Construction Safety Accidents Research Articles

Research on scenario deduction and emergency decision-making evaluation for construction safety accidents

This study introduces an innovative “scenario-response” analysis framework for emergency management in construction safety accidents. Leveraging Bayesian Network (BN) technology, this framework systematically analyzes and evaluates the potential risks and developmental paths of such accidents. In-depth dissection of construction safety accident cases led to the creation of a comprehensive scenario knowledge meta-representation model. This model effectively captures both the micro-mechanisms and the macro-evolution of accidents. Furthermore, the study employs the entropy weight-TOPSIS model to scientifically evaluate emergency decision-making plans. This approach has resulted in the establishment of a comprehensive accident emergency response evaluation indicator system. It provides a quantitative basis for decision-making, aiding those managing construction safety accidents. The study’s empirical segment, focusing on a case analysis of a deep foundation pit collapse accident, illustrates key preventive measures. It highlights that timely reporting by supervisory personnel, enhanced supervision, and strict punishment of violations are crucial in preventing and controlling accident development in deep foundation pit construction. Additionally, the study validates the practical effectiveness and reliability of the proposed method. It offers valuable insights and guidance for both safety management practices and theoretical research within the construction industry.

A pre-control method in safety risk management of metro construction

ABSTRACT With the rapid development of metro construction in China, construction safety accidents occur frequently due to the lack of effective safety risk controls. Continuously improving the level of safety risk pre-control has become a significant factor in the safety managements of metro construction. This paper presents a reasoning model for safety risk pre-control measures in metro construction using case-based reasoning. To establish the case database, a frame-based knowledge representation method is adopted based on the characteristics of metro construction safety accident cases. Additionally, a two-level retrieval strategy combining hierarchical retrieval and nearest-neighbor algorithms is used for case retrieval. Furthermore, a new approach, the cloud model, is proposed to determine the attribute weight of metro construction safety accidents. This approach addresses quantitative attributes with fuzziness and randomness. Finally, a specific metro station is taken as a case to validate the feasibility of the approach. The results showed that the case-based reasoning method enables engineers to quickly and effectively develop risk pre-control measures based on previous cases. In addition, the use of case-based reasoning in decision-making enhances knowledge sharing and reuse in the metro construction industry. This paper presents an effective reasoning approach for pre-control measure analysis in metro construction, allowing engineers to quickly and efficiently carry out scientific and reasonable pre-control measures from past cases.

Construction safety management based on building information modeling technology

With the progression of society, urban construction has been rapidly advancing, yet the incidence of construction safety accidents has surged. In order to effectively prevent construction safety incidents, construction safety management has become increasingly significant. To achieve the safety management of construction projects, Building Information Modeling (BIM) technology is introduced. It can help construction safety management departments better understand every link in the entire construction process. In this paper, an integrated analysis of BIM technology and the current state of construction safety is presented, aiming to explore the problems existing in the current construction safety management and provide construction personnel and management staff with a comprehensive understanding of predictable risk factors, effective preemptive measures, safety inspections, identification of safety hazards, and other associated issues, thereby guaranteeing the occupational safety and security of construction personnel and property. Consequently, the accident prevention model undergoes a transformation from a reactive approach to a proactive one.

Risk assessment of construction safety accidents based on association rule mining and Bayesian network

Risk assessment of construction safety accidents based on association rule mining and Bayesian network

Group cognitive characteristics of construction Workers’ unsafe behaviors from personalized management

Unsafe behavior management is an effective way to prevent construction safety accidents. However, unsafe behavior management lacks personalization and needs to incorporate cognitive factors. Therefore, this study explored the group cognitive characteristics of workers' unsafe behaviors under common individual factors. Firstly, the cognitive model of unsafe behavior was validated using structural equation modeling and bootstrap techniques. The results showed a distal mediating between the cognitive processes of unsafe behaviors. Second, the cognitive processes of workers’ unsafe behaviors were analyzed using multiple group analysis for three individual factors: age, work experience, and education. The results showed that age is not a significant individual factor in group cognitive characteristics, education and work experience are significant individual factors with group cognitive characteristics of unsafe behavior. Workers with shorter experience are prone to cognitive failure in risk identification, others' influence, self-efficacy, and work skills. Workers with longer experience are prone to cognitive failure in safety awareness, others' pressure, and execution ability. Low-education workers are prone to cognitive failure in risk identification and pursuing energy-saving. High-education workers are prone to cognitive failure in safety awareness, self-efficacy, and work skills. Finally, targeted measures are given for significant individual factors based on group cognitive characteristics of unsafe behavior in order to construct widely used personalized group cognitive management for unsafe behavior.

