Empirical Model for the Retained Stability Index of Asphalt Mixtures Using Hybrid Machine Learning Approach
Moisture susceptibility is a complex phenomenon that induces various distresses in asphalt pavements and can be assessed by the Retained Stability Index (RSI). This study proposes a robust model to predict the RSI using a hybrid machine learning technique, including Artificial Neural Network (ANN) a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Applied System Innovation |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-5577/6/5/93 |
| _version_ | 1797574852630020096 |
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| author | Yazeed S. Jweihan Mazen J. Al-Kheetan Musab Rabi |
| author_facet | Yazeed S. Jweihan Mazen J. Al-Kheetan Musab Rabi |
| author_sort | Yazeed S. Jweihan |
| collection | DOAJ |
| description | Moisture susceptibility is a complex phenomenon that induces various distresses in asphalt pavements and can be assessed by the Retained Stability Index (RSI). This study proposes a robust model to predict the RSI using a hybrid machine learning technique, including Artificial Neural Network (ANN) and Gene Expression Programming. The model is expressed as a simple and direct mathematical function with input variables of mineral filler proportion (F%), water absorption rate of combined aggregate (Ab%), asphalt content (AC%), and air void content (Va%). A relative importance analysis ranked AC% as the most influential variable on RSI, followed by Va%, F%, and Ab%. The experimental RSI results of 150 testing samples of various mixes were utilized along with other data points generated by the ANN to train and validate the proposed model. The model promotes a high level of accuracy for predicting the RSI with a 96.6% coefficient of determination (R<sup>2</sup>) and very low errors. In addition, the sensitivity of the model has been verified by considering the effect of the variables, which is in line with the results of network connection weight and previous studies in the literature. F%, Ab%, and Va% have an inverse relationship with the RSI values, whereas AC% has the opposite. The model helps forecast the water susceptibility of asphalt mixes by which the experimental effort is minimized and the mixes’ performance can be improved. |
| first_indexed | 2024-03-10T21:27:58Z |
| format | Article |
| id | doaj.art-fb221a807fc74e4bb1ee215e269b347f |
| institution | Directory Open Access Journal |
| issn | 2571-5577 |
| language | English |
| last_indexed | 2024-03-10T21:27:58Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied System Innovation |
| spelling | doaj.art-fb221a807fc74e4bb1ee215e269b347f2023-11-19T15:35:26ZengMDPI AGApplied System Innovation2571-55772023-10-01659310.3390/asi6050093Empirical Model for the Retained Stability Index of Asphalt Mixtures Using Hybrid Machine Learning ApproachYazeed S. Jweihan0Mazen J. Al-Kheetan1Musab Rabi2Civil and Environmental Engineering Department, College of Engineering, Mutah University, Mutah, P.O. Box 7, Karak 61710, JordanCivil and Environmental Engineering Department, College of Engineering, Mutah University, Mutah, P.O. Box 7, Karak 61710, JordanDepartment of Civil Engineering, Jerash University, Jerash 26150, JordanMoisture susceptibility is a complex phenomenon that induces various distresses in asphalt pavements and can be assessed by the Retained Stability Index (RSI). This study proposes a robust model to predict the RSI using a hybrid machine learning technique, including Artificial Neural Network (ANN) and Gene Expression Programming. The model is expressed as a simple and direct mathematical function with input variables of mineral filler proportion (F%), water absorption rate of combined aggregate (Ab%), asphalt content (AC%), and air void content (Va%). A relative importance analysis ranked AC% as the most influential variable on RSI, followed by Va%, F%, and Ab%. The experimental RSI results of 150 testing samples of various mixes were utilized along with other data points generated by the ANN to train and validate the proposed model. The model promotes a high level of accuracy for predicting the RSI with a 96.6% coefficient of determination (R<sup>2</sup>) and very low errors. In addition, the sensitivity of the model has been verified by considering the effect of the variables, which is in line with the results of network connection weight and previous studies in the literature. F%, Ab%, and Va% have an inverse relationship with the RSI values, whereas AC% has the opposite. The model helps forecast the water susceptibility of asphalt mixes by which the experimental effort is minimized and the mixes’ performance can be improved.https://www.mdpi.com/2571-5577/6/5/93moisture damagemachine learningasphalt pavementrelative importance |
| spellingShingle | Yazeed S. Jweihan Mazen J. Al-Kheetan Musab Rabi Empirical Model for the Retained Stability Index of Asphalt Mixtures Using Hybrid Machine Learning Approach Applied System Innovation moisture damage machine learning asphalt pavement relative importance |
| title | Empirical Model for the Retained Stability Index of Asphalt Mixtures Using Hybrid Machine Learning Approach |
| title_full | Empirical Model for the Retained Stability Index of Asphalt Mixtures Using Hybrid Machine Learning Approach |
| title_fullStr | Empirical Model for the Retained Stability Index of Asphalt Mixtures Using Hybrid Machine Learning Approach |
| title_full_unstemmed | Empirical Model for the Retained Stability Index of Asphalt Mixtures Using Hybrid Machine Learning Approach |
| title_short | Empirical Model for the Retained Stability Index of Asphalt Mixtures Using Hybrid Machine Learning Approach |
| title_sort | empirical model for the retained stability index of asphalt mixtures using hybrid machine learning approach |
| topic | moisture damage machine learning asphalt pavement relative importance |
| url | https://www.mdpi.com/2571-5577/6/5/93 |
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