Discernment of seismic soil liquefaction is a complex and non-linear procedure that is affected by diversified factors of uncertainties and complexity.The Bayesian belief network(BBN)is an effective tool to present a ...Discernment of seismic soil liquefaction is a complex and non-linear procedure that is affected by diversified factors of uncertainties and complexity.The Bayesian belief network(BBN)is an effective tool to present a suitable framework to handle insights into such uncertainties and cause–effect relationships.The intention of this study is to use a hybrid approach methodology for the development of BBN model based on cone penetration test(CPT)case history records to evaluate seismic soil liquefaction potential.In this hybrid approach,naive model is developed initially only by an interpretive structural modeling(ISM)technique using domain knowledge(DK).Subsequently,some useful information about the naive model are embedded as DK in the K2 algorithm to develop a BBN-K2 and DK model.The results of the BBN models are compared and validated with the available artificial neural network(ANN)and C4.5 decision tree(DT)models and found that the BBN model developed by hybrid approach showed compatible and promising results for liquefaction potential assessment.The BBN model developed by hybrid approach provides a viable tool for geotechnical engineers to assess sites conditions susceptible to seismic soil liquefaction.This study also presents sensitivity analysis of the BBN model based on hybrid approach and the most probable explanation of liquefied sites,owing to know the most likely scenario of the liquefaction phenomenon.展开更多
This paper is devoted to the probabilistic stability analysis of a tunnel face excavated in a two-layer soil. The interface of the soil layers is assumed to be positioned above the tunnel roof. In the framework of lim...This paper is devoted to the probabilistic stability analysis of a tunnel face excavated in a two-layer soil. The interface of the soil layers is assumed to be positioned above the tunnel roof. In the framework of limit analysis, a rotational failure mechanism is adopted to describe the face failure considering different shear strength parameters in the two layers. The surrogate Kriging model is introduced to replace the actual performance function to perform a Monte Carlo simulation. An active learning function is used to train the Kriging model which can ensure an efficient tunnel face failure probability prediction without loss of accuracy. The deterministic stability analysis is given to validate the proposed tunnel face failure model. Subsequently, the number of initial sampling points, the correlation coefficient, the distribution type and the coefficient of variability of random variables are discussed to show their influences on the failure probability. The proposed approach is an advisable alternative for the tunnel face stability assessment and can provide guidance for tunnel design.展开更多
Support vector regression （SVR） method is a novel type of learning machine algorithms, which is seldom applied to the development of urban atmospheric quality models under multiple socio-economic factors. This study...Support vector regression （SVR） method is a novel type of learning machine algorithms, which is seldom applied to the development of urban atmospheric quality models under multiple socio-economic factors. This study presents four SVR models by selecting linear, radial basis, spline, and polynomial functions as kernels, respectively for the prediction of urban dust fall levels. The inputs of the models are identified as industrial coal consumption, population density, traffic flow coefficient, and shopping density coefficient. The training and testing results show that the SVR model with radial basis kernel performs better than the other three both in the training and testing processes. In addition, a number of scenario analyses reveal that the most suitable parameters （insensitive loss function e, the parameter to reduce the influence of error C, and discrete level or average distribution of parameters σ） are 0.001, 0.5, and 2 000, respectively.展开更多
