning for improving model generalizability and uncovering industry-specific patterns. This study contributes to the field of bankruptcy prediction by providing a robust framework for accurate and interpretable predictions for both academic research and practical applications. Future work will focus on further enhancing prediction accuracy to identify more potential bankruptcies.

Abstract: This study assesses the impact of ISO/IEC 17025 accreditation on quality performance, operational efficiency, and customer satisfaction at Eskom’s Flow Laboratory, a key calibration facility in South Africa’s power generation sector. It investigates how ac-creditation has influenced technical competence, service delivery, and customer con-fidence. Data was collected through questionnaires from 82 customers across sectors including power generation (28%), environmental testing (24%), manufacturing (20%), mining (15%), and other industries (13%). Results show strong positive perceptions of post-accreditation performance, with an overall satisfaction mean of 4.55/5 and 88% agreement among respondents. The highest scores were for result accuracy and relia-bility (4.7), staff competence (4.6), and customer willingness to recommend the lab (4.8). Furthermore, 91% agreed that accreditation improved confidence in test results, while 88% cited stronger quality control and traceability. Reported benefits included enhanced efficiency (85%), transparency (83%), and international recognition (87%). Overall, ISO/IEC 17025 accreditation has significantly strengthened the laboratory’s technical credibility, operational consistency, and client trust. The study concludes that sustaining these gains will require ongoing investment in staff development, customer engagement, and technology, in support of Eskom’s broader goals of operational ex-cellence, reliability, and customer satisfaction.

Abstract: Oversight of U.S. airports under FAA Part 139 is primarily checklist-driven, whereas the ICAO Safety Management System (SMS) emphasizes proactive, performance-based safety practices. Despite widespread adoption of both approaches, quantitative comparisons remain limited. This study develops a structured mapping framework aligning FAA Part 139 requirements with the four ICAO SMS pillars: safety policy, risk management, safety assurance, and safety promotion. Publicly available FAA inspection findings were normalized using OPSNET operational exposure data and compared with ICAO’s Universal Safety Oversight Audit Programme (USOAP) indicators to identify areas of overlap and divergence and to assess whether checklist compliance and SMS routines produce consistent outcomes. The study presents a detailed crosswalk between the two frameworks, descriptive statistics on inspection deficiencies, and comparative assessments of regulatory coverage. Regression modeling evaluates the relationship between SMS implementation levels and FAA inspection outcomes while controlling for airport size and operational context. Findings indicate that Part 139 oversight performs effectively in high-consequence areas, including Aircraft Rescue and Firefighting (ARFF), while SMS contributes to early hazard detection and strengthens safety culture. Based on these results, a hybrid oversight model integrating mandatory inspections with predictive SMS practices is proposed to enhance U.S. aviation safety and support alignment with international standards.

Abstract:

