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基于双向循环神经网络 (BiRNN) 和双向长期短期记忆 (BiLSTM) 的项目建设安全水平的评估模型设计

Ming Ge¹, Yongbo Yuan¹

¹School of Infrastructure Engineering, Dalian University of Technology, Dalian, Liaoning, China.

PeerJ. Computer science

|December 9, 2024

概括

本研究引入了用于建筑安全评估的混合深度学习模型,改进了风险识别. 双向循环神经网络 (BiRNN) 和双向长期短期记忆 (BiLSTM) 模型为更安全的建筑施工提供了更高的准确性.

科学领域:

土木工程土木工程是指土木工程.
人工智能的人工智能
建设管理建设管理.

背景情况:

在建筑安全评估中用于多元回归的深度学习集成提出了重大挑战.
对从业人员信息和现场条件的定量分析对于可靠的安全评估至关重要.

研究的目的:

开发和评估混合深度学习模型,用于准确的建筑安全评估.
通过使用 Dropout 机制,提高安全评估模型的概括能力.

主要方法:

利用分析层次过程 (AHP) 来量化建筑安全能力的四个关键方面.
开发了一种混合模型,将双向循环神经网络 (BiRNN) 和双向长短期记忆 (BiLSTM) 与 Dropout 结合起来.
分析了与操作员条件,组织因素,现场管理和不安全行为相关的19个二次因果因素.

主要成果:

与传统方法相比,BiRNN-BiLSTM混合模型表现出优异的性能.
达到0.48的平均平方误差 (MSE),根平均平方误差 (RMSE) 的0.69.
报告的平均绝对误差 (MAE) 为0.54%,平均绝对百分比误差 (MAPE) 为3.36%.

结论:

BiRNN-BiLSTM模型准确地识别了潜在的建筑安全风险.
开发的模型为建设中的有效项目管理提供了可靠的决策支持.
混合深度学习方法为推进建筑安全评估提供了一个有希望的方向.

关键词:

在 AHP AHP 中.这就是为什么BiLSTM.一个RNNN的BiRNNN.建筑施工建筑施工建筑施工放弃放弃放弃放弃

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