The taxi drivers' cruising pattern was learned using GPS trajectory data collected in Shenzhen,China.By employing zero-inflated Poisson model,the impacts of land use and previous pick-up experience on cruising dec...The taxi drivers' cruising pattern was learned using GPS trajectory data collected in Shenzhen,China.By employing zero-inflated Poisson model,the impacts of land use and previous pick-up experience on cruising decision were measured.The cruising strategies of different types of drivers as well as the top one driver were examined.The results indicate that both land use and previous pick-up experience affect travel behavior with the former's influence(7.07×10-4 measured by one of the coefficients in zero-inflated Poisson model) being greater than the latter's(4.58×10-5) in general,but the comparison also varies across the types of drivers.Besides,taxi drivers' day-to-day learning feature is also proved by the results.According to comparison of the cruising behavior of the most efficient and inefficient driver,an efficient cruising strategy was proposed,that is,obeying the distribution of land use in choice of cruising area,while learning from pick-up experience in selection of detailed cruising location.By learning taxi drivers' cruising pattern,the development of measures of regulating travel behaviors is facilitated,important factor for traffic organization and planning is identified,and an efficient cruising strategy for taxi drivers is provided.展开更多
Experimentation data of perspex glass sheet cutting, using CO2 laser, with missing values were modelled with semi-supervised artificial neural networks. Factorial design of experiment was selected for the verification...Experimentation data of perspex glass sheet cutting, using CO2 laser, with missing values were modelled with semi-supervised artificial neural networks. Factorial design of experiment was selected for the verification of orthogonal array based model prediction. It shows improvement in modelling of edge quality and kerf width by applying semi-supervised learning algorithm, based on novel error assessment on simulations. The results are expected to depict better prediction on average by utilizing the systematic randomized techniques to initialize the neural network weights and increase the number of initialization. Missing values handling is difficult with statistical tools and supervised learning techniques; on the other hand, semi-supervised learning generates better results with the smallest datasets even with missing values.展开更多
A learning controller of nonhonolomic robot in real-time based on support vector machine(SVM)is presented.The controller includes two parts:one is kinematic controller based on nonlinear law,and the other is dynamic c...A learning controller of nonhonolomic robot in real-time based on support vector machine(SVM)is presented.The controller includes two parts:one is kinematic controller based on nonlinear law,and the other is dynamic controller based on SVM.The kinematic controller is aimed to provide desired velocity which can make the steering system stable.The dynamic controller is aimed to transform the desired velocity to control torque.The parameters of the dynamic system of the robot are estimated through SVM learning algorithm according to the training data of sliding windows in real time.The proposed controller can adapt to the changes in the robot model and uncertainties in the environment.Compared with artificial neural network(ANN)controller,SVM controller can converge to the reference trajectory more quickly and the tracking error is smaller.The simulation results verify the effectiveness of the method proposed.展开更多
基金Projects(2016YFE0200100,2018YFC1505300-5.3)supported by the National Key Research&Development Plan of ChinaProject(51639002)supported by the Key Program of National Natural Science Foundation of China
文摘Discernment of seismic soil liquefaction is a complex and non-linear procedure that is affected by diversified factors of uncertainties and complexity.The Bayesian belief network(BBN)is an effective tool to present a suitable framework to handle insights into such uncertainties and cause–effect relationships.The intention of this study is to use a hybrid approach methodology for the development of BBN model based on cone penetration test(CPT)case history records to evaluate seismic soil liquefaction potential.In this hybrid approach,naive model is developed initially only by an interpretive structural modeling(ISM)technique using domain knowledge(DK).Subsequently,some useful information about the naive model are embedded as DK in the K2 algorithm to develop a BBN-K2 and DK model.The results of the BBN models are compared and validated with the available artificial neural network(ANN)and C4.5 decision tree(DT)models and found that the BBN model developed by hybrid approach showed compatible and promising results for liquefaction potential assessment.The BBN model developed by hybrid approach provides a viable tool for geotechnical engineers to assess sites conditions susceptible to seismic soil liquefaction.This study also presents sensitivity analysis of the BBN model based on hybrid approach and the most probable explanation of liquefied sites,owing to know the most likely scenario of the liquefaction phenomenon.
