Search Results (115)

Understanding human perceptual strategies in high-stakes environments, such as crime scene investigations, is essential for developing cognitive models that reflect expert decision-making. This study presents an immersive experimental framework that utilizes virtual reality (VR) and eye-tracking technologies to capture and analyze visual attention during simulated forensic tasks. A${360}^{\circ }$ panoramic crime scene, constructed using the Nikon KeyMission 360 camera, was integrated into a VR system with HTC Vive and Tobii Pro eye-tracking components. A total of 46 undergraduate students aged 19 to 24–23, from the National University of Singapore in Singapore and 23 from the Central Police University in Taiwan—participated in the study, generating over 2.6 million gaze samples (IRB No. 23-095-B). The collected eye-tracking data were analyzed using statistical summarization, temporal alignment techniques (Earth Mover’s Distance and Needleman-Wunsch algorithms), and machine learning models, including K-means clustering, random forest regression, and support vector machines (SVMs). Clustering achieved a classification accuracy of 78.26%, revealing distinct visual behavior patterns across participant groups. Proficiency prediction models reached optimal performance with a random forest regression ($R^2$ = 0.7034), highlighting scan-path variability and fixation regularity as key predictive features. These findings demonstrate that eye-tracking metrics—particularly sequence-alignment-based features—can effectively capture differences linked to both experiential training and cultural context. Beyond its immediate forensic relevance, the study contributes a structured methodology for encoding visual attention strategies into analyzable formats, offering valuable insights for cognitive modeling, training systems, and human-centered design in future perceptual intelligence applications. Furthermore, our work advances the development of autonomous vehicles by modeling how humans visually interpret complex and potentially hazardous environments. By examining expert and novice gaze patterns during simulated forensic investigations, we provide insights that can inform the design of autonomous systems required to make rapid, safety-critical decisions in similarly unstructured settings. The extraction of human-like visual attention strategies not only enhances scene understanding, anomaly detection, and risk assessment in autonomous driving scenarios, but also supports accelerated learning of response patterns for rare, dangerous, or otherwise exceptional conditions—enabling autonomous driving systems to better anticipate and manage unexpected real-world challenges. Full article

The construction of large underground caverns fundamentally differs from building and above ground civil infrastructure projects due to their complex geometries and variable geological conditions. These projects are complex and challenging because a large amount of data is generated from dispersed, independent, and heterogeneous sources. The underground construction industry often uses traditional project management techniques to manage complex interactions between these data sources that are hardly linked, and independent decisions are often made without considering all the relevant aspects. In this context, cavern construction exhibits uncertainties and risks due to unforeseen circumstances, an intricate design, and ineffective information management. Existing research has considered general BIM semantic models at the design stage; however, the digital transformation of cavern construction remains underdeveloped and fails to integrate digital construction throughout the project lifecycle. To address that gap, a novel BIM-based multi-model cavern information modeling framework is presented here to improve project management, construction, and delivery by integrating multiple interlinked data models and project performance data for large underground cavern construction. Data models of cavern construction processes are linked to propose an extension of the Industry Foundation Classes (IFC) schema based on the cavern-specific elements, relationships, and property set definitions. To illustrate the potential of the proposed framework, a theoretical application to the powerhouse cavern construction is presented. The results indicate that the framework has significant potential to improve construction efficiency and safety and establish a robust foundation for the digital transformation of underground cavern projects. The theoretical implementation on the Neelum–Jhelum powerhouse cavern showed that the framework enabled a 92 m cavern realignment to avoid fault zones, achieved a 12.4% reduction in rock bolt usage, and a 9.8% reduction in shotcrete volume. These quantitative improvements illustrate its potential to enhance safety, reduce material costs, and optimize construction efficiency compared to conventional workflows. Full article

