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

Hosein Garoosiha,Jamal Ahmadi,Hossein Bayat,Antonio Formisano

doi:10.1080/23311916.2019.1609179

Hosein Garoosiha, Jamal Ahmadi + Show 2 more

Open Access

https://doi.org/10.1080/23311916.2019.1609179

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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.

Highlights

Shrinkage is the concrete volume changes because of internal moisture losses, drying fresh concrete, internal water absorption by anhydrous cement particles and carbon dioxide penetration which appears without any stress increasing in the free or unrestrained members
Research significance The primary objective of this paper is to develop a method based on artificial neural network technique for the prediction of concrete shrinkage strain
● The shrinkage prediction model such as ACI209, B3, and GL2000 have a considerable limitation in the input data

Summary

Introduction

Shrinkage is the concrete volume changes because of internal moisture losses (drying shrinkage), drying fresh concrete (plastic shrinkage), internal water absorption by anhydrous cement particles (autogenous shrinkage) and carbon dioxide penetration (carbonation shrinkage) which appears without any stress increasing in the free or unrestrained members. The ANN method has been used to the calculation of concrete volume changes over time In this regard, Ball and Buyle (2013) presented a predicting model for estimation of concrete shrinkage based on artificial neural network technique. In studies which were done in this area, the effect of several factors such as additives and pozzolanic materials are not generally considered Regarding this issue, this research attempts to provide a comprehensive model based on neural network techniques to predict shrinkage strain in concrete with different cement type and broader ranges of the water to cement ratios. The primary object of this study is the prediction of the shrinkage strain from the input data which include concrete properties and environmental condition According to this aim, the standard back-propagation network has been utilized for configuration of the artificial neural network. Even if the value of network performance is lower, the accuracy of the network

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