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Dynamic prediction models of rock quality designation in tunneling projects
Transportation Geotechnics ( IF 5.3 ) Pub Date : 2020-12-29 , DOI: 10.1016/j.trgeo.2020.100497
Arsalan Mahmoodzadeh , Mokhtar Mohammadi , Hunar Farid Hama Ali , Sazan Nariman Abdulhamid , Hawkar Hashim Ibrahim , Krikar M Gharrib Noori

Machine learning (ML) is becoming an appealing tool in various fields of civil engineering, such as tunneling. A very important issue in tunneling is to know the geological condition of the tunnel route before the construction. Various geological and geotechnical parameters can be considered according to data availability to define tunnels' ground conditions. The Rock Quality Designation (RQD) is one of the most important parameters that are very effective in tunnel geology. This article aims to maximize the prediction accuracy of the RQD parameter along a tunnel route through continuous updating techniques. For this purpose, four ML methods of K-nearest neighbor (KNN), Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Decision Tree (DT) were considered. All the RQD observations along the tunnel route were considered as the models’ inputs. For predicting the RQD status along the entire tunnel route, the ML models use the regression technique. For checking the applicability of the models, the Hamru road tunnel in Iran was used. The models were updated twice to assess the update effect on the results achieved during the tunnel construction. In each prediction phase, all the prediction results were compared using different statistical evaluation criteria and the actual mode. Finally, the comparative tests' findings showed that predictions of the GPR model with R2 = 0.8746/root mean square error (RMSE) = 3.5942101, R2 = 0.9328/RMSE = 2.5580977, and R2 = 0.9433/RMSE = 1.8016325 are generally well-suited to actual results for pre-update, first update, and second update phases, respectively. The updating procedure also leads to prediction models that are more accurate and less uncertain than the previous prediction stage.



中文翻译:

隧道工程中岩体质量动态预测模型

机器学习(ML)在土木工程的各个领域(例如隧道)中正成为一种有吸引力的工具。隧道施工中一个非常重要的问题是在施工之前了解隧道路线的地质状况。可以根据数据可用性考虑各种地质和岩土参数来定义隧道的地面条件。岩石质量指定(RQD)是在隧道地质中非常有效的最重要参数之一。本文旨在通过连续更新技术来最大化沿隧道路线的RQD参数的预测准确性。为此,考虑了K最近邻(KNN),高斯过程回归(GPR),支持向量回归(SVR)和决策树(DT)的四种ML方法。沿隧道路线的所有RQD观测值均视为模型的输入。为了预测整个隧道路线上的RQD状态,ML模型使用回归技术。为了检查模型的适用性,使用了伊朗的哈姆鲁公路隧道。对模型进行了两次更新,以评估更新对隧道施工过程中获得的结果的影响。在每个预测阶段,使用不同的统计评估标准和实际模式对所有预测结果进行比较。最后,比较测试的结果表明,带有R的GPR模型的预测 对模型进行了两次更新,以评估更新对隧道施工过程中获得的结果的影响。在每个预测阶段,使用不同的统计评估标准和实际模式对所有预测结果进行比较。最后,比较测试的结果表明,带有R的GPR模型的预测 对模型进行了两次更新,以评估对隧道施工过程中获得的结果的更新效果。在每个预测阶段,使用不同的统计评估标准和实际模式对所有预测结果进行比较。最后,比较测试的结果表明,带有R的GPR模型的预测2  = 0.8746 /均方根误差(RMSE)= 3.5942101,R 2  = 0.9328 / RMSE = 2.5580977和R 2  = 0.9433 / RMSE = 1.8016325通常非常适合于更新前,第一次更新和第二次的实际结果分别更新阶段。更新过程还导致预测模型比先前的预测阶段更准确且不确定性更高。

更新日期:2020-12-29
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