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| 1 | 基于特征选择和贝叶斯优化 LightGBM 的注塑制品尺寸预测显示文摘提出了一种基于Pearson相关系数特征选择和经贝叶斯优化的LightGBM算法模型对注塑制品的尺寸进行回归预测的方法。首先对注塑数据预处理,计算各特征与待预测注塑制品尺寸的Pearson相关系数,筛选出与注塑制品尺寸相关性较强的特征,然后使用经贝叶斯优化的LightGBM算法对处理后的数据进行训练和测试。通过与随机森林、支持向量机以及人工神经网络算法进行对比验证,发现所提出的贝叶斯优化LightGBM算法比其它三种算法具有更高的预测精度,且能准确反映尺寸的变化趋势。 | 宋建 陈广森 陈敬福 徐百平 | 2021 | 工程塑料应用2021，49，8： | 8 |
| 2 | 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 |
| 3 | 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 |
| 4 | 基于极端梯度提升回归模型的电梯钢丝绳磨损预测方法显示文摘目前对电梯钢丝绳磨损量预测的研究还存在不足,针对这一问题,采用基于极端梯度提升(XGBoost)算法的机器学习方法,对电梯钢丝绳磨损率的预测进行了理论分析、数据采集和实验测试研究。首先,在目标损失函数中增加了额外的正则化项,并使用了贝叶斯超参数优化,提出了优化后的算法BO-XGBoost;然后,用自制的电梯钢丝绳疲劳试验机对曳引轮直径、载荷力、频率和包角这4个因素进行了试验,得到了用于预测钢丝绳磨损率的数据;最后,用BO-XGBoost算法对钢丝绳磨损率进行了预测分析,同时与多元线性回归(MLR)、随机森林(RF)以及支持向量机(SVM)等机器学习算法进行了比较。研究结果表明:BO-XGBoost算法的建模效果和回归效果最好,其泛化能力也最高,能适应不同工况的实验,在预测钢丝绳磨损率方面优于其他几个模型,预测值与试验值达到了99.1%的准确率,证明了该方法的有效性。 | 陈向俊 傅军平 陈栋栋 李科 李黎苹 吕林锋 | 2022 | 机电工程2022，39，4： | 4 |
| 5 | Prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms显示文摘The main purpose of blasting operation is to produce desired and optimum mean size rock fragments.Smaller or fine fragments cause the loss of ore during loading and transportation,whereas large or coarser fragments need to be further processed,which enhances production cost.Therefore,accurate prediction of rock fragmentation is crucial in blasting operations.Mean fragment size(MFS) is a crucial index that measures the goodness of blasting designs.Over the past decades,various models have been proposed to evaluate and predict blasting fragmentation.Among these models,artificial intelligence(AI)-based models are becoming more popular due to their outstanding prediction results for multiinfluential factors.In this study,support vector regression(SVR) techniques are adopted as the basic prediction tools,and five types of optimization algorithms,i.e.grid search(GS),grey wolf optimization(GWO),particle swarm optimization(PSO),genetic algorithm(GA) and salp swarm algorithm(SSA),are implemented to improve the prediction performance and optimize the hyper-parameters.The prediction model involves 19 influential factors that constitute a comprehensive blasting MFS evaluation system based on AI techniques.Among all the models,the GWO-v-SVR-based model shows the best comprehensive performance in predicting MFS in blasting operation.Three types of mathematical indices,i.e.mean square error(MSE),coefficient of determination(R^(2)) and variance accounted for(VAF),are utilized for evaluating the performance of different prediction models.The R^(2),MSE and VAF values for the training set are 0.8355,0.00138 and 80.98,respectively,whereas 0.8353,0.00348 and 82.41,respectively for the testing set.Finally,sensitivity analysis is performed to understand the influence of input parameters on MFS.It shows that the most sensitive factor in blasting MFS is the uniaxial compressive strength. | Enming Li Fenghao Yang Meiheng Ren Xiliang Zhang Jian Zhou Manoj Khandelwal | 2021 | Journal of Rock Mechanics and Geotechnical Engineering2021，13，6： | 3 |
| 6 | 基于鲸群优化随机森林算法的非平衡数据分类显示文摘为了提高非平衡数据分类的准确性,采用随机森林算法用于数据分类,并结合鲸鱼优化算法对随机森林弱分类器权重进行优化求解,以增强随机森林算法对非平衡数据分类的适应性。首先,建立基于随机森林的非平衡数据分类模型。通过随机森林的多个决策树弱分类器进行分类,有效解决样本不均衡导致的分类困难问题。接着,采用鲸群优化算法对弱分类器权重进行优化求解,将分类准确率均值作为鲸群优化适应度函数,以提高弱分类器权重投票对最终分类结果的精度。最后,采用经过鲸群优化得到的随机森林模型进行非平衡数据分类。实验证明,通过合理设置鲸群优化算法参数,可以获得分类准确度更高的随机森林弱分类器权重,相较于常用非平衡数据分类算法,文中算法能够获得更优的分类性能。 | 叶丽珠 郑冬花 刘月红 牛少华 | 2022 | 南京邮电大学学报(自然科学版)2022，42，6： | 3 |
