Search Results (676)

Wastewater treatment plants (WWTPs) are essential infrastructure for urban water management. Their stable operation is critical to effluent quality and public safety. However, wastewater treatment involves complex biochemical processes and extensive electromechanical equipment. Hazards such as sludge flotation in secondary clarifiers, fire, electric shock, and toxic gas poisoning may occur. These hazards can threaten worker safety and reduce treatment efficiency. Traditional inspection mainly relies on manual patrols, fixed-camera monitoring, and experience-based judgment. These methods often have low efficiency, limited coverage, and delayed responses. To address these limitations, this paper investigates robot-based hazard detection for WWTPs. A multisource hazard detection dataset is constructed for secondary clarifiers and confined spaces, including images collected by an inspection robot. Object detection models are then applied to identify typical hazards. Comparative experiments are conducted using Faster R-CNN and several YOLO-series models. YOLOv12 achieves mAP@0.5 values of 0.917 and 0.819 for sludge flotation detection and confined space hazard detection, respectively. It also provides a good balance between detection performance and inference efficiency. The results demonstrate that robot vision combined with object detection can support intelligent inspection in WWTPs. Full article

The construction industry remains among the most dangerous occupations in the world and safety management in this industry remains hierarchical in nature, being designed by engineers, regulatory agencies, and site managers and not by those who actually face the highest risk of danger. This paper argues that this asymmetry in who gets to design safety is systemic, arising from the intersection of organisational hierarchy, national power distance, and multi-tier subcontracting, all of which erode both accountability and worker voice. Drawing on a theoretical framework that integrates the safety-voice, power-distance, and systems-theoretic accident model and processes (STAMP) literatures with a structural analysis of Istanbul’s construction sector, the paper shows how legal, contractual, and cultural mechanisms suppress the feedback processes that resilience-oriented safety management depends on. Drawing on Turkish labour-law thresholds for union recognition, subcontracting data, and a case of worker protest during the construction of one of Istanbul’s largest infrastructure projects, the paper identifies three mechanisms of exclusion: statutory representation thresholds that structurally exclude construction workers, subcontracting chains that diffuse responsibility for safety outcomes away from the workers who bear the risk, and organisational hierarchies that discourage safety voice long before it reaches any formal channel. The paper concludes with a set of design principles for reintegrating worker feedback into safety control structures, offered as a contribution to resilience engineering practice. Full article

Occupational safety management in combined cycle power plant (CCPP) construction requires methods that can both prioritize multiple tasks and explain why critical scenarios arise. This article presents a hybrid case-study methodology integrating questionnaire-based semi-quantitative 5 × 5 risk matrices with task-focused HAZOP. An anonymous structured questionnaire was completed by 105 workers at a CCPP construction site (response rate 75.0%), covering eight major construction activities and four occupational groups. Likelihood and severity ratings were processed in RStudio 2025.09.2 to produce 5 × 5 risk matrices, distribution plots, and “High + Critical” prioritization indices. The highest-ranked tasks were then subjected to HAZOP in PHAWorks RA Edition (Version 1.0.9834). “Work at height” emerged as the highest-ranked perceived-risk activity across all groups, followed by lifting/load handling, chemicals/flammables, and electrical works. Safety technicians tended to assign more conservative ratings than labor personnel, indicating role-dependent differences in risk perception. The HAZOP analysis suggested that the plausible HAZOP-informed accident scenarios and control weaknesses were not only technical but also organizational, including failures in permit-to-work, lockout/tagout, exclusion-zone control, fall protection, and chemical handling discipline. The integrated hybrid workflow bridges rapid semi-quantitative prioritization and structured causal analysis, supporting the translation of field-based perceptions into targeted and actionable safety controls for dynamic energy-project worksites. The findings highlight the critical role of organizational discipline alongside technical measures and support the integration of structured risk assessment within occupational health and safety management systems in dynamic construction environments. Full article

