Development of deep neural network model to predict the compressive strength of FRCM confined columns

The present study describes a reliability analysis of the strength model for predicting concrete columns confinement influence with Fabric-Reinforced Cementitious Matrix(FRCM).through both physical models and Deep Neural Network model(artificial neural network(ANN)with double and triple hidden layers).The database of 330 samples collected for the training model contains many important parameters,i.e.,section type(circle or square),corner radius rc,unconfined concrete strength fco,thickness nt,the elastic modulus of fiber Ef,the elastic modulus of mortar Em.The results revealed that the proposed ANN models well predicted the compressive strength of FRCM with high prediction accuracy.The ANN model with double hidden layers(APDL-1)was shown to be the best to predict the compressive strength of FRCM confined columns compared with the ACI design code and five physical models.Furthermore,the results also reveal that the unconfined compressive strength of concrete,type of fiber mesh for FRCM,type of section,and the corner radius ratio,are the most significant input variables in the efficiency of FRCM confinement prediction.The performance of the proposed ANN models(including double and triple hidden layers)had high precision with R higher than 0.93 and RMSE smaller than 0.13,as compared with other models from the literature available.

Dimensionality reduction and prediction of soil consolidation coefficient using random forest coupling with Relief algorithm

The consolidation coefficient of soil(C_(v))is a crucial parameter used for the design of structures leaned on soft soi.In general,the C_(v) is determined experimentally in the laboratory.However,the experimental tests are time-consuming as well as expensive.Therefore,researchers tried several ways to determine C_(v) via other simple soil parameters.In this study,we developed a hybrid model of Random Forest coupling with a Relief algorithm(RF-RL)to predict the C_(v) of soil.To conduct this study,a database of soil parameters collected from a case study region in Vietnam was used for modeling.The performance of the proposed models was assessed via statistical indicators,namely Coefficient of determination(R^(2)),Root Mean Squared Error(RMSE),and Mean Absolute Error(MAE).The proposal models were constructed with four sets of soil variables,including 6,7,8,and 13 inputs.The results revealed that all models performed well with a high performance(R^(2)>0.980).Although the RF-RL model with 13 variables has the highest prediction accuracy(R^(2)=0.9869),the difference compared with other models was negligible(i.e.,R^(2)=0.9824,0.9850,0.9825 for the cases with 6,7,8 inputs,respectively).Thus,it can be concluded that the hybrid model of RF-RL can be employed to predict C_(v) based on the basic soil parameters.

Application of machine learning technique for predicting and evaluating chloride ingress in concrete

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 1D(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.