The degradation of concrete structure in the marine environment is often related to chloride-induced corrosion of reinforcement steel.Therefore,the chloride concentration in concrete is a vital parameter for estimating the corrosion level of reinforcement steel.This research aims at predicting the chloride content in concrete using three hybrid models of gradient boosting(GB),artificial neural network(ANN),and random forest(RF)in combination with particle swarm optimization(PSO).The input variables for modeling include exposure condition,water/binder ratio(W/B),cement content,silica fume,time exposure,and depth of measurement.The results indicate that three models performed well with high accuracy of prediction(R2≥0.90).Among three hybrid models,the model using GB_PSO achieved the highest prediction accuracy(R2=0.9551,RMSE=0.0327,and MAE=0.0181).Based on the results of sensitivity analysis using SHapley Additive exPlanation(SHAP)and partial dependence plots ID(PDP-1D),it was found that the exposure condition and depth of measurement were the two most vital variables affecting the prediction of chloride content.When the number of different exposure conditions is larger than two,the exposure significantly impacted the chloride content of concrete because the chloride ion ingress is affected by both chemical and physical processes.This study provides an insight into the evaluation and prediction of the chloride content of concrete in the marine environment.

Van Quan TRAN;Van Loi GIAP;Dinh Phien VU;Riya Catherine GEORGE;Lanh Si HO

Department of Civil Engineering,University of Transport Technology,Hanoi 100000,Vietnam;Civil and Environmental Engineering Program,Graduate School of Advanced Science and Engineering,Hiroshima University,Hiroshima 739-8527,Japan

结构与土木工程前沿

[1]Van Quan TRAN;Van Loi GIAP;Dinh Phien VU;Riya Catherine GEORGE;Lanh Si HO-.Application of machine learning technique for predicting and evaluating chloride ingress in concrete)[J].结构与土木工程前沿,2022(09):1153-1169

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