Construction workers face disproportionate rates of fatal and nonfatal injuries, including falls, heat strain, overexertion, and struck-by incidents, that passive personal protective equipment cannot continuously monitor. Smart garments, which embed sensors and microelectronics directly into textile apparel, offer a deployable infrastructure for continuous worker-level physiological and kinematic monitoring. This PRISMA-guided systematic review synthesizes 76 records (search executed 31 December 2025; coverage 2000–2026), of which 63 are empirically validated and used for benchmarking. Four analytical contributions are made: (i) a tiered corpus classification separating construction-direct, transferable, and contextual evidence; (ii) stratification of all performance claims by validation context; (iii) explicit transferability assessment for records from healthcare, occupational safety, sports, and rehabilitation; and (iv) a deployment-maturity framework evaluating readiness across technical performance, validation maturity, operational durability, human and organizational acceptance, and system integration, extending beyond single-dimension scores such as NASA TRL. Only 7 of 63 empirical records (11%) were field or field-simulated, of which 6 reached TRL 5; most other systems remain at TRL 4. The framework suggests that technical performance is less often the primary limiting dimension than validation maturity, durability, and integration, although this conclusion is constrained by the dominance of laboratory and transferable evidence. Full article

Driven by Vision 2030, the construction industry in the Kingdom of Saudi Arabia (KSA) has expanded rapidly. This expansion has raised concerns about safety performance and created a need to identify the factors that most strongly influence safety outcomes. This study is an exploratory, perception-based investigation into the perceived importance of factors affecting construction safety performance and into the perceived value of emerging Industry 4.0 technologies in improving it. Data were collected from 68 construction engineering professionals using a structured questionnaire covering 50 safety-related factors across five domains: management, worker, equipment and environmental, organisational and policy, and technology integration. The Importance Index (I) method was used to analyse the responses, and the domain-level results are reported as descriptive prioritisations rather than as statistically validated constructs. The three highest-rated items were lack of management commitment to safety programmes (I = 91.2%), inadequate and infrequent safety training (I = 89.7%), and absence of safety awareness among top management (I = 88.5%), followed closely by weak enforcement of safety regulations (I = 87.4%). At the domain level, management-related factors recorded the highest mean (I = 81.7%), ahead of worker-related factors (I = 79.5%) and technology integration (I = 75.0%). Although respondents identified artificial intelligence (AI), the Internet of Things (IoT), virtual and augmented reality (VR/AR), building information modelling (BIM), and drones as valuable safety enablers, they rated organisational and human factors as more important than the technologies themselves. Extreme heat and adverse weather conditions (I = 81.7%) emerged as a leading environmental risk, underlining the need for climate-specific safety interventions in the KSA context. Heat stress could be considered a notable concern in this context, as construction work in KSA is often performed outdoors during extended periods of high summer temperatures, which can elevate the risk of heat exhaustion, heat stroke, and fatigue-related incidents. This contrasts with the more temperate conditions of many international comparator studies and helps to explain why respondents treat heat as a leading environmental concern. To the authors’ knowledge, this is among the few exploratory studies to bring management, workforce, policy, environmental, and Industry 4.0 technology determinants together within a single KSA-specific framework. The findings contribute to both theory and practice in construction safety by identifying priority areas for intervention and by providing a basis for future large-scale confirmatory studies. Full article

The construction industry is inherently high-risk, with workers’ unsafe behaviors directly causing most safety incidents. As smart technologies are widely deployed on construction sites, new forms of risk-taking behavior have emerged, but their underlying mechanisms remain poorly understood. Grounded in Human–Technology–Organization (HTO) theory, this study establishes a multi-factor coupling analytical framework and employs a mixed NCA–fsQCA method to empirically analyze data from 312 workers across two smart construction sites in Beijing. The results show that no single antecedent variable acts as a necessary condition for either type of high-risk-taking behavior, though each variable exerts distinct bottleneck constraints. Five configurations driving high-risk behaviors are identified: smart technology adaptability serves as the core condition for automation trust bias behaviors, while individual risk-taking propensity and task situational pressure are universal core factors for both behavior types. These findings uncover the multi-dimensional coupling logic of risk-taking behaviors and offer theoretical and practical insights for targeted safety management in smart construction contexts. Full article

