Driven piles are used in many geological environments as a practical and convenient structural component.Hence,the determination of the drivability of piles is actually of great importance in complex geotechnical appl...Driven piles are used in many geological environments as a practical and convenient structural component.Hence,the determination of the drivability of piles is actually of great importance in complex geotechnical applications.Conventional methods of predicting pile drivability often rely on simplified physicalmodels or empirical formulas,whichmay lack accuracy or applicability in complex geological conditions.Therefore,this study presents a practical machine learning approach,namely a Random Forest(RF)optimized by Bayesian Optimization(BO)and Particle Swarm Optimization(PSO),which not only enhances prediction accuracy but also better adapts to varying geological environments to predict the drivability parameters of piles(i.e.,maximumcompressive stress,maximum tensile stress,and blow per foot).In addition,support vector regression,extreme gradient boosting,k nearest neighbor,and decision tree are also used and applied for comparison purposes.In order to train and test these models,among the 4072 datasets collected with 17model inputs,3258 datasets were randomly selected for training,and the remaining 814 datasets were used for model testing.Lastly,the results of these models were compared and evaluated using two performance indices,i.e.,the root mean square error(RMSE)and the coefficient of determination(R2).The results indicate that the optimized RF model achieved lower RMSE than other prediction models in predicting the three parameters,specifically 0.044,0.438,and 0.146;and higher R^(2) values than other implemented techniques,specifically 0.966,0.884,and 0.977.In addition,the sensitivity and uncertainty of the optimized RF model were analyzed using Sobol sensitivity analysis and Monte Carlo(MC)simulation.It can be concluded that the optimized RF model could be used to predict the performance of the pile,and it may provide a useful reference for solving some problems under similar engineering conditions.展开更多
The thinking of co evolution is applied to the optimization of retaining and protecting structure for deep foundation excavation, and the system of optimization of anchored row piles for deep foundation pit has been a...The thinking of co evolution is applied to the optimization of retaining and protecting structure for deep foundation excavation, and the system of optimization of anchored row piles for deep foundation pit has been already developed successfully. For the co evolution algorithm providing an evolutionary mechanism to simulate ever changing problem space, it is an optimization algorithm that has high performance, especially applying to the optimization of complicated system of retaining and protecting for deep foundation pit. It is shown by many engineering practices that the co evolution algorithm has obvious optimization effect, so it can be an important method of optimization of retaining and protecting for deep foundation pit. Here the authors discuss the co evolution model, object function, all kinds of constraint conditions and their disposal methods, and several key techniques of system realization.展开更多
The optimization of the inter-helix spacing is a key issue of the axial bearing capacity of helical piles.In this paper,based on the cavity expansion,an analytical approach considering the small-strain stiffness,stren...The optimization of the inter-helix spacing is a key issue of the axial bearing capacity of helical piles.In this paper,based on the cavity expansion,an analytical approach considering the small-strain stiffness,strength,compressibility and stress level of sand around the helical pile was proposed to analyze the influence zone of the helices to determine the optimal inter-helix spacing in sand.The calculation results of the proposed method were verified using the centrifuge test data and finite element analysis for helical pile in Congleton HST95 sand.They were also compared with those using the Meyerhof pile foundation theory.The results show that the optimal inter-helix spacing based on Meyerhof pile foundation theory differs significantly from the measurement.The range of the influence zone for the helices in sand calculated by the cavity expansion theory matches with the data from literature.The calculation results with the proposed method are consistent with the range of the optimal spacing ratio inferred in the centrifuge tests.The results based on the two-dimensional(2D)finite element model(FEM)are also basically consistent with the calculated analytical solution.展开更多
An optimization mathematical model of the pile forces for piled breasting dolphins in the open sea under various loading conditions is presented. The optimum layout with the well distributed pile forces and the least ...An optimization mathematical model of the pile forces for piled breasting dolphins in the open sea under various loading conditions is presented. The optimum layout with the well distributed pile forces and the least number of piles is achieved by the multiplier penalty function method. Several engineering cases have been calculated and compared with the result of the conventional design method. It is shown that the number of piles can be reduced at least by 10%～20% and the piles' bearing state is improved greatly.展开更多
为解决水深45.000 m深海风机钢管桩基础安装作业可靠性差和精度低等问题,对一种新型深海风机钢管桩基础安装用导向架进行结构优化。采用有限元法(Finite Element Method, FEM)与试验相结合的方法,从环境参数与作用载荷、结构形式、作业...为解决水深45.000 m深海风机钢管桩基础安装作业可靠性差和精度低等问题,对一种新型深海风机钢管桩基础安装用导向架进行结构优化。采用有限元法(Finite Element Method, FEM)与试验相结合的方法,从环境参数与作用载荷、结构形式、作业工况和结构强度与结构稳定性等方面对导向架进行综合研究。经海试验证,优化的导向架的打桩精度与打桩高效性均满足技术指标要求,可大幅提高深海风机钢管桩基础安装作业速度和质量。展开更多
基金supported by the National Science Foundation of China(42107183).
