Prediction of maximum thermal crack width of RC abutments utilizing actual construction data and study on influential parameters using neural networks

Abstract

In this study, an attempt was made to predict the maximum width of thermal cracking in RC abutments using field data. The data was obtained from Japanese Yamaguchi prefecture database which is a database of concrete structures in the prefecture constructed with appropriate concreting work. Reliable data was chosen carefully to avoid incorporation of possible human errors. Feed-forward multilayer perceptron neural network was used for prediction. k-fold cross validation was performed to avoid overfitting. By using the trained NN, several parametric studies were conducted to observe the influences of different parameters. Main idea of this research was to solve the complex problem of predicting the maximum crack width by using the parameters which are easier to obtain in the field. The results have shown the potential of predicting the crack width and observable influence of several parameters. The results will be helpful in proposing countermeasures to mitigate harmful thermal cracks.