After the 2021 military coup in Myanmar, many people sought safety, decent work, and social protection in neighboring Thailand. This study examines how legal documentation status and Sense of Place (SOP) are associated with the workplace retention intentions of irregular Myanmar migrant workers in Mae Sot, Thailand. SOP is conceptualized through three key dimensions: emotional bonds to the surrounding environment (place attachment), the extent to which the place meets everyday needs (place dependence), and the way migrants identify with their host context (place identity). To explore these dynamics, we adopted an explanatory sequential mixed-methods approach, surveying 101 respondents between January and April 2024 and subsequently conducting in-depth interviews with 30 individuals between June and July 2024. Given the sensitivity of irregular migration and undocumented legal status, obtaining survey responses was challenging because many potential participants were concerned about privacy, identification, and possible legal exposure. Quantitative data were analyzed using descriptive comparisons, bivariate association tests with Goodman-calibrated minimum Bayes factors, LASSO-assisted variable screening, principal component analysis, and Bayesian mixed-effects ordinal probit models. The descriptive and bivariate results show that legal documentation status is positively associated with all SOP dimensions, job satisfaction, and willingness to remain in the current job. In the candidate model comparison, the Bayesian specification without legal status showed slightly better predictive performance than the corresponding specification with legal status. The primary five-category Bayesian mixed-effects model indicated positive conditional associations of Job Satisfaction, Place Attachment, and the structural workplace index with willingness to stay. It also indicated heterogeneity in the Place Attachment association across observed job tenure groups; this pattern is interpreted as a cross-sectional between-group difference rather than as evidence of individual change over time. Complementary Bayesian sensitivity analyses evaluate the multidimensional SOP construct, the incremental predictive contribution of legal status, sensitivity to alternative priors, and model diagnostics. The qualitative interviews further show that legal documentation is widely perceived as enabling broader employment opportunities, safer access to basic services, employer support, labor rights protection, and reduced exposure to exploitation. Overall, this study highlights the complementary roles of legal recognition, workplace satisfaction, and emotional rootedness in shaping migrant retention, offering implications for decent work, migrant well-being, reduced inequalities, and sustainable labor and migration policies in Thailand’s border regions, particularly in relation to SDG 8 and SDG 10. Full article

Construction activities within the Oil and Gas Industry present many challenges and inherent dangers for workers. Many current solutions available in the market lack real-time insights and predictive capabilities for proactive decision-making and operational safety. This industry demands robust solutions to enhance safety, improve security, and increase productivity, and this is where Human Digital Twins (HDTs), Wearables, and Artificial Intelligence (AI) play an important role. HDTs aim to create a dynamic digital replica of workers, integrating data from wearables, IIoT sensors, information systems, and any other data source available, enabling continuous monitoring of physiological and cognitive conditions. Complementing HDTs, AI-powered wearables collect enriched data from embedded sensors, such as heart rate, blood oxygen, or fatigue, and integrate deep learning algorithms to predict potential incidents directly on the edge before they happen, thanks to their high computing capabilities. This review represents the state-of-the-art for HDTs and AI-powered wearables for the Oil and Gas industry. It explores current technological developments, their applications, the challenges and limitations of deploying them into a real-world project, and also synthesizes current research and industry practices, highlighting their potential in human health and safety. Additionally, it identifies future research directions to overcome existing barriers. Full article

