PDF(Pubmed)
Abstract：
Despite ongoing safety efforts, construction sites experience a concerningly high accident rate. Notwithstanding that policies and research to reduce the risk of accidents in the construction industry have been active for a long time, the accident rate in the construction industry is considerably higher than in other industries. This trend may likely be further exacerbated by the rapid growth of large-scale construction projects driven by urban population expansion. Consequently, accurately predicting recovery periods of accidents at construction sites in advance and proactively investing in measures to mitigate them is critical for efficiently managing construction projects. Therefore, the purpose of this study is to propose a framework for developing accident prediction models based on the Deep Neural Network (DNN) algorithm according to the scale of the construction site. This study suggests DNN models and applies the DNN for each construction site scale to predict accident recovery periods. The model performance and accuracy were evaluated using mean absolute error (MAE) and root-mean-square error (RMSE) and compared with the widely used multiple regression analysis model. As a result of model comparison, the DNN models showed a lower prediction error rate than the regression analysis models for both small-to-medium and large construction sites. The findings and framework of this study can be applied as the opening stage of accident risk assessment using deep learning techniques, and the introduction of deep learning technology to safety management according to the scale of the construction site is provided as a guideline.
摘要：
尽管正在进行安全工作，建筑工地的事故率非常高。尽管长期以来一直积极推行降低建筑业事故风险的政策和研究,建筑业的事故率大大高于其他行业。城市人口扩张带动的大规模建设项目的快速增长可能会进一步加剧这一趋势。因此,提前准确预测建筑工地事故的恢复期，并积极投资缓解事故的措施，对于有效管理建筑项目至关重要。因此,本研究的目的是根据施工现场的规模，提出一个基于深度神经网络（DNN）算法开发事故预测模型的框架。这项研究提出了DNN模型，并将DNN应用于每个施工现场规模，以预测事故恢复期。使用平均绝对误差（MAE）和均方根误差（RMSE）评估模型的性能和准确性，并与广泛使用的多元回归分析模型进行比较。作为模型比较的结果，对于中小型和大型建筑工地，DNN模型的预测错误率均低于回归分析模型。本研究的结果和框架可以作为使用深度学习技术进行事故风险评估的开始阶段。并根据施工现场的规模将深度学习技术引入安全管理中作为指南。