文摘Driven piles are used in many geological environments as a practical and convenient structural component.Hence,the determination of the drivability of piles is actually of great importance in complex geotechnical applications.Conventional methods of predicting pile drivability often rely on simplified physicalmodels or empirical formulas,whichmay lack accuracy or applicability in complex geological conditions.Therefore,this study presents a practical machine learning approach,namely a Random Forest(RF)optimized by Bayesian Optimization(BO)and Particle Swarm Optimization(PSO),which not only enhances prediction accuracy but also better adapts to varying geological environments to predict the drivability parameters of piles(i.e.,maximumcompressive stress,maximum tensile stress,and blow per foot).In addition,support vector regression,extreme gradient boosting,k nearest neighbor,and decision tree are also used and applied for comparison purposes.In order to train and test these models,among the 4072 datasets collected with 17model inputs,3258 datasets were randomly selected for training,and the remaining 814 datasets were used for model testing.Lastly,the results of these models were compared and evaluated using two performance indices,i.e.,the root mean square error(RMSE)and the coefficient of determination(R2).The results indicate that the optimized RF model achieved lower RMSE than other prediction models in predicting the three parameters,specifically 0.044,0.438,and 0.146;and higher R^(2) values than other implemented techniques,specifically 0.966,0.884,and 0.977.In addition,the sensitivity and uncertainty of the optimized RF model were analyzed using Sobol sensitivity analysis and Monte Carlo(MC)simulation.It can be concluded that the optimized RF model could be used to predict the performance of the pile,and it may provide a useful reference for solving some problems under similar engineering conditions.
基金National Natural Science Foundation of China( 5 986 80 0 1)
文摘The thinking of co evolution is applied to the optimization of retaining and protecting structure for deep foundation excavation, and the system of optimization of anchored row piles for deep foundation pit has been already developed successfully. For the co evolution algorithm providing an evolutionary mechanism to simulate ever changing problem space, it is an optimization algorithm that has high performance, especially applying to the optimization of complicated system of retaining and protecting for deep foundation pit. It is shown by many engineering practices that the co evolution algorithm has obvious optimization effect, so it can be an important method of optimization of retaining and protecting for deep foundation pit. Here the authors discuss the co evolution model, object function, all kinds of constraint conditions and their disposal methods, and several key techniques of system realization.
基金Financial support from the National Natural Science Foundation of China (Grant Nos. 52078427, 51978588 and 41901073)
文摘The optimization of the inter-helix spacing is a key issue of the axial bearing capacity of helical piles.In this paper,based on the cavity expansion,an analytical approach considering the small-strain stiffness,strength,compressibility and stress level of sand around the helical pile was proposed to analyze the influence zone of the helices to determine the optimal inter-helix spacing in sand.The calculation results of the proposed method were verified using the centrifuge test data and finite element analysis for helical pile in Congleton HST95 sand.They were also compared with those using the Meyerhof pile foundation theory.The results show that the optimal inter-helix spacing based on Meyerhof pile foundation theory differs significantly from the measurement.The range of the influence zone for the helices in sand calculated by the cavity expansion theory matches with the data from literature.The calculation results with the proposed method are consistent with the range of the optimal spacing ratio inferred in the centrifuge tests.The results based on the two-dimensional(2D)finite element model(FEM)are also basically consistent with the calculated analytical solution.
基金TheworkwassupportedbytheNationalFoundationofHighPerformanceComputation (No .9810 0 5 )
文摘An optimization mathematical model of the pile forces for piled breasting dolphins in the open sea under various loading conditions is presented. The optimum layout with the well distributed pile forces and the least number of piles is achieved by the multiplier penalty function method. Several engineering cases have been calculated and compared with the result of the conventional design method. It is shown that the number of piles can be reduced at least by 10%～20% and the piles' bearing state is improved greatly.
文摘为解决水深45.000 m深海风机钢管桩基础安装作业可靠性差和精度低等问题,对一种新型深海风机钢管桩基础安装用导向架进行结构优化。采用有限元法(Finite Element Method, FEM)与试验相结合的方法,从环境参数与作用载荷、结构形式、作业工况和结构强度与结构稳定性等方面对导向架进行综合研究。经海试验证,优化的导向架的打桩精度与打桩高效性均满足技术指标要求,可大幅提高深海风机钢管桩基础安装作业速度和质量。