Tunnel construction poses significant safety challenges due to confined spaces, limited visibility, and the dynamic movement of labourers and machinery. This study addresses a critical gap in real-time, bidirectional proximity monitoring by developing and validating a prototype early-warning system that integrates real-time location systems (RTLS) with long-range (LoRa) wireless communication and ultra-wideband (UWB) positioning. The system comprises Arduino nano microcontrollers, organic light-emitting diode (OLED) displays, and piezo buzzers to detect and signal proximity breaches between workers and equipment. Using an action research approach, three pilot case studies were conducted in a simulated tunnel environment to test the system’s effectiveness in both static and dynamic risk scenarios. The results showed that the system accurately tracked proximity and generated timely alerts when safety thresholds were crossed, although minor delays of 5–8 s and slight positional inaccuracies were noted. These findings confirm the system’s capacity to enhance situational awareness and reduce reliance on manual safety protocols. The study contributes to the tunnel safety literature by demonstrating the feasibility of low-cost, real-time monitoring solutions that simultaneously track labour and machinery. The proposed RTLS framework offers practical value for safety managers and informs future research into automated safety systems in complex construction environments. Full article

This study explores the use of Large Language Models (LLMs), specifically GPT, for different safety management applications in the construction industry. Many studies have explored the integration of GPT in construction safety for various applications; their primary focus has been on the feasibility of such integration, often using GPT models for specific applications rather than a thorough evaluation of GPT’s limitations and capabilities. In contrast, this study aims to provide a comprehensive assessment of GPT’s performance based on established key criteria. Using structured use cases, this study explores GPT’s strength and weaknesses in four construction safety areas: (1) delivering personalized safety training and educational content tailored to individual learner needs; (2) automatically analyzing post-accident reports to identify root causes and suggest preventive measures; (3) generating customized safety guidelines and checklists to support site compliance; and (4) providing real-time assistance for managing daily safety tasks and decision-making on construction sites. LLMs and NLP have already been employed in each of these four areas for improvement, making them suitable areas for further investigation. GPT demonstrated acceptable performance in delivering evidence-based, regulation-aligned responses, making it valuable for scaling personalized training, automating accident analyses, and developing safety protocols. Additionally, it provided real-time safety support through interactive dialogues. However, the model showed limitations in deeper critical analysis, extrapolating information, and adapting to dynamic environments. The study concludes that while GPT holds significant promise for enhancing construction safety, further refinement is necessary. This includes fine-tuning for more relevant safety-specific outcomes, integrating real-time data for contextual awareness, and developing a nuanced understanding of safety risks. These improvements, coupled with human oversight, could make GPT a robust tool for safety management. Full article

Extensive research in the field of construction safety has predominantly focused on identifying the causes and impacts of construction accidents, evaluating safety plans, assessing the effectiveness of safety education materials, and analyzing relevant policies. However, comparatively limited attention has been given to the systematic formation, management, and utilization of safety-related information and knowledge. Despite significant advancements in information and knowledge management technologies across the architecture, engineering, and construction (AEC) industries, their application in construction safety remains underdeveloped. This study addresses this gap by proposing a novel ontology-based framework specifically designed for construction safety management. Unlike previous models, the proposed ontology integrates diverse safety regulations and terminologies into a unified and semantically structured knowledge model. It comprises three primary superclasses covering key areas of construction safety, with an initial focus on fall hazards—one of the most frequent and severe risks, particularly in roofing activities. This domain-specific approach not only improves semantic clarity and standardization but also enhances reusability and extensibility for other risk domains. The ontology was developed using established methodologies and validated through reasoning tools and competency questions. By providing a formally structured, logic-driven knowledge base, the model supports automated safety reasoning, facilitates communication among stakeholders, and lays the foundation for future intelligent safety management systems in construction. This research contributes a validated, extensible, and regulation-aligned ontology model that addresses critical challenges in safety information integration, sharing, and application. Full article

Driven by the global energy transition and the pursuit of “dual carbon” goals, sustainability risks within the coal supply chain have emerged as a central obstacle impeding the low-carbon transformation of high-carbon industries. To address the critical gap in systematic and multidimensional risk assessments for coal supply chains, this study proposes a hybrid framework that integrates the analytic hierarchy process (AHP) with the fuzzy comprehensive evaluation (FCE) method. Utilizing the Delphi method and the coefficient of variation technique, this study develops a risk assessment system encompassing eight primary criteria and forty sub-criteria. These indicators cover economic, operational safety, ecological and environmental, management policy, demand, sustainable supply, information technology, and social risks. An empirical analysis is conducted, using a prominent Chinese coal enterprise as a case study. The findings demonstrate that the overall risk level of the enterprise is “moderate”, with demand risk, information technology risk, and social risk ranking as the top three concerns. This underscores the substantial impact of accelerated energy substitution, digital system vulnerabilities, and stakeholder conflicts on supply chain resilience. Further analysis elucidates the transmission mechanisms of critical risk nodes, including financing constraints, equipment modernization delays, and deficiencies in end-of-pipe governance. Targeted strategies are proposed, such as constructing a diversified financing matrix, developing a blockchain-based data-sharing platform, and establishing a community co-governance mechanism. These measures offer scientific decision-making support for the coal industry’s efforts to balance “ensuring supply” with “reducing carbon emissions”, and provide a replicable risk assessment paradigm for the sustainable transformation of global high-carbon supply chains. Full article

