Open AccessPrediction of Concrete Compressive Strength Using Artificial Intelligence Methods
Author(s) -
Hansen Muliauwan,
Doddy Prayogo,
G. Gaby,
K. Harsono
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1625/1/012018
Subject(s) - compressive strength , artificial neural network , support vector machine , computer science , artificial intelligence , machine learning , predictive modelling , materials science , composite material
Concrete is one of the most used materials in buildings today; yet, predicting the accurate concrete compressive strength remains challenging because of the highly complex relationship between its mixture. An accurate method of predicting concrete compressive strength can provide a significant advantage to the construction material industry, particularly within the concrete material industry. Many methods can be used to build the prediction model of concrete compressive strength. However, the traditional methods have so many shortcomings, including expensive experimental costs and the inability to formulate an accurate complex relationship between the components of a concrete mixture with the compressive strength. To overcome this issue, this study applies multiple artificial intelligence (AI) methods to find the most accurate input and output relationships within concrete mixtures. The three types of AI methods that will be used in this study are artificial neural networks (ANN), support vector machine (SVM), and linear regression (LR). This study uses 1030 data samples from concrete compressive strength tests obtained from University of California, Irvine, to demonstrate the use of AI prediction models. The obtained results of the simulation show that these artificial intelligence methods can build predictive models without conducting any expensive experiments in the laboratory with good accuracy.