| 7 | Prediction of rockhead using a hybrid N-XGBoost machine learning framework显示文摘The spatial information of rockhead is crucial for the design and construction of tunneling or underground excavation.Although the conventional site investigation methods(i.e.borehole drilling) could provide local engineering geological information,the accurate prediction of the rockhead position with limited borehole data is still challenging due to its spatial variation and great uncertainties involved.With the development of computer science,machine learning(ML) has been proved to be a promising way to avoid subjective judgments by human beings and to establish complex relationships with mega data automatically.However,few studies have been reported on the adoption of ML models for the prediction of the rockhead position.In this paper,we proposed a robust probabilistic ML model for predicting the rockhead distribution using the spatial geographic information.The framework of the natural gradient boosting(NGBoost) algorithm combined with the extreme gradient boosting(XGBoost)is used as the basic learner.The XGBoost model was also compared with some other ML models such as the gradient boosting regression tree(GBRT),the light gradient boosting machine(LightGBM),the multivariate linear regression(MLR),the artificial neural network(ANN),and the support vector machine(SVM).The results demonstrate that the XGBoost algorithm,the core algorithm of the probabilistic NXGBoost model,outperformed the other conventional ML models with a coefficient of determination(R2)of 0.89 and a root mean squared error(RMSE) of 5.8 m for the prediction of rockhead position based on limited borehole data.The probabilistic N-XGBoost model not only achieved a higher prediction accuracy,but also provided a predictive estimation of the uncertainty.Thus,the proposed N-XGBoost probabilistic model has the potential to be used as a reliable and effective ML algorithm for the prediction of rockhead position in rock and geotechnical engineering. | Xing Zhu Jian Chu Kangda Wang Shifan Wu Wei Yan Kiefer Chiam | 2021 | Journal of Rock Mechanics and Geotechnical Engineering2021，13，6： | 3 |
| 8 | 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 |
| 9 | 基于XGBoost算法的胶凝砂砾石劈拉强度预测分析显示文摘将水泥质量浓度、砂率、水胶比和粉煤灰质量浓度设为输入变量,28 d劈拉强度设为输出变量,用极端梯度提升树(XGBoost)算法对胶凝砂砾石(CSG)的劈拉强度进行预测,并与随机森林(RF)算法的预测结果进行对比,以决策系数(R^(2))、均方根误差(RMSE)、平均绝对误差(MAE)和平均百分比误差(MAPE)作为评估标准对2种算法进行对比分析.结果表明:XGBoost算法的R2为0.968 1,具有高度的预测准确性;相比表现良好的RF算法,XGBoost算法测试集的RMSE和MAE均降低了0.003, MAPE降低了0.32%,表明XGBoost算法能够对CSG劈拉强度进行更为精准的预测. | 郭磊 李泽宣 田青青 郭利霞 高航 | 2023 | 建筑材料学报2023，26，4： | 1 |
| 10 | Improved prediction of clay soil expansion using machine learning algorithms and meta-heuristic dichotomous ensemble classifiers显示文摘Soil swelling-related disaster is considered as one of the most devastating geo-hazards in modern history.Hence,proper determination of a soil’s ability to expand is very vital for achieving a secure and safe ground for infrastructures.Accordingly,this study has provided a novel and intelligent approach that enables an improved estimation of swelling by using kernelised machines(Bayesian linear regression(BLR)&bayes point machine(BPM)support vector machine(SVM)and deep-support vector machine(D-SVM));(multiple linear regressor(REG),logistic regressor(LR)and artificial neural network(ANN)),tree-based algorithms such as decision forest(RDF)&boosted trees(BDT).Also,and for the first time,meta-heuristic classifiers incorporating the techniques of voting(VE)and stacking(SE)were