Journal Article

Concrete compressive strength using artificial neural networks

Asteris, Panagiotis G., Mokos, Vaseilios G.

2020

Overview

The non-destructive testing of concrete structures with methods such as ultrasonic pulse velocity and Schmidt rebound hammer test is of utmost technical importance. Non-destructive testing methods do not require sampling, and they are simple, fast to perform, and efficient. However, these methods result in large dispersion of the values they estimate, with significant deviation from the actual (experimental) values of compressive strength. In this paper, the application of artificial neural networks (ANNs) for predicting the compressive strength of concrete in existing structures has been investigated. ANNs have been systematically used for predicting the compressive strength of concrete, utilizing both the ultrasonic pulse velocity and the Schmidt rebound hammer experimental results, which are available in the literature. The comparison of the ANN-derived results with the experimental findings, which are in very good agreement, demonstrates the ability of ANNs to estimate the compressive strength of concrete in a reliable and robust manner. Thus, the (quantitative) values of weights for the proposed neural network model are provided, so that the proposed model can be readily implemented in a spreadsheet and accessible to everyone interested in the procedure of simulation.

• Publisher: Springer London,Springer Nature B.V
• Subject: Artificial Intelligence / Artificial neural networks / Compressive strength / Computational Biology/Bioinformatics / Computational Science and Engineering / Computer Science / Computer simulation / Concrete / Concrete properties / Concrete structures / Data Mining and Knowledge Discovery / Destructive testing / Hammers / Image Processing and Computer Vision / Neural networks / Nondestructive testing / Original Article / Probability and Statistics in Computer Science / Spreadsheets / Ultrasonic testing