Analysis of the Characteristics of Fatal Accidents in the Construction Industry in China Based on Statistical Data

Xu, Qingwei; Xu, Kaili

doi:10.3390/ijerph18042162

Cited by 53 publications

(28 citation statements)

References 46 publications

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“…To better understand the nature of these occupational incidents among electrical contractors—and therefore to discover effective safety interventions to prevent injuries [ 7 ], especially fatalities—this study analyzed a large national database of occupational accidents to detect which statistically significant influential factors contribute to injuries and deaths among electrical contractors. This study has collected data from the Occupational Safety and Health Administration’s (OSHA’s) Integrated Management Information System (IMIS) accidents database from 2007 to 2013.…”

Section: Introductionmentioning

confidence: 99%

Trends in Catastrophic Occupational Incidents among Electrical Contractors, 2007–2013

Gholizadeh

Onuchukwu

Esmaeili

2021

IJERPH

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This study used methodologies of descriptive and quantitative statistics to identify the contributing factors most affecting occupational accident outcomes among electrical contracting enterprises, given an accident occurred. Accident reports were collected from the Occupational Safety and Health Administration’s fatality and catastrophe database. To ensure the reliability of the data, the team manually codified more than 600 incidents through a comprehensive content analysis using injury-classification standards. Inclusive of both fatal and non-fatal injuries, the results showed that most accidents happened in nonresidential buildings, new construction, and small projects (i.e., $50,000 or less). The main source of injuries manifested in parts and materials (46%), followed by tools, instruments, and equipment (19%), and structure and surfaces (16%). The most frequent types of injuries were fractures (31%), electrocutions (27%), and electrical burns (14%); the main injured body parts were upper extremities (25%), head (23%), and body system (18%). Among non-fatal cases, falls (37%), exposure to electricity (36%), and contact with objects (19%) caused most injuries; among fatal cases, exposure to electricity was the leading cause of death (50%), followed by falls (28%) and contact with objects (19%). The analysis also investigated the impact of several accident factors on the degree of injuries and found significant effects from such factors such as project type, source of injury, cause of injury, injured part of body, nature of injury, and event type. In other words, the statistical probability of a fatal accident—given an accident occurrence—changes significantly based on the degree of these factors. The results of this study, as depicted in the proposed decision tree model, revealed that the most important factor for predicting the nature of injury (electrical or non-electrical) is: whether the source of injury is parts and materials; followed by whether the source of injury is tools, instruments, and equipment. In other words, in predicting (with a 94.31% accuracy) the nature of injury as electrical or non-electrical, whether the source of injury is parts and materials and whether the source of injury is tools, instruments, and equipment are very important. Seven decision rules were derived from the proposed decision tree model. Beyond these outcomes, the described methodology contributes to the accident-analysis body of knowledge by providing a framework for codifying data from accident reports to facilitate future analysis and modeling attempts to subsequently mitigate more injuries in other fields.

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Section: Introductionmentioning

confidence: 99%

Trends in Catastrophic Occupational Incidents among Electrical Contractors, 2007–2013

Gholizadeh

Onuchukwu

Esmaeili

2021

IJERPH

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“…China’s construction industry is likewise in a precarious state in terms of safety. From 2010 to 2019, around 7275 construction workers died in workplace accidents, with an average of 1.99 deaths per day ( Xu and Xu, 2021 ). Furthermore, since 2015, the number of safety accidents and fatalities in housing and municipal construction has shown an increasing trend year by year ( Zhang J. et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Formation Mechanism and Dynamic Evolution Laws About Unsafe Behavior of New Generation of Construction Workers Based on China’s Construction Industry: Application of Grounded Theory and System Dynamics

Wang

et al. 2022

Front. Psychol.

