BIM and ontology-based knowledge management for dam safety monitoring

The construction industry has a high accident rate, making safety training for construction workers increasingly important in addressing this issue. Previous research on construction safety training has made significant progress, but existing literature reviews lack a comprehensive summary of the development of construction safety training, an evaluation of the effectiveness of current training methods, and an analysis of potential improvements in the future. Therefore, this study addresses these challenges through systematic literature review, scientometric analysis, and meta-analysis. A total of 92 publications since 2001 were included in this study after searching and selection based on the preferred reporting items commonly used in psychological and medical systematic reviews and meta-analyses. This study identified three phases of construction safety training research: determination of the necessity and significance of construction safety training, exploration of different methods for construction safety training, and development of construction safety training tools using advanced technologies. Findings from the scientometric analysis revealed that prevention and hazard recognition are key research topics in the field. Critical findings of prevention and hazard recognition were summarized and discussed to have a comprehensive understanding of current progress. The results of the meta-analysis indicated that current safety training methods are generally effective in improving construction safety, though the effectiveness in field studies is lower than in laboratory studies. Thus, researchers should understand why safety training performance is different between laboratories and construction sites and then control the differences to enhance the effectiveness of construction safety training in the future. This study identified that personalized safety training is a foreseeable development trend and suggested more efforts on the improvement of long-term safety training effectiveness. Additionally, safety training methods should be revised and optimized for the needs of intelligent construction and prefabricated construction. These findings offer new insights into construction safety training for researchers and policymakers. 

Development of Safety Training in Construction: Literature Review, Scientometric Analysis, and Meta-Analysis

Prefabricated Construction (PC) is one of the modern construction techniques where some components of a structure or an entire structure are manufactured in a controlled environment and then installed on the construction site. The Architectural Engineering and Construction (AEC) industry is gradually implementing PC by integrating with Building Information Modeling (BIM), Artificial Intelligence (AI), and Internet of Things (IoT) applications. A three-step procedure adopted in this study comprises a bibliometric search, followed by quantitative analysis and qualitative analysis, to figure out the recent research trends in PC aided with BIM, AI and IoT applications and to draw the research trends and knowledge gap. The quantitative analysis pinpoints key research patterns, influential authors, and notable articles; the qualitative assessment spotlights five central themes, including the integration of BIM, AI, and IoT with PC design, quality improvement, performance and productivity enhancement, information and knowledge management, and the barriers to PC adoption. Crucially, future research directions such as automated design developments, robotic fabrication, applications' compatibility, strategic decision-making, and PC operation and maintenance are highlighted, underscoring the potential for refining PC adoption in the industry. This review provides a roadmap for future research to aid researchers and practitioners in optimizing PC's potential in the AEC sector. 

The convergence of BIM, AI and IoT: Reshaping the future of prefabricated construction

Reimagining construction safety: unveiling the impact of building information modeling (BIM) implementation

Generating risk response measures for subway construction by fusion of knowledge and deep learning

The extensive application of building information modeling (BIM) technology has brought opportunities and challenges to safety risk management in the field of prefabricated building construction. It is of great significance to provide timely information and knowledge for safety risk decisions in prefabricated building construction, and to display this information visually. In response, based on the ontology theory and using the Revit software, in this study we aimed to establish a monitoring system for the construction of prefabricated buildings, which was verified through a practical case. The results revealed that, first, ontology technology can be applied in the Revit software through plug-in integration, and knowledge regarding construction safety risk management in prefabricated building construction can be shared, reused, and accumulated using this system. Second, problems with the design and construction models of prefabricated buildings that do not meet the specification requirements can be detected by the monitoring system in the Revit software. Third, automatic risk identification and response methods using ontology theory and BIM technologies can effectively promote construction safety risk management performance in relation to prefabricated buildings. These findings examine the application of ontology to the field of prefabricated construction safety risk management for the first time, enrich the research on ontology technology, and contribute to safety risk management in the construction of prefabricated buildings.

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Safety Risk Management of Prefabricated Building Construction Based on Ontology Technology in the BIM Environment

Traffic sign recognition (TSR) system is essential for autonomous vehicle and is vulnerable to security threats from adversarial attacks. The existing adversarial attacks for TSR are invasive and suffer from poor concealment and high computational complexity, and thus have low feasibility in real-world scenarios. This paper proposes a non-invasive modulated LED illumination-based adversarial attack scheme. By generating luminance flashes imperceptible to human eyes through fast intensity modulation of lighting such as LED streetlights and exploiting the rolling shutter mechanism of CMOS sensors of in-vehicle imaging system, the proposed attack scheme can successfully perform adversarial attacks on TSR system by implanting luminance information perturbations into the images acquired by autonomous vehicle and thus poisoning the image data fed into TSR system. Depending on the modulation frequency and pattern of LED illumination, the proposed attack scheme enables denial of service (DoS) attack that leads to traffic sign detection failure and escape attack that leads to traffic sign misclassification, with the advantages of superior concealment, low computational complexity and high practical feasibility. Experiments are conducted with two benchmark datasets (GTSDB and GTSRB) and two state-of-the-art models of TSR detection and TSR classification, YOLOv5m and Sill-Net respectively, in both the digital and physical world. Experimental results show that the proposed DoS attack on the TSR detection model (YOLOv5m) can reach the success rate of 90.00% and the proposed escape attack on the TSR classification model (Sill-Net) can achieve the success rate of 35.00%.

MLIA: modulated LED illumination-based adversarial attack on traffic sign recognition system for autonomous vehicle

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Safety Risk Management of Prefabricated Building Construction Based on Ontology Technology in the BIM Environment

MLIA: modulated LED illumination-based adversarial attack on traffic sign recognition system for autonomous vehicle