Development of a New Modified Sonar Inspired Optimization based on Machine Learning Methods for Evaluating Compressive of High-Performance Concrete
The nonlinearity observed in high-performance concrete (HPC) can be attributed to its distinctive features. This study examines the effectiveness of expert frameworks in determining compressive strength, aiming to enhance accuracy through the development of a master artificial neural network (ANN) s...
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| Format: | Article |
| Language: | English |
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Semnan University
2024-11-01
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| Series: | Journal of Rehabilitation in Civil Engineering |
| Subjects: | |
| Online Access: | https://civiljournal.semnan.ac.ir/article_8675_f4d674809dab27713f7d2bfef322f59e.pdf |
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| author | Ali Nikkhoo Amin Moshtagh Mehri Mehrnia |
| author_facet | Ali Nikkhoo Amin Moshtagh Mehri Mehrnia |
| author_sort | Ali Nikkhoo |
| collection | DOAJ |
| description | The nonlinearity observed in high-performance concrete (HPC) can be attributed to its distinctive features. This study examines the effectiveness of expert frameworks in determining compressive strength, aiming to enhance accuracy through the development of a master artificial neural network (ANN) system utilizing the sonar inspired optimization (SIO) algorithm. The ANN model employs exploratory data to establish initial optimal weights and biases, thereby improving precision. Comparison with previous studies validates the accuracy of the proposed system, demonstrating that the SIO-ANN hybrid model offers finer estimation of high-performance concrete properties. Results consistently show a coefficient of determination (R2) exceeding 0.972 and a 50%-67% reduction in error rates compared to conventional fitting curve approaches. Parameters such as population, weight, and bias within the SIO-ANN framework are continuously updated and optimized to achieve optimal values efficiently. Additionally, the SIO-ANN model exhibits superior runtime performance compared to other models. Consequently, the proposed SIO-ANN approach emerges as a viable alternative for accurately assessing and predicting the compressive strength of high-performance concrete. |
| format | Article |
| id | doaj-art-d3bb88f6723d444a93b47cd953c8b8c8 |
| institution | Kabale University |
| issn | 2345-4415 2345-4423 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Semnan University |
| record_format | Article |
| series | Journal of Rehabilitation in Civil Engineering |
| spelling | doaj-art-d3bb88f6723d444a93b47cd953c8b8c82025-01-21T20:47:45ZengSemnan UniversityJournal of Rehabilitation in Civil Engineering2345-44152345-44232024-11-0112411613510.22075/jrce.2024.33564.20258675Development of a New Modified Sonar Inspired Optimization based on Machine Learning Methods for Evaluating Compressive of High-Performance ConcreteAli Nikkhoo0Amin Moshtagh1Mehri Mehrnia2Associate Professor, Faculty of Engineering, University of Science and Culture, Tehran, IranPh.D. Student, Department of Civil Engineering, University of Science and Culture, Tehran, IranPh.D. Student, Department of Biomedical Engineering, Northwestern University, United StatesThe nonlinearity observed in high-performance concrete (HPC) can be attributed to its distinctive features. This study examines the effectiveness of expert frameworks in determining compressive strength, aiming to enhance accuracy through the development of a master artificial neural network (ANN) system utilizing the sonar inspired optimization (SIO) algorithm. The ANN model employs exploratory data to establish initial optimal weights and biases, thereby improving precision. Comparison with previous studies validates the accuracy of the proposed system, demonstrating that the SIO-ANN hybrid model offers finer estimation of high-performance concrete properties. Results consistently show a coefficient of determination (R2) exceeding 0.972 and a 50%-67% reduction in error rates compared to conventional fitting curve approaches. Parameters such as population, weight, and bias within the SIO-ANN framework are continuously updated and optimized to achieve optimal values efficiently. Additionally, the SIO-ANN model exhibits superior runtime performance compared to other models. Consequently, the proposed SIO-ANN approach emerges as a viable alternative for accurately assessing and predicting the compressive strength of high-performance concrete.https://civiljournal.semnan.ac.ir/article_8675_f4d674809dab27713f7d2bfef322f59e.pdfhigh-performance concretesonar inspired optimizationoptimizationartificial neural networkprediction |
| spellingShingle | Ali Nikkhoo Amin Moshtagh Mehri Mehrnia Development of a New Modified Sonar Inspired Optimization based on Machine Learning Methods for Evaluating Compressive of High-Performance Concrete Journal of Rehabilitation in Civil Engineering high-performance concrete sonar inspired optimization optimization artificial neural network prediction |
| title | Development of a New Modified Sonar Inspired Optimization based on Machine Learning Methods for Evaluating Compressive of High-Performance Concrete |
| title_full | Development of a New Modified Sonar Inspired Optimization based on Machine Learning Methods for Evaluating Compressive of High-Performance Concrete |
| title_fullStr | Development of a New Modified Sonar Inspired Optimization based on Machine Learning Methods for Evaluating Compressive of High-Performance Concrete |
| title_full_unstemmed | Development of a New Modified Sonar Inspired Optimization based on Machine Learning Methods for Evaluating Compressive of High-Performance Concrete |
| title_short | Development of a New Modified Sonar Inspired Optimization based on Machine Learning Methods for Evaluating Compressive of High-Performance Concrete |
| title_sort | development of a new modified sonar inspired optimization based on machine learning methods for evaluating compressive of high performance concrete |
| topic | high-performance concrete sonar inspired optimization optimization artificial neural network prediction |
| url | https://civiljournal.semnan.ac.ir/article_8675_f4d674809dab27713f7d2bfef322f59e.pdf |
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