The construction sector represents approximately 13% of global gross domestic product (GDP) and over 5% in Spain, employing more than one million workers. Despite its economic importance, the sector exhibits low digitalization levels and persistently high accident rates, contrasting with other industries that have successfully integrated digital technologies for safety improvement. Objective: This study evaluates the technical, operational, and regulatory feasibility of implementing digital tools and artificial intelligence (AI) in occupational risk prevention (ORP) within the Spanish construction sector. It focuses on identifying applicable technologies, assessing professionals’ perceptions of their practical utility, and analyzing key implementation barriers. Methodology: A mixed-method approach was employed in four stages: (1) a systematic literature review of digital safety tools; (2) a survey of 97 construction professionals using purposive sampling and validated through pretesting (Cronbach’s α = 0.82); (3) an analysis of official accident statistics; and (4) expert consensus using the Delphi method (three rounds, 75% consensus threshold). Results: Virtual reality (VR), augmented reality (AR), and mixed reality (MR) applications were identified as highly beneficial for training and awareness, with 78.2% of professionals supporting their use for safety training. Building Information Modeling (BIM) and drones were highlighted as the most valued tools for risk management and site supervision. Main implementation barriers include a lack of digital skills (35%), insufficient budget (30%), and high tool costs (25%). Contribution: This study proposes a mixed-method methodological framework—quantitative and qualitative—adapted to national contexts and validated through a Delphi consensus process. The framework prioritizes key technologies and identifies targeted strategies to overcome critical implementation barriers. Full article

Non-Intrusive Load Monitoring (NILM) technology, enabled by high-precision electrical data acquisition sensors at household entry points, facilitates real-time monitoring of electricity consumption, enhancing user interaction with smart home systems and reducing electrical safety risks. However, the growing diversity of household appliances and limitations in NILM accuracy and robustness necessitate innovative solutions. Additionally, outdated public datasets fail to capture the rapid evolution of modern appliances. To address these challenges, we constructed a high-sampling-rate voltage–current dataset, measuring 15 common household appliances across diverse scenarios in a controlled laboratory environment tailored to regional grid standards (220 V/50 Hz). We propose an AI-driven NILM method that integrates power-mapped, color-coded voltage–current (V–I) trajectories with frequency-domain features to significantly improve load recognition accuracy and robustness. By leveraging deep learning frameworks, this approach enriches temporal feature representation through chromatic mapping of instantaneous power and incorporates frequency-domain spectrograms to capture dynamic load behaviors. A novel channel-wise attention mechanism optimizes multi-dimensional feature fusion, dynamically prioritizing critical information while suppressing noise. Comparative experiments on the custom dataset demonstrate superior performance, particularly in distinguishing appliances with similar load profiles, underscoring the method’s potential for advancing smart home energy management, user-centric energy feedback, and social informatics applications in complex electrical environments. Full article

As an important part of the emerging energy portfolio, the coordinated development of the photovoltaic (PV) industry and ecological environment is a core factor in realizing the high-quality development of the energy industry. Xinjiang, located in northwestern China, possesses vast open land, abundant solar radiation, and low land-use conflict, making it a strategic hub for large-scale PV power station deployment. However, the region’s fragile ecological background is highly sensitive to land-use changes induced by PV infrastructure expansion. Therefore, scientifically evaluating the ecological impacts of PV construction is essential to support environmentally informed operation and maintenance (O&M) strategies.This study investigates the spatial distribution of PV installations and their macro-scale ecological effects across Xinjiang from 2000 to 2020. Utilizing multi-temporal satellite remote sensing data and geospatial analysis techniques on the Google Earth Engine (GEE) platform, we constructed a Remote Sensing Ecological Index (RSEI) model to quantify the long-term ecological response to PV development. It was found that PV installations were concentrated in unutilized land (37.10%) and grassland (34.45%), with the smallest proportion being found in forested land (1.68%). Nearly 70% of the PV areas showed an improving trend in the ecological environment index, and there were significantly more ecological quality-improving areas than degraded areas (69% vs. 31%). There were significant regional differences, and the highest ecological environment index was found in 2020 for the Northern Xinjiang Altay PV area (0.30), while the lowest (0.10) was observed in Hetian in southern Xinjiang. The results of this study provide a spatial optimization basis for the integration of PV development and ecological protection in Xinjiang and provide practical guidance to help the government to formulate a comprehensive management strategy of “PV + ecology”, which will help to realize the synergistic development of clean energy development and ecological safety. Full article

