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| 1 | 用基于树的Bagging和Boosting集成技术预测硬岩矿山岩爆显示文摘岩爆预测对地下硬岩矿山的设计和施工至关重要。使用三种基于树的集成方法,对由102个历史案例(即1998—2011年期间14个硬岩矿山数据)组成的岩爆数据库进行了检查,以用于有岩爆倾向矿井的岩爆预测。该岩爆数据集包含六个广泛接受的倾向性指标,即:开挖边界周围的最大切向应力(MTS)、完整岩石的单轴抗压强度(UCS)和单轴抗拉强度(UTS)、应力集中系数(SCF)、岩石脆性指数(BI)和应变能储存指数(EEI)。以分类树作为基准分类器的两种Boosting算法(AdaBoost.M1,SAMME)和Bagging算法进行了评估,评估了它们学习岩爆的能力。将可用数据集随机分为训练集(整个数据集的2/3)和测试集(其余数据集)。采用重复10倍交叉验证(CV)作为调整模型超参数的验证方法,并利用边际分析和变量相对重要性分析了各集成学习模型特征。根据重复10倍交叉验证结果,对岩爆数据集的精度分析表明,与AdaBoost.M1、SAMME算法和岩爆经验判据相比,Bagging方法是预测硬岩矿山岩爆的最佳方法。 | 王世鸣 周健 李传奇 Danial Jahed ARMAGHANI 李夕兵 Hani SMITRI | 2021 | Journal of Central South University2021，28，2： | 19 |
| 2 | Application of several optimization techniques for estimating TBM advance rate in granitic rocks显示文摘This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of granite.For this purpose,extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang-Selangor raw water transfer tunnel in Malaysia.Rock properties consisting of uniaxial compressive strength(UCS),Brazilian tensile strength(BTS),rock mass rating(RMR),rock quality designation(RQD),quartz content(q)and weathered zone as well as machine specifications including thrust force and revolution per minute(RPM)were measured to establish comprehensive datasets for optimization.Accordingly,to estimate the advance rate of TBM,two new hybrid optimization techniques,i.e.an artificial neural network(ANN)combined with both imperialist competitive algorithm(ICA)and particle swarm optimization(PSO),were developed for mechanical tunneling in granitic rocks.Further,the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice.To evaluate the accuracy of the proposed models for both testing and training datasets,various statistical indices including coefficient of determination(R^2),root mean square error(RMSE)and variance account for(VAF)were utilized herein.The values of R^2,RMSE,and VAF ranged in 0.939-0.961,0.022-0.036,and 93.899-96.145,respectively,with the PSO-ANN hybrid technique demonstrating the best performance.It is concluded that both the optimization techniques,i.e.PSO-ANN and ICA-ANN,could be utilized for predicting the advance rate of TBMs;however,the PSO-ANN technique is superior. | Danial Jahed Armaghani Mohammadreza Koopialipoor Aminaton Marto Saffet Yagiz | 2019 | Journal of Rock Mechanics and Geotechnical Engineering2019，11，4： | 15 |
| 3 | Predicting TBM penetration rate in hard rock condition:A comparative study among six XGB-based metaheuristic techniques显示文摘A reliable and accurate prediction of the tunnel boring machine(TBM)performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects.This research aims to develop six hybrid models of extreme gradient boosting(XGB)which are optimized by gray wolf optimization(GWO),particle swarm optimization(PSO),social spider optimization(SSO),sine cosine algorithm(SCA),multi verse optimization(MVO)and moth flame optimization(MFO),for estimation of the TBM penetration rate(PR).To do this,a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation,the rock mass rating,Brazilian tensile strength(BTS),rock mass weathering,the uniaxial compressive strength(UCS),revolution per minute and trust force per cutter(TFC),were set as inputs and TBM PR was selected as model output.Together with the mentioned six hybrid models,four single models i.e.,artificial neural network,random forest regression,XGB and support vector regression were also built to estimate TBM PR for comparison purposes.These models were designed conducting several parametric studies on their most important parameters and then,their performance capacities were assessed through the use of root mean square error,coefficient of determination,mean absolute percentage error,and a10-index.Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of(0.1453,and 0.1325),R^(2) of(0.951,and 0.951),mean absolute percentage error(4.0689,and 3.8115),and a10-index of(0.9348,and 0.9496)in training and testing phases,respectively.The developed hybrid PSO-XGB can be introduced as an accurate,powerful and applicable technique in the field of TBM performance prediction.By conducting sensitivity analysis,it was found that UCS,BTS and TFC have the deepest impacts on the TBM PR. | Jian Zhou Yingui Qiu Danial Jahed Armaghani Wengang Zhang Chuanqi Li Shuangli Zhu Reza Tarinejad | 2021 | Geoscience Frontiers2021，12，3： | 10 |