utilised.Different independent scenarios of explanatory features’combination that influence soil behaviour in swelling were investigated.Preliminary results indicated BLR as possessing the highest amount of deviation from the predictor variable(the actual swell-strain).REG and BLR performed slightly better than ANN while the meta-heuristic learners(VE and SE)produced the best overall performance(greatest R2 value of 0.94 and RMSE of 0.06%exhibited by VE).CEC,plasticity index and moisture content were the features considered to have the highest level of importance.Kernelized binary classifiers(SVM,D-SVM and BPM)gave better accuracy(average accuracy and recall rate of 0.93 and 0.60)compared to ANN,LR and RDF.Sensitivity-driven diagnostic test indicated that the meta-heuristic models’best performance occurred when ML training was conducted using k-fold validation technique.Finally,it is recommended that the concepts developed herein be deployed during the preliminary phases of a geotechnical or geological site characterisation by using the best performing meta-heuristic models via their background coding resource. | E.U.Eyo S.J.Abbey T.T.Lawrence F.K.Tetteh | 2022 | Geoscience Frontiers2022，13，1： | 1 |
| 11 | 基于PSO-RF模型的复杂地层双模盾构土压掘进模式下密封舱压力预测显示文摘依托广州地铁7号线2期工程洪圣沙-裕丰围区间隧道工程,采用粒子群算法优化随机森林方法(particle swarm optimization-random forest algorithm,PSO-RF)建立双模盾构土压掘进模式下密封舱压力预测模型。通过对盾构掘进参数进行相关性分析,筛选出对密封舱压力影响较大的掘进参数,包括螺机转速、螺机扭矩、刀盘转速、推进速度、贯入度、刀盘扭矩、总推力,将筛选出的掘进参数作为预测模型输入参数,密封舱压力作为模型的输出参数,对密封舱压力进行预测。结果表明:采用PSO-RF预测模型能够有效预测双模盾构密封舱压力;相比于传统神经网络预测模型,PSO-RF模型预测精度更高,平均绝对误差均在10%以内,预测值和实际值的拟合优度R^(2)为0.9014,在预测精度及模型的泛化能力上明显优于BP神经网络。 | 张斌 佟彬 刘国强 周子豪 王树英 | 2023 | 隧道与地下工程灾害防治2023，5，1： | 1 |
| 12 | 基于加权随机森林的番茄氮元素缺乏分级模型研究显示文摘基于叶面颜色特征建立番茄氮元素缺乏分级模型判别准确率可达0.8以上。夏季定植的番茄叶片表面会覆盖粘质腺毛,粘质腺毛利于番茄吸收水分和营养元素,相同营养液氮离子浓度下叶片黄化过程异于未覆盖粘质腺毛的叶片。故仅基于叶面颜色特征建立分级模型,其准确率降至0.65。覆盖粘质腺毛番茄其叶片周长和叶面积两个形状特征均小于未覆盖粘质腺毛的番茄叶片,本文将番茄叶片两个形状特征结合原有叶面颜色特征共同作为模型输入,建立新的番茄氮元素缺乏分级模型。搭建图像采集系统,该图像采集单元由树莓派和其相机模块构建,使用WiFi或4G网络完成智能手机、图像采集单元、本地计算机之间无线数据传输。智能手机通过Web界面可远程控制采集图像并将图像传输到云平台存储。本地计算机对图像进行预处理提取叶片形状、颜色特征后输入模型进行预测,并输出预测结果。试验结果表明,图像采集系统春季和夏季平均温度在19.7~28.3℃范围内,光照在1125~9543 lx范围内均可正常使用,采集的图像经预处理分割后降低了环境光线的影响。使用优化后的加权随机森林模型,基于形状特征和颜色特征相结合的叶片氮元素缺乏分级判别准确率可达0.83。 | 李莉 蓝天 赵奇慧 孟繁佳 | 2021 | 农业机械学报2021，52，11： | 1 |
| 13 | Classification of clustered microseismic events in a coal mine using machine learning显示文摘Discrimination of seismicity distributed in different areas is essential for reliable seismic risk assessment in mines.Although machine learning has been widely applied in seismic data processing,feasibility and reliability of applying this technique to classify spatially clustered seismic events in underground mines are yet to be investigated.In this research,two groups of seismic events with a minimum local magnitude(ML) of-3 were observed in an underground coal mine.They were respectively located around a dyke and the longwall face.Additionally,two types of undesired signals were also recorded.Four machine learning methods,i.e.random forest(RF),support vector machine(SVM),deep convolutional neural network(DCNN),and residual neural network(ResNN),were used for classifying these signals.The results obtained based on a primary dataset showed that these seismic events could be classified with at least 91% accuracy.The DCNN using seismogram images as the inputs reached the best performance with more than 94% accuracy.As mining is a dynamic progress which could change the characteristics of seismic signals,the temporal variance in the prediction performance