Dynamic Fall Risk Assessment Framework for Construction Workers Based on Dynamic Bayesian Network and Computer Vision

Piao, Yanmei; Xu, Wenpei; Wang, Ting-Kwei; Chen, Jieh Haur

doi:10.1061/(asce)co.1943-7862.0002200

Cited by 15 publications

(5 citation statements)

References 71 publications

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“…However, in future work, we plan to extend this algorithm to predict the behavior of workers working at the height of up to 3.5 m to cover all KOSHA regulations associated with the A-type ladder. We plan to develop an early risk assessment framework with the safety risks index by considering risks and severity to classify risks as low, medium, and high for more advanced practical usability in managing risks while working at a height on the ladder [32]. We plan to create a larger dataset by collecting images from various construction sites to detect relevant objects for a more practical application of CV-based safety monitoring.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…Ding et al [31] introduced a deep learning-based hybrid model comprising a CNN and long short-term memory that automatically identified unsafe behavior by detecting workers working on a ladder. Piao et al [32] proposed a dynamic fall risk assessment framework for construction workers that combined CV and the Bayesian network to reduce FFH by automatically detecting risk factors and improving risk assessment efficiency and used working on a ladder as a case study. Chen et al [33] introduced a proactive worker safety risk evaluation framework using position and worker posture as quantitative indicators to classify workers' behavior.…”

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Fall Prevention From Ladders Utilizing a Deep Learning-Based Height Assessment Method

et al. 2022

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According to the Center for Construction Research and Training (CPWR) and the Korea Occupational Safety & Health Agency (KOSHA), falls from ladders are a leading cause of fatalities. The current safety inspection process to enforce height-related rules is manual and time-consuming. It requires the physical presence of a safety manager, for whom it is sometimes impossible to monitor an entire area in which ladders are being used. Deep learning-based computer vision technology has the potential to capture a large amount of useful information from a digital image. Therefore, this paper presents a deep learning-based height assessment method using a single known value in an image to measure working height, monitor compliance to safety rules, and ensure worker safety. The proposed method comprises (1) extraction of safety rules from the KOSHA database related to the A-type ladder; (2) object detection (Single Shot Multibox Detector SSD) (3) a height-computing module (HCM) to estimate the working height of the worker (how high a worker is from the ground); and (4) classification of worker behavior (using the developed SSD-based HCM) based on the best practices derived from the KOSHA database. The developed algorithm has been tested on four different scenarios based on KOSHA safety rules, with heights ranging from under 1.2 m to over 2 m. Additionally, the proposed method was evaluated on 300 images for binary classification (safe and unsafe) and achieved an overall accuracy of 85.33%, verifying its feasibility for intelligent height estimation and compliance monitoring. INDEX TERMS Falls from ladders, vision intelligence-based monitoring, construction safety rules, visionbased height estimation, deep learning. I. INTRODUCTIONConstruction work is likely to expose workers to hazardous situations because of its distinctive, dynamic, and complex nature. Compared with the workers in other industries, those in the construction industry are more prone to occupational accidents that could potentially lead to fatalities [1]-[3]. The most common construction accidents include fall from height (FFH), collision with objects, electrocution, and being stuckThe associate editor coordinating the review of this manuscript and approving it for publication was Turgay Celik . in between machinery. Among these, FFH is the most frequent cause of accidents at construction sites. According to the U.S. Bureau of Labor Statistics, the number of fatalities due to falls to a lower level increased by 26% from 2011 to 2016, which includes ladders (836 fatal injuries) and rooftops (763). The most common height of a fall was reported as 30 ft (658 deaths) [4]. In addition, the Center for Construction Research and Training (CPWR) reports that ladder (93)-related accidents were a leading cause of fatal injuries from 2011 to 2019 [5]. Moreover, in South Korea, industrial accidents are increasing, with accidents at construction sites

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Section: Discussion and Future Workmentioning

confidence: 99%

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confidence: 99%

Fall Prevention From Ladders Utilizing a Deep Learning-Based Height Assessment Method

et al. 2022

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“…proposed a dynamic risk evolution assessment framework for construction sites. It automatically detected on-site risk factor information, to improve efficiency of risk assessment, and reduce the risk of construction workers falling from heights [26]. Starting from safety in an emergency, and from the perspective of scenario derivation, Yuan, C.F.…”

Section: Dynamic Bayesian Networkmentioning

confidence: 99%

Analysis on Dynamic Evolution of the Cost Risk of Prefabricated Building Based on DBN

