Search Results (552)

Road construction, inspection and maintenance are activities that often require workers near heavy equipment, traffic, and dangerous materials. This proximity to potential hazards along with the characteristics of highway and street work zones—transient and in restricted areas—increases the possibility of accidents and near-misses. Recent developments in remote-controlled technology can provide workers and inspectors with the ability to conduct activities from a safer distance. This paper aims to scan and evaluate several promising remote-controlled technologies that could be used to improve safety in highway and streets work zones. The technology scanning highlighted over twenty technologies in several levels of development that met this goal. Each technology was briefly evaluated not only based on safety features but also on productivity, data processing, and requirements for implementation. Finally, recommendations for implementation of selected technologies were provided. This consolidated review provides a unique and timely resource for researchers and practitioners. Full article

Unsafe behaviors by construction workers are a primary cause of accidents in commercial building construction. While traditional studies focus on the frequency of violations, they often overlook the disorder and unpredictability of such behaviors. This study introduces “Unsafe Behavior Entropy” as a new index to measure the disorder of workers’ behaviors, complementing traditional violation frequency. Utilizing a dataset from a large-scale commercial building construction project in Wuhan, China, this research uses Partial Least Squares Regression (PLSR) and Gray Relational Analysis (GRA) to examine the influence of six key factors, including safety meeting coverage and supervision density. The PLSR results indicate that the number of workers supervised per safety officer is the most critical driver of both frequency and entropy, while the coverage rate of entry safety education significantly impacts behavioral stability. GRA findings further reveal a high degree of correlation between management interventions and reductions in behavioral disorder. The study concludes that optimizing safety resource allocation and standardizing educational processes are fundamental to controlling human-related risks. By integrating the dual perspectives of frequency and entropy, this research provides a more comprehensive framework for safety management in complex building projects. Full article

Renewable energy sources, and wind energy in particular, constitute a central pillar of energy policy at both national and European levels. Nevertheless, the deployment of onshore wind farms is frequently associated with spatial, environmental, and social conflicts, making the evaluation of existing projects imperative. The present study aimed to assess the sustainability of existing onshore wind farms in the Region of Thessaly, with particular emphasis on their spatial planning, technical characteristics, and environmental impacts. The methodological framework consists of four distinct stages: (i) identification and spatial mapping of existing wind farms in the study area, (ii) assessment of the compliance of existing wind installations with the Specific Framework for Spatial Planning and Sustainable Development for Renewable Energy Sources (SFSPSD–RES), (iii) application of the Rapid Impact Assessment Matrix (RIAM) to enable a systematic and comparable evaluation of the impacts of wind installations on specific environmental and anthropogenic parameters, and (iv) estimation of project hazard and operational vulnerability through the application of Operational Risk Management (ORM). Geographic Information Systems (GISs) were employed for data processing and spatial analysis. The assessment showed that 40% of the evaluated wind farms fully comply with all eleven exclusion criteria of the SFSPSD-RES, whereas the remaining 60% show partial compliance, failing to meet between one and three criteria. RIAM results indicate that the most significant adverse impacts (−D and −C) during construction are associated with morphology/soils and the natural environment, mainly due to loss/fragmentation of vegetation and disturbance of fauna, and, in some cases, in areas of increased sensitivity. During operation, the main negative effects (−D and −C) relate to landscape and visual quality, as well as continued disturbance to the natural environment. At the same time, the operation generates important positive effects (+E) on the atmospheric environment through reduced CO₂ emissions. The ORM analysis further shows that the most important risks for most wind farms arise during construction (ORM = 2 and 3), particularly from serious worker accidents during lifting, roadworks, and foundation activities. The study demonstrates that the sustainability of existing wind installations depends on a complex set of spatial, environmental, and technical factors. The proposed framework integrates spatial compliance screening, RIAM-based environmental impact assessment, and ORM-based risk and opportunity evaluation. This connection links the importance of impacts with their operational manageability during construction and operation phases, as well as across sustainability dimensions. Consequently, the study provides a more decision-focused approach for assessing existing wind farms and supporting policy development. Full article