The rapid evolution of information technology has positioned digital intelligence as a transformative force across socio-economic domains, necessitating rigorous scholarly examination of its applications and implications. This paper systematically explores the digital intelligence empowerment in Building Lifecycle Management (BLM) under the framework of Construction 4.0. Employing a mixed-methods approach, the research combines a systematic literature review with bibliometric visualization analysis using CiteSpace to map the intellectual landscape, identify key technological drivers (for example, Building Information Modeling, Internet of Things, artificial intelligence, and blockchain), and elucidate integration mechanisms across planning, design, construction, and operational phases. A comparative case study of BLM adoption further demonstrates the transformative impacts of digital intelligence on project efficiency, sustainability, and safety. The research highlights the role of digital intelligence in addressing industry challenges, including resource waste (global construction generates 30% of total waste), safety risks, and stagnant productivity, while fostering innovation and sustainable development. This study advances a holistic model for digital transformation in BLM, offering actionable insights for stakeholders to bridge the academia–industry divide and prioritize strategic investments in interoperable systems, workforce upskilling, and governance frameworks. Full article

With rapid urbanization and surging traffic volumes, traffic accident data have become high-dimensional, multi-source, heterogeneous, and spatiotemporally dynamic, posing challenges for traditional statistical methods and machine learning models to simultaneously account for data heterogeneity and nonlinear interactions. Knowledge graphs, by constructing structured semantic networks that integrate accident events, participants, environmental factors, and other multidimensional elements, inherently support multi-source information fusion and reasoning. In this study, following a top-down ontology design principle, we construct a California Traffic Accident Knowledge Graph (TAKG) encompassing over one hundred elements, and propose an enhanced TransD embedding model. Our model introduces entity–attribute projection vectors into the dynamic mapping mechanism to explicitly encode domain attributes, and designs a dual-limit scoring loss function to independently regulate the positive and negative sample boundaries. Experimental results demonstrate that our method significantly outperforms traditional translation-based models on the self-built TAKG as well as on the FB15K-237 and WN18RR benchmark datasets. This research provides a solid data foundation and algorithmic support for downstream traffic accident risk prediction and intelligent traffic safety management. Full article

Construction remains one of the most hazardous industries, with high accident rates driven by insufficient planning, coordination, and safety training. While extended reality (XR) technologies, encompassing virtual, augmented, and mixed reality, have shown promise in improving safety outcomes, existing applications are typically isolated, lacking integration across the project lifecycle and alignment with digital methodologies such as those found in Construction 4.0. This study proposes a comprehensive workflow and framework for the integration of XR technologies into construction safety management, grounded in Building Information Modelling, Lean Construction, and Prevention through Design. This methodology structures the use of XR to support safety planning, training, inspection, and control, with a focus on lifecycle integration and proactive risk mitigation. Implementation examples are presented to illustrate the framework’s applicability and scalability. These demonstrate how XR can support immersive walkthroughs, synchronisation with BIM data, and simulation of human–machine interactions. This study contributes a structured, replicable approach that addresses the current fragmentation of XR safety applications, offering both a theoretical basis and practical guidance for adopting XR in construction safety workflows. Full article

