Cargando…
Compressive Strength Prediction of Rice Husk Ash Concrete Using a Hybrid Artificial Neural Network Model
The combination of rice husk ash and common concrete both reduces carbon dioxide emission and solves the problem of agricultural waste disposal. However, the measurement of the compressive strength of rice husk ash concrete has become a new challenge. This paper proposes a novel hybrid artificial ne...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10145703/ https://www.ncbi.nlm.nih.gov/pubmed/37109970 http://dx.doi.org/10.3390/ma16083135 |
_version_ | 1785034400635813888 |
---|---|
author | Li, Chuanqi Mei, Xiancheng Dias, Daniel Cui, Zhen Zhou, Jian |
author_facet | Li, Chuanqi Mei, Xiancheng Dias, Daniel Cui, Zhen Zhou, Jian |
author_sort | Li, Chuanqi |
collection | PubMed |
description | The combination of rice husk ash and common concrete both reduces carbon dioxide emission and solves the problem of agricultural waste disposal. However, the measurement of the compressive strength of rice husk ash concrete has become a new challenge. This paper proposes a novel hybrid artificial neural network model, optimized using a reptile search algorithm with circle mapping, to predict the compressive strength of RHA concrete. A total of 192 concrete data with 6 input parameters (age, cement, rice husk ash, super plasticizer, aggregate, and water) were utilized to train proposed model and compare its predictive performance with that of five other models. Four statistical indices were adopted to evaluate the predictive performance of all the developed models. The performance evaluation indicates that the proposed hybrid artificial neural network model achieved the most satisfactory prediction accuracy regarding R(2) (0.9709), VAF (97.0911%), RMSE (3.4489), and MAE (2.6451). The proposed model also had better predictive accuracy than that of previously developed models on the same data. The sensitivity results show that age is the most important parameter for predicting the compressive strength of RHA concrete. |
format | Online Article Text |
id | pubmed-10145703 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-101457032023-04-29 Compressive Strength Prediction of Rice Husk Ash Concrete Using a Hybrid Artificial Neural Network Model Li, Chuanqi Mei, Xiancheng Dias, Daniel Cui, Zhen Zhou, Jian Materials (Basel) Article The combination of rice husk ash and common concrete both reduces carbon dioxide emission and solves the problem of agricultural waste disposal. However, the measurement of the compressive strength of rice husk ash concrete has become a new challenge. This paper proposes a novel hybrid artificial neural network model, optimized using a reptile search algorithm with circle mapping, to predict the compressive strength of RHA concrete. A total of 192 concrete data with 6 input parameters (age, cement, rice husk ash, super plasticizer, aggregate, and water) were utilized to train proposed model and compare its predictive performance with that of five other models. Four statistical indices were adopted to evaluate the predictive performance of all the developed models. The performance evaluation indicates that the proposed hybrid artificial neural network model achieved the most satisfactory prediction accuracy regarding R(2) (0.9709), VAF (97.0911%), RMSE (3.4489), and MAE (2.6451). The proposed model also had better predictive accuracy than that of previously developed models on the same data. The sensitivity results show that age is the most important parameter for predicting the compressive strength of RHA concrete. MDPI 2023-04-16 /pmc/articles/PMC10145703/ /pubmed/37109970 http://dx.doi.org/10.3390/ma16083135 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, Chuanqi Mei, Xiancheng Dias, Daniel Cui, Zhen Zhou, Jian Compressive Strength Prediction of Rice Husk Ash Concrete Using a Hybrid Artificial Neural Network Model |
title | Compressive Strength Prediction of Rice Husk Ash Concrete Using a Hybrid Artificial Neural Network Model |
title_full | Compressive Strength Prediction of Rice Husk Ash Concrete Using a Hybrid Artificial Neural Network Model |
title_fullStr | Compressive Strength Prediction of Rice Husk Ash Concrete Using a Hybrid Artificial Neural Network Model |
title_full_unstemmed | Compressive Strength Prediction of Rice Husk Ash Concrete Using a Hybrid Artificial Neural Network Model |
title_short | Compressive Strength Prediction of Rice Husk Ash Concrete Using a Hybrid Artificial Neural Network Model |
title_sort | compressive strength prediction of rice husk ash concrete using a hybrid artificial neural network model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10145703/ https://www.ncbi.nlm.nih.gov/pubmed/37109970 http://dx.doi.org/10.3390/ma16083135 |
work_keys_str_mv | AT lichuanqi compressivestrengthpredictionofricehuskashconcreteusingahybridartificialneuralnetworkmodel AT meixiancheng compressivestrengthpredictionofricehuskashconcreteusingahybridartificialneuralnetworkmodel AT diasdaniel compressivestrengthpredictionofricehuskashconcreteusingahybridartificialneuralnetworkmodel AT cuizhen compressivestrengthpredictionofricehuskashconcreteusingahybridartificialneuralnetworkmodel AT zhoujian compressivestrengthpredictionofricehuskashconcreteusingahybridartificialneuralnetworkmodel |