Background: Work at heights is a high-risk occupational activity, with falls being a leading cause of fatal accidents in construction and industrial maintenance. Conventional safety training often does not fully prepare workers for real-world hazards. Immersive virtual reality (IVR) has emerged as a promising training tool, providing controlled and realistic simulations of hazardous scenarios. This hypothesis-generating pilot study evaluates the feasibility and effectiveness of IVR in enhancing practical skills, safety perception, and physiological responses during work-at-height training. Methods: This controlled trial will recruit first-time trainees from the National Learning Service (SENA) of Colombia. Participants will be assigned to an intervention group, receiving IVR training before field-based practical sessions, or a control group, receiving standard theoretical instruction. Outcomes include practical skill acquisition, ergonomic risk, cognitive performance, and physiological responses, including heart rate variability measured with validated devices. Assessments will be performed using standardized tools, and data will be analyzed with repeated-measures ANOVA and regression models to compare groups. Conclusions: By integrating practical, cognitive, ergonomic, and physiological measures, this study will provide evidence on whether IVR improves the effectiveness of work-at-height training beyond conventional methods. Findings may inform future strategies to enhance occupational safety training in high-risk work environments. Full article

Project ownership is a critical factor that shapes safety management systems and accident patterns in construction. However, the Ministry of Employment and Labor (MOEL) industrial accident database, which is the largest construction accident database in Korea, does not include project ownership information. To address this limitation, this study developed a fine-tuned KLUE-BERT framework that automatically classifies project ownership using unstructured text fields (site name, client name, and workplace name) in MOEL data. Training data were constructed through manual classification of the 2018–2023 approved statistics and data augmentation. The proposed model achieved high classification performance. Multilayered statistical analyses were conducted using the classified 2014–2023 construction accident data across six key accident variables: accident type, accident cause, construction scale, accident severity, occupation, and worker tenure. The results revealed statistically significant associations between project ownership and all six variables. Public projects exhibited relatively high proportions of accidents involving construction machinery and vehicles, whereas private projects exhibited higher proportions of fall- and scaffold-related accidents. This study presents a novel artificial intelligence-based framework that generates analytical variables absent from the original data and demonstrates its utility through large-scale statistical analysis. The findings provide empirical evidence to support the development of project ownership-specific construction safety policies. Limitations include potential data leakage from pre-split augmentation and generalizability limited to Korean construction data. Full article

Electric power station confined spaces are high-risk and complex environments characterized by significant illumination variations. Whether safety helmets are properly worn directly affects the operational safety of workers in confined spaces. However, helmet detection in such environments faces several challenges, including drastic lighting changes and difficulties in small-object detection. Moreover, existing object detection models typically contain a large number of parameters, making real-time helmet detection difficult to deploy on field devices with limited computational resources. To address these issues, this paper proposes a lightweight safety helmet wearing detection algorithm named GSA-YOLO. To mitigate the effects of severe illumination variation and detail loss in confined spaces, a GCA-C2f module integrating GhostConv and the CBAM attention mechanism is embedded into the backbone network. This design reduces the number of parameters and computational cost while enhancing the model’s feature extraction capability under challenging lighting conditions. To improve detection performance for occluded targets, an improved efficient channel attention (I-ECA) mechanism is introduced into the neck structure, which suppresses irrelevant channel features and enhances occluded object detection accuracy. Furthermore, to alleviate missed detections of small objects and inaccurate localization under low-light conditions, a P2 detection branch is added to the head, and the WIoU loss function is adopted to dynamically adjust the weights of hard and easy samples, thereby improving small-object detection accuracy and localization robustness. A confined space helmet detection dataset containing 5000 images was constructed through on-site data collection for model training and validation. Experimental results demonstrate that the proposed GSA-YOLO achieves an mAP@0.5 of 91.2% on the self-built dataset with only 2.3 M parameters, outperforming the baseline model by 2.9% while reducing the parameter count by 23.6%. The experimental results verify that the proposed algorithm is suitable for environments with significant illumination variation and small-object detection challenges. It provides a lightweight and efficient solution for on-site helmet detection in confined space scenarios, thereby contributing to the reduction in industrial safety accidents. Full article

