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| 1 | State-of-the-art review of soft computing applications in underground excavations显示文摘Soft computing techniques are becoming even more popular and particularly amenable to model the complex behaviors of most geotechnical engineering systems since they have demonstrated superior predictive capacity,compared to the traditional methods.This paper presents an overview of some soft computing techniques as well as their applications in underground excavations.A case study is adopted to compare the predictive performances of soft computing techniques including eXtreme Gradient Boosting(XGBoost),Multivariate Adaptive Regression Splines(MARS),Artificial Neural Networks(ANN),and Support Vector Machine(SVM) in estimating the maximum lateral wall deflection induced by braced excavation.This study also discusses the merits and the limitations of some soft computing techniques,compared with the conventional approaches available. | Wengang Zhang Runhong Zhang Chongzhi Wu Anthony Teck Chee Goh Suzanne Lacasse Zhongqiang Liu Hanlong Liu | 2020 | Geoscience Frontiers2020，11，4： | 33 |
| 2 | 边坡位移智能预测算法显示文摘目的:边坡位移预测是实现滑坡灾害预报的有效手段,对降低滑坡灾害导致的损失具有重要意义。本文针对三峡库区广泛分布的'阶跃型'滑坡,采用三种不同的机器学习算法:长短期记忆(LSTM)神经网络、随机森林(RF)算法和门控递归单元(GRU),预测三个不同的三峡库区边坡位移,并对比三种算法的预测精度,从而选择适用于边坡位移预测的机器学习算法。创新点:1.建立了基于时间序列分解和机器学习算法的动态预测模型,并能够准确预测边坡位移。2.对比了不同的机器学习算法预测边坡周期项位移的精度。方法:1.基于时间序列分解原理,将边坡累积位移分解为趋势项位移和周期项位移。2.利用多项式拟合对边坡趋势项位移进行预测。3.基于位移影响因素采用三种机器学习模型(LSTM、GRU和RF)预测边坡周期项位移。结论:1.本文提出的基于时间序列分解和机器学习算法的动态预测模型可以准确预测三峡库区'阶跃型'边坡位移。2.LSTM和GRU算法可以充分利用滑坡历史信息,精确预测边坡位移的周期项。 | Zhong-qiang LIU Dong GUO Suzanne LACASSE Jin-hui LI Bei-bei YANG Jung-chan CHOI | 2020 | Journal of Zhejiang University-Science A(Applied Physics & Engineering)2020，21，6： | 12 |
| 3 | Modelling of shallow landslides with machine learning algorithms显示文摘This paper introduces three machine learning(ML)algorithms,the‘ensemble'Random Forest(RF),the‘ensemble'Gradient Boosted Regression Tree(GBRT)and the Multi Layer Perceptron neural network(MLP)and applies them to the spatial modelling of shallow landslides near Kvam in Norway.In the development of the ML models,a total of 11 significant landslide controlling factors were selected.The controlling factors relate to the geomorphology,geology,geo-environment and anthropogenic effects:slope angle,aspect,plan curvature,profile curvature,flow accumulation,flow direction,distance to rivers,water content,saturation,rainfall and distance to roads.It is observed that slope angle was the most significant controlling factor in the ML analyses.The performance of the three ML models was evaluated quantitatively based on the Receiver Operating Characteristic(ROC)analysis.The results show that the‘ensemble'GBRT machine learning model yielded the most promising results for the spatial prediction of shallow landslides,with a 95%probability of landslide detection and 87%prediction efficiency. | Zhongqiang Liu Graham Gilbert Jose Mauricio Cepeda Asgeir Olaf Kydland Lysdahl Luca Piciullo Heidi Hefre Suzanne Lacasse | 2021 | Geoscience Frontiers2021，12，1： | 3 |
| 4 | A conceptual framework for quantitative estimation of physical vulnerability to landslides显示文摘 | Marco Uzielli Farrokh Nadim Suzanne Lacasse | 2008 | Engineering Geology2008，102，： | 1 |
| 5 | Displacement Prediction in ColluvialLandslides,Three Gorges Reservoir,China显示文摘 | Juan Du Kunlong Yin Suzanne Lacasse | 2013 | Landslides2013，，10： | 1 |
| 6 | Bayesian partial pooling to reduce uncertainty in overcoring rock stress estimation显示文摘The state of in situ stress is a crucial parameter in subsurface engineering,especially for critical projects like nuclear waste repository.As one of the two ISRM suggested methods,the overcoring(OC)method is widely used to estimate the full stress tensors in rocks by independent regression analysis of the data from each OC test.However,such customary independent analysis of individual OC tests,known as no pooling,is liable to yield unreliable test-specific stress estimates due to various uncertainty sources involved in the OC method.To address this problem,a practical and no-cost solution is considered by incorporating into OC data analysis additional information implied within adjacent OC tests,which are usually available in OC measurement campaigns.Hence,this paper presents a Bayesian partial pooling(hierarchical)model for combined analysis of adjacent OC tests.We performed five case studies using OC test data made at a nuclear waste repository research site of Sweden.The results demonstrate that partial pooling of adjacent OC tests indeed allows borrowing of information across adjacent tests,and yields improved stress tensor estimates with reduced uncertainties simultaneously for all individual tests than they are independently analysed as no pooling,particularly for those unreliable no pooling stress estimates.A further model comparison shows that the partial pooling model also gives better predictive performance,and thus confirms that the information borrowed across adjacent OC tests is relevant and effective. | Yu Feng Ke Gao Suzanne Lacasse | 2024 | Journal of Rock Mechanics and Geotechnical Engineering2024，16，4： | 0 |
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