The assessment of Levenberg–Marquardt and Bayesian Framework training algorithm for prediction of concrete shrinkage by the artificial neural network

Abstract

Shrinkage and creep are the main concrete volume changes over time. This unacceptable concrete deformation leads to stress and cracks creation where eventually reduces the service life of concrete structures. According to this, the prediction of shrinkage and creep strain in concrete structures with acceptable accuracy is the significance essential. The extensive investigation accomplished by several researchers has created different relationships and models for forecasting of shrinkage and creep strain based on experimental and analytical observation. Despite effective efforts in this regard, existing models do not have sufficient accuracy for anticipate of shrinkage strain. According to this, in this research, it has been attempted to provide a shrinkage predicting model based on the artificial neural network technique with the application of RILEM database. Also, it has been tried to determine the accuracy of the proposed model in comparison to the existing standard models by statistical analysis. According to the obtained results, by application of the neural network technique, the shrinkage strain could be predicted with acceptable accuracy especial in the extended period.

Keywords

• shrinkage strains
• prediction models
• artificial neural network
• training algorithm
• modern concrete