In order to analyse the behaviour of concrete exposed to chloride attack, 243 specimens of 5 cm in diameter and 10 cm in thickness were prepared in order to analyse the influence of water/cement ratio, mineral additions, type of cement, period of curing and the level of exposure to the penetration of chloride ions.

The aim of this paper was to get chloride depth penetration and chloride diffusion of concrete specimens under conditions of drying–wetting cycles. Based on the experimental results, an Artificial Neural Network (ANN) modelling was used to map the relationship between the variables analysed and the ion penetration depth. Results obtained showed that ANN modelling proved to be efficient to estimate the depth of chloride penetration and chloride diffusion coefficients in concrete and the parameters that most influence the depth of chloride penetration was the type of cement, the type of addition and the cure time.

为了分析遭受氯离子侵蚀的混凝土的性能，制备了243个直径5厘米，厚度10厘米的试样，以分析水灰比，矿物添加，水泥类型，养护时间和暴露于氯离子渗透的水平。

本文的目的是在干湿循环条件下获得混凝土样品的氯化物深度渗透和氯化物扩散。根据实验结果，使用人工神经网络（ANN）建模来映射分析的变量与离子穿透深度之间的关系。获得的结果表明，ANN模型被证明可以有效地估计混凝土中氯离子渗透的深度和氯离子扩散系数，而影响氯离子渗透深度的最主要参数是水泥的类型，添加的类型和固化时间。