Data-Driven Dynamic Bayesian Network Model for Safety Resilience Evaluation of Prefabricated Building Construction

Due to factors such as the availability of assembly equipment, technology, and site management level, prefabricated building construction safety accidents often occur. To ensure the safety of prefabricated buildings and effectively reduce the accident rate, the concept of resilience is introduced into the safety management of prefabricated buildings. Based on the resilience absorption capacity, adaptation capacity, recovery capacity, and optimization capacity, a comprehensive evaluation index system for the safety resilience of prefabricated buildings is established. By combining prior knowledge with structural learning and parameter learning, a dynamic Bayesian network (DBN) model is constructed to dynamically evaluate the safety resilience of prefabricated buildings. Through forward causal reasoning and backward diagnostic reasoning, the dynamic safety resilience value of prefabricated buildings and the chain of maximum failure causes are obtained. Finally, by conducting a sensitivity analysis on the target nodes, the key influencing factors of the safety resilience of prefabricated construction are identified, and improvement suggestions for enhancing resilience are proposed. The results indicate that establishing a resilience safety culture, preventing unsafe behaviors of personnel, safety management, and supervision on the construction site, emergency management actions, and building a risk management information system are crucial factors influencing the safety resilience of prefabricated buildings. The enhancement of absorption capacity has the greatest impact on the safety resilience of prefabricated buildings.

Analysis on safety management measures in civil engineering construction

Building construction involves many complex processes such as high-altitude operations, mechanized equipment operations, and transportation and handling of building materials. Construction is a high-risk industry with frequent accidents. Therefore, some efficient and practical preventive measures need to be proposed immediately to reduce the occurrence of these accidents. This paper introduces different types of construction accidents and analyzes the reasons in combination with the theory of accident causation, to provide implementable ways to improve the safety performance of construction projects. Finally, a few precautions are offered in this paper to prevent the similar accidents from happening. The research results show that measures such as improving the safety evaluation system, strengthening the operating environment, strictly controlling the installation and dismantling links, enhancing the lighting conditions at night, and controlling minor accidents can effectively prevent the occurrence of construction safety accidents.

Research on Safety Risk Transfer in Subway Shield Construction Based on Text Mining and Complex Networks

Subway construction is often in a complex natural and human-machine operating environment, and that complicated setting leads to subway construction being more prone to safety accidents, which can cause substantial casualties and monetary losses. Thus, it is necessary to investigate the safety risks of subway construction. The existing literature on the identification and assessment of subway construction safety risks (SCSR) is susceptible to the influence of subjective factors. Moreover, although existing studies have explored the interrelationships between different risks, these studies usually analyze the interrelationships of single risks, lack the study of risk chain transfer relationships, and fail to find out the key path of risk transfer. Therefore, this paper innovatively combines text mining, association rules, and complex networks to deep mine subway construction safety incident reports and explore the risk transfer process. Firstly, it uses text mining technology to identify subway construction safety risks. Then, association rules are introduced to explore the causal relationships among safety risks. Finally, the key safety risks and important transfer paths of subway construction safety accidents (SCSA) are obtained based on the complex network model. Research results show that (a) improper safety management, unimplemented safety subject responsibilities, violation of operation rules, non-perfect safety responsibilities system and insufficient safety education and training are the key safety risks in SCSA; (b) two shorter key risk transfer paths in the subway construction safety network can be obtained: insufficient safety education and training→lower safety awareness→violation of operation rules→safety accidents; insufficient safety checks or hidden trouble investigations→violation of operation rules→safety accidents; (c) in the process of risk transfer, the risk can be controlled by controlling the key safety risk or cutting off the transfer paths. This paper provides new ideas and methods for SCSR identification and influence element mining, and the results of the study help safety managers propose accurate subway construction safety risk control measures.

