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| 1 | Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network显示文摘In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead to the flyrock phenomenon.Flyrock can damage structures or nearby equipment in the surrounding areas and inflict harm to humans,especially workers in the working sites.Thus,prediction of flyrock is of high importance.In this investigation,examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried out.One hundred and fifty-two blasting events in three open-pit granite mines in Johor,Malaysia,were monitored to collect field data.The collected data include blasting parameters and rock mass properties.Site-specific weathering index(WI),geological strength index(GSI) and rock quality designation(RQD)are rock mass properties.Multi-layer perceptron(MLP),random forest(RF),support vector machine(SVM),and hybrid models including Harris Hawks optimization-based MLP(known as HHO-MLP) and whale optimization algorithm-based MLP(known as WOA-MLP) were developed.The performance of various models was assessed through various performance indices,including a10-index,coefficient of determination(R^(2)),root mean squared error(RMSE),mean absolute percentage error(MAPE),variance accounted for(VAF),and root squared error(RSE).The a10-index values for MLP,RF,SVM,HHO-MLP and WOA-MLP are 0.953,0.933,0.937,0.991 and 0.972,respectively.R^(2) of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models. | Bhatawdekar Ramesh Murlidhar Hoang Nguyen Jamal Rostami XuanNam Bui Danial Jahed Armaghani Prashanth Ragam Edy Tonnizam Mohamad | 2021 | Journal of Rock Mechanics and Geotechnical Engineering2021，13，6： | 6 |
| 2 | 机器学习方法在滑坡易发性评价中的应用显示文摘中国山区多、地形复杂,构造发育、地质灾害隐患分布广泛。滑坡作为山区最具灾难性的地质灾害之一,严重威胁着人民群众的生命及财产安全。构建滑坡易发性模型能够量化滑坡发生的可能性,对制定防灾措施、减少潜在风险具有重要作用。由于经验驱动模型难以量化,且往往依赖主观判断,近年来,滑坡易发性模型的精度与准确度在从经验驱动和统计理论模型向新兴机器学习方向发展的过程中得到提升。对目前滑坡易发性评价常用的机器学习模型进行综合评述,并针对三峡库区的案例研究,对不同的机器学习技术进行广泛分析和比较。机器学习模型通过结合实地调查资料和历史数据,可绘制滑坡易发性地图,辅助制定滑坡减缓策略。根据滑坡易发性预测模型的准确性和效率,评价几种常用算法的优势和局限性。结果表明,与一些常用的滑坡易发性制图方法相比,基于树结构的集成算法模型性能更好。此外,高质量的数据库十分重要,深度学习算法的更多应用还有待进一步研究探索。 | 马彦彬 李红蕊 王林 仉文岗 朱正伟 杨海清 王鲁琦 袁兴中 | 2022 | 土木与环境工程学报(中英文)2022，44，1： | 5 |
| 3 | Optimized functional linked neural network for predicting diaphragm wall deflection induced by braced excavations in clays显示文摘Deep excavation during the construction of underground systems can cause movement on the ground,especially in soft clay layers.At high levels,excessive ground movements can lead to severe damage to adjacent structures.In this study,finite element analyses(FEM)and the hardening small strain(HSS)model were performed to investigate the deflection of the diaphragm wall in the soft clay layer induced by braced excavations.Different geometric and mechanical properties of the wall were investigated to study the deflection behavior of the wall in soft clays.Accordingly,1090 hypothetical cases were surveyed and simulated based on the HSS model and FEM to evaluate the wall deflection behavior.The results were then used to develop an intelligent model for predicting wall deflection using the functional linked neural network(FLNN)with different functional expansions and activation functions.Although the FLNN is a novel approach to predict wall deflection;however,in order to improve the accuracy of the FLNN model in predicting wall deflection,three swarm-based optimization algorithms,such as artificial bee colony(ABC),Harris’s hawk’s optimization(HHO),and hunger games search(HGS),were hybridized to the FLNN model to generate three novel intelligent models,namely ABC-FLNN,HHO-FLNN,HGS-FLNN.The results of the hybrid models were then compared with the basic FLNN and MLP models.They revealed that FLNN is a good solution for predicting wall deflection,and the application of different functional expansions and activation functions has a significant effect on the outcome predictions of the wall deflection.It is remarkably interesting that the performance of the FLNN model was better than the MLP model with a mean absolute error(MAE)of 19.971,root-mean-squared error(RMSE)of 24.574,and determination coefficient(R^(2))of 0.878.Meanwhile,the performance of the MLP model only obtained an MAE of 20.321,RMSE of 27.091,and R^(2)of 0.851.Furthermore,the results also indicated that the proposed hybrid models,i.e.,ABC-FLNN,HHO-FLNN,HGS-FLNN,yielded more superior performances than those of the FLNN and MLP models in terms of the prediction of deflection behavior of diaphragm walls with an MAE in the range of 11.877 to 12.239,RMSE in the range of 15.821 to 16.045,and R^(2)in the range of 0.949 to 0.951.They can be used as an alternative tool to simulate diaphragm wall deflections under different conditions with a high degree of accuracy. | Chengyu Xie Hoang Nguyen Yosoon Choi Danial Jahed Armaghani | 2022 | Geoscience Frontiers2022，13，2： | 3 |