Recent advances in artificial intelligence (AI) and computer vision technologies have enabled practical applications in industrial environments where safety, quality, and productivity are critical. To support both researchers and practitioners, this survey categorizes AI-based vision systems by their functional objectives rather than algorithmic classification. Specifically, representative implementations are organized across key application areas including safety monitoring, product quality inspection, assembly line support, and worker productivity enhancement. Most of the surveyed studies are in the manufacturing and construction sectors, where real-world deployments have demonstrated measurable improvements. Unlike many previous reviews, this survey focuses on image-centric applications, using visually interpretable outputs such as photographs, video frames, and real-world examples to illustrate the on-site usability of AI vision systems. It also organizes prior work by functional roles and practical deployment considerations, rather than algorithm-centric evaluations, to provide practitioners with actionable insights for industrial adoption. Full article

The construction industry is a critical pillar of Hong Kong’s economic development and urban growth, yet it continues to face significant safety challenges amid increasing construction activity and workforce demands. While efforts to accelerate housing development and infrastructure projects are essential for addressing societal needs, they must be balanced with effective measures to prevent accidents and manage workplace hazards. This paper reviews the common causes, patterns, and emerging trends of construction-related accidents in Hong Kong and examines the Tai Po Wang Fuk Court fire as a representative case study of systemic safety failure. Using documentary evidence on a fire that caused 168 deaths, 79 injuries, and spread across seven of eight towers, the study identifies five aligned failure layers. Drawing upon archival records, official reports, legislative documents, and public accounts, the study applies James Reason’s Swiss Cheese Model to demonstrate how deficiencies in material selection, worker behaviour, fire protection systems, contractor management, and regulatory oversight aligned to enable the incident. Building on these findings, the paper proposes a 5E framework—Engineering, Education, Enforcement, Engagement, and Evaluation—to provide a structured approach for strengthening construction safety management and fire risk governance. The framework offers practical guidance for policymakers, regulators, contractors, property managers, and other stakeholders seeking to enhance safety culture, improve regulatory compliance, and promote resilience in the construction sector. The findings contribute to the broader discourse on construction safety by demonstrating how systemic failures can be translated into targeted interventions for preventing similar incidents in the future. Full article

Through textual analysis of 15 English-language articles by seven predominantly U.S.-based sex worker journalists published between 2014 and 2025 across 11 online platforms, this article examines how sex worker journalists aim to influence public opinion around anti-porn policy. Sex workers report on legislation such as the Kids Online Safety Act (KOSA), age-verification laws, the Stop Internet Sexual Exploitation Act (SISEA), and the Survivors of Human Trafficking Fight Back Act, maintaining that these laws cause harm to sex workers. In the process, they challenge sex work stigma perpetuated by the mainstream media and stake alternative validity claims. They also construct sex worker expertise based on their lived experience in the sex trades through new modes of address and by citing sex workers alongside other progressive organizers. This, furthermore, supports coalition building between the sex workers’ rights movement and other movements for social justice. Full article

With the continuous expansion of government investment in transportation infrastructure, transportation investment has increased, and bridge engineering has flourished. However, safety accidents frequently occur during the construction stage, so the safety situation of bridge construction units remains unfavorable. Bridge construction is a high-risk occupational activity because operations are performed under complex site conditions, variable geological and hydrological environments, long construction periods, and frequent interaction among workers, machinery, materials, technologies, and surrounding environments. Existing bridge construction safety assessment methods have improved hazard identification and risk prioritization; however, many still have difficulty representing uncertainty, hesitation, and subjective judgment in expert-based occupational safety evaluation. To address this problem, this study proposes an integrated Pythagorean fuzzy TOPSIS decision-support framework for occupational safety risk prioritization in bridge construction projects. A safety risk indicator system is established from five dimensions: human, management, material, technical, and environmental factors. Expert judgments are expressed using linguistic variables and converted into Pythagorean fuzzy numbers. The Pythagorean fuzzy weighted average operator is used to aggregate multi-expert evaluation information, and criterion importance is determined from expert-based Pythagorean fuzzy assessments. The model is applied to a bridge reconstruction project, followed by sensitivity analysis and comparison with several existing methods. The results indicate that low work quality, non-standard construction, construction site environment, lack of safety awareness, and insufficient construction technology are the most critical occupational safety risk indicators. The proposed method provides a practical decision-support tool for identifying priority safety risks under fuzzy and uncertain evaluation conditions. Full article

