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The construction industry is accident-prone, and unsafe behaviors of construction workers have been identified as a leading cause of accidents. One important countermeasure to prevent accidents is monitoring and managing those unsafe behaviors. The most popular way of detecting and identifying workers’ unsafe behaviors is the computer vision-based...
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... study elaborated the current research on the identification of unsafe behaviors at construction sites from three directions: motion recognition, object recognition, interaction recognition. And, it provided an overview of related research, as shown in Table 1. Based on the above, most of existing research or products focused only on the workers' behaviors (i.e., motions) recognition or object recognition, very limited research considered the interaction between man-machine/material. Considering the importance of identifying construction workers' unsafe interaction between man-machine/material and the limitations of existing research, this study contributes a method that combines object recognition with motion recognition, which is very important for interaction identification. ...Context 2
... identification results were drawn into a confusion matrix, as shown in Figure 11. The accuracy of behavior risk evaluation considering safety signs was shown in Table 10. ...Context 3
... identification results were drawn into a confusion matrix, as shown in Figure 11. The accuracy of behavior risk evaluation considering safety signs was shown in Table 10. ...Context 4
... identification results were drawn into a confusion matrix, as shown in Figure 12. The comparison results of behavior identification accuracy based on YOLOv5 and YOLO-NAS were shown in Table 11. For Type I behaviors, the results show the overall identification accuracy of Type I behaviors was 91.96%, and the overall accuracy of Throwing and Operating were 83.93% and 100.00%. ...Context 5
... identification results were drawn into a confusion matrix, as shown in Figure 12. The comparison results of behavior identification accuracy based on YOLOv5 and YOLO-NAS were shown in Table 11. For Type I behaviors, the results show the overall identification accuracy of Type I behaviors was 91.96%, and the overall accuracy of Throwing and Operating were 83.93% and 100.00%. ...Context 6
... the choice of motion recognition technology is particularly important. Secondly, the experimental tasks (i.e., behaviors in Table 1) were selected based on the field studies, but the participants in this study were not real construction workers and were recruited from a convenience sample. Thirdly, detecting and extracting the meaning of safety signs, which was used for the behaviors risk evaluation, was convenient and effective, especially for computer vision-based intelligent systems. ...Context 7
... the choice of motion recognition technology is particularly important. Secondly, the experimental tasks (i.e., behaviors in Table 1) were selected based on the field studies, but the participants in this study were not real construction workers and were recruited from a convenience sample. Thirdly, detecting and extracting the meaning of safety signs, which was used for the behaviors risk evaluation, was convenient and effective, especially for computer vision-based intelligent systems. ...Citations
... This method was then used for activity classification through LSTM. Li et al. [60] identified three types of construction worker behaviors: throwing, operating, and crossing, using YOLO and ST-GCN. Torabi et al. [61] proposed a YOWO53 model that recognizes construction activities. ...
Recognition and classification for construction activities help to monitor and manage construction workers. Deep learning and computer vision technologies have addressed many limitations of traditional manual methods in complex construction environments. However, distinguishing different workers and establishing a clear recognition logic remain challenging. To address these issues, we propose a novel construction activity recognition method that integrates multiple deep learning algorithms. To complete this research, we created three datasets: 727 images for construction entities, 2546 for posture and orientation estimation, and 5455 for worker re-identification. First, a YOLO v5-based model is trained for worker posture and orientation detection. A person re-identification algorithm is then introduced to distinguish workers by tracking their coordinates, body and head orientations, and postures over time, then estimating their attention direction. Additionally, a YOLO v5-based object detection model is developed to identify ten common construction entity objects. The worker’s activity is determined by combining their attentional orientation, positional information, and interaction with detected construction entities. Ten video clips are selected for testing, and a total of 745 instances of workers are detected, achieving an accuracy rate of 88.5%. With further refinement, this method shows promise for a broader application in construction activity recognition, enhancing site management efficiency.
... The point cloud data for the detected scaffolding and guardrails were compared with safety regulation data to automatically check for safety regulation violations. Peilin et al. [60] introduced a method for detecting human behavior involving three actions: throwing (e.g., throwing a hammer, throwing a bottle), operating (e.g., turning on a switch, putting down a bottle), and crossing (e.g., crossing a railing, crossing obstacles). The outcomes were promising for monitoring the unsafe behavior of construction workers. ...