| 4 | Hybrid ensemble soft computing approach for predicting penetration rate of tunnel boring machine in a rock environment显示文摘This study implements a hybrid ensemble machine learning method for forecasting the rate of penetration(ROP) of tunnel boring machine(TBM),which is becoming a prerequisite for reliable cost assessment and project scheduling in tunnelling and underground projects in a rock environment.For this purpose,a sum of 185 datasets was collected from the literature and used to predict the ROP of TBM.Initially,the main dataset was utilised to construct and validate four conventional soft computing(CSC)models,i.e.minimax probability machine regression,relevance vector machine,extreme learning machine,and functional network.Consequently,the estimated outputs of CSC models were united and trained using an artificial neural network(ANN) to construct a hybrid ensemble model(HENSM).The outcomes of the proposed HENSM are superior to other CSC models employed in this study.Based on the experimental results(training RMSE=0.0283 and testing RMSE=0.0418),the newly proposed HENSM is potential to assist engineers in predicting ROP of TBM in the design phase of tunnelling and underground projects. | Abidhan Bardhan Navid Kardani Anasua GuhaRay Avijit Burman Pijush Samui Yanmei Zhang | 2021 | Journal of Rock Mechanics and Geotechnical Engineering2021，13，6： | 2 |
| 5 | 风井深基坑开挖对坑角外浅基础建筑物的影响显示文摘以杭州市某风井深基坑工程为研究对象,基于小应变土体本构模型和霍克-布朗岩石本构模型,建立了基坑开挖三维数值模型。通过与实测数据对比,验证了模型的有效性。在此模型基础上,对坑角外浅基础建筑进行适当简化,研究了不同距离下浅基础建筑墙体沉降、倾斜、扭转以及基础水平位移的变化规律。研究结果表明:随着建筑物与基坑距离增大,其墙体不均匀沉降及倾斜值呈先增大后减小再反向增大的趋势,其墙体扭转程度呈先减小后增大再减小的趋势,其基础水平位移呈逐渐减小趋势,但当减小到一定程度,基础位移方向会发生改变。 | 丁士龙 孙志浩 张恒志 窦炳珺 郑凯达 徐长节 | 2021 | 华东交通大学学报2021，38，6： | 2 |
| 6 | A predictive equation for residual strength using a hybrid of subset selection of maximum dissimilarity method with Pareto optimal multi-gene genetic programming显示文摘More accurate and reliable estimation of residual strength friction angle(/r)of clay is crucial in many geotechnical engineering applications,including riverbank stability analysis,design,and assessment of earthen dam slope stabilities.However,a general predictive equation for/r,with applicability in a wide range of effective parameters,remains an important research gap.The goal of this study is to develop a more accurate equation for/r using the Pareto Optimal Multi-gene Genetic Programming(POMGGP)approach by evaluating a comprehensive dataset of 290 experiments compiled from published literature databases worldwide.A new framework for integrated equation derivation proposed that hybridizes the Subset Selection of Maximum Dissimilarity Method(SSMD)with Multi-gene Genetic Programming(MGP)and Pareto-optimality(PO)to find an accurate equation for/r with wide range applicability.The final predictive equation resulted from POMGGP modeling was assessed in comparison with some previously published machine learning-based equations using statistical error analysis criteria,Taylor diagram,revised discrepancy ratio(RDR),and scatter plots.Base on the results,the POMGGP has the lowest uncertainty with U95=2.25,when compared with Artificial Neural Network(ANN)(U95=2.3),Bayesian Regularization Neural Network(BRNN)(U95=2.94),Levenberg-Marquardt Neural Network(LMNN)(U95=3.3),and Differential Evolution Neural Network(DENN)(U95=2.37).The more reliable results in estimation of/r derived by POMGGP with reliability 59.3%,and resiliency 60%in comparison with ANN(reliability=30.23%,resiliency=28.33%),BRNN(reliability=10.47%,resiliency=10.39%),LMNN(reliability=19.77%,resiliency=20.29%)and DENN(reliability=27.91%,resiliency=24.19%).Besides the simplicity and ease of application of the new POMGGP equation to a broad range of conditions,using the uncertainty,reliability,and resilience analysis confirmed that the derived equation for/r significantly outperformed other existing machine learning methods,including the ANN,BRNN,LMNN,and DENN equations。 | Hossien Riahi-Madvar Mahsa Gholami Bahram Gharabaghi Seyed Morteza Seyedian | 2021 | Geoscience Frontiers2021，12，5： | 1 |
