Carbonation is one of the most aggressive phenomena affecting reinforced concrete structures and causing their degradation over time. Once reinforcement is altered by carbonation, the structure will no longer fulfill service requirements. For this purpose, the present work estimates the lifetime of fly ash concrete by developing a carbonation depth prediction model that uses an artificial neural network technique. A collection of 300 data points was made from experimental results available in the published literature. Backpropagation training of a three-layer perceptron was selected for the calculation of weights and biases of the network to reach the desired performance. Six parameters affecting carbonation were used as input neurons: binder content, fly ash substitution rate, water/binder ratio, CO₂ concentration, relative humidity, and concrete age. Moreover, experimental validation carried out for the developed model shows that the artificial neural network has strong potential as a feasible tool to accurately predict the carbonation depth of fly ash concrete. Finally, a mathematical formula is proposed that can be used to successfully estimate the service life of fly ash concrete.

碳化是影响钢筋混凝土结构并导致其随时间退化的最具侵略性的现象之一。一旦碳化作用改变了钢筋，该结构将不再满足使用要求。为此，目前的工作通过开发使用人工神经网络技术的碳化深度预测模型来估计粉煤灰混凝土的寿命。根据已发表文献中可用的实验结果收集了 300 个数据点。选择三层感知器的反向传播训练来计算网络的权重和偏差，以达到所需的性能。影响碳化的六个参数用作输入神经元：粘合剂含量、飞灰替代率、水/粘合剂比、CO ₂浓度、相对湿度和混凝土年龄。此外，对开发的模型进行的实验验证表明，人工神经网络作为准确预测粉煤灰混凝土碳化深度的可行工具具有强大的潜力。最后，提出了可用于成功估算粉煤灰混凝土使用寿命的数学公式。