Construction sites implement various safety management activities, including toolbox meetings, risk assessments, and safety knowledge assessments, to reduce accidents. Multiple-choice question (MCQ)-based assessments are widely used to evaluate worker safety competencies. However, the effectiveness of MCQ assessments depends critically on distractor quality; incorrect options must be plausible enough to challenge uninformed respondents while remaining clearly distinguishable from knowledgeable ones. Manual distractor creation requires substantial expertise and is prone to inconsistency, whereas large language models (LLMs) often generate options that lack domain relevance. This paper proposes context-aware multipath adaptive safety scoring (CoMPASS), an algorithm that integrates construction safety domain knowledge with LLM capabilities for MCQ distractor generation. CoMPASS operates through two pathways: CoMPASS-H leverages a hierarchical hazard factor ontology for hazard identification questions, whereas CoMPASS-R uses hybrid retrieval-augmented generation (RAG) for risk control questions. An evaluation using 50 real construction accident cases with a robotic assessment test (RAT) using frontier LLMs as virtual examinees demonstrated that CoMPASS-R achieved a 90% quality pass rate, whereas all baseline methods failed to meet the composite quality criteria. The proposed framework provides a scalable approach to generating assessment content that supports effective safety management at construction sites. Full article

Construction accident reports contain rich causal evidence; however, their unstructured narratives make systematic analysis difficult. Recent advances in large language models (LLMs) have created new opportunities to leverage such information at scale. This study develops an integrated LLM–BERT–network framework for analyzing construction accident causation. Based on 347 official construction accident investigation reports, a DeepSeek-based pipeline with human-in-the-loop quality control was used to extract causal keywords describing direct and indirect causes, yielding 2572 keywords. A BERT-based semantic normalization procedure then consolidated synonymous expressions, reducing 811 deduplicated keywords to 104 normalized terms (an 87.2% reduction in vocabulary size). A manual sample-based evaluation further supported the reliability of the LLM-based extraction and BERT-based normalization procedures. The normalized keywords were further organized into a hierarchical taxonomy and used to construct a directed keyword-association network linking indirect and direct causes for structured relational analysis. To strengthen methodological rigor, additional validation and analytical experiments were conducted, including manual sample-based evaluation of keyword extraction, sensitivity analysis of normalization settings, and examination of representative failure cases. The results support the reliability and robustness of the proposed framework. The analysis indicates that behavior-related factors and management deficiencies occupy structurally important positions in the directed network. Overall, the findings suggest that construction accidents arise from the interaction of human, managerial, environmental, material, and technical factors rather than isolated single causes. Effective prevention therefore requires system-oriented interventions that strengthen worker competence, supervision, training, accountability, and hazard identification. Full article

Despite the implementation of safety-engineered devices (SEDs) in Germany, percutaneous sharps injuries (PSIs) caused by medical devices remain a major occupational risk for healthcare workers. The aim of this study was to analyze the frequency of PSIs and the circumstances of SED-associated PSIs in hospitals, medical practices, and nursing homes. Routine data from a statutory accident insurance provider for 2015–2024 were used to analyze PSI trends (n = 481,575), and survey data from online questionnaires were used to analyze circumstances of PSIs (n = 791). Routine data showed a slight decline (6.1%) in PSIs over the past 10 years across all sectors. Hospitals and medical practices had the highest rates (30.2 and 21.6 PSIs per 1000 full-time equivalents, respectively). The devices most frequently involved were blood collection needles in hospitals and medical practices and insulin pens in nursing homes. Overall, 43.1% of PSIs were related to the improper disposal of used devices. Around 31.1% of PSIs were associated with SEDs. Around 33% of SED-related injuries occurred during disposal. High workload and distraction were the most frequently reported causes of injuries. Regular training should be provided to raise staff awareness of the proper handling and disposal of used devices. Full article

