This study focuses on the consolidation behavior and mathematical interpretation of partially-saturated ground improved by impervious column inclusion.The constitutive relations for soil skeleton,pore air and pore wat...This study focuses on the consolidation behavior and mathematical interpretation of partially-saturated ground improved by impervious column inclusion.The constitutive relations for soil skeleton,pore air and pore water for partially saturated soils are proposed in the context of partially-saturated ground improved by impervious column inclusion.Settlement equation and dissipation equations of excess pore air/water pressures for a partially saturated improved ground are then derived.The semi-analytical solutions for ground settlement and pore pressure dissipation are then obtained through the Laplace transform and validated by the existing solutions for two special cases in the literature and the numerical results obtained from the finite difference method.A series of parametric studies is finally conducted to investigate the influence of some key factors on consolidation of partially saturated ground improved by impervious column inclusion.Based on the parametric study,it can be found that a higher value of the area replacement ratio or modulus of the pile results in a longer dissipation time of excess pore air pressure(PAP),a shorter dissipation time of excess pore water pressure(PWP),and a lower normalized settlement.展开更多
As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance le...As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers.On the other hand,a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule.The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications.The previously-proposed intelligent techniques in this field are mostly based on a single or base model with a low level of accuracy.Hence,this study aims to introduce a hybrid randomforest(RF)technique optimized by global harmony search with generalized oppositionbased learning(GOGHS)for forecasting TBM advance rate(AR).Optimizing the RF hyper-parameters in terms of,e.g.,tree number and maximum tree depth is the main objective of using the GOGHS-RF model.In the modelling of this study,a comprehensive databasewith themost influential parameters onTBMtogetherwithTBM AR were used as input and output variables,respectively.To examine the capability and power of the GOGHSRF model,three more hybrid models of particle swarm optimization-RF,genetic algorithm-RF and artificial bee colony-RF were also constructed to forecast TBM AR.Evaluation of the developed models was performed by calculating several performance indices,including determination coefficient(R2),root-mean-square-error(RMSE),and mean-absolute-percentage-error(MAPE).The results showed that theGOGHS-RF is a more accurate technique for estimatingTBMAR compared to the other applied models.The newly-developedGOGHS-RFmodel enjoyed R2=0.9937 and 0.9844,respectively,for train and test stages,which are higher than a pre-developed RF.Also,the importance of the input parameters was interpreted through the SHapley Additive exPlanations(SHAP)method,and it was found that thrust force per cutter is the most important variable on TBMAR.The GOGHS-RF model can be used in mechanized tunnel projects for predicting and checking performance.展开更多
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 sche...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.展开更多
Road pavement surfaces need routine and regular monitoring and inspection to keep the surface layers in high-quality condition.However,the population growth and the increases in the number of vehicles and the length o...Road pavement surfaces need routine and regular monitoring and inspection to keep the surface layers in high-quality condition.However,the population growth and the increases in the number of vehicles and the length of road networks worldwide have required researchers to identify appropriate and accurate road pavement monitoring techniques.The vibration-based technique is one of the effective techniques used to measure the condition of pavement degradation and the level of pavement roughness.The consistency of pavement vibration data is directly proportional to the intensity of surface roughness.Intense fluctuations in vibration signals indicate possible defects at certain points of road pavement.However,vibration signals typically need a series of pre-processing techniques such as filtering,smoothing,segmentation,and labelling before being used in advanced processing and analyses.This research reports the use of noise-cancelling and datasmoothing techniques,including high pass filter,moving average method,median,Savitzky-Golay filter,and extracting peak envelope method,to enhance raw vibration signals for further processing and classification.The results show significant variations in the impact of noise-cancelling and data-smoothing techniques on raw pavement vibration signals.According to the results,the high pass filter is a more accurate noise-cancelling and data smoothing technique on road pavement vibration data compared to other data filtering and data smoothing methods.展开更多
Pavement monitoring plays a vital role in maintaining sustainable road network conditions and provides road users with satisfactory comfort riding.Regular and routine monitoring provide clear information on road condi...Pavement monitoring plays a vital role in maintaining sustainable road network conditions and provides road users with satisfactory comfort riding.Regular and routine monitoring provide clear information on road conditions and the level of damage to pavement surfaces.In this study,a vibration-based method was used as a monitoring technique to evaluate the pavement surface conditions on local roads.Pre-processing techniques were used to cancel noise and prepare the data for feature extraction and prediction.Two multiclassification Machine Learning(ML)models were used,including Random Forest(RF)and Decision Tree(DT),for the automated classification and detection of different types of pavement distresses.In addition,a Support Vector Machine(SVM)technique was used to develop a binary ML model for the same classification and detection purposes.The results showed that the developed ML models provide high accuracy in predicting the road degradation classification with about 93%accuracy using the RF and 90%accuracy using the DT.Using the SVM model,the overall average accuracy of detection and classification of pavement defects was about 96%.展开更多
基金The financial support from National Natural Science Foundation of China (Grant Nos. 12172211 and 52078021)Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, China (Grant No. R201904)
文摘This study focuses on the consolidation behavior and mathematical interpretation of partially-saturated ground improved by impervious column inclusion.The constitutive relations for soil skeleton,pore air and pore water for partially saturated soils are proposed in the context of partially-saturated ground improved by impervious column inclusion.Settlement equation and dissipation equations of excess pore air/water pressures for a partially saturated improved ground are then derived.The semi-analytical solutions for ground settlement and pore pressure dissipation are then obtained through the Laplace transform and validated by the existing solutions for two special cases in the literature and the numerical results obtained from the finite difference method.A series of parametric studies is finally conducted to investigate the influence of some key factors on consolidation of partially saturated ground improved by impervious column inclusion.Based on the parametric study,it can be found that a higher value of the area replacement ratio or modulus of the pile results in a longer dissipation time of excess pore air pressure(PAP),a shorter dissipation time of excess pore water pressure(PWP),and a lower normalized settlement.