| 7 | Probabilistic assessment of slope failure considering anisotropic spatial variability of soil properties显示文摘Anisotropic spatial variability of soil properties is frequently encountered in geotechnical engineering practice due to the complex depositional process.To quantitatively evaluate the response of slope failure related to anisotropic spatial variability of soil properties and reveal the underlying influence of anisotropic spatial variability of soil properties on the slope reliability,this study integrates the random finite difference method(RFDM)into a probabilistic assessment framework and adopts general spatial variability and a cohesive-frictional soil slope example for illustration.A parametric analysis is carried out to investigate the influence of general anisotropic spatial variability of soil properties on slope failure probability and failure characteristics.The results show that the directional angles of scales of fluctuation of general anisotropic spatial variability significantly affect the slope failure probability.The dominant failure mode is the intermediate type in most cases of general anisotropic spatial variability,which is distinguished from the shallow failure mode occurring in the homogenous state.Overestimation of cross-correlation between c and u(qc;u),scales of fluctuation(dmax and dmin)in general anisotropic spatially variable soils significantly influences the average slip mass volumes of deep and multi-slip failure mode.Compared with transverse anisotropic spatial variability,general anisotropic spatial variability significantly ampli-fies the effects of qc;u,dmax and dmin on slope reliability. | Longlong Chen Wengang Zhang Fuyong Chen Dongming Gu Lin Wang Zhenyu Wang | 2022 | Geoscience Frontiers2022，13，3： | 1 |
| 8 | Analysis of ground surface settlement in anisotropic clays using extreme gradient boosting and random forest regression models显示文摘Excessive ground surface settlement induced by pit excavation(i.e.braced excavation) can potentially result in damage to the nearby buildings and facilities.In this paper,extensive finite element analyses have been carried out to evaluate the effects of various structural,soil and geometric properties on the maximum ground surface settlement induced by braced excavation in anisotropic clays.The anisotropic soil properties considered include the plane strain shear strength ratio(i.e.the ratio of the passive undrained shear strength to the active one) and the unloading shear modulus ratio.Other parameters considered include the support system stiffness,the excavation width to excavation depth ratio,and the wall penetration depth to excavation depth ratio.Subsequently,the maximum ground surface settlement of a total of 1479 hypothetical cases were analyzed by various machine learning algorithms including the ensemble learning methods(extreme gradient boosting(XGBoost) and random forest regression(RFR)algorithms).The prediction models developed by the XGBoost and RFR are compared with that of two conventional regression methods,and the predictive accuracy of these models are assessed.This study aims to highlight the technical feasibility and applicability of advanced ensemble learning methods in geotechnical engineering practice. | Runhong Zhang Yongqin Li Anthony T.C.Goh Wengang Zhang Zhixiong Chen | 2021 | Journal of Rock Mechanics and Geotechnical Engineering2021，13，6： | 1 |
| 9 | Dynamic prediction of landslide life expectancy using ensemble system incorporating classical prediction models and machine learning显示文摘With the development of landslide monitoring system,many attempts have been made to predict landslide failure-time utilizing monitoring data of displacements.Classical models(e.g.,Verhulst,GM(1,1),and Saito models)that consider the characteristics of landslide displacement to determine the failuretime have been investigated extensively.In practice,monitoring is continuously implemented with monitoring data-set updated,meaning that the predicted landslide life expectancy(i.e.,the lag between the predicted failure-time and time node at each instant of conducting the prediction)should be re-evaluated with time.This manner is termed“dynamic prediction”.However,the performances of the classical models have not been discussed in the context of the dynamic prediction yet.In this study,such performances are investigated firstly,and disadvantages of the classical models are then reported,incorporating the monitoring data from four real landslides.Subsequently,a more qualified ensemble model is proposed,where the individual classical models are integrated by machine learning(ML)-based meta-model.To evaluate the quality of the models under the dynamic prediction,a novel indicator termed“discredit index(b)”is proposed,and a higher value of b indicates lower prediction quality.It is found that Verhulst and Saito models would produce predicted results with significantly higher b,while GM(1,1)model would indicate results with the highest mean absolute error.Meanwhile,the ensemble models are found to be more accurate and qualified than the classical models.Here,the performance of decision tree regression-based ensemble model is the best among the various ML-based ensemble models. | Lei-Lei Liu Hao-Dong Yin Ting Xiao Lei Huang Yung-Ming Cheng | 2024 | Geoscience Frontiers2024，15，2： | 0 |
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