Ge Ming, Yuan Yongbo
School of Infrastructure Engineering, Dalian University of Technology, Dalian, Liaoning, China.
PeerJ Comput Sci. 2024 Oct 18;10:e2351. doi: 10.7717/peerj-cs.2351. eCollection 2024.
Integrating deep learning methods for multi-element regression analysis poses a challenge in constructing safety evaluations for building construction. To address this challenge, this paper evaluates the integration of construction safety by quantitatively analyzing practitioners' information and on-site construction conditions. The analytic hierarchy process (AHP) method quantifies construction safety capabilities, considering four key aspects: operators' primary conditions, organizational personnel's working conditions, on-site management conditions, and analysis of unsafe behaviors. A comprehensive set of 19 secondary causal factors is constructed. Furthermore, a hybrid model based on bidirectional recurrent neural network (BiRNN) and bidirectional long short-term memory (BiLSTM) is developed for construction safety evaluation, enhancing the model's generalization ability by introducing the Dropout mechanism. Experimental results demonstrate that the fusion of BiRNN and BiLSTM methods outperforms traditional methods in construction safety evaluation, yielding mean squared error (MSE) and root mean squared error (RMSE) values of 0.48 and 0.69 and mean absolute error (MAE) and mean absolute percentage error (MAPE) values of 0.54 and 3.36%, respectively. The case study affirms that BiRNN-BiLSTM can accurately identify potential safety risks, providing reliable decision support for project management.
将深度学习方法集成到多元素回归分析中，在构建建筑施工安全评估方面带来了挑战。为应对这一挑战，本文通过定量分析从业者信息和现场施工条件来评估施工安全的集成情况。层次分析法（AHP）对施工安全能力进行量化，考虑四个关键方面：操作人员的基本条件、组织人员的工作条件、现场管理条件以及不安全行为分析。构建了一套全面的19个二级因果因素。此外，还开发了一种基于双向递归神经网络（BiRNN）和双向长短期记忆（BiLSTM）的混合模型用于施工安全评估，通过引入Dropout机制提高了模型的泛化能力。实验结果表明，BiRNN和BiLSTM方法的融合在施工安全评估中优于传统方法，均方误差（MSE）和均方根误差（RMSE）值分别为0.48和0.69，平均绝对误差（MAE）和平均绝对百分比误差（MAPE）值分别为0.54和3.36%。案例研究证实，BiRNN - BiLSTM能够准确识别潜在安全风险，为项目管理提供可靠的决策支持。