Analysis on the Relationship between Safety and Unsafe Behavior in Building Construction

With the continuous improvement of the safety management level of construction technology, although the occurrence of construction safety accidents is generally declining, but there are still more construction safety accidents. Research shows that construction safety is significantly related to workers' safety behavior. In order to reduce safety accidents, it is necessary to improve workers' safety awareness, improve construction environment and enhance management requirements. Improving construction safety is helpful to promote economic benefits and enhance the image of enterprises, and protect the life and health of workers.

Construction and application of knowledge graph for construction accidents based on deep learning

PurposeLearning from safety accidents and sharing safety knowledge has become an important part of accident prevention and improving construction safety management. Considering the difficulty of reusing unstructured data in the construction industry, the knowledge in it is difficult to be used directly for safety analysis. The purpose of this paper is to explore the construction of construction safety knowledge representation model and safety accident graph through deep learning methods, extract construction safety knowledge entities through BERT-BiLSTM-CRF model and propose a data management model of data–knowledge–services.Design/methodology/approachThe ontology model of knowledge representation of construction safety accidents is constructed by integrating entity relation and logic evolution. Then, the database of safety incidents in the architecture, engineering and construction (AEC) industry is established based on the collected construction safety incident reports and related dispute cases. The construction method of construction safety accident knowledge graph is studied, and the precision of BERT-BiLSTM-CRF algorithm in information extraction is verified through comparative experiments. Finally, a safety accident report is used as an example to construct the AEC domain construction safety accident knowledge graph (AEC-KG), which provides visual query knowledge service and verifies the operability of knowledge management.FindingsThe experimental results show that the combined BERT-BiLSTM-CRF algorithm has a precision of 84.52%, a recall of 92.35%, and an F1 value of 88.26% in named entity recognition from the AEC domain database. The construction safety knowledge representation model and safety incident knowledge graph realize knowledge visualization.Originality/valueThe proposed framework provides a new knowledge management approach to improve the safety management of practitioners and also enriches the application scenarios of knowledge graph. On the one hand, it innovatively proposes a data application method and knowledge management method of safety accident report that integrates entity relationship and matter evolution logic. On the other hand, the legal adjudication dimension is innovatively added to the knowledge graph in the construction safety field as the basis for the postincident disposal measures of safety accidents, which provides reference for safety managers' decision-making in all aspects.

Real-Time Early Safety Warning for Personnel Intrusion Behavior on Construction Sites Using a CNN Model

The high number of annual safety accidents and casualties reflects the problems of slow detection of safety accidents and untimely early warnings in current construction safety management, and China urgently needs new methods and technologies to improve the safety management efficiency of the construction industry. However, there are fewer achievements in the use of new technologies for intelligent construction safety management, and most of the research focuses on intrusion detection and specific event alarms, which cannot be well implemented for systematic early warning functions. Based on the existing research and the characteristics of early warning scenarios, this study introduces the convolutional neural network (CNN) to build a video image recognition and classification model to give early safety warnings for intrusion behavior in hazard areas of construction and demonstrates the warning effect and accuracy with practical cases. First, it clarifies the early warning demand information, such as the attributes of construction personnel and hazard areas. Then, the construction model is realized by multi-scale hierarchical feature extraction mapping, the Softmax classification function, and the argmax function. Finally, from the empirical analysis, it can be seen that an early safety warning based on the CNN model has an accurate ability to identify the intrusion behavior of construction site personnel, which can reduce the probability of construction safety accidents to a certain extent, and provide enlightenment for further realization of intelligent construction sites.

