Prediction of High-Performance Concrete Strength Using a Hybrid Artificial Intelligence Approach

Prediction of High-Performance Concrete Strength Using a Hybrid Artificial Intelligence Approach

This study introduces an improved artificial intelligence (AI) approach called intelligence optimized support vector regression (IO-SVR) for estimating the compressive strength of high-performance concrete (HPC). The nonlinear functional mapping between the HPC materials and compressive strength is...

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Bibliographic Details
Main Authors: Prayogo Doddy, Wong Foek Tjong, Tjandra Daniel
Format: Article
Language:English
Published: EDP Sciences 2018-01-01
Series:MATEC Web of Conferences
Online Access:https://doi.org/10.1051/matecconf/201820306006
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spelling doaj-6533848841a24a64a62f2c8268e2a16e2021-03-02T10:10:23ZengEDP SciencesMATEC Web of Conferences2261-236X2018-01-012030600610.1051/matecconf/201820306006matecconf_iccoee2018_06006Prediction of High-Performance Concrete Strength Using a Hybrid Artificial Intelligence ApproachPrayogo DoddyWong Foek TjongTjandra DanielThis study introduces an improved artificial intelligence (AI) approach called intelligence optimized support vector regression (IO-SVR) for estimating the compressive strength of high-performance concrete (HPC). The nonlinear functional mapping between the HPC materials and compressive strength is conducted using the AI approach. A dataset with 1,030 HPC experimental tests is used to train and validate the prediction model. Depending on the results of the experiments, the forecast outcomes of the IO-SVR model are of a much higher quality compared to the outcomes of other AI approaches. Additionally, because of the high-quality learning capabilities, the IO-SVR is highly recommended for calculating HPC strength.https://doi.org/10.1051/matecconf/201820306006
collection DOAJ
language English
format Article
sources DOAJ
author Prayogo Doddy
Wong Foek Tjong
Tjandra Daniel
spellingShingle Prayogo Doddy
Wong Foek Tjong
Tjandra Daniel
Prediction of High-Performance Concrete Strength Using a Hybrid Artificial Intelligence Approach
MATEC Web of Conferences
author_facet Prayogo Doddy
Wong Foek Tjong
Tjandra Daniel
author_sort Prayogo Doddy
title Prediction of High-Performance Concrete Strength Using a Hybrid Artificial Intelligence Approach
title_short Prediction of High-Performance Concrete Strength Using a Hybrid Artificial Intelligence Approach
title_full Prediction of High-Performance Concrete Strength Using a Hybrid Artificial Intelligence Approach
title_fullStr Prediction of High-Performance Concrete Strength Using a Hybrid Artificial Intelligence Approach
title_full_unstemmed Prediction of High-Performance Concrete Strength Using a Hybrid Artificial Intelligence Approach
title_sort prediction of high-performance concrete strength using a hybrid artificial intelligence approach
publisher EDP Sciences
series MATEC Web of Conferences
issn 2261-236X
publishDate 2018-01-01
description This study introduces an improved artificial intelligence (AI) approach called intelligence optimized support vector regression (IO-SVR) for estimating the compressive strength of high-performance concrete (HPC). The nonlinear functional mapping between the HPC materials and compressive strength is conducted using the AI approach. A dataset with 1,030 HPC experimental tests is used to train and validate the prediction model. Depending on the results of the experiments, the forecast outcomes of the IO-SVR model are of a much higher quality compared to the outcomes of other AI approaches. Additionally, because of the high-quality learning capabilities, the IO-SVR is highly recommended for calculating HPC strength.
url https://doi.org/10.1051/matecconf/201820306006
work_keys_str_mv AT prayogododdy predictionofhighperformanceconcretestrengthusingahybridartificialintelligenceapproach
AT wongfoektjong predictionofhighperformanceconcretestrengthusingahybridartificialintelligenceapproach
AT tjandradaniel predictionofhighperformanceconcretestrengthusingahybridartificialintelligenceapproach
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