This study focuses on improving pre-emptive risk recognition and safety checks to prevent workplace accidents. It underscores improvements by addressing existing research issues, suggesting potential enhancements through system development. Work approval procedures and workers’ prior risk awareness, through the confirmation of work safety standards in physically separated work areas, are fundamental methods of preventing serious accidents and creating a safe work environment. A key factor concerning worker safety is recognizing the potential accident risk factors (or hazards) in advance through practical job hazard analysis and consequently take risk-reduction measures in case the safety standards are not met. Despite the crucial significance of pre-awareness of work risks at the majority of construction sites, tools to enhance this awareness are currently limited. Furthermore, real-time detection of work risks and the implementation of risk reduction measures are contingent upon a monitoring environment and a robust safety culture. This study proposed construction worker location-tracking technology that recognizes personal identification (ID). A safety check system based on location tracking combining personal quick response code (QR code) recognition and computer vision technology to automatically identify workers’ personal identities and track their physical location was proposed. A real-time safety check system was implemented to classify automatically whether workers have confirmed hazards and to approve a work process in high-risk workplaces by supervisors. Location-tracking technology with ID recognition performed the following two safety management functions. First, if a construction worker does not pre-check the work risk information before entering the work zone, the geofencing technology automatically classifies workers as those who are not aware of job hazards. Secondly, the safety manager or supervisor entering the on-site work zone possesses the authority to halt work if the work environment fails to meet safety standards and can issue warnings regarding risky situations. Essential functions were validated through a case study involving preliminary testing within the development system. To assess the practical application of the system, virtual simulations were conducted using recorded videos from a construction site to replicate the two essential functions of the system. The system was constructed using an Apache server and Python code, and for testing purposes, the names of the workers were randomized.
Machine learning, a key thruster of Construction 4.0, has seen exponential publication growth in the last ten years. Many studies have identified ML as the future, but few have critically examined the applications and limitations of various algorithms in construction management. Therefore, this article comprehensively reviewed the top 100 articles from 2018 to 2023 about ML algorithms applied in construction risk management, provided their strengths and limitations, and identified areas for improvement. The study found that integrating various data sources, including historical project data, environmental factors, and stakeholder information, has become a common trend in construction risk. However, the challenges associated with the need for extensive and high-quality datasets, models’ interpretability, and construction projects’ dynamic nature pose significant barriers. The recommendations presented in this paper can facilitate interdisciplinary collaboration between traditional construction and machine learning, thereby enhancing the development of specialized algorithms for real-world projects.
Construction workers’ behaviors directly affects labor productivity and their own safety, thereby influencing project quality. Recognizing and monitoring the construction-related behaviors is therefore crucial for high-quality management and orderly construction site operation. Recent strides in computer vision technology suggest its potential to replace traditional manual supervision approaches. This paper explores research on monitoring construction workers’ behaviors using computer vision. Through bibliometrics and content-based analysis, the authors present the latest research in this area from three perspectives: "Detection, Localization, and Tracking for Construction Workers," "Recognition of Workers’ Construction Activities," and "Occupational Health and Safety Behavior Monitoring." In terms of the literature’s volume, there has been a notable increase in this field. Notably, the focus on safety-related literature is predominant, underscoring the concern for occupational health. Vision algorithms have witnessed an increase in the utilization of object detection. The ongoing and future research trajectory is anticipated to involve multi-algorithm integration and an emphasis on enhancing robustness. Then the authors summarize the review from engineering impact and technical suitability, and analyze the limitations of current research from the perspectives of technical approaches and application scenarios. Finally, it discusses future research directions in this field together with generative AI models. Furthermore, the authors hope this paper can serves as a valuable reference for both scholars and engineers.
Currently, artificial intelligence (AI) technologies are becoming a strategic vector for the development of companies in the construction sector. The introduction of "smart solutions" at all stages of the life cycle of building materials, products and structures is observed everywhere. Among the variety of applications of AI methods, a special place is occupied by the development of the theory and technology of creating artificial systems that process information from images obtained during construction monitoring of the structural state of objects. This paper discusses the process of developing an innovative method for analyzing the presence of cracks that arose after applying a load and delamination as a result of the technological process, followed by estimating the length of cracks and delamination using convolutional neural networks (CNN) when assessing the condition of aerated concrete products. The application of four models of convolutional neural networks in solving a problem in the field of construction flaw detection using computer vision is shown; the models are based on the U-Net and LinkNet architecture. These solutions are able to detect changes in the structure of the material, which may indicate the presence of a defect. The developed intelligent models make it possible to segment cracks and delamination and calculate their lengths using the author's SCALE technique. It was found that the best segmentation quality was shown by a model based on the LinkNet architecture with static augmentation: precision = 0.73, recall = 0.80, F1 = 0.73 and IoU = 0.84. The use of the considered algorithms for segmentation and analysis of cracks and delamination in aerated concrete products using various convolutional neural network architectures makes it possible to improve the quality management process in the production of building materials, products and structures.