Structure detection(SD) has emerged as a critical technology for ensuring the safety and longevity of infrastructure, particularly in housing and civil engineering. Traditional SD methods often rely on manual inspections, which are time-consuming, labor-intensive, and prone to human error, especially in complex environments such as dense urban settings or aging buildings with deteriorated materials. Recent advances in autonomous systems—such as Unmanned Aerial Vehicles (UAVs) and climbing robots—have shown promise in addressing these limitations by enabling efficient, real-time data collection. However, challenges persist in accurately detecting and analyzing structural defects (e.g., masonry cracks, concrete spalling) amidst cluttered backgrounds, hardware constraints, and the need for multi-scale feature integration . The integration of machine learning (ML) and deep learning (DL) has revolutionized SD by enabling automated feature extraction and robust defect recognition. For instance, RepConv architectures have been widely adopted for multi-scale object detection, while attention mechanisms like TAM (Technology Acceptance Model) have improved spatial feature fusion in complex scenes. Nevertheless, existing works often focus on singular sensing modalities (e.g., UAVs alone) or neglect the fusion of complementary data streams (e.g., ground-based robot imagery) to enhance detection accuracy. Furthermore, computational redundancy in multi-scale processing and inconsistent bounding box regression in detection frameworks remain underexplored. This study addresses these gaps by proposing a generalized safety inspection system that synergizes UAV and stair-climbing robot data. We introduce a novel multi-scale targeted feature extraction path (Rep-FasterNet TAM block) to unify automated RepConv-based feature refinement with dynamic scale fusion, reducing computational overhead while preserving critical structural details. For detection, we combine traditional methods with remote sensor fusion to mitigate feature loss during image upsampling/downsampling, supported by a structural model GIOU [Mathematical Definition：GIOU=IOU-（C-U）/C] that enhances bounding box regression through shape/scale-aware constraints and real-time analysis. By siting our work within the context of recent reviews on ML/DL for SD, we demonstrate how our hybrid approach bridges the gap between autonomous inspection hardware and AI-driven defect analysis, offering a scalable solution for large-scale housing safety assessments. In response to challenges in detecting objects accurately during housing safety assessments—including large/dense objects, complex backgrounds, and hardware limitations—we propose a generalized inspection system leveraging data from UAVs and stair-climbing robots. To address multi-scale feature extraction inefficiencies, we design a Rep-FasterNet TAM block that integrates RepConv for automated feature refinement and a multi-scale attention module to enhance spatial feature consistency. For detection, we combine dynamic scale remote feature fusion with traditional methods, supported by a structural GIOU model that improves bounding box regression through shape/scale constraints and real-time analysis. Experiments demonstrate that our system increases masonry/concrete assessment accuracy by 11.6% and 20.9%, respectively, while reducing manual drawing restoration workload by 16.54%. This validates the effectiveness of our hybrid approach in unifying autonomous inspection hardware with AI-driven analysis, offering a scalable solution for SD in housing infrastructure.

Abstract: Traditional route optimization frameworks often suffer from "spatial blindness," ad-dressing the problem through abstract matrices devoid of geographical context, which yields suboptimal solutions in complex landscapes. To address this fundamental methodological gap, this study proposes the Iterative Score Propagation Algorithm (ISPA), a transparent, GNN-inspired framework that reframes optimization from a set of isolated points to a holistic corridor problem. The robustness and superiority of ISPA were rigorously tested against established Multi-Criteria Decision-Making (MCDM) methods (WLC, TOPSIS, VIKOR). This comparative analysis was conducted across three diverse engineering scenarios—ranging from balanced engineering (rural high-way) and cost-centric optimization (pipeline) to experience maximization (trekking trail)—and under two distinct weighting philosophies (objective Entropy and subjec-tive AHP). The holistic analysis reveals that ISPA achieves the highest final score (0.815) across all six test conditions, demonstrating both the highest overall mean per-formance and the greatest stability. Furthermore, its flexible cost function successfully modeled unconventional objectives, such as a "climbing reward," showcasing a para-digm shift from cost minimization to experience maximization. We conclude that ISPA's superior performance stems from its structural advantage in contextualizing spatial data, rather than a dependency on any specific weighting scheme. Conse-quently, this work introduces not just a novel algorithm but a new, spatially-aware approach that transforms route planning from a static calculation into a dynamic de-sign and scenario analysis tool for planners and engineers.

Abstract: The present study examines the mechanisms of overtopping failure and the evolution laws of homogeneous earth embankments during the flood season. To this end, the Changkai embankment in Fuzhou City, Jiangxi Province, was selected as a case study, with seven groups of indoor model tests conducted to consider the effects of different embankment top widths, embankment heights, river water depths, and river flow rates. The test results are as follows: Overtopping failure of earth embankments can be categorised into three distinct stages. The breach formation process can be categorised into three stages: vertical erosion (stage I), breach expansion (stage II) and breach stabilisation (stage III). River water levels and inflow rates were identified as pivotal factors influencing the final morphology of the breach and the flow velocity within it. Conversely, the height of the dike was found to have little influence on the shape of the breach and the flow velocity. The breach width ranges from 6 cm to 12 cm. An increase in water depth, corresponding to a greater difference in water levels on both sides of the river, has been observed to result in a deeper breach and faster widening rate. Elevated water levels have been shown to increase the potential energy of the water, which is subsequently converted into greater kinetic energy during breach formation. This, in turn, increases the flow velocity at the breach. However, a negative correlation has been observed between inflow velocity and flow at the breach. This paper combines the material properties of the embankment to discuss the overtopping failure mechanism and the breach evolution law of homogeneous earth embankments. This provides a basis for preventing and controlling embankment failure disasters.