基金Projects supported by the China Scholarship Council
文摘This paper is devoted to the probabilistic stability analysis of a tunnel face excavated in a two-layer soil. The interface of the soil layers is assumed to be positioned above the tunnel roof. In the framework of limit analysis, a rotational failure mechanism is adopted to describe the face failure considering different shear strength parameters in the two layers. The surrogate Kriging model is introduced to replace the actual performance function to perform a Monte Carlo simulation. An active learning function is used to train the Kriging model which can ensure an efficient tunnel face failure probability prediction without loss of accuracy. The deterministic stability analysis is given to validate the proposed tunnel face failure model. Subsequently, the number of initial sampling points, the correlation coefficient, the distribution type and the coefficient of variability of random variables are discussed to show their influences on the failure probability. The proposed approach is an advisable alternative for the tunnel face stability assessment and can provide guidance for tunnel design.
基金Projects(2007JT3018, 2008JT1013, 2009FJ4056) supported by the Key Project in Hunan Science and Technology Program, ChinaProject(20090161120014) supported by the New Teachers Sustentation Fund in Doctoral Program, Ministry of Education, China
文摘Support vector regression （SVR） method is a novel type of learning machine algorithms, which is seldom applied to the development of urban atmospheric quality models under multiple socio-economic factors. This study presents four SVR models by selecting linear, radial basis, spline, and polynomial functions as kernels, respectively for the prediction of urban dust fall levels. The inputs of the models are identified as industrial coal consumption, population density, traffic flow coefficient, and shopping density coefficient. The training and testing results show that the SVR model with radial basis kernel performs better than the other three both in the training and testing processes. In addition, a number of scenario analyses reveal that the most suitable parameters （insensitive loss function e, the parameter to reduce the influence of error C, and discrete level or average distribution of parameters σ） are 0.001, 0.5, and 2 000, respectively.
基金Project(50908099)supported by the National Natural Science Foundation of ChinaProject(201104493)supported by the Doctoral Program of Higher Education of China
文摘The taxi drivers' cruising pattern was learned using GPS trajectory data collected in Shenzhen,China.By employing zero-inflated Poisson model,the impacts of land use and previous pick-up experience on cruising decision were measured.The cruising strategies of different types of drivers as well as the top one driver were examined.The results indicate that both land use and previous pick-up experience affect travel behavior with the former's influence(7.07×10-4 measured by one of the coefficients in zero-inflated Poisson model) being greater than the latter's(4.58×10-5) in general,but the comparison also varies across the types of drivers.Besides,taxi drivers' day-to-day learning feature is also proved by the results.According to comparison of the cruising behavior of the most efficient and inefficient driver,an efficient cruising strategy was proposed,that is,obeying the distribution of land use in choice of cruising area,while learning from pick-up experience in selection of detailed cruising location.By learning taxi drivers' cruising pattern,the development of measures of regulating travel behaviors is facilitated,important factor for traffic organization and planning is identified,and an efficient cruising strategy for taxi drivers is provided.
文摘Experimentation data of perspex glass sheet cutting, using CO2 laser, with missing values were modelled with semi-supervised artificial neural networks. Factorial design of experiment was selected for the verification of orthogonal array based model prediction. It shows improvement in modelling of edge quality and kerf width by applying semi-supervised learning algorithm, based on novel error assessment on simulations. The results are expected to depict better prediction on average by utilizing the systematic randomized techniques to initialize the neural network weights and increase the number of initialization. Missing values handling is difficult with statistical tools and supervised learning techniques; on the other hand, semi-supervised learning generates better results with the smallest datasets even with missing values.
基金Project(60910005)supported by the National Natural Science Foundation of China
文摘A learning controller of nonhonolomic robot in real-time based on support vector machine(SVM)is presented.The controller includes two parts:one is kinematic controller based on nonlinear law,and the other is dynamic controller based on SVM.The kinematic controller is aimed to provide desired velocity which can make the steering system stable.The dynamic controller is aimed to transform the desired velocity to control torque.The parameters of the dynamic system of the robot are estimated through SVM learning algorithm according to the training data of sliding windows in real time.The proposed controller can adapt to the changes in the robot model and uncertainties in the environment.Compared with artificial neural network(ANN)controller,SVM controller can converge to the reference trajectory more quickly and the tracking error is smaller.The simulation results verify the effectiveness of the method proposed.