The rapid expansion of artificial intelligence (AI) is revolutionizing economic systems by reshaping production, labor organization, and productivity patterns. In the construction industry, which remains highly labor-intensive, project-based, and structurally heterogeneous across European countries, AI can support productivity, planning, safety monitoring, cost estimation, and decision-making. However, its implementation also poses specific challenges, including fragmented workflows, heterogeneous construction sites, limited digital skills, poor data interoperability, high adoption costs, resistance to organizational change, and temporary adjustment costs that may delay the visibility of productivity gains. In this paper, we evaluate the effect of Artificial Intelligence (AI) on labor productivity in the construction sector of European Union countries, taking into account labor input and sector output, and search for structural trends from a systems perspective. AI adoption is defined as the share of construction businesses that use at least one AI technology. Productivity is estimated per worker and per hour using a dataset of EU countries from 2023 to 2024. The effects of AI adoption, labor input, and output on productivity are evaluated using multivariate log-linear regressions in SPSS. Hierarchical and K-means clustering reveal groups of countries with comparable digital and labor performance. The results demonstrate that construction output is the key driver of labor productivity. In contrast, AI adoption shows a small, negative association with productivity, consistent with short-term adjustment costs in the early stages of digital transformation. Cluster analysis reveals diverse country profiles in AI use, productivity, and labor intensity. Overall, the findings underscore the need for a gradual approach to managing digital change in construction, supported by complementary organizational and skills-based measures. Full article

To more effectively address the wide range of safety risks faced by construction workers on job sites, machine learning (ML)–based computer vision and augmented reality (AR) technologies are increasingly being employed to enhance efficiency, safety, and productivity. However, current AR construction safety tools only provide passive information for the user to then decide how to use that information. This study leverages advanced computer vision coupled with AR to work with site managers and on-site workers to make operational safety decisions using real-time, visual information of potential hazards. A YOLOv11 model trained to detect the presence or lack of personal protective equipment was developed and tested by creating a local ML computing environment using camera feeds. The detection results were compiled and displayed in real time on a web-based interface developed with Hypertext Preprocessor and on a Microsoft HoloLens 2 heads-up display. The system was successfully field-tested on a construction site. Full article

Current construction safety research has paid limited attention to the relationship between stable individual differences and hazard-related visual attention. This study combined personality assessment and eye-tracking technology to investigate visual attention allocation and hazard recognition among construction workers in static work-at-height scenarios. Personality traits were assessed using the Chinese Big Five Personality Inventory Brief Version, and 30 participants with extreme trait profiles were selected for eye-tracking experiments in two representative work-at-height scenarios. Eight eye-tracking indicators were analyzed across four dimensions: attentional span, attentional stability, attentional allocation, and attentional shifting. An AHP-based evaluation framework was further developed to assess visual attention efficacy. The results showed descriptive differences in hazard-related visual attention patterns across personality-trait groups. Individuals high in agreeableness and conscientiousness exhibited more hazard-oriented visual allocation and higher visual attention efficacy, whereas those high in openness and extraversion showed stronger exploratory tendencies and lower efficiency in allocating attention to high-risk areas. Individuals high in neuroticism showed intermediate overall performance but relatively weaker attentional organization. Sensitivity analysis indicated that the ranking results remained stable under moderate weight perturbations. These findings provide a quantitative framework for comparing visual attention efficacy across personality-trait groups and offer preliminary support for differentiated safety training, risk communication, and attentional guidance in construction safety management. Full article