One of the common excavation methods in the construction of urban infrastructures as well as water and wastewater facilities is the excavation through soldier pile walls.The maximum lateral displacement of pile wall i...One of the common excavation methods in the construction of urban infrastructures as well as water and wastewater facilities is the excavation through soldier pile walls.The maximum lateral displacement of pile wall is one of the important variables in controlling the stability of the excavation and its adjacent structures.Nowadays,the application of machine learning methods is widely used in engineering sciences due to its low cost and high speed of calculation.This paper utilized three intelligent machine learning algorithms based on the excavation method through soldier pile walls,namely eXtreme gradient boosting(XGBoost),least square support vector regressor(LS-SVR),and random forest(RF),to predict maximum lateral displacement of pile walls.The results showed that the implemented XGBoost model performed excellently and could make predictions for maximum lateral displacement of pile walls with the mean absolute error of 0.1669,the highest coefficient of determination 0.9991,and the lowest root mean square error 0.3544.Although the LS-SVR,and RF models were less accurate than the XGBoost model,they provided good prediction results of maximum lateral displacement of pile walls for numerical outcomes.Furthermore,a sensitivity analysis was performed to determine the most effective parameters in the XGBoost model.This analysis showed that soil elastic modulus and excavation height had a strong influence on of maximum lateral displacement of pile wall prediction.展开更多
This paper introduces a new prefabricated recyclable double-row piles retaining system for excavations in silty clay ground.Laboratory model test and numerical simulation are conducted to study the system behavior upo...This paper introduces a new prefabricated recyclable double-row piles retaining system for excavations in silty clay ground.Laboratory model test and numerical simulation are conducted to study the system behavior upon excavation.The horizontal displacement(δ_(h)),Von Mises stress(δ_(M)),strain(ε),ground surface settlement(δ_(v)),and earth pressure are systematically investigated.Furthermore,the monitoring data of 13 excavation cases supported by double-row piles retaining system are presented and discussed.The experimental results can basically match the numerical results,and the maximumδ_(M),maximum bending moment(M_(max)),maximum horizontal displacement(δ_(hm))of structural members are all less than the tolerance limits.The ground surface settlement model of double-row piles retaining system consists of three zones,i.e.,rebound influence zone,primary influence zone and secondary influence zone.The dhm values are 0.07%–1.42%of the excavation depth(He).The maximum ground surface settlement(δ_(vm))is generally less than dhm.The ratio ofδ_(vm)=δ_(hm)varies between 0.09 and 0.76,with an average value of 0.5.The observed earth pressure on the retained side of front pile(paf)is about 0.53–0.57γH below the excavation surface.Above the excavation surface,p_(af)decreases dramatically when getting closer to the ground surface.展开更多
文摘One of the common excavation methods in the construction of urban infrastructures as well as water and wastewater facilities is the excavation through soldier pile walls.The maximum lateral displacement of pile wall is one of the important variables in controlling the stability of the excavation and its adjacent structures.Nowadays,the application of machine learning methods is widely used in engineering sciences due to its low cost and high speed of calculation.This paper utilized three intelligent machine learning algorithms based on the excavation method through soldier pile walls,namely eXtreme gradient boosting(XGBoost),least square support vector regressor(LS-SVR),and random forest(RF),to predict maximum lateral displacement of pile walls.The results showed that the implemented XGBoost model performed excellently and could make predictions for maximum lateral displacement of pile walls with the mean absolute error of 0.1669,the highest coefficient of determination 0.9991,and the lowest root mean square error 0.3544.Although the LS-SVR,and RF models were less accurate than the XGBoost model,they provided good prediction results of maximum lateral displacement of pile walls for numerical outcomes.Furthermore,a sensitivity analysis was performed to determine the most effective parameters in the XGBoost model.This analysis showed that soil elastic modulus and excavation height had a strong influence on of maximum lateral displacement of pile wall prediction.
基金supported by the National Key Research and Development Program of China(Grant No.2021YFC3100803)the National Natural Science Founda tion of China(Grant Nos.52208380 and 52078506)+2 种基金the Guangdong Basic and Applied Basic ResearchFoundation,China(Grant No.2023A1515012159)Open Research Fund of State Key Laboratory of Geomechanics and Geotechnical Engineering,Institute of Rock and Soil Mechanics,Chinese Academy of Sciences(Grant No.SKLGME021022)the Science and Technology Program of Guangzhou Municipal Construction Group Co.,Ltd.,China(Grant No.2022-KJ004).
文摘This paper introduces a new prefabricated recyclable double-row piles retaining system for excavations in silty clay ground.Laboratory model test and numerical simulation are conducted to study the system behavior upon excavation.The horizontal displacement(δ_(h)),Von Mises stress(δ_(M)),strain(ε),ground surface settlement(δ_(v)),and earth pressure are systematically investigated.Furthermore,the monitoring data of 13 excavation cases supported by double-row piles retaining system are presented and discussed.The experimental results can basically match the numerical results,and the maximumδ_(M),maximum bending moment(M_(max)),maximum horizontal displacement(δ_(hm))of structural members are all less than the tolerance limits.The ground surface settlement model of double-row piles retaining system consists of three zones,i.e.,rebound influence zone,primary influence zone and secondary influence zone.The dhm values are 0.07%–1.42%of the excavation depth(He).The maximum ground surface settlement(δ_(vm))is generally less than dhm.The ratio ofδ_(vm)=δ_(hm)varies between 0.09 and 0.76,with an average value of 0.5.The observed earth pressure on the retained side of front pile(paf)is about 0.53–0.57γH below the excavation surface.Above the excavation surface,p_(af)decreases dramatically when getting closer to the ground surface.