Coupled Analysis of Safety Risks in Bridge Construction Based on N-K Model and SNA

Bridge construction accidents are often caused by a variety of factors, so it is particularly important to explore the role mechanism of bridge construction accident risk factors to effectively prevent construction safety accidents and ensure the smooth construction of bridges. We collect the causes of bridge construction accidents in China from 2006 to 2023, take 126 typical cases as research samples, analyze the primary risk factors of bridge construction from four aspects (human factors, equipment factors, management factors, and environmental factors), establish a library of secondary risk factors with reference to the literature research, introduce the theory of risk coupling, and analyze the coupling mechanism and types of risk factors of bridge construction accidents. The N-K random Boolean network model (N-K model) quantifies the coupling relationship between risk factors, assesses the risk level, and uses social network analysis (SNA) to analyze the network of bridge construction accident risk factors. The results indicate that the more factors involved in risk coupling, the greater the safety risks in bridge construction. Human factors are susceptible to the influence of other elements, and environmental and management factors can directly or indirectly impact other factors. In addition, operational errors, a lack of supervision and management, inadequate safety inspections, poor management personnel, and insufficient technical capabilities are also key risk factors that need to be prevented and controlled.

Coupling Analysis of Tunnel Construction Safety Risks Based on N-K Model and SD Causality Diagram

Tunnel construction is characterized by its large scale, long periods and vulnerability to environmental impact, which pose great challenges to tunnel construction safety. In order to analyze the coupling mechanism of tunnel construction safety risks and assess these risks, we conducted a study on the coupling evaluation of these risks in order to improve tunnel construction safety risk management. By analyzing 150 accident cases related to tunnel construction safety, an N-K model (natural killing model) was constructed to quantify the risk level of each coupling form from four aspects—personnel risk factors, equipment risk factors, environmental risk factors and management risk factors—and the SD (system dynamics) causality diagram was used to construct risk element conduction paths and identify the key influencing factors of different coupling forms. The research results show that with the increase in risk coupling factors, the risk of tunnel construction safety accidents also increases; weak personnel safety awareness, aging and wear of equipment, poor operating environment and construction site management chaos are the key risk factors whose prevention needs to be focused on. The related research results can provide a new method for decision makers to assess tunnel construction safety risks and enrich the research on tunnel construction safety risk management.

Skeleton-based automatic assessment and prediction of intrusion risk in construction hazardous areas

Intrusion behavior in hazardous areas is one of the major causes of construction safety accidents including falls from height, strikes by objects, etc. Implementing automatic and precise assessment of intrusions to enhance safety performance is of great importance in construction areas. Due to the large area of construction sites and diverse human behaviors, it is difficult to accurately predict worker behavior, resulting in many intrusions detected after the occurrence. Notably, computer vision-based skeleton extraction can provide a promising non-contact solution for assessing intrusions. This paper presents a novel intrusion behavior detection and evaluation approach by defining a safety buffer zone and using two key quantitative elements, i.e. the motion state and orientation posture of intruders. An indoor experiment was conducted by employing skeleton detection technology with safety knowledge to demonstrate the feasibility and effectiveness of the assessment method. The participants’ risk levels were evaluated separately and simultaneously based on the motion and posture. The risk level was compared based on various evaluated methods and the ground truth. The results show that a satisfying accuracy of intrusion assessment can be achieved at different risk levels. Appropriate warning and intervention methods can be implemented to mitigate the occurrence or reduce the severity of intrusions and thus reduce safety accidents with the use of the proposed method.

Exploring the metro construction accidents and causations for improving safety management based on data mining and network theory

PurposeThe purpose of this study is to identify the causative factors of metro construction safety accidents, analyze the correlation between accidents and causative factors and assist in developing safety management strategies for improving safety performance in the context of the Chinese construction industry.Design/methodology/approachTo achieve these objectives, 13 types and 48 causations were determined based on 274 construction safety accidents in China. Then, 204 cause-and-effect relationships among accidents and causations were identified based on data mining. Next, network theory was employed to develop and analyze the metro construction accident causation network (MCACN).FindingsThe topological characteristics of MCACN were obtained, it is both a small-world network and a scale-free network. Controlling critical causative factors can effectively control the occurrence of metro construction accidents. Degree centrality strategy is better than closeness centrality strategy and betweenness centrality strategy.Research limitations/implicationsIn practice, it is very difficult to quantitatively identify and determine the importance of different accidents and causative factors. The weights of nodes and edges are failed to be assigned when constructing MCACN.Practical implicationsThis study provides a theoretical basis and feasible management reference for construction enterprises in China to control construction risks and reduce safety accidents. More safety resources should be allocated to control critical risks. It is recommended that safety managers implement degree centrality strategy when making safety-related decisions.Originality/valueThis paper establishes the MCACN model based on data mining and network theory, identifies the properties and clarifies the mechanism of metro construction accidents and causations.