Safety performance remains a persistent challenge in the construction industry due to hazardous working conditions, dynamic site environments, and complex organizational structures. Despite regulatory advances and technical safety controls, accident rates remain high, suggesting that formal mechanisms alone are insufficient. Addressing this gap, this study examines safety accountability as a central organizational mechanism and investigates how it influences construction workers’ safety performance through behavioral processes and leadership conditions. Drawing on accountability theory and social learning theory, we propose a moderated parallel mediation model in which safety monitoring and safety learning function as mediators, while inclusive leadership behavior serves as a contextual moderator. Data were collected from 629 construction workers employed in large-scale projects in Istanbul and Ankara, Türkiye, using a two-wave survey design to mitigate common method bias. Hypotheses were tested using confirmatory factor analysis and Hayes’ PROCESS macro. The results indicate that safety accountability does not exert a significant direct effect on safety performance; rather, its influence is fully transmitted through safety monitoring and safety learning, with monitoring emerging as the stronger mediating mechanism. Moreover, inclusive leadership behavior significantly strengthens the accountability-driven pathways leading to improved safety outcomes. By integrating accountability structures, behavioral processes, and leadership context, this study advances construction safety research and provides evidence-based guidance for enhancing occupational safety performance in high-risk construction environments. Full article

Artificial intelligence (AI) has become the leading technology for digital transformation in various industries. However, the digitalization of construction project management (e.g., cost, time, and safety) in the context of AI technology implementation is still limited. Therefore, this paper aims to conduct a systematic literature review of AI technologies in construction project cost, time, and safety management, and identify mainstream application areas, cross-domain synthesis, challenges, research gaps, and future research directions. By adopting the PRISMA approach, a systematic literature review was conducted to retrieve 392 articles from the Scopus database. The results presented mainstream application areas of construction project cost (i.e., cost estimation, cost prediction, cost index forecasting, cost control, cost optimization), time (i.e., planning and scheduling, delay risk prediction, time optimization, cycle time prediction), and safety (i.e., workers’ safety monitoring, on-site safety monitoring, personal protective equipment (PPE) detection, safety report text analysis, fall risk monitoring, safety accident prediction, and safety hazard identification and risk assessment). Moreover, the cross-domain synthesis, challenges, and research gaps of AI technologies in construction project management were discussed. Based on these findings, this paper suggests future directions to extend research in this domain. This paper would contribute to the construction project management research domain by providing key application areas and useful research directions, thus promoting digital transformation in the sector. Full article

In the construction industry, struck-by accidents involving heavy equipment such as crawler excavators are a leading cause of worker fatalities and injuries. Existing vision-based hazard detection methods are limited by approximate evaluations, reliance on specific references, and neglect of spatial relationships between equipment and workers, making them inadequate for complex dynamic construction environments. This study aims to address these limitations by proposing a precise and adaptable struck-by hazard detection method. The method integrates four core modules: object tracking via the YOLOv5-DeepSORT model to detect workers, excavators, and their key components; activity recognition to identify the operational states of excavators, working or static, and workers, driver or field worker; proximity estimation based on stereo vision using the BGNet model and camera calibration to calculate 3D spatial distances; and safety identification to assess worker safety status in real time. Validated through three virtual construction scenarios, flat ground, rugged terrain, slope, the method achieved high safety status identification accuracies of 92.71%, 90.04%, and 94.25% respectively. The results demonstrate its robustness in adapting to diverse construction environments and accurately capturing equipment–worker spatial interactions. This research expands the application scope of hazard monitoring in complex settings, enhances safety identification efficiency, and provides a reliable technical solution for improving construction site safety management. Full article