The construction industry is transforming significantly, with emerging technologies reshaping project management by enhancing efficiency, sustainability, and safety. This study examines the integration of these innovations into Chad’s construction sector, drawing on insights from 79 industry participants. Given Chad’s unique economic and infrastructural landscape, understanding the practical implementation of these technologies is crucial. This research demonstrated strong reliability and validity through exploratory factor analysis, with a KMO value above 0.75, statistical significance at p < 0.001, and a Cronbach’s Alpha exceeding 0.8. Using Promax rotation, this study identified 15 key factors, providing valuable insights into how technologies such as Building Information Modeling (BIM), Artificial Intelligence (AI), the Internet of Things (IoT), and Digital Twin technology are transforming construction processes. These tools enhance design accuracy, facilitate real-time decision-making, and minimize material waste while supporting global sustainability goals, including the United Nations’ Sustainable Development Goals (SDGs). Examining the adoption of these technologies within Chad is particularly important, as the country faces unique challenges that demand tailored solutions. While digital transformation in the construction industry has been widely studied worldwide and in Africa, Chad’s industry remains relatively unexplored in this regard. This research bridges this gap by identifying both the opportunities and the barriers to technological integration in the sector. Embracing these innovations could help modernize Chad’s construction industry, addressing persistent inefficiencies and promoting environmental sustainability. However, widespread adoption is hindered by significant challenges, including high implementation costs, limited access to advanced tools, and a shortage of skilled professionals. Overcoming these obstacles will require strategic investments in education, infrastructure, and supportive policies. By fully leveraging technological advancements, Chad has the potential to build a more competitive, resilient, and sustainable construction industry, driving national development while aligning with global sustainability initiatives. Full article

Traditional construction safety management, reliant on manual inspections and heuristic judgments, increasingly fails to address the dynamic, multi-dimensional risks of modern projects, perpetuating fragmented safety governance and reactive hazard mitigation. This study proposes an integrated building information modeling (BIM)–AI platform to unify safety supervision across the project lifecycle, synthesizing spatial-temporal data from BIM with AI-driven probabilistic models and IoT-enabled real-time monitoring for sustainable construction safety management. Employing a Design Science Research methodology, the platform’s phase-agnostic architecture bridges technical–organizational divides, while the Multilayer Neural Risk Coupling Assessment framework quantifies interdependencies among structural, environmental, and human risk factors. Prototype testing in real-world projects demonstrates improved risk detection accuracy, reduced reliance on manual processes, and enhanced cross-departmental collaboration. The system transitions safety regimes from compliance-based protocols to proactive, data-empowered governance. This approach offers scalability across diverse projects. The BIM-AI intelligent fusion platform proposed in this study builds an intelligent construction paradigm with synergistic development of safety governance and sustainability through whole lifecycle risk coupling analysis and real-time dynamic monitoring, which realizes a proactive safety supervision system while significantly reducing construction waste and accident prevention mechanisms. Full article

Synthetic aperture radar (SAR) ship detection holds significant application value in maritime monitoring, marine traffic management, and safety maintenance. Despite remarkable advances in deep-learning-based detection methods, performance remains constrained by the vast size differences between ships, limited feature information of small targets, and complex environmental interference in SAR imagery. Although many studies have separately tackled small target identification and multi-scale detection in SAR imagery, integrated approaches that jointly address both challenges within a unified framework for SAR ship detection are still relatively scarce. This study presents MC-ASFF-ShipYOLO (Monte Carlo Attention—Adaptively Spatial Feature Fusion—ShipYOLO), a novel framework addressing both small target recognition and multi-scale ship detection challenges. Two key innovations distinguish our approach: (1) We introduce a Monte Carlo Attention (MCAttn) module into the backbone network that employs random sampling pooling operations to generate attention maps for feature map weighting, enhancing focus on small targets and improving their detection performance. (2) We add Adaptively Spatial Feature Fusion (ASFF) modules to the detection head that adaptively learn spatial fusion weights across feature layers and perform dynamic feature fusion, ensuring consistent ship representations across scales and mitigating feature conflicts, thereby enhancing multi-scale detection capability. Experiments are conducted on a newly constructed dataset combining HRSID and SSDD. Ablation experiment results demonstrate that, compared to the baseline, MC-ASFF-ShipYOLO achieves improvements of 1.39% in precision, 2.63% in recall, 2.28% in ${AP}_{50}$, and 3.04% in $AP$, indicating a significant enhancement in overall detection performance. Furthermore, comparative experiments show that our method outperforms mainstream models. Even under high-confidence thresholds, MC-ASFF-ShipYOLO is capable of predicting more high-quality detection boxes, offering a valuable solution for advancing SAR ship detection technology. Full article