A method for assessing the crossed risk of construction safety

Tian, Zhen; Chen, Qianqian; Zhang, Taihui

doi:10.1016/j.ssci.2021.105531

Cited by 15 publications

(7 citation statements)

References 40 publications

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Section: Methodsmentioning

confidence: 99%

“…There are plenty of literature to provide explanations for risk identification methods of construction accidents (Sharan and Narayanan, 2020;Siraj and Fayek, 2019). Attribute classification initially referred to the research results of Tian et al (2021). However, we believe that on-site management factors should be distinguished from enterprise institutional factors in order to reduce the ambiguity in the description.…”

Section: Methodsmentioning

confidence: 99%

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An association rule mining model for evaluating the potential correlation of construction cross operation risk

Chen

Tian

Liu

et al. 2022

ECAM

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PurposeCross operation is a common operation method in the building construction process nowadays. Due to the crossover, each other's operations are disturbed, and risks also interact. This superimposed relationship of risks is worthy of attention. The study aims to develop a model for analyzing cross-working risks. This model can quantify the correlation of various risk factors.Design/methodology/approachThe concept of cross operation and the cross types involved are clarified. The risk factors were extracted from cross-operation accidents. The association rule mining (ARM) was used to analyze the results of various cross-types accidents. With the help of visualization tools, the intensity distribution and correlation path of the relationship between each factor were obtained. A complete cross-operation risk analysis model was established.FindingsThe application of ARM method proves that there are obvious risk correlation deviations in different types of cross operations. A high-frequency risk common to all cross operations is on-site safety inspection and process supervision, but the subsequent problems are different. Cutting off the high-lift risk chain timely according to the results obtained by ARM can reduce or eliminate the danger of high-frequency risk factors.Originality/valueThis is the first systematic analysis of cross-work risk in the construction. The study determined the priority of risk management. The results contribute to targeted cross-work control to reduce accidents caused by cross-work.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

An association rule mining model for evaluating the potential correlation of construction cross operation risk

Chen

Tian

Liu

et al. 2022

ECAM

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“…Hänninen studied the role of Bayesian networks in maritime accident prevention and safety modeling, discussed some of the challenges in applying Bayesian networks, and analyzed the possibilities of combining data with expert knowledge and the ways in which models can be easily updated when more accurate evidence is obtained [ 37 ]. In addition, many scholars have used BNs to carry out a significant amount of research in biology, construction, hydrology, supply chains, oil and gas, and other fields [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ]. However, the above research needs to accurately know the probability value of each variable before using a BN deductive reasoning algorithm for analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

An Integrated Quantitative Risk Assessment Method for Underground Engineering Fires

Yuan

Zhu

Zhang

et al. 2022

IJERPH

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Fires are one of the main disasters in underground engineering. In order to comprehensively describe and evaluate the risk of underground engineering fires, this study proposes a UEF risk assessment method based on EPB-FBN. Firstly, based on the EPB model, the static and dynamic information of the fire, such as the cause, occurrence, hazard, product, consequence, and emergency rescue, was analyzed. An EPB model of underground engineering fires was established, and the EPB model was transformed into a BN structure through the conversion rules. Secondly, a fuzzy number was used to describe the state of UEF variable nodes, and a fuzzy conditional probability table was established to describe the uncertain logical relationship between UEF nodes. In order to make full use of the expert knowledge and empirical data, the probability was divided into intervals, and a triangulated fuzzy number was used to represent the linguistic variables judged by experts. The α-weighted valuation method was used for de-fuzzification, and the exact conditional probability table parameters were obtained. Through fuzzy Bayesian inference, the key risk factors can be identified, the sensitivity value of key factors can be calculated, and the maximum risk chain can be found in the case of known evidence. Finally, the method was applied to the deductive analysis of three scenarios. The results show that the model can provide realistic analysis ideas for fire safety evaluation and emergency management of underground engineering. The proposed EPB risk assessment model provides a new perspective for the analysis of UEF accidents and contributes to the ongoing development of UEF research.

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“…With the rapid development of the construction industry, new types of buildings, such as supertall, green, and intelligent buildings, are driving the expansion of the construction scale and the diversification of design structures, thereby increasing both the volume of work and construction complexity, while imposing greater demands on safety management [2][3][4]. However, factors unique to the construction industry-such as inconsistent labor quality, high resource demands, long project cycles, and complex construction conditions-position it as one of the industries with the highest safety risks [5][6][7]. The construction safety production sector currently confronts a series of deep-rooted challenges and issues involving various facets of safety production risk management [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…The smaller its value is, the better it represents the tree structure. Assuming that I L and I R are the sets of instances of the left and right nodes after splitting I = I L ∪ I R , by measuring the difference in the scoring function before and after breaking, the split point with the most significant difference is determined to be the optimal splitting point (7). Node splitting is accomplished after splitting at the optimal splitting point.…”

mentioning

confidence: 99%

Research on the Prediction of Sustainable Safety Production in Building Construction Based on Text Data

Fan,

Wang,

Liu

et al. 2024

Sustainability

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Given the complexity and variability of modern construction projects, safety risk management has become increasingly challenging, while traditional methods exhibit deficiencies in handling complex dynamic environments, particularly those involving unstructured text data. Consequently, this study proposes a text data-based risk prediction method for building construction safety. Initially, heuristic Chinese automatic word segmentation, which incorporates mutual information, information entropy statistics, and the TF-IDF algorithm, preprocesses text data to extract risk factor keywords and construct accident attribute variables. At the same time, the Spearman correlation coefficient is utilized to eliminate the multicollinearity between feature variables. Next, the XGBoost algorithm is employed to develop a model for predicting the risks associated with safe production. Its performance is optimized through three experimental scenarios. The results indicate that the model achieves satisfactory overall performance after hyperparameter tuning, with the prediction accuracy and F1 score reaching approximately 86%. Finally, the SHAP model interpretation technique identifies critical factors influencing the safety production risk in building construction, highlighting project managers’ attention to safety, government regulation, safety design, and emergency response as critical determinants of accident severity. The main objective of this study is to minimize human intervention in risk assessment and to construct a text data-based risk prediction model for building construction safety production using the rich empirical knowledge embedded in unstructured accident text, with the aim of reducing safety production accidents and promoting the sustainable development of construction safety in the industry. This model not only enables a paradigm shift toward intelligent risk control in safety production but also provides theoretical and practical insights into decision-making and technical support in safety production.

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A method for assessing the crossed risk of construction safety

Cited by 15 publications

References 40 publications

An association rule mining model for evaluating the potential correlation of construction cross operation risk

An association rule mining model for evaluating the potential correlation of construction cross operation risk

An Integrated Quantitative Risk Assessment Method for Underground Engineering Fires

Research on the Prediction of Sustainable Safety Production in Building Construction Based on Text Data

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