Stiffness Moduli Modelling and Prediction in Four-Point Bending of Asphalt Mixtures: A Machine Learning-Based Framework
Stiffness modulus represents one of the most important parameters for the mechanical characterization of asphalt mixtures (AMs). At the same time, it is a crucial input parameter in the process of designing flexible pavements. In the present study, two selected mixtures were thoroughly investigated...
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MDPI AG
2023-10-01
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| Online Access: | https://www.mdpi.com/2673-4109/4/4/59 |
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| author | Nicola Baldo Fabio Rondinella Fabiola Daneluz Pavla Vacková Jan Valentin Marcin D. Gajewski Jan B. Król |
| author_facet | Nicola Baldo Fabio Rondinella Fabiola Daneluz Pavla Vacková Jan Valentin Marcin D. Gajewski Jan B. Król |
| author_sort | Nicola Baldo |
| collection | DOAJ |
| description | Stiffness modulus represents one of the most important parameters for the mechanical characterization of asphalt mixtures (AMs). At the same time, it is a crucial input parameter in the process of designing flexible pavements. In the present study, two selected mixtures were thoroughly investigated in an experimental trial carried out by means of a four-point bending test (4PBT) apparatus. The mixtures were prepared using spilite aggregate, a conventional 50/70 penetration grade bitumen, and limestone filler. Their stiffness moduli (SM) were determined while samples were exposed to 11 loading frequencies (from 0.1 to 50 Hz) and 4 testing temperatures (from 0 to 30 °C). The SM values ranged from 1222 to 24,133 MPa. Observations were recorded and used to develop a machine learning (ML) model. The main scope was the prediction of the stiffness moduli based on the volumetric properties and testing conditions of the corresponding mixtures, which would provide the advantage of reducing the laboratory efforts required to determine them. Two of the main soft computing techniques were investigated to accomplish this task, namely decision trees with the Categorical Boosting algorithm and artificial neural networks. The outcomes suggest that both ML methodologies achieved very good results, with Categorical Boosting showing better performance (MAPE = 3.41% and R<sup>2</sup> = 0.9968) and resulting in more accurate and reliable predictions in terms of the six goodness-of-fit metrics that were implemented. |
| first_indexed | 2024-03-08T20:53:49Z |
| format | Article |
| id | doaj.art-8702e02354714569a54e579ffc3b1dcc |
| institution | Directory Open Access Journal |
| issn | 2673-4109 |
| language | English |
| last_indexed | 2024-03-08T20:53:49Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | CivilEng |
| spelling | doaj.art-8702e02354714569a54e579ffc3b1dcc2023-12-22T14:00:47ZengMDPI AGCivilEng2673-41092023-10-01441083