During the construction of cast-in-place piles in warm permafrost,the heat carried by concrete and the cement hydration reaction can cause strong thermal disturbance to the surrounding permafrost.Since the bearing cap...During the construction of cast-in-place piles in warm permafrost,the heat carried by concrete and the cement hydration reaction can cause strong thermal disturbance to the surrounding permafrost.Since the bearing capacity of the pile is quite small before the full freeze-back,the quick refreezing of the native soils surrounding the cast-in-place pile has become the focus of the infrastructure construction in permafrost.To solve this problem,this paper innovatively puts forward the application of the artificial ground freezing(AGF)method at the end of the curing period of cast-in-place piles in permafrost.A field test on the AGF was conducted at the Beiluhe Observation and Research Station of Frozen Soil Engineering and Environment(34°51.2'N,92°56.4'E)in the Qinghai Tibet Plateau(QTP),and then a 3-D numerical model was established to investigate the thermal performance of piles using AGF under different engineering conditions.Additionally,the long-term thermal performance of piles after the completion of AGF under different conditions was estimated.Field experiment results demonstrate that AGF is an effective method to reduce the refreezing time of the soil surrounding the piles constructed in permafrost terrain,with the ability to reduce the pile-soil interface temperatures to below the natural ground temperature within 3 days.Numerical results further prove that AGF still has a good cooling effect even under unfavorable engineering conditions such as high pouring temperature,large pile diameter,and large pile length.Consequently,the application of this method is meaningful to save the subsequent latency time and solve the problem of thermal disturbance in pile construction in permafrost.The research results are highly relevant for the spread of AGF technology and the rapid building of pile foundations in permafrost.展开更多
Geotechnical engineering deals with materials（e.g. soil and rock） that, by their very nature, exhibit varied and uncertain behavior due to the imprecise physical processes associated with the formation of these mate...Geotechnical engineering deals with materials（e.g. soil and rock） that, by their very nature, exhibit varied and uncertain behavior due to the imprecise physical processes associated with the formation of these materials. Modeling the behavior of such materials in geotechnical engineering applications is complex and sometimes beyond the ability of most traditional forms of physically-based engineering methods. Artificial intelligence（AI） is becoming more popular and particularly amenable to modeling the complex behavior of most geotechnical engineering applications because it has demonstrated superior predictive ability compared to traditional methods. This paper provides state-of-the-art review of some selected AI techniques and their applications in pile foundations, and presents the salient features associated with the modeling development of these AI techniques. The paper also discusses the strength and limitations of the selected AI techniques compared to other available modeling approaches.展开更多
The Hong Kong–Zhuhai–Macao Bridge(HZMB)involved the installation of 120 mega-cylinders with a diameter of 22 m,weights up to 513 t,and penetration depths up to 33 m using an eight-vibratory hammer group.Due to the l...The Hong Kong–Zhuhai–Macao Bridge(HZMB)involved the installation of 120 mega-cylinders with a diameter of 22 m,weights up to 513 t,and penetration depths up to 33 m using an eight-vibratory hammer group.Due to the lack of engineering experience on the drivability of large-diameter cylinders under multiple vibratory hammers,predicting the penetration rate and time of steel cylinders is an open challenge that has a considerable impact on the construction control of the HZMB.In this study,the vibratory penetration of large-diameter steel cylinders in the HZMB is investigated based on geological surveys,field monitoring,and drivability analysis.The vibratory penetration rate,installation accuracy,and dynamic responses of the steel cylinders at both the eastern and western artificial islands are analyzed.The dynamic soil resistance has a great influence on the cylinder drivability.However,the current design methods for estimating the vibratory driving soil resistance are proven inaccurate without considering the scale effects.Therefore,a modified method with a normalized effective area ratio A_(r,eff)is proposed in this study to calculate the vibratory soil resistance for open-ended thin-wall cylinders under unplugged conditions.Considering the scale effects on the vibratory driving soil resistance,the proposed method leads to closer results to the measured data,providing a reference for future engineering practice.展开更多
In the recent era,piled raft foundation(PRF)has been considered an emergent technology for offshore and onshore structures.In previous studies,there is a lack of illustration regarding the load sharing and interaction...In the recent era,piled raft foundation(PRF)has been considered an emergent technology for offshore and onshore structures.In previous studies,there is a lack of illustration regarding the load sharing and interaction behavior which are considered the main intents in the present study.Finite element(FE)models are prepared with various design variables in a double-layer soil system,and the load sharing and interaction factors of piled rafts are estimated.The obtained results are then checked statistically with nonlinear multiple regression(NMR)and artificial neural network(ANN)modeling,and some prediction models are proposed.ANN models are prepared with Levenberg-Marquardt(LM)algorithm for load sharing and interaction factors through backpropagation technique.The factor of safety(FS)of PRF is also estimated using the proposed NMR and ANN models,which can be used for developing the design strategy of PRF.展开更多
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
基金supported by the National Natural Science Foundation of China(Grant No.42071095)the Program of the State Key Laboratory of Frozen Soil Engineering(Grant No.SKLFSE-ZQ-59)+1 种基金the Science and Technology Project of Gansu Province(Grant No.22JR5RA086)the Science and Technology Research and Development Program of the Qinghai-Tibet Group Corporation(Grant No.QZ2022-G02).
