RISK PREDICTION AND DIAGNOSIS OF WATER SEEPAGE IN OPERATIONAL SHIELD TUNNELS BASED ON RANDOM FOREST
RISK PREDICTION AND DIAGNOSIS OF WATER SEEPAGE IN OPERATIONAL SHIELD TUNNELS BASED ON RANDOM FOREST
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基于随机森林的运营盾构隧道渗水风险预测与诊断
DOI：
10.3846/jcem.2021.14901
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发表时间：
2021-10
影响因子：
4.3
通讯作者：
Xianjia Wang
中科院分区：
文献类型：
--
作者：
Liu Yang;Chen Hongyu;Zhang Limao;Xianjia Wang
渗水是隧道施工中最主要的缺陷之一，直接影响隧道的施工安全。有效预测和诊断WS是目前急需解决的问题。本文提出了WS等级的标准和评价指标体系，并从监测记录中构建了一个样本数据集进行演示。首先，采用自举重采样方法建立随机森林渗流风险预测模型。其次，采用5重交叉验证法选择最优分支和参数，建立射频预测训练模型；此外，为了说明该方法的有效性，本文还以武汉地铁3号线运营阶段为例进行了研究。结果表明，钢筋截面的剥落面积、裂缝宽度和损失率对WS有较大影响。最后对试验数据进行预测，并计算预测结果误差指数。与支持向量机、人工神经网络等一些传统机器学习方法的预测相比，射频预测精度最高，最接近真实值，显示了模型的准确性及其应用潜力。
Water seepage (WS) is a paramount defect during tunnel operation and directly affects the operational safety of tunnels. Effectively predicting and diagnosing WS are problems that urgently need to be solved. This paper presents a standard and an evaluation index system for WS grades and constructs a sample dataset from monitoring recoreds for demonstration purposes. First, we use bootstrap resampling to build a random forest (RF) seepage risk prediction model. Second, the optimal branch and parameters are selected by the 5-fold cross-validation method to establish the RF prediction training model. Additionally, to illustrate the effectiveness of the method, the operational stage of Wuhan Metro Line 3 in China is taken as a case study. The results conclude that the segment spalling area, crack width, and loss rate of the rebar cross-section have a strong influence on WS. Finally, the test data are predicted, and the prediction result error index is calculated. Compared with the predictions of some traditional machine learning methods, such as support vector machines and artificial neural networks, RF prediction has the highest accuracy and is the closest to the true value, which demonstrates the accuracy of the model and its application potential.
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影响因子：
2.8
作者：
Zhengjun Mao;Xiaokang Wang;Ning An;Xiaojun Li;Rongyu Wei;Yaqiong Wang;Hao Wu
通讯作者：
Zhengjun Mao;Xiaokang Wang;Ning An;Xiaojun Li;Rongyu Wei;Yaqiong Wang;Hao Wu
DOI：
10.1177/2399808319894580
发表时间：
2019-12
期刊：
Environment and Planning B: Urban Analytics and City Science
影响因子：
--
作者：
Shutian Zhou;Guofang Zhai;Yuwen Lu;Yijun Shi
通讯作者：
Shutian Zhou;Guofang Zhai;Yuwen Lu;Yijun Shi
影响因子：
7.1
作者：
He, Bao-Jie;Zhu, Jin;Wang, Junsong
通讯作者：
Wang, Junsong
影响因子：
4
作者：
Jian Zhou;P. G. Asteris;D. J. Armaghani;B. Pham
通讯作者：
Jian Zhou;P. G. Asteris;D. J. Armaghani;B. Pham
影响因子：
2.2
作者：
C. Behrens
通讯作者：
C. Behrens