Artificial Neural Network Method for Predicting Compressive Strength of Normal Concrete

Lombok Island is an archipelago that has a source Natural resources such as sand and gravel are abundant. This material is one of the components of concrete. Concrete is a frequently used material in Indonesia. Compressive strength testing of concrete typically requires a large number of samples and a considerable amount of time. To expedite and simplify this process, researchers employ computer-based intelligence techniques, namely the Artificial Neural Network (ANN) method. This research involved a series of laboratory tests for normal concrete's compressive strength. The obtained data was then processed using MATLAB with the ANN modeling method for training. The research results indicated a Mean Absolute Percentage Error (MAPE) of 0.02% during the training process and 1.54% during testing. This demonstrates that the developed ANN modeling exhibits a high level of accuracy with low error. Therefore, the empirical formula obtained can be used for predicting the compressive strength of normal concrete with a good degree of precision.

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Publication Details

Journal
Journal of Advanced Civil and Environmental Engineering
Published
2024-12-31
DOI
https://doi.org/10.30659/jacee.7.2.171-177
Primary Topic
Infrastructure Maintenance and Monitoring
Type
article
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article

Artificial Neural Network Method for Predicting Compressive Strength of Normal Concrete

Auliya Isti Makrifa, Jauhari Prasetiawan, Dhiafah Hera Darayani, J Juanita
Journal of Advanced Civil and Environmental Engineering
Infrastructure Maintenance and Monitoring
article

Artificial Neural Network Method for Predicting Compressive Strength of Normal Concrete

Auliya Isti Makrifa, Jauhari Prasetiawan, Dhiafah Hera Darayani, J Juanita
article en

Abstract

Lombok Island is an archipelago that has a source Natural resources such as sand and gravel are abundant. This material is one of the components of concrete. Concrete is a frequently used material in Indonesia. Compressive strength testing of concrete typically requires a large number of samples and a considerable amount of time. To expedite and simplify this process, researchers employ computer-based intelligence techniques, namely the Artificial Neural Network (ANN) method. This research involved a series of laboratory tests for normal concrete's compressive strength. The obtained data was then processed using MATLAB with the ANN modeling method for training. The research results indicated a Mean Absolute Percentage Error (MAPE) of 0.02% during the training process and 1.54% during testing. This demonstrates that the developed ANN modeling exhibits a high level of accuracy with low error. Therefore, the empirical formula obtained can be used for predicting the compressive strength of normal concrete with a good degree of precision.

Journal of Advanced Civil and Environmental EngineeringVol. 7(2)
Sultan Agung Islamic University (ID), Universitas Islam Al-Azhar Mataram (ID)
Openalex Percentile: Top 70%
Infrastructure Maintenance and Monitoring
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Artificial Neural Network Method for Predicting Compressive Strength of Normal Concrete — Auliya Isti Makrifa, Jauhari Prasetiawan, et al. · Journal of Advanced Civil and Environmental Engineering (2024) | TGRS Research Map | TGRS