Title

Prediction of Compressive Strength and Modulus of Elasticity of Concrete using Machine Learning Models

Abstract

This paper presents machine learning (ML) models for high fidelity prediction of compressive strength and modulus of elasticity (MOE) of concrete in relation to primary attributes of its mixture design. Two comprehensive databases, consisting of over 1000 and 500 data-records consolidated from technical publications, were used for training and testing the ML models that included random forests (RF), support vector machine (SVM) and multilayer perceptron artificial neural network (MLP-ANN). The metrics used for evaluation of prediction performance included five different statistical parameters and composite performance index (CPI). Results show that the RF model consistently outperforms the other two ML models in terms of prediction accuracy. Overall, machine learning is a very powerful and efficient tool for prediction of concrete properties as well as for the optimization of its mixture design to meet a set of desired performance criteria.

Meeting Name

Materials Science and Technology 2019, MS & T 2019 (2019: Sep. 29-Oct. 3, Portland, OR)

Department(s)

Civil, Architectural and Environmental Engineering

Second Department

Electrical and Computer Engineering

Third Department

Materials Science and Engineering

Research Center/Lab(s)

Re-Cast Tier1 University Transportation Center

Comments

Computational tasks were conducted in the Materials Research Center of Missouri S&T. The first and fourth author would like to acknowledge funding provided by the Leonard Wood Institute (LWI). The second author would like to acknowledge funding provided by the RECAST University Transportation Center (at Missouri S&T) and Missouri Department of Transportation (MoDOT). The third and last authors would like to acknowledge funding provided by the National Science Foundation (NSF; CMMI: 1661609).

Keywords and Phrases

Artificial neural network; Compressive strength; Machine learning; Modulus of elasticity; Random forest; Support vector machine

International Standard Book Number (ISBN)

978-087339770-4

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2019 Materials Science and Technology, All rights reserved.

Publication Date

01 Oct 2019

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