基金the National Natural Science Foundation of China(Grant 42177164)the Distinguished Youth Science Foundation of Hunan Province of China(2022JJ10073).
文摘As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers.On the other hand,a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule.The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications.The previously-proposed intelligent techniques in this field are mostly based on a single or base model with a low level of accuracy.Hence,this study aims to introduce a hybrid randomforest(RF)technique optimized by global harmony search with generalized oppositionbased learning(GOGHS)for forecasting TBM advance rate(AR).Optimizing the RF hyper-parameters in terms of,e.g.,tree number and maximum tree depth is the main objective of using the GOGHS-RF model.In the modelling of this study,a comprehensive databasewith themost influential parameters onTBMtogetherwithTBM AR were used as input and output variables,respectively.To examine the capability and power of the GOGHSRF model,three more hybrid models of particle swarm optimization-RF,genetic algorithm-RF and artificial bee colony-RF were also constructed to forecast TBM AR.Evaluation of the developed models was performed by calculating several performance indices,including determination coefficient(R2),root-mean-square-error(RMSE),and mean-absolute-percentage-error(MAPE).The results showed that theGOGHS-RF is a more accurate technique for estimatingTBMAR compared to the other applied models.The newly-developedGOGHS-RFmodel enjoyed R2=0.9937 and 0.9844,respectively,for train and test stages,which are higher than a pre-developed RF.Also,the importance of the input parameters was interpreted through the SHapley Additive exPlanations(SHAP)method,and it was found that thrust force per cutter is the most important variable on TBMAR.The GOGHS-RF model can be used in mechanized tunnel projects for predicting and checking performance.
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.
文摘Road pavement surfaces need routine and regular monitoring and inspection to keep the surface layers in high-quality condition.However,the population growth and the increases in the number of vehicles and the length of road networks worldwide have required researchers to identify appropriate and accurate road pavement monitoring techniques.The vibration-based technique is one of the effective techniques used to measure the condition of pavement degradation and the level of pavement roughness.The consistency of pavement vibration data is directly proportional to the intensity of surface roughness.Intense fluctuations in vibration signals indicate possible defects at certain points of road pavement.However,vibration signals typically need a series of pre-processing techniques such as filtering,smoothing,segmentation,and labelling before being used in advanced processing and analyses.This research reports the use of noise-cancelling and datasmoothing techniques,including high pass filter,moving average method,median,Savitzky-Golay filter,and extracting peak envelope method,to enhance raw vibration signals for further processing and classification.The results show significant variations in the impact of noise-cancelling and data-smoothing techniques on raw pavement vibration signals.According to the results,the high pass filter is a more accurate noise-cancelling and data smoothing technique on road pavement vibration data compared to other data filtering and data smoothing methods.
文摘Pavement monitoring plays a vital role in maintaining sustainable road network conditions and provides road users with satisfactory comfort riding.Regular and routine monitoring provide clear information on road conditions and the level of damage to pavement surfaces.In this study,a vibration-based method was used as a monitoring technique to evaluate the pavement surface conditions on local roads.Pre-processing techniques were used to cancel noise and prepare the data for feature extraction and prediction.Two multiclassification Machine Learning(ML)models were used,including Random Forest(RF)and Decision Tree(DT),for the automated classification and detection of different types of pavement distresses.In addition,a Support Vector Machine(SVM)technique was used to develop a binary ML model for the same classification and detection purposes.The results showed that the developed ML models provide high accuracy in predicting the road degradation classification with about 93%accuracy using the RF and 90%accuracy using the DT.Using the SVM model,the overall average accuracy of detection and classification of pavement defects was about 96%.