of DCNN was also investigated to assess the reliability of this classifier during mining.A cascaded workflow consisting of database update,model training,signal prediction,and results review was established.By progressively calibrating the DCNN model,it achieved up to 99% prediction accuracy.The results demonstrated that machine learning is a reliable tool for the automatic discrimination of spatially clustered seismicity in underground mining. | Yi Duan Yiran Shen Ismet Canbulat Xun Luo Guangyao Si | 2021 | Journal of Rock Mechanics and Geotechnical Engineering2021，13，6： | 1 |
| 14 | Tunnel boring machine vibration-based deep learning for the ground identification of working faces显示文摘Tunnel boring machine(TBM) vibration induced by cutting complex ground contains essential information that can help engineers evaluate the interaction between a cutterhead and the ground itself.In this study,deep recurrent neural networks(RNNs) and convolutional neural networks(CNNs) were used for vibration-based working face ground identification.First,field monitoring was conducted to obtain the TBM vibration data when tunneling in changing geological conditions,including mixed-face,homogeneous,and transmission ground.Next,RNNs and CNNs were utilized to develop vibration-based prediction models,which were then validated using the testing dataset.The accuracy of the long short-term memory(LSTM) and bidirectional LSTM(Bi-LSTM) models was approximately 70% with raw data;however,with instantaneous frequency transmission,the accuracy increased to approximately 80%.Two types of deep CNNs,GoogLeNet and ResNet,were trained and tested with time-frequency scalar diagrams from continuous wavelet transformation.The CNN models,with an accuracy greater than 96%,performed significantly better than the RNN models.The ResNet-18,with an accuracy of 98.28%,performed the best.When the sample length was set as the cutterhead rotation period,the deep CNN and RNN models achieved the highest accuracy while the proposed deep CNN model simultaneously achieved high prediction accuracy and feedback efficiency.The proposed model could promptly identify the ground conditions at the working face without stopping the normal tunneling process,and the TBM working parameters could be adjusted and optimized in a timely manner based on the predicted results. | Mengbo Liu Shaoming Liao Yifeng Yang Yanqing Men Junzuo He Yongliang Huang | 2021 | Journal of Rock Mechanics and Geotechnical Engineering2021，13，6： | 1 |
| 15 | Data-driven estimation of joint roughness coefficient显示文摘Joint roughness is one of the most important issues in the hydromechanical behavior of rock mass.Therefore,the joint roughness coefficient(JRC)estimation is of paramount importance in geomechanics engineering applications.Studies show that the application of statistical parameters alone may not produce a sufficiently reliable estimation of the JRC values.Therefore,alternative data-driven methods are proposed to assess the JRC values.In this study,Gaussian process(GP),K-star,random forest(RF),and extreme gradient boosting(XGBoost)models are employed,and their performance and accuracy are compared with those of benchmark regression formula(i.e.Z2,Rp,and SDi)for the JRC estimation.To analyze the models’performance,112 rock joint profile datasets having eight common statistical parameters(R_(ave),R_(max),SD_(h),iave,SD_(i),Z_(2),R_(p),and SF)and one output variable(JRC)are utilized,of which 89 and 23 datasets are used for training and validation of