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首页> 外文期刊>Journal of Construction Engineering and Management >Accident Analysis for Construction Safety Using Latent Class Clustering and Artificial Neural Networks
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Accident Analysis for Construction Safety Using Latent Class Clustering and Artificial Neural Networks

机译：基于潜在类聚类和人工神经网络的施工安全事故分析

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Despite many improvements in safety management, the construction industry still has the highest potential for occupational injuries including High Severe (HS) work events, which result in injuries or fatalities, and Low Severe (LS) work events, which cause near misses or nonserious injuries. The analysis of incidents is highly dependent on the quality of records. Problems in recording and the heterogeneity of incident data may create conflicts while analyzing the relationship between attributes. The objective of the study was to develop a novel model to predict the outcomes of construction incidents using Latent Class Clustering Analysis (LCCA) and Artificial Neural Networks (ANNs) and determine necessary preventative actions. ANN has been used for many years to investigate the nonlinear relation between attributes and generate a logic between them. Herein, ANN was used to perform severity analyses of incidents utilizing real data, which were collected from various construction sites anonymously. Many factors affect the performance of ANN, including the size of the input and the heterogeneity of data. LCCA was used to seek out better performance and accuracy in ANN applications by reducing the heterogeneity of the incidents. By applying LCCA, attributes that possess different probabilities were clustered together and put into the ANN model. Then, the study concluded by providing a necessary preventative measure according to the result of incidents forecasted in advance. The research has two significant contributions. First, the hybrid model revealed promising results as the performance of the ANN-based predictive model was enhanced by addressing the heterogeneity of data. Second, the study presented professionals with practical preventative actions to avoid construction incidents according to the results of prediction.
机译：尽管在安全管理方面进行了许多改进，但建筑行业仍具有最高的职业伤害潜力，包括导致重伤或死亡的高严重（HS）工作事件，以及导致未命中或严重伤害的低严重（LS）工作事件。 。事件的分析高度依赖于记录的质量。在分析属性之间的关系时，记录问题和事件数据的异构性可能会产生冲突。这项研究的目的是开发一种新型模型，以使用潜在类聚类分析（LCCA）和人工神经网络（ANN）预测建筑事故的结果，并确定必要的预防措施。人工神经网络已被用于研究属性之间的非线性关系并在它们之间产生逻辑。在此，人工神经网络用于利用真实数据对事件进行严重性分析，这些数据是从各个施工现场匿名收集的。许多因素会影响ANN的性能，包括输入的大小和数据的异质性。通过减少事故的异质性，LCCA用于在ANN应用中寻求更好的性能和准确性。通过应用LCCA，将具有不同概率的属性聚类在一起，并放入ANN模型中。然后，研究根据预先预测的事件结果提供了必要的预防措施，从而得出结论。该研究有两个重要贡献。首先，由于解决了数据的异质性，基于神经网络的预测模型的性能得到了增强，因此混合模型显示出了可喜的结果。其次，该研究根据预测结果为专业人员提供了实际的预防措施，以避免施工事故。

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