Predicting Rapid Chloride Permeability of Self-Consolidating Concrete: A Comparative Study on Statistical and Neural Network Models

Document Type

Article

Publication Date

7-2013

Publication Title

Construction and Building Materials

Volume

44

First page number:

381

Last page number:

390

Abstract

This paper is intended to compare robustness of linear and nonlinear regressions, and neural network prediction models in estimating rapid chloride permeability of self-consolidating concretes based on their mixture proportions. Several models were developed by varying number of independent variables and samples (mixtures) allotted to training and testing. The results of this study showed the superior performance of neural network models in comparison with the prediction models obtained by linear and nonlinear regressions, particularly when testing evaluations were chosen from the boundaries of mixture proportions. Within the linear and nonlinear prediction models, power relationships produced the most consistent performance.

Keywords

Concrete; Concrete construction; Extrapolation; Interpolation; Linear and nonlinear regressions; Neural network; Neural networks (Computer science); Prediction; Rapid chloride permeability test; Regression analysis; Self-consolidating concrete

Disciplines

Civil and Environmental Engineering | Civil Engineering | Construction Engineering and Management | Structural Engineering

Language

English

Permissions

Use Find in Your Library, contact the author, or interlibrary loan to garner a copy of the item. Publisher policy does not allow archiving the final published version. If a post-print (author's peer-reviewed manuscript) is allowed and available, or publisher policy changes, the item will be deposited.

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