| 4 | Modeling of wireless SAW temperature sensor and associated antenna显示文摘Surface acoustic wave(SAW)resonator used as wireless sensor was characterized and the parameters of its MBVD(Modified Butterworth-Van Dyke)model were extracted versus temperature.The extracted parameters lead toevaluate the resonator performancesin terms of Temperature coefficient of frequency(TCF)and quality factor(Q).An antenna was then associated with the SAW resonator and the entire system has been characterized and modeled.The good agreement experiment-simulation allows to define the optimum operating conditions of the wireless sensor. | Laurent ALLIES Eloi BLAMPAIN Hamid M'JAHED Gerard PRIEUR Omar ELMAZRIA | 2014 | Instrumentation2014，1，1： | 10 |
| 5 | 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 |
| 6 | Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization显示文摘The advance rate(AR)of a tunnel boring machine(TBM)under hard rock conditions is a key parameter in the successful implementation of tunneling engineering.In this study,we improved the accuracy of prediction models by employing a hybrid model of extreme gradient boosting(XGBoost)with Bayesian optimization(BO)to model the TBM AR.To develop the proposed models,1286 sets of data were collected from the Peng Selangor Raw Water Transfer tunnel project in Malaysia.The database consists of rock mass and intact rock features,including rock mass rating,rock quality designation,weathered zone,uniaxial compressive strength,and Brazilian tensile strength.Machine specifications,including revolution per minute and thrust force,were considered to predict the TBM AR.The accuracies of the predictive models were examined using the root mean squares error(RMSE)and the coefficient of determination(R^(2))between the observed and predicted yield by employing a five-fold cross-validation procedure.Results showed that the BO algorithm can capture better hyper-parameters for the XGBoost prediction model than can the default XGBoost model.The robustness and generalization of the BO-XGBoost model yielded prominent results with RMSE and R^(2) values of 0.0967 and 0.9806(for the testing phase),respectively.The results demonstrated the merits of the proposed BO-XGBoost model.In addition,variable importance through mutual information tests was applied to interpret the XGBoost model and demonstrated that machine parameters have the greatest impact as compared to rock mass and material properties. | Jian Zhou Yingui Qiu Shuangli Zhu Danial Jahed Armaghani Manoj Khandelwal Edy Tonnizam Mohamad | 2021 | Underground Space2021，6，5： | 4 |
| 7 | Wireless and batteryless SAW sensors,a promising solution for harsh environments显示文摘In this paper,general principle of the Surface Acoustic Wave(SAW) sensor in wired and wireless con-figurations will be developed and a review of recent works concerning the field of high temperature applications will be presented.The first part will be devoted to aspects of data transmission and processing.Both configurations of SAW de-vice,delay line and resonator,will be discussed as well as the remote interrogation techniques used to collect and to proc-ess signal.The second part will be devoted to the material aspects.Indeed,knowing that the conventional piezoelectric substrates such as quartz or lithium niobate cannot be used at high temperature,the choice of the material constituting the SAW device(substrate & electrodes) is one of the challenges to face.We will focus our discussion on the Langasite,the current reference for high temperature applications,and on the AlN/Sapphire structure,the very promising alternative for application where the use of high frequency is required. | H.M'Jahed T.Aubert G.Prieur O.Elmazria | 2011 | 电子测量与仪器学报2011，25，7： | 4 |
| 8 | 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 |
| 9 | Intelligent rockburst prediction model with sample category balance using feedforward neural network and Bayesian optimization显示文摘The rockburst prediction becomes more and more challenging due to the development of deep underground projects and constructions.Increasing numbers of intelligent algorithms are used to predict and prevent rockburst.This paper investigated the drawbacks of neural networks in rockburst prediction,and aimed at these shortcomings,Bayesian optimization and the synthetic minority oversampling technique+Tomek Link(SMOTETomek)were applied to efficiently develop the feedforward neural network(FNN)model for rockburst prediction.In this regard,314 real rockburst cases were collected to establish a database for modeling.The database was divided into a training set(80%)and a test set(20%).The maximum