models,respectively.The interpretability of the developed XGBoost model is presented in terms of feature importance ranking,partial dependence plots(PDPs),feature interaction,and local interpretable model-agnostic explanations(LIME)techniques.Analyses of results show that machine learning models demonstrate higher accuracy and precision for estimating JRC values compared with the benchmark empirical equations,indicating the generalization ability of the data-driven models in better estimation accuracy. | Hadi Fathipour-Azar | 2021 | Journal of Rock Mechanics and Geotechnical Engineering2021，13，6： | 1 |
| 16 | Improved prediction of shear wave velocity for clastic sedimentary rocks using hybrid model with core data显示文摘Accurate measurement of acoustic velocities of sedimentary rocks is essential for prediction of rock elastic constants and well failure analysis during drilling operations.Direct measurement by advanced logging tools such as dipole sonic imager is not always possible.For older wells,such data are not available in most cases.Therefore,it is an alternate way to develop a reliable correlation to estimate the shear wave velocity from existing log and/or core data.The objective of this research is to investigate the nature of dependency of different reservoir parameters on the shear wave velocity(VS) of clastic sedimentary rocks,and to identify the parameter/variable which shows the highest level of dependency.In the study,data-driven connectionist models are developed using machine learning approach of least square support vector machine(LSSVM).The coupled simulated annealing(CSA) approach is utilized to optimize the tuning and kernel parameters in the model development.The performance of the simulation-based model is evaluated using statistical parameters.It is found that the most dependency predictor variable is the compressional wave velocity,followed by the rock porosity,bulk density and shale volume in turn.A new correlation is developed to estimate VS,which captures the most influential parameters of sedimentary rocks.The new correlation is verified and compared with existing models using measured data of sandstone,and it exhibits a minimal error and high correlation coefficient(R^(2)-0.96).The hybridized LSSVM-CSA connectionist model development strategy can be applied for further analysis to predict rock mechanical properties.Additionally,the improved correlation of VS can be adopted to estimate rock elastic constants and conduct wellbore failure analysis for safe drilling and field development decisions,reducing the exploration costs. | Mohammad Islam Miah | 2021 | Journal of Rock Mechanics and Geotechnical Engineering2021，13，6： | 1 |
| 17 | 考虑土体电势分布影响的真空预压联合电渗耦合解析解显示文摘真空预压联合电渗加固技术能够充分发挥真空预压法和电渗法的优势,进而有效改善软基处理效果。研究表明,当采用真空预压联合电渗法加固软基时,土体电势呈现非线性分布规律。然而,目前有关该联合加固技术的理论研究尚未考虑土体电势非线性分布对软基处理过程的影响且在理论推导中通常假设土体电势为线性分布,显然,这与试验结果有所出入。对此,本文首先以阳极为研究对象建立了二维平面应变条件下真空预压联合电渗法耦合固结模型。在模型中考虑了井阻效应、真空荷载沿土层深度衰减以及土体电势非线性变化的影响。然后,基于实际土体电势分布规律分别给出了阳极影响区域内平均超静孔隙水压力和土体固结度的理论解析解。最后,结合具体的模型试验案例对该解析解的合理性进行了验证,证明了该解析解的准确性和科学性。该解析解能够合理预测真空预压联合电渗法处理过程中超静孔隙水压力和土体固结度的变化情况,能够为真空预压联合电渗加固技术后续的工程运用提供借鉴和参考。 | 冯建挺 沈扬 许俊红 施文 | 2021 | Journal of Central South University2021，28，8： | 1 |
| 18 | 基于人工神经网络多模型迁移学习的隧(巷)道机械化掘进装备控制参数自主决策方法显示文摘TBM工法是一种应用于隧(巷)道建设的机械化掘进方法。为实现TBM稳态段掘进过程中装备自身对操作参数的优化决策,建立一种基于迁移学习思想的TBM控制参数自主决策模型。首先提出核心优化策略:在充分尊重现场经验的基础上,提高掘进效率、降低掘进能耗,并以稳态段贯入度(PRev)、刀盘转速(RPM)为输出参数完成数学建模;其次构建优化决策模型,模型基本架构采用深度神经网络,包含2个源域子模型和1个目标域主干模型,分别执行控制参数回归、破岩比能预测、控制参数优化决策任务,并通过迁移学习和网络层冻结的方法实现了源域与目标域的统一;而后确定模型关键超参数取值,采用正交试验和贝叶斯优化相结合的方式确定了源域子模型的最优超参数组合,基于层次分析法确定了目标域主干模型目标函数的关键权重;最后,依托吉林引松供水工程总干线输水隧洞四标段TBM施工数据集(4459组有效数据),对所建立模型进行训练和测试。结果表明,TBM稳态段净掘进速率平均提升了15.55%,刀盘破岩比能平均下降了7.13%,并降低了方差,改善了稳态段掘进过程的整体平稳性。研究成果可为长大隧道、矿山巷道建设工程中的TBM智能化控制系统提供技术支撑。 | 高峰 黄兴 刘泉声 殷欣 伯音 王心语 | 2023 | 岩石力学与工程学报2023，42，6： | 0 |
