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Complex risk factors make metro construction safety accidents prone to occur, and there are various types of accidents. Accident reports record detailed information about different types of accidents in text form. However, effectively utilizing such unstructured data presents a significant challenge. Text mining (TM) provides a viable foundation for addressing this challenge, but related studies have limitations in risk feature extraction and lack of in-depth analysis capability. To address the deficiencies of existing studies and provide a feasible strategy for identifying key risk factors in the metro construction domain, this paper proposes an integrated model combining TM and machine learning–based Bayesian networks. Firstly, the term frequency-inverse document frequency (TF-IDF) algorithm in TM was used to separately extract the direct and indirect cause factors from the accident reports, with the missing factors supplemented using the TextRank algorithm. Then, depending on the assumption of whether to consider the conditional independence between factors, an improved naive Bayesian network (NBN) and a tree-augmented naive Bayesian network (TAN) were built based on the extracted factors and the corresponding accident types, respectively, for further in-depth analysis. Finally, the training set was divided to train the two network models, and sensitivity analysis was used to identify the key risk factors. Using 162 accident reports from China as an application example, the results showed that TAN exhibited a higher average accuracy (79.62%) in the test set compared with the improved NBN (71.75%), and the importance of risk factors for different accident types was successfully ranked from multiple perspectives using TAN. Meanwhile, some new insights into metro accidents in China were obtained, which can support decision-making for accident prevention and control. In conclusion, this paper effectively addresses the relevant limitations of accident text utilization and presents a novel approach for metro construction safety management. Analyzing accident texts can help gain insights from objective historical data to support safety management efforts. However, accident texts are often unstructured and contain a lot of irrelevant content. How to quickly extract valid information from accident text and use it to analyze accidents in depth is of continuous interest to safety managers. In particular, those models that have real-time decision support capabilities in addition to theoretical insights. This paper proposes an integrated model that combines text mining and machine-learning Bayesian networks. This model achieves comprehensive textual feature extraction, multifaceted accident causation analysis, and allows safety managers to input current accident information into the model to obtain real-time decision support for accident prevention and control. Although the proposed model is developed for metro construction, it can be slightly adapted by incorporating the characteristics of accident texts from similar domains to obtain an integrated model suitable for these domains, so as to effectively control the occurrence of safety accidents. [ABSTRACT FROM AUTHOR]

Construction collapse accidents are one of the most serious accidents. A variety of causal factors interact with each other, leading to the occurrence of accidents. Some causal factors may cause other causal factors to happen one after another, eventually leading to the occurrence of accidents. The propagation process of causal factors that can lead to construction collapse accident must be clarified. Therefore, this study aims to analyze the propagation path of construction collapse causal factors based on network theory and cascading failure model. First, this study collected and screened the cases of construction collapse accidents, and conducted a statistical analysis of their basic information. Second, a construction collapse accident causation (CCAC) framework was established to identify causal factors in accident cases. Then, a CCAC network model was established. Based on multiple measurement criteria in complex network theory, the causal factors were ranked. After that, through the cascading failure analysis of the causal factors, the critical causal factor-accident propagation path was obtained. It is found that construction scheme and organization is at the beginning and is the source of other causal factors. Finally, five cases were analyzed to verify the rationality of the results. This paper provides a solution to study the propagation path of causal factors and find out the characteristics of the CCAC mechanism through a large number of accident cases, which has theoretical and practical significance for the prevention of construction collapse accidents. [ABSTRACT FROM AUTHOR]

The construction industry suffers from frequent and expensive safety accidents, significantly affecting construction project performance. Numerous data-driven classification models have been developed to categorize construction accident outcomes. While critical influencing factors provide insights for safety prevention, existing models have given less attention to the cost of accidents—an important indicator influencing management decisions. This study aims to develop accident cost prediction models that examine crucial precursors of safety accidents, offering guidance for construction safety prevention from a financial perspective. This study collected 1,606 accident reports from the Chinese construction industry between 2005 and 2022 to address this gap. Three ensemble data-driven methods, namely random forest, extreme gradient boosting regressor (XGBoost), and natural gradient boosting regressor (NGBoost) were employed to develop accident cost prediction models. Based on the performance comparison, the random forest regression model for accident cost was determined to be the best prediction model. To extract the critical attributes affecting safety accident costs, this study utilized shapely additive explanations (SHAP) value to analyze the sensitivity and influence of input variables of data-driven models. The findings showed that collapse has the greatest impact on accident costs, as indicated by the highest mean SHAP value, followed by falling from height. Furthermore, factors such as year, safety supervision, drawing, and construction plan are noteworthy in affecting accident cost prediction. Safety department, protection, and work conditions hold a slightly higher degree of influence compared to contracting arrangement, safety culture, safety supervision, training and examination, and mechanical equipment on the model output. This study provides a dimension that might be overlooked in the investigation of safety accidents in the construction industry and the insights provided by findings will contribute to the development of targeted safety accident prevention strategies. [ABSTRACT FROM AUTHOR]

Workers' motion trajectories or spatial tracks on construction sites contain useful safety-related information. Existing safety management research on worker trajectory typically analyzes the interactions between worker motion trajectory and risk sources. The historical accident-free zone of a group of workers is a reflection of the potential safety zones on a construction site. Few studies have investigated construction workers' trajectory safety risks from the integrated perspectives of group and hazard sources. Therefore this study developed a novel and integrated safety risk diagnosis method combining hazard source and group movement distributions to fully utilize the phone Global Positioning System (GPS) trajectory information of construction workers. The proposed method diagnoses workers' risk exposures by considering workers' trajectories in unsafe and safe areas. In addition, the method uses expert confidence and comprehensive decision indexes to determine the feature weights. Furthermore, the proposed safety risk diagnosis method adopts the grey optimization Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) diagnosis model to diagnose workers' risk management priorities and behavior adjustment direction. An actual project site was used to assess the performance of the proposed diagnostic method. The results show that the developed method provides a quantitative means for project managers to measure workers' spatial-temporal risk exposure, diagnose safety risks, and plan for safety controls. The proposed integrated method provides a new perspective to make full use of workers' trajectory information and helps to provide practical and specific data-driven safety guidance for construction managers and workers. [ABSTRACT FROM AUTHOR]

Worksite accidents in the construction and infrastructure industries result in cost overruns and schedule downtimes. Previous research has analyzed cost and schedule deviations caused by worksite accidents within these industries. However, a quantitative analysis of accidents at the subsector level and comparisons at the organizational level have yet to be undertaken. Toward this aim, worksite accident data are collected across different industry subsectors and organizations of different sizes, and an integrated risk register is constructed to capture cost and schedule deviations caused by worksite accidents. The findings indicate that structural and installation services are the main contributors to workplace accidents in terms of cost and time deviations. Moreover, organizational size is an important factor in the impact analysis of worksite accidents. Large organizations in the structural services were found to make a significant impact on cost overruns and schedule downtimes. They are followed by small organizations in the installation services subsector and medium/large organizations in the heavy and civil engineering subsector. The findings in this study reveal that improvements and investments in safety training and workplace communications are needed for efficient accident prevention. [ABSTRACT FROM AUTHOR]