Real-time prediction of tunnel face conditions using XGBoost Random Forest algorithm

IF 2.9 3区 工程技术 Q2 ENGINEERING, CIVIL Frontiers of Structural and Civil Engineering Pub Date : 2024-01-23 DOI:10.1007/s11709-023-0044-4
Lei-jie Wu, Xu Li, Ji-dong Yuan, Shuang-jing Wang
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Abstract

Real-time perception of rock conditions based on continuously collected data to meet the requirements of continuous Tunnel Boring Machine (TBM) construction presents a critical challenge that warrants increased attention. To achieve this goal, this paper establishes real-time prediction models for fractured and weak rock mass by comparing 6 different algorithms using real-time data collected by the TBM. The models are optimized in terms of selecting metric, selecting input features, and processing imbalanced data. The results demonstrate the following points. (1) The Youden’s index and area under the ROC curve (AUC) are the most appropriate performance metrics, and the XGBoost Random Forest (XGBRF) algorithm exhibits superior prediction and generalization performance. (2) The duration of the TBM loading phase is short, usually within a few minutes after the disc cutter contacts the tunnel face. A model based on the features during the loading phase has a miss rate of 21.8%, indicating that it can meet the early warning needs of TBM construction well. As the TBM continues to operate, the inclusion of features calculated from subsequent data collection can continuously correct the results of the real-time prediction model, ultimately reducing the miss rate to 16.1%. (3) Resampling the imbalanced data set can effectively improve the prediction by the model, while the XGBRF algorithm has certain advantages in dealing with the imbalanced data issue. When the model gives an alarm, the TBM operator and on-site engineer can be reminded and take some necessary measures for avoiding potential tunnel collapse. The real-time predication model can be a useful tool to increase the safety of TBM excavation.

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使用 XGBoost 随机森林算法实时预测隧道工作面状况
根据连续采集的数据对岩石状况进行实时感知,以满足隧道掘进机(TBM)连续施工的要求,这是一个严峻的挑战,需要引起更多的关注。为实现这一目标,本文利用 TBM 采集的实时数据,通过比较 6 种不同算法,建立了断裂岩体和软弱岩体的实时预测模型。这些模型在选择度量、选择输入特征和处理不平衡数据等方面进行了优化。结果表明了以下几点。(1) 尤登指数和 ROC 曲线下面积(AUC)是最合适的性能指标,XGBoost 随机森林(XGBRF)算法表现出更优越的预测和泛化性能。(2) TBM 加载阶段持续时间很短,通常在圆盘铣刀接触隧道面后几分钟内完成。基于加载阶段特征的模型失误率为 21.8%,表明该模型能够很好地满足 TBM 施工的预警需求。随着隧道掘进机的持续运行,加入通过后续数据采集计算出的特征,可以不断修正实时预测模型的结果,最终将失误率降低到 16.1%。(3) 对不平衡数据集进行重采样可以有效提高模型的预测效果,而 XGBRF 算法在处理不平衡数据问题上具有一定的优势。当模型发出警报时,可以提醒隧道掘进机操作员和现场工程师,并采取一些必要措施避免潜在的隧道坍塌。实时预测模型是提高 TBM 挖掘安全性的有用工具。
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来源期刊
CiteScore
5.20
自引率
3.30%
发文量
734
期刊介绍: Frontiers of Structural and Civil Engineering is an international journal that publishes original research papers, review articles and case studies related to civil and structural engineering. Topics include but are not limited to the latest developments in building and bridge structures, geotechnical engineering, hydraulic engineering, coastal engineering, and transport engineering. Case studies that demonstrate the successful applications of cutting-edge research technologies are welcome. The journal also promotes and publishes interdisciplinary research and applications connecting civil engineering and other disciplines, such as bio-, info-, nano- and social sciences and technology. Manuscripts submitted for publication will be subject to a stringent peer review.
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