tangential stress,uniaxial compressive strength,tensile strength,stress ratio,brittleness ratio,and elastic strain energy were selected as input parameters.Bayesian optimization was implemented to find the optimal hyperparameters in FNN.To eliminate the effects of imbalanced category,SMOTETomek was adopted to process the training set to obtain a balanced training set.The FNN developed by the balanced training set received 90.48% accuracy in the test set,and the accuracy improved 12.7% compared to the imbalanced training set.For interpreting the FNN model,the permutation importance algorithm was introduced to analyze the relative importance of input variables.The elastic strain energy was the most essential variable,and some measures were proposed to prevent rockburst.To validate the practicability,the FNN developed by the balanced training set was utilized to predict rockburst in Sanshandao Gold Mine,China,and it had outstanding performance(accuracy 100%). | Diyuan Li Zida Liu Peng Xiao Jian Zhou Danial Jahed Armaghani | 2022 | Underground Space2022，7，5： | 2 |
| 10 | 车轮外形设计的数值优化方法显示文摘介绍了一种根据既定的轮轨接触特性来设计铁道车辆车轮外形的数值优化方法。 | Hamid Jahed 李艳(译) 刘新明(校) | 2009 | 国外铁道车辆2009，，2： | 2 |
| 11 | A simple, flexible and high-throughput cloning system for plant genome editing via CRISPR-Cas system显示文摘CRISPR-Cas9 system is now widely used to edit a target genome in animals and plants. Cas9 protein derived from Streptococcus pyogenes(Sp Cas9) cleaves double-stranded DNA targeted by a chimeric single-guide RNA(sg RNA). For plant genome editing, Agrobacterium-mediated T-DNA transformation has been broadly used to express Cas9 proteins and sg RNAs under the control of Ca MV 35 S and U6/U3 promoter, respectively. We here developed a simple and high-throughput binary vector system to clone a 19 20 bp of sg RNA, which binds to the reverse complement of a target locus, in a large T-DNA binary vector containing an Sp Cas9 expressing cassette. Twostep cloning procedures:(1) annealing two target-specific oligonucleotides with overhangs specific to the Aar I restriction enzyme site of the binary vector; and(2) ligating the annealed oligonucleotides into the two Aar I sites of the vector, facilitate the high-throughput production of the positive clones. In addition, Cas9-coding sequence and U6/U3 promoter can be easily exchanged via the GatewayTMsystem and unique Eco RI/Xho I sites on the vector, respectively. We examined the mutation ratio and patterns when we transformed these constructs into Arabidopsis thaliana and a wild tobacco, Nicotiana attenuata. Our vector system will be useful to generate targeted large-scale knock-out lines of model as well as non-model plant. | Hyeran Kim Sang-Tae Kim Jahee Ryu Min Kyung Choi Jiyeon Kweon Beum-Chang Kang Hyo-Min Ahn Suji Bae Jungeun Kim Jin-Soo Kim Sang-Gyu Kim | 2016 | Journal of Integrative Plant Biology2016，58，8： | 2 |
| 12 | Proteomic evaluation reveals that olfactory ensheathing cells but not Schwann cells express calponin显示文摘 | Boyd JG Jahed A McDonald TG | 2006 | Gila2006，53，4： | 1 |
| 13 | A numerical optimization technique for design of wheel profiles显示文摘 | Hamid Jahed Behrooz Farshi Mohammad A. Eshraghi Asghar Nasr | 2006 | Wear2006，，1： | 1 |
| 14 | Upper and low- er fatigue life limits model using energy-based fatigue properties显示文摘 | JAHED H VARVANI-FARAHANI A | 2006 | International Journal of Fatigue2006，28，5： | 1 |
| 15 | Metallographene nanocomposite electrocatalytic platform for the determination of toxic metal ions显示文摘 | Willemse C M Tlhomelang K Jahed N | 2011 | Sensors2011，1，4： | 1 |
| 16 | Cyclic behaviour of wrought magnesium alloy under multiaxial load显示文摘 | ALBINMOUSA J JAHED H LAMBERT S | 2011 | International Journal of Fatigue2011，33，： | 1 |
| 17 | olfactorv ensheathing cells express smooth muscle alpha-aetin in vitro and in vivo显示文摘 | Jahed A Rowland JW McDonald T | 2007 | J Comp Neurol2007，503，2： | 1 |
| 18 | Multiaxial behaviour of wrought magnesium alloys: A review and suitability of energy-based fatigue life model显示文摘 | ALBINMOUSA J JAHED H | 2014 | Theoretical and Applied FractureMechanics2014，73，： | 1 |
| 19 | Optimum design of inhomogeneous non-uniform rotating discs显示文摘 | FARSHI B JAHED H MEHRABIAN A | 2004 | Computers and Structures2004，82，： | 1 |
| 20 | An axisymmetric method of creep analysis for primary and secondary creep 显示文摘 | Jahed H Bidabadi J | 2003 | The International Journal of Pressure Vessels and Piping2003，80，9： | 1 |
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