| 19 | Bayesian optimization with adaptive surrogate models for automated experimental design显示文摘Bayesian optimization(BO)is an indispensable tool to optimize objective functions that either do not have known functional forms or are expensive to evaluate.Currently,optimal experimental design is always conducted within the workflow of BO leading to more efficient exploration of the design space compared to traditional strategies.This can have a significant impact on modern scientific discovery,in particular autonomous materials discovery,which can be viewed as an optimization problem aimed at looking for the maximum(or minimum)point for the desired materials properties.The performance of BO-based experimental design depends not only on the adopted acquisition function but also on the surrogate models that help to approximate underlying objective functions.In this paper,we propose a fully autonomous experimental design framework that uses more adaptive and flexible Bayesian surrogate models in a BO procedure,namely Bayesian multivariate adaptive regression splines and Bayesian additive regression trees.They can overcome the weaknesses of widely used Gaussian process-based methods when faced with relatively high-dimensional design space or non-smooth patterns of objective functions.Both simulation studies and real-world materials science case studies demonstrate their enhanced search efficiency and robustness. | Bowen Lei Tanner Quinn Kirk Anirban Bhattacharya Debdeep Pati Xiaoning Qian Raymundo Arroyave Bani K.Mallick | 2021 | npj Computational Materials2021，，1： | 0 |
| 20 | Dimensionality reduction and prediction of soil consolidation coefficient using random forest coupling with Relief algorithm显示文摘The consolidation coefficient of soil(C_(v))is a crucial parameter used for the design of structures leaned on soft soi.In general,the C_(v) is determined experimentally in the laboratory.However,the experimental tests are time-consuming as well as expensive.Therefore,researchers tried several ways to determine C_(v) via other simple soil parameters.In this study,we developed a hybrid model of Random Forest coupling with a Relief algorithm(RF-RL)to predict the C_(v) of soil.To conduct this study,a database of soil parameters collected from a case study region in Vietnam was used for modeling.The performance of the proposed models was assessed via statistical indicators,namely Coefficient of determination(R^(2)),Root Mean Squared Error(RMSE),and Mean Absolute Error(MAE).The proposal models were constructed with four sets of soil variables,including 6,7,8,and 13 inputs.The results revealed that all models performed well with a high performance(R^(2)>0.980).Although the RF-RL model with 13 variables has the highest prediction accuracy(R^(2)=0.9869),the difference compared with other models was negligible(i.e.,R^(2)=0.9824,0.9850,0.9825 for the cases with 6,7,8 inputs,respectively).Thus,it can be concluded that the hybrid model of RF-RL can be employed to predict C_(v) based on the basic soil parameters. | Hai-Bang LY Huong-Lan Thi VU Lanh Si HO Binh Thai PHAM | 2022 | Frontiers of Structural and Civil Engineering2022，16，2： | 0 |
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