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Construction workers’ unsafe behavior is a major cause of safety accidents and injuries, therefore, a profound understanding of the formation process and evolution laws about construction workers’ unsafe behavior is conducive to taking measures to prevent incidents. At present, the new generation of construction workers (NGCWs) born after 1980 are gradually becoming the main force at construction sites in China. Given that generational differences of construction workers can cause the discrepancies in their thoughts and attitudes when engaging in safety-related activities, this study aims to investigate the formation mechanism and dynamic evolution laws about NGCWs’ unsafe behavior based on the context of China’s construction industry. From the perspective of behavior motivation, in-depth semi-structured interviews with 18 NGCWs and 7 grassroots managers were conducted, and data analysis followed a three-step coding process based on grounded theory. Through continuous comparison, abstraction and analysis, the stimulus-organism-response theory was introduced and expanded to construct a three-stage formation mechanism model. On this basis, the causal diagram and stock flow diagram were developed based on system dynamics principles to reflect the dynamic feedback relationships of the factors in the static formation mechanism model, and simulation was carried out using Vensim PLE software. The results show that three types of internal needs and three types of external incentives stimulate corresponding motivations for NGCWs’ unsafe behavior. Two types of individual factors, five types of situational factors and behavior result play an influencing role in the decision-making process of externalizing motivation into behavior. Under the synergistic effect of multiple factors, the level of unsafe behavior displays a downward trend, and the rate of decrease is slow first and then fast. Furthermore, among individual factors and situational factors, safety awareness and safety management system have the most significant effect on the level of unsafe behavior, while situational factors play a more obvious role. The findings can provide theoretical support and practical references to China’s construction companies and government departments for the purpose of improving NGCWs’ unsafe behavior.

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“…Furthermore, falling objects account for several industrial injuries. For instance, a worker can get trapped in a factory as a result of falling objects, thus sustaining traumatic brain injuries, spinal injury, or broken bones, including death, in cases of larger objects falling from high above, as reported in a recent death in Korea of a worker due to falling objects from a broken crane, and about 6005 fatal accidents in China's construction industry resulting in 7275 deaths from 2010 to 2019 [5].…”

Section: Introductionmentioning

confidence: 99%

Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor

et al. 2021

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Factory shop floor workers are exposed to threats and accidents due to their encounters with tools, equipment, and toxic materials. There are cases of occupational accidents resulting in injuries to workers and precipitating lawsuits, which on the other hand affect company’s operational cost. To ensure the safety of workers within the shop floor, there is a need for proactive activity monitoring. Such activities include detection of falling objects, abnormal vibration, and movement of humans within an acceptable area of the factory floor. Breathing sensor-based monitoring of workers in the smart factory shop floor can also be implemented. This is for the detection of human activity, especially in cases where workers are in isolation with no available emergency assistance. Internet of Things (IoT), Industrial Internet of Things (IIoT), and machine learning (ML) have enabled so many possibilities in this area. In this study, we present a simple test-bed, which is made up of a vibration sensor, a breathing and movement sensor, and a Light Detection and Ranging (LIDAR) sensor. These sensors were used to gather normal and abnormal data of human activities at the factory. We developed a dataset based on possible real-life situations and it is made up of about 10,000 data points. The data was split with a ratio of 75:25 for training and testing the model. We investigated the performance of different ML algorithms, including support vector machine (SVM), linear regression, naive Bayes (NB), K-nearest neighbor (KNN), and convolutional neural network (CNN). From our experiments, the CNN model outperformed other algorithms with an accuracy of 99.45%, 99.78%,100%, and 100%, respectively, for vibration, movement, breathing, and distance. We have also successfully developed a dataset to assist the research community in this field.

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Analysis of the Characteristics of Fatal Accidents in the Construction Industry in China Based on Statistical Data

Cited by 53 publications

References 46 publications

Trends in Catastrophic Occupational Incidents among Electrical Contractors, 2007–2013

Trends in Catastrophic Occupational Incidents among Electrical Contractors, 2007–2013

Formation Mechanism and Dynamic Evolution Laws About Unsafe Behavior of New Generation of Construction Workers Based on China’s Construction Industry: Application of Grounded Theory and System Dynamics

Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor

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