Machine learning for predictions of strains due to long-term effects and temperature in concrete structures

Abstract

The effect of creep, shrinkage and temperature, and the development of strains in concrete over time are complex problems which are hard to define accurate models for. In this thesis, the possibility of using machine learning to define more accurate models for predictions of strain due to long-term effects and temperature in concrete has been investigated. A simply supported concrete beam has been modelled. Strain results for the long-term effects have been obtained with DIANA FEA while strains from the temperature effects were calculated with MATLAB. The Neural Network Time Series toolbox in MATLAB was used for training neural network models with the total strain results. Several models were trained for different points at the beam with time and temperature as inputs and strain as output.

It was found that when the neural network was trained with three years of strain signals the models that were generated had good generalization and could predict strain signals with high accuracy. The length of the strain signal used for training, the complexity of the signal, whether the general trend of the signal changes after the period used for training and the amount of noise in the signal affects the performance of the models. It was seen that it is possible to train neural network models that can eliminate some of the strain due to temperature effects when predicting future strains using a constant temperature as input. The predicted strains followed the general trend of the long-term strains and the yearly variation due to temperature was eliminated. However, the models could not completely remove the daily strain variation due to temperature. Based on the findings in this thesis and other studies done on machine learning, it seems like there is potential for using neural networks to predict strains with long-term effects with higher accuracy than the material models used for calculating these effects today.