基于TPE-XGBoost的地下洞室有害气体风险评估预测

Risk assessment and prediction of harmful gases in underground caverns based on TPE-XGBoost

摘要

地下洞室施工过程中有害气体风险评估是施工安全管理的重要组成部分。然而，由于洞室有害气体浓度受自身扩散、岩石类型、施工机械选型、通风方案等多方因素联合影响，有害气体风险评估工作往往难以进行。基于此，通过洞室施工有害气体及其影响因素的大数据监测，考虑数据多维与非线性特征，采用极限梯度提升(eXtreme Gradient Boosting, XGBoost)集成学习算法，构建地下洞室有害气体风险评估模型，并利用树结构TPE(Tree-structured Parzen Estimator)算法优化模型超参数。结合实例，预测有害气体风险评估结果，并验证模型准确性，研究结果表明：XGBoost模型对地下洞室有害气体的风险评估准确率、召回率、F1分数均达到了85.8%,模型表现出准确的预测能力。与XGBoost、SVM、Decision Tree、AdaBoost等模型相比，经超参数优化后的XGBoost模型准确率分别提升了6.8%、54.0%、10.3%、100.9%,证明模型较其他模型能够更为有效地实现地下洞室有害气体风险评估工作，为施工安全管理提供理论与技术指导。

Abstract

The risk assessment of harmful gases during the construction of underground caverns is an important component of construction safety management. However, due to the combined influence of multiple factors such as self diffusion, rock type, selection of construction machinery, ventilation scheme, etc., the risk assessment of harmful gases in caverns is often difficult to carry out. Based on this, big data monitoring of harmful gases and their influencing factors during tunnel construction was used, taking into account the multidimensional and nonlinear characteristics of the data. The eXtreme Gradient Boosting(XGBoost) ensemble learning algorithm is adopted to construct a risk assessment model for harmful gases in underground tunnels, and the Tree-structured Parzen Estimator(TPE) algorithm is used to optimize the hyperparameters of the model. By combining examples, the risk assessment result of harmful gases were predicted and the accuracy of the model was verified. The research result showed that the XGBoost model achieved an accuracy rate, recall rate, and F1 score of 85.8% for the risk assessment of harmful gases in underground caverns, demonstrating the model's accurate predictive ability. Compared with XGBoost, SVM, Decision Tree, and AdaBoost, the accuracy of the XGBoost model optimized by hyperparameters has been improved by 6.8%, 54.0%, 10.3%, and 100.9%, respectively. This proves that the model can more effectively implement the risk assessment of harmful gases in underground caverns, providing theoretical and technical guidance for construction safety management.

关键词

地下洞室施工 / 有害气体浓度 / 风险评估 / XGBoost / TPE

Key words

underground tunnel construction / harmful gas concentration / risk assessment / XGBoost / TPE

Author summay

尹成福(1979—), 男, 正高级工程师, 硕士, 主要从事水利水电工程施工技术研究。E-mail: [e-mail]

基金资助

国家自然科学基金项目(52209163)