Wang

Yuan³

et al. 2022

Sustainability

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Prefabricated building constitutes the development trend of the construction industry in the future. However, many uncertainties in the construction process will surely lead to a higher cost. Therefore, it is necessary to study the cost risk evolution and transfer mechanism in the implementation process of this project. A dynamic evolution model for the cost risk of prefabricated buildings has been established in this paper. First of all, a matrix for cost risk of prefabricated buildings was established based on the WSR (Wuli-Shili-Renli) model, and all risk factors in the implementation stage were classified in accordance with the WSR principle. Second, a DBN-based dynamic evolution model was established based on the risk matrix, and the structure and node parameters of the Dynamic Bayesian Network were determined with the aid of the K2 structure learning algorithm and parameter learning method. In view of the probability change process of risks over time, the dynamic evolution path of risks was predicted in different cases through causal reasoning and diagnostic reasoning. Eventually, the model was applied into construction projects. The research results show that: because prefabricated components need to be made by prefabricated component factories, the management systems of prefabricated component factories are usually not perfect, and the probability of management risks is higher. The occurrence of management risks not only has an impact on other risks at the current time node, but also causes other risks to occur in the subsequent transportation and construction phases at the next moment, which eventually leads to the occurrence of risk events.

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“…Accident diagnosis and management [23] Fall from height; construction workers [24] Construction safety; predictive analysis; tunnel construction [28] CN Complex network Human error; safety assessment [32] explanation, prediction Near-miss; metro construction; safety management [42] Construction safety; subway construction [25] description, explanation Unsafe behaviors; accident prevention; urban railway construction [46] Safety management; design for safety (DFS); prevention through design (PTD); subway construction [69] Accident analysis; railway operational accident [70] Accident analysis; metro operation hazard network (MOHN) [71] Deep foundation pit; subway construction [17] Construction workers; unsafe behavior [72] Unsafe behavior; accident prevention; urban railway [73] Accident level; accident chain; construction [44] description, explanation, control Human factor analysis (HFA); occupational safety [48] Organizational synchronization; construction delay factors [74] CNN Convolutional neural network Fall prevention; personnel protective equipment [75] explanation, prediction, control Construction safety; guardrail detection [29] FNN Fuzzy neural network Worker-machine safety; intelligent assessment [76] explanation, prediction, control NN Neural network;…”

Section: Network Approaches Research Objects and Analysis Processmentioning

confidence: 99%

“…Compared with traditional safety analysis and evaluation methods such as ETA, FTA, and FMECA, the Bayesian network is mainly used to replace or improve traditional safety analysis and evaluation methods by virtue of its fine description of safety factors and their interactions, as well as its better quantitative performance [19][20][21]. Moreover, as the application scope of the Bayesian network increases, information technologies such as data mining and artistic intelligence are gradually being introduced into the research of the Bayesian network, which greatly improves the dynamics, accuracy, and timeliness of security assessment methods [23,24]. In the construction industry, artificial intelligence, big data, cloud computing, and deep learning have gradually been combined with research and practice, which not only shows a stronger analytical ability for the original security problems, but also plays an important role in finding and solving new security problems.…”

Section: Future Direction Based On Keyword Cluster Analysismentioning

confidence: 99%

Research Paradigm of Network Approaches in Construction Safety and Occupational Health

Liu

Cui

et al. 2022

IJERPH

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Construction safety accidents seriously threaten the lives and health of employees; however, the complexity of construction safety problems continues to increase. Network approaches have been widely applied to address accident mechanics. This study aims to review related studies on construction safety and occupational health (CSOH) and summarize the research paradigm of recent decades. We solicited 119 peer-reviewed journal articles and performed a bibliometric analysis as the foundation of the future directions, application bottlenecks, and research paradigm. (1) Based on the keyword cluster, future directions are divided into four layers: key directions, core themes, key problems, and important methods. (2) The network approaches are not independently applied in the CSOH research. It needs to rely on different theories or be combined with other methods and models. However, in terms of approach applications, there are still some common limitations that restrict its application and development. (3) The research paradigm of network analysis process can be divided into four stages: description, explanation, prediction, and control. When the same network method encounters different research objects, it focuses on different analysis processes and plays different roles.

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Dynamic Fall Risk Assessment Framework for Construction Workers Based on Dynamic Bayesian Network and Computer Vision

Cited by 15 publications

References 71 publications

Fall Prevention From Ladders Utilizing a Deep Learning-Based Height Assessment Method

Fall Prevention From Ladders Utilizing a Deep Learning-Based Height Assessment Method

Analysis on Dynamic Evolution of the Cost Risk of Prefabricated Building Based on DBN

Research Paradigm of Network Approaches in Construction Safety and Occupational Health

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