Background: Safety climate is widely used to explain and prevent occupational accidents in industrial settings; however, the field remains conceptually fragmented, with multiple measurement instruments coexisting without consensus on the core dimensions that define the construct, limiting the comparability of findings and the effectiveness of organizational interventions. Objectives: This study aims to identify, organize, and synthesize the determinants of safety climate reported in validated instruments applied in industrial settings through a systematic literature review. Methods: The review was conducted in accordance with the PRISMA 2020 guidelines, with searches performed in the Scopus and Web of Science databases, resulting in the inclusion of 27 empirical studies published between 2015 and 2025. Dimensions reported in the instruments were extracted, grouped by conceptual similarity, and integrated into a common structure. The synthesis examined determinant recurrence across instruments and interpreted the findings in light of the psychometric quality of the measures, as assessed using the COSMIN framework. Results: The results indicate that despite the diversity of scales, safety climate determinants derived from measurement instruments consistently converge into four domains: Health and Safety Management, Organizational Safety Resources, Worker Involvement, and Working Conditions. The convergence of these domains across independent instruments, considered alongside the methodological robustness of their validation procedures, indicates a conceptually coherent structural core predominantly supported by instruments with confirmatory structural validation. Conclusions: By integrating conceptual structure and measurement quality, this study contributes to reducing fragmentation in the literature and provides an empirical basis for the development, adaptation, and selection of safety climate instruments, with direct implications for research and safety management in industrial environments. Full article

Safety monitoring of scaffolding operations is essential for preventing accidents in high-altitude construction. This study proposes an integrated computer vision and multi-criterion decision-making (MCDM) framework that combines object detection, pose estimation, Analytic Network Process (ANP) and ELECTRE III methods to evaluate safety risks of construction workers. Specifically, computer vision techniques are employed to extract objective visual evidence related to workers’ behaviors, protective equipment (PPE) usage, and working environments, which serve as the basis for subsequent safety risk quantification. A four-criterion system, including action risk, PPE compliance, working height, and structural integrity, is established. Weights are determined via the ANP, and risk ranking is conducted using ELECTRE III. Experiments on a self-built dataset achieved an mAP@0.5 of 92.3%, a segmentation IoU of 67.2%, and a pose OKS@0.5 of 89.6%. The evaluation results correlate strongly with expert assessments (Kendall’s τ = 0.79). The proposed framework effectively identifies unsafe behaviors and quantifies safety risks, providing reliable decision support for intelligent construction safety management. Full article

Forestry operations expose workers to a high risk of health constraints, accidents, and injuries. We are trying to protect them and implement many effective countermeasures; nevertheless, the development of new forestry machines remains a long process, with limited safety and ergonomic feedback, usually provided only at a late stage in the design process. In this study, we propose a practical digital ergonomics workflow that combines inertial motion capture, standardized risk scoring, and digital human modelling to improve and shorten human-centered and safer design of forestry machinery. We validated the approach in a field pilot on a prototype milling–spraying device for standing trees. Two experienced operators performed a full work-cycle (carry → install → operate → dismantle → return), during which their whole-body kinematics were captured in real forest conditions. These were then evaluated using kinematic metrics, RULA, OWAS, and a heart-rate-based load index. Based on these ergonomical and risk findings, we translate motion-derived risk ‘hotspots’ into real redesign targets (grip/handle geometry, weight distribution, support elements, and control layout), outlining an updated forestry-specific DHM/HDT (digital human modeling; human digital twin) framework that explicitly incorporates terrain and environmental constraints to accelerate the iteration of safer prototypes. The updated digital modeling framework will be used in the design of the new, more complex machine—“Semi-autonomous system for optimizing degraded soils by deep injection”. This machine contains a much more complex and advanced structure, including a tractor with an attachment tool for specialized deep soil injection. We suppose that using motion capture data, human digital twins, and digital human models can effectively support designing and the development process to avoid human-related construction nonconformities of this complex machine even before the final machine prototype is produced for functional field testing. Full article