Prediction of Construction and Production Safety Accidents in China Based on Time Series Analysis Combination Model

At present, the data related to construction safety accidents in China have the characteristics of small sample size, large fluctuation, large base, nonlinearity, etc. It is of great significance in accident prevention to break through the limitations of traditional prediction models in dealing with time series and reduce the contingency of prediction results. The purpose of this study was to use the trend decomposition method to reduce the fluctuation of non-stationary time series and use the combination of an autoregressive integrated moving average model and Grey model with fractional order accumulation to accurately predict construction accidents. This paper analyzed the number of production safety accidents and deaths in housing municipal engineering in China from 2009 to 2019, which makes monthly, quarterly and annual forecasts and compares forecast results. The FOAGM model is based on a genetic algorithm and can forecast individual months separately, which improves the overall forecast of accident numbers. The rolling forecast was used to provide an idea of hierarchical optimization of the forecast results for the annual accident number forecast with a small number of samples. The study also emphasized that the prediction of the death toll is affected by larger and above accidents. By using the CRITIC method, the impacts were quantified, so as to revise the prediction results.

Prediction on construction industry safety performance based on AHP and grey system

With the frequent occurrence of construction safety accidents, it is imperative to improve the safety performance of the construction industry. To predict safety performance scientifically, the number of construction accident fatalities was selected as an indicator of safety performance. This study combined the analytic hierarchy process (AHP) and grey relational analysis (GRA) to identify labor productivity, construction area of the house, and the number of construction enterprises and employees of the construction enterprises as the main factors affecting the number of construction accident fatalities. Then, this study used the grey prediction theory to establish a GM(1,5) model to predict the number of safety accident fatalities in 2013-2019. The results showed that the model has great prediction accuracy and can be used to predict future construction safety performance. Through the prediction of safety performance, managers can evaluate the development trends of construction projects. Therefore, they can take timely measures to reduce the occurrence of safety accidents. Meanwhile, the study provides a new optimization method for the development of the GM(1,N) model and the application of real data.

Cause analysis of construction safety accidents in China using association rules

In China the number of accidents and deaths in construction industry is increasing rapidly in recent years. Cause analysis is important to reveal the main factors contributing to the accidents, and therefore improve the safety mechanism for accident reduction in future. In this paper, 653 construction safety accidents in China which are fully investigated and formulated by a set of country-specific factors are used as the source data of potential causal relationships among the factors. By utilizing association rules a total of 13 main factors contributing to accident causation are explored, and 9 factors significantly impacting on the severity of accidents are identified. The discovered association rules offer scientific grounds for Chinese construction safety management and decision support. The novelty of this study is to present a different perspective during cause analysis in which the factors impacting the construction site safety and the consequences of construction accidents are revealed respectively.

Research Paradigm of Network Approaches in Construction Safety and Occupational Health.

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.

Co-workers' guanxi and construction workers' safety behavior: The mediating role of group identification.

The construction industry in China is characterized by higher safety risk, and construction workers' unsafe behaviors are one of the main causes of construction safety accidents, thus, designing scientific mechanisms that motivate and cultivate the construction workers to adopt safety behaviors becomes the key to the construction safety problem. Existing studies have examined some of the factors leading to workers' safety behavior (WSB) at the social, organizational, and individual levels, but ignore investigating the impact of co-workers' guanxi (CWG) on WSB. Thus, this research utilized exploratory factor analysis, confirmatory factor analysis, and structural equation modeling to examine the impact of CWG on WSB, and the mediating role of group identification (GI) in the relationship between CWG and WSB. Results show that CWG can directly or indirectly influence WSB, GI can exert a partial mediating effect on the relationship between CWG and GI. The research results enrich the research on c guanxi and causation of WSB, and provide a reference for project managers to carry out relationship-related safety management and industry regulations.