文摘During the construction of cast-in-place piles in warm permafrost,the heat carried by concrete and the cement hydration reaction can cause strong thermal disturbance to the surrounding permafrost.Since the bearing capacity of the pile is quite small before the full freeze-back,the quick refreezing of the native soils surrounding the cast-in-place pile has become the focus of the infrastructure construction in permafrost.To solve this problem,this paper innovatively puts forward the application of the artificial ground freezing(AGF)method at the end of the curing period of cast-in-place piles in permafrost.A field test on the AGF was conducted at the Beiluhe Observation and Research Station of Frozen Soil Engineering and Environment(34°51.2'N,92°56.4'E)in the Qinghai Tibet Plateau(QTP),and then a 3-D numerical model was established to investigate the thermal performance of piles using AGF under different engineering conditions.Additionally,the long-term thermal performance of piles after the completion of AGF under different conditions was estimated.Field experiment results demonstrate that AGF is an effective method to reduce the refreezing time of the soil surrounding the piles constructed in permafrost terrain,with the ability to reduce the pile-soil interface temperatures to below the natural ground temperature within 3 days.Numerical results further prove that AGF still has a good cooling effect even under unfavorable engineering conditions such as high pouring temperature,large pile diameter,and large pile length.Consequently,the application of this method is meaningful to save the subsequent latency time and solve the problem of thermal disturbance in pile construction in permafrost.The research results are highly relevant for the spread of AGF technology and the rapid building of pile foundations in permafrost.
文摘Geotechnical engineering deals with materials（e.g. soil and rock） that, by their very nature, exhibit varied and uncertain behavior due to the imprecise physical processes associated with the formation of these materials. Modeling the behavior of such materials in geotechnical engineering applications is complex and sometimes beyond the ability of most traditional forms of physically-based engineering methods. Artificial intelligence（AI） is becoming more popular and particularly amenable to modeling the complex behavior of most geotechnical engineering applications because it has demonstrated superior predictive ability compared to traditional methods. This paper provides state-of-the-art review of some selected AI techniques and their applications in pile foundations, and presents the salient features associated with the modeling development of these AI techniques. The paper also discusses the strength and limitations of the selected AI techniques compared to other available modeling approaches.
基金supported by the National Natural Science Foundation of China(52001267)Tianjin Port Engineering Institute Co.,Ltd.,and Eunsung O&C Offshore Marine and Construction(EUNSUNG19EG01).
文摘The Hong Kong–Zhuhai–Macao Bridge(HZMB)involved the installation of 120 mega-cylinders with a diameter of 22 m,weights up to 513 t,and penetration depths up to 33 m using an eight-vibratory hammer group.Due to the lack of engineering experience on the drivability of large-diameter cylinders under multiple vibratory hammers,predicting the penetration rate and time of steel cylinders is an open challenge that has a considerable impact on the construction control of the HZMB.In this study,the vibratory penetration of large-diameter steel cylinders in the HZMB is investigated based on geological surveys,field monitoring,and drivability analysis.The vibratory penetration rate,installation accuracy,and dynamic responses of the steel cylinders at both the eastern and western artificial islands are analyzed.The dynamic soil resistance has a great influence on the cylinder drivability.However,the current design methods for estimating the vibratory driving soil resistance are proven inaccurate without considering the scale effects.Therefore,a modified method with a normalized effective area ratio A_(r,eff)is proposed in this study to calculate the vibratory soil resistance for open-ended thin-wall cylinders under unplugged conditions.Considering the scale effects on the vibratory driving soil resistance,the proposed method leads to closer results to the measured data,providing a reference for future engineering practice.
文摘In the recent era,piled raft foundation(PRF)has been considered an emergent technology for offshore and onshore structures.In previous studies,there is a lack of illustration regarding the load sharing and interaction behavior which are considered the main intents in the present study.Finite element(FE)models are prepared with various design variables in a double-layer soil system,and the load sharing and interaction factors of piled rafts are estimated.The obtained results are then checked statistically with nonlinear multiple regression(NMR)and artificial neural network(ANN)modeling,and some prediction models are proposed.ANN models are prepared with Levenberg-Marquardt(LM)algorithm for load sharing and interaction factors through backpropagation technique.The factor of safety(FS)of PRF is also estimated using the proposed NMR and ANN models,which can be used for developing the design strategy of PRF.
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.
