Prediction of chloride content in concrete using ANN and CART

Abstract

Chloride-induced corrosion of concrete structures in marine areas is a serious problem and is generally affected by several factors. Chloride concentration is an important parameter for estimating the corrosion state of concrete. In this research, first chloride concentration at various depths of concrete specimens was measured using the accelerated chloride penetration test method under laboratory conditions, simulating a marine environment after 4·5 and 9 months. Then the obtained experimental dataset of 162 in 9 months of exposure was used to develop classification and regression trees (CARTs) and an artificial neural network (ANN) as subsets of artificial intelligence methods. Environmental condition, penetration depth, water-to-cementitious material ratio and silica fume mass were considered as input parameters, and chloride concentration was taken as the output parameter. Finally, results for the two methods were compared with the experimental observations to evaluate their accuracy in phases of training and testing. As a further aspect to the study, prediction of chloride concentration as a function of the exposure time and unavailable testing parameters was carried out. The results showed that ANN and CART have good ability and accuracy for predicting the chloride concentration in concrete under marine environment conditions. In the present research, the ANN method showed more accuracy.

Degradation Models and Maintenance Strategies for Reinforced Concrete Structures in Coastal Environments under Climate Change: A Review

“…However, the model depends on the available data, so its reliability in predicting damage may vary depending on the structure and historical data. Asghshahr, Rahai, and Ashrafi [110] used laboratory data on chloride penetration to develop classification and regression trees (CARTs) and ANN models to predict chloride concentration considering environmental conditions, penetration depth, water-to-cementitious material ratio, and silica fume mass as input parameters. The results showed a good ability and accuracy of the models for predicting the chloride concentration in concrete under marine environment conditions, with the best accuracy shown by the ANN model.…”

An efficient machine learning approach for predicting concrete chloride resistance using a comprehensive dataset

“…In the context of the limited dataset, Slika & Saad (2016) 43 used Ensemble Kalman Filter (EnKF) on the limited dataset of NC to predict Chloride concentration. Regarding SF concrete, 162 datasets were collected by Asghshahr et al (2016) 44 , and ANN, along with classification and regression tree (CART) methods, were utilized to predict the Chloride concentration. Only 4 input variables were considered in their models, including environmental condition, penetration depth, W/B ratio, and SF.…”

Artificial Neural Network with a Cross-Validation Technique to Predict the Material Design of Eco-Friendly Engineered Geopolymer Composites

“…In civil engineering research, ANN has been successfully applied to anticipate the strength criteria of hardened concrete [ 19 ] and the workability properties of fresh concrete [ 20 ]. In addition to these studies, several important and advanced parametric studies, such as those estimating the bond strength of structural concrete [ 21 , 22 ], the spalling [ 23 ] damage assessment of concrete [ 24 , 25 ], analyzing sections of deep beams [ 26 ], estimating the fracture parameters of geopolymer composites [ 27 ], and accessing the properties of FRP columns [ 28 ] have been successfully conducted. Additionally, durability studies on various factors such as corrosion inhibition [ 29 ], chloride penetration [ 30 , 31 ] and other aspects have also been successfully undertaken.…”