Predicting the rheological properties of bitumen-filler mastic using machine learning techniques
This study uses the artificial neural network and response surface methodology to develop two models for predicting the rheological properties, complex modulus (G*) and phase angle (δ) of bitumen-filler mastic. It also analyses and evaluates the accuracy of both models by determining the coefficient...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2023
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| Online Access: | http://journalarticle.ukm.my/22757/ http://journalarticle.ukm.my/22757/7/11.pdf |
| _version_ | 1848815679270027264 |
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| author | Abdalrhman Milad, Amirah Haziqah Mohamad Zaki, Nur Izzi Md. Yusoff, |
| author_facet | Abdalrhman Milad, Amirah Haziqah Mohamad Zaki, Nur Izzi Md. Yusoff, |
| author_sort | Abdalrhman Milad, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | This study uses the artificial neural network and response surface methodology to develop two models for predicting the rheological properties, complex modulus (G*) and phase angle (δ) of bitumen-filler mastic. It also analyses and evaluates the accuracy of both models by determining the coefficient of determination (R2), mean squared error (MSE), and root mean squared error (RMSE). The prediction models use the G* and δ data from a previous study by researchers at the Nottingham Transportation Engineering Centre to determine three types of bitumen-filler mastic (limestone, cement and grit stone) with varying filler concentrations of 15, 35, 40 and 65%. The analysis shows that both models perform well in predicting the rheological properties of bitumen-filler mastic. A comparison of the two models shows that the artificial neural network (ANN) has higher accuracy than the response surface methodology model, with an R2 value exceeding 0.92. The results of the ANN achieve a higher R2 value and lower MSE and RMSE values. In summary, the performance of the artificial neural network model is better than the response surface methodology model, which uses the full quadratic, pure quadratic, linear and interaction mathematical methods. |
| first_indexed | 2025-11-15T00:53:49Z |
| format | Article |
| id | oai:generic.eprints.org:22757 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:53:49Z |
| publishDate | 2023 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:227572023-12-29T06:39:36Z http://journalarticle.ukm.my/22757/ Predicting the rheological properties of bitumen-filler mastic using machine learning techniques Abdalrhman Milad, Amirah Haziqah Mohamad Zaki, Nur Izzi Md. Yusoff, This study uses the artificial neural network and response surface methodology to develop two models for predicting the rheological properties, complex modulus (G*) and phase angle (δ) of bitumen-filler mastic. It also analyses and evaluates the accuracy of both models by determining the coefficient of determination (R2), mean squared error (MSE), and root mean squared error (RMSE). The prediction models use the G* and δ data from a previous study by researchers at the Nottingham Transportation Engineering Centre to determine three types of bitumen-filler mastic (limestone, cement and grit stone) with varying filler concentrations of 15, 35, 40 and 65%. The analysis shows that both models perform well in predicting the rheological properties of bitumen-filler mastic. A comparison of the two models shows that the artificial neural network (ANN) has higher accuracy than the response surface methodology model, with an R2 value exceeding 0.92. The results of the ANN achieve a higher R2 value and lower MSE and RMSE values. In summary, the performance of the artificial neural network model is better than the response surface methodology model, which uses the full quadratic, pure quadratic, linear and interaction mathematical methods. Penerbit Universiti Kebangsaan Malaysia 2023 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/22757/7/11.pdf Abdalrhman Milad, and Amirah Haziqah Mohamad Zaki, and Nur Izzi Md. Yusoff, (2023) Predicting the rheological properties of bitumen-filler mastic using machine learning techniques. Jurnal Kejuruteraan, 35 (4). pp. 889-899. ISSN 0128-0198 https://www.ukm.my/jkukm/volume-3504-2023/ |
| spellingShingle | Abdalrhman Milad, Amirah Haziqah Mohamad Zaki, Nur Izzi Md. Yusoff, Predicting the rheological properties of bitumen-filler mastic using machine learning techniques |
| title | Predicting the rheological properties of bitumen-filler mastic using machine learning techniques |
| title_full | Predicting the rheological properties of bitumen-filler mastic using machine learning techniques |
| title_fullStr | Predicting the rheological properties of bitumen-filler mastic using machine learning techniques |
| title_full_unstemmed | Predicting the rheological properties of bitumen-filler mastic using machine learning techniques |
| title_short | Predicting the rheological properties of bitumen-filler mastic using machine learning techniques |
| title_sort | predicting the rheological properties of bitumen-filler mastic using machine learning techniques |
| url | http://journalarticle.ukm.my/22757/ http://journalarticle.ukm.my/22757/ http://journalarticle.ukm.my/22757/7/11.pdf |