Evaluating the performance of existing concrete structures is essential in civil engineering,with compressive strength serving as an indicator of performance.Non-destructive testing(NDT)techniques are commonly employe...Evaluating the performance of existing concrete structures is essential in civil engineering,with compressive strength serving as an indicator of performance.Non-destructive testing(NDT)techniques are commonly employed due to their cost-effectiveness and the ability to assess structural integrity without causing damage.However,NDT methods often yield less accurate results than destructive testing(DT),which,although highly reliable,is costly and invasive.To address this limitation,recent research has focused on developing predictive models that correlate DT and NDT outcomes using machine learning techniques.This study explores the application of Support Vector Machine(SVM)models,enhanced with optimization techniques,to improve prediction accuracy.Experimental concrete practical samples,ranging from M10 to M40 grade,were prepared and tested at 14 and 28 days of curing,totaling 126 laboratory specimens.Additionally,231 field samples were collected from a 20-year-old structure to reflect in situ conditions.The performance of SVM was improved using optimization algorithms such as Bayesian Optimization and Genetic Algorithms(GA).Among various kernel functions tested,the Gaussian non-linear kernel proved most effective in modeling the complex relationship between NDT and DT results.The SVM model optimized using Bayesian methods and a Gaussian kernel achieved superior performance,with a high coefficient of determination(R²=0.9771)and significantly lower error metrics,including Mean Squared Error(MSE),Root Mean Squared Error(RMSE),and Mean Absolute Error(MAE).Bayesian-optimized SVM with a Gaussian kernel offers a highly accurate and practical tool for predicting compressive strength from NDT data,enhancing decision-making in structural assessment.展开更多
文摘Evaluating the performance of existing concrete structures is essential in civil engineering,with compressive strength serving as an indicator of performance.Non-destructive testing(NDT)techniques are commonly employed due to their cost-effectiveness and the ability to assess structural integrity without causing damage.However,NDT methods often yield less accurate results than destructive testing(DT),which,although highly reliable,is costly and invasive.To address this limitation,recent research has focused on developing predictive models that correlate DT and NDT outcomes using machine learning techniques.This study explores the application of Support Vector Machine(SVM)models,enhanced with optimization techniques,to improve prediction accuracy.Experimental concrete practical samples,ranging from M10 to M40 grade,were prepared and tested at 14 and 28 days of curing,totaling 126 laboratory specimens.Additionally,231 field samples were collected from a 20-year-old structure to reflect in situ conditions.The performance of SVM was improved using optimization algorithms such as Bayesian Optimization and Genetic Algorithms(GA).Among various kernel functions tested,the Gaussian non-linear kernel proved most effective in modeling the complex relationship between NDT and DT results.The SVM model optimized using Bayesian methods and a Gaussian kernel achieved superior performance,with a high coefficient of determination(R²=0.9771)and significantly lower error metrics,including Mean Squared Error(MSE),Root Mean Squared Error(RMSE),and Mean Absolute Error(MAE).Bayesian-optimized SVM with a Gaussian kernel offers a highly accurate and practical tool for predicting compressive strength from NDT data,enhancing decision-making in structural assessment.
