Small ground anchors are widely used to fix securing tents in disaster relief efforts.Given the urgent nature of rescue operations,it is crucial to obtain prompt and accurate estimations of their pullout capacity.In t...Small ground anchors are widely used to fix securing tents in disaster relief efforts.Given the urgent nature of rescue operations,it is crucial to obtain prompt and accurate estimations of their pullout capacity.In this study,a stacking machine learning(ML)model is developed for the rapid estimation of pullout capacity offered by small ground anchors used for temporary tents,leveraging cone penetration data.The proposed stacking model incorporates three ML algorithms as the base regression models:K-nearest neighbors(KNN),support vector regression(SVR),and extreme gradient boosting(XGBoost).A dataset comprising 119 in-situ anchor pullout tests,where the cone penetration data were measured,is utilized to train and assess the stacking model performance.Three metrics,i.e.,coefficient of determination(R2),mean absolute error(MAE),and root mean square error(RMSE),are employed to evaluate the predictive accuracy of the proposed model and compare its performance against four popular ML models and an empirical formula to highlight the advantages of the proposed stacking approach.The results affirm that the proposed stacking model outperforms other ML models and the empirical approach as achieving higher R2 and lower MAE and RMSE and more predicted data points falling within 20%error line.Thus,the proposed stacking model holds promising potential as a solution for efficiently predicting the pullout capacity of small ground anchors.展开更多
基金support provided by National Natural Science Foundation of China(Grant No.52108297)Postdoctoral Research Foundation of China(Grant No.2021M692742)+2 种基金Special Support Project of China Postdoctoral Foundation(Grant No.2023T160560)Qin Chuang Yuan Imported High-level Innovation and Entrepreneurship Talent Project(Grant No.OCYRCXM-2022-29)Fundamental Research Funds for the Central Universities,CHD(Grant No.300102212301).
文摘Small ground anchors are widely used to fix securing tents in disaster relief efforts.Given the urgent nature of rescue operations,it is crucial to obtain prompt and accurate estimations of their pullout capacity.In this study,a stacking machine learning(ML)model is developed for the rapid estimation of pullout capacity offered by small ground anchors used for temporary tents,leveraging cone penetration data.The proposed stacking model incorporates three ML algorithms as the base regression models:K-nearest neighbors(KNN),support vector regression(SVR),and extreme gradient boosting(XGBoost).A dataset comprising 119 in-situ anchor pullout tests,where the cone penetration data were measured,is utilized to train and assess the stacking model performance.Three metrics,i.e.,coefficient of determination(R2),mean absolute error(MAE),and root mean square error(RMSE),are employed to evaluate the predictive accuracy of the proposed model and compare its performance against four popular ML models and an empirical formula to highlight the advantages of the proposed stacking approach.The results affirm that the proposed stacking model outperforms other ML models and the empirical approach as achieving higher R2 and lower MAE and RMSE and more predicted data points falling within 20%error line.Thus,the proposed stacking model holds promising potential as a solution for efficiently predicting the pullout capacity of small ground anchors.
