The unique structure and complex deformation characteristics of concrete face rockfill dams(CFRDs)create safety monitoring challenges.This study developed an improved random forest(IRF)model for dam health monitoring ...The unique structure and complex deformation characteristics of concrete face rockfill dams(CFRDs)create safety monitoring challenges.This study developed an improved random forest(IRF)model for dam health monitoring modeling by replacing the decision tree in the random forest(RF)model with a novel M5'model tree algorithm.The factors affecting dam deformation were preliminarily selected using the statistical model,and the grey relational degree theory was utilized to reduce the dimensions of model input variables.Finally,a deformation prediction model of CFRDs was established using the IRF model.The ten-fold cross-validation method was used to quantitatively analyze the parameters affecting the IRF algorithm.The performance of the established model was verified using data from three specific measurement points on the Jishixia dam and compared with other dam deformation prediction models.At point ES-10,the performance evaluation indices of the IRF model were superior to those of the M5'model tree and RF models and the classical support vector regression(SVR)and back propagation(BP)neural network models,indicating the satisfactory performance of the IRF model.The IRF model also outperformed the SVR and BP models in settlement prediction at points ES2-8 and ES4-10,demonstrating its strong anti-interference and generalization capabilities.This study has developed a novel method for forecasting and analyzing dam settlements with practical significance.Moreover,the established IRF model can also provide guidance for modeling health monitoring of other structures.展开更多
To ensure the safety of buildings surrounding foundation pits, a study was made on a settlement monitoring and trend prediction method. A statistical testing method for analyzing the stability of a settlement monitori...To ensure the safety of buildings surrounding foundation pits, a study was made on a settlement monitoring and trend prediction method. A statistical testing method for analyzing the stability of a settlement monitoring datum has been discussed. According to a comprehensive survey, data of 16 stages at operating control point, were verified by a standard t test to determine the stability of the operating control point. A stationary auto-regression model, AR(p), used for the observation point settlement prediction has been investigated. Given the 16 stages of the settlement data at an observation point, the applicability of this model was analyzed. Settlement of last four stages was predicted using the stationary auto-regression model AR (1); the maximum difference between predicted and measured values was 0.6 mm, indicating good prediction results of the model. Hence, this model can be applied to settlement predictions for buildings surrounding foundation pits.展开更多
As urbanization accelerates,the metro has become an important means of transportation.Considering the safety problems caused by metro construction,ground settlement needs to be monitored and predicted regularly,especi...As urbanization accelerates,the metro has become an important means of transportation.Considering the safety problems caused by metro construction,ground settlement needs to be monitored and predicted regularly,especially when a new metro line crosses an existing one.In this paper,we propose a settlement-probability prediction model with a Bayesian emulator(BE)based on the Gaussian prior(GP),that is,a GPBE.In addition,considering the distortion characteristics of monitoring data,the data is denoised using wavelet decomposition(WD),so the final prediction model is WD-GPBE.In particular,the effects of different prediction ratios and moving windows on prediction performance are explored,and the optimal number of moving windows is determined.In addition,the predicted value for GPBE based on the original data is compared with the predicted value for WD-GPBE based on the denoised data.One year of settlement-monitoring data collected by a structural health monitoring(SHM)system installed on the Nanjing Metro is used to demonstrate the effectiveness of WDGPBE and GPBE for predicting settlement.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.51979224)the China National Funds for Distinguished Young Scientists(Grant No.52125904).
文摘The unique structure and complex deformation characteristics of concrete face rockfill dams(CFRDs)create safety monitoring challenges.This study developed an improved random forest(IRF)model for dam health monitoring modeling by replacing the decision tree in the random forest(RF)model with a novel M5'model tree algorithm.The factors affecting dam deformation were preliminarily selected using the statistical model,and the grey relational degree theory was utilized to reduce the dimensions of model input variables.Finally,a deformation prediction model of CFRDs was established using the IRF model.The ten-fold cross-validation method was used to quantitatively analyze the parameters affecting the IRF algorithm.The performance of the established model was verified using data from three specific measurement points on the Jishixia dam and compared with other dam deformation prediction models.At point ES-10,the performance evaluation indices of the IRF model were superior to those of the M5'model tree and RF models and the classical support vector regression(SVR)and back propagation(BP)neural network models,indicating the satisfactory performance of the IRF model.The IRF model also outperformed the SVR and BP models in settlement prediction at points ES2-8 and ES4-10,demonstrating its strong anti-interference and generalization capabilities.This study has developed a novel method for forecasting and analyzing dam settlements with practical significance.Moreover,the established IRF model can also provide guidance for modeling health monitoring of other structures.
基金Project 50279005 supported by the National Natural Science Foundation of China
文摘To ensure the safety of buildings surrounding foundation pits, a study was made on a settlement monitoring and trend prediction method. A statistical testing method for analyzing the stability of a settlement monitoring datum has been discussed. According to a comprehensive survey, data of 16 stages at operating control point, were verified by a standard t test to determine the stability of the operating control point. A stationary auto-regression model, AR(p), used for the observation point settlement prediction has been investigated. Given the 16 stages of the settlement data at an observation point, the applicability of this model was analyzed. Settlement of last four stages was predicted using the stationary auto-regression model AR (1); the maximum difference between predicted and measured values was 0.6 mm, indicating good prediction results of the model. Hence, this model can be applied to settlement predictions for buildings surrounding foundation pits.
基金the Humanities and Social Sciences Research Project of Ministry of Education of China(No.23YJCZH037)the Educational Science Planning Project of Zhejiang Province(No.2023SCG222)+3 种基金the Foundation of the State Key Laboratory of Mountain Bridge and Tunnel Engi‐neering of China(No.SKLBT-2210)the National Key R&D Program of China(No.2022YFC3802301)the National Natural Science Foundation of China(No.52178306)the Scientific Research Project of Zhejiang Provincial Department of Educa-tion(No.Y202248682),China.
文摘As urbanization accelerates,the metro has become an important means of transportation.Considering the safety problems caused by metro construction,ground settlement needs to be monitored and predicted regularly,especially when a new metro line crosses an existing one.In this paper,we propose a settlement-probability prediction model with a Bayesian emulator(BE)based on the Gaussian prior(GP),that is,a GPBE.In addition,considering the distortion characteristics of monitoring data,the data is denoised using wavelet decomposition(WD),so the final prediction model is WD-GPBE.In particular,the effects of different prediction ratios and moving windows on prediction performance are explored,and the optimal number of moving windows is determined.In addition,the predicted value for GPBE based on the original data is compared with the predicted value for WD-GPBE based on the denoised data.One year of settlement-monitoring data collected by a structural health monitoring(SHM)system installed on the Nanjing Metro is used to demonstrate the effectiveness of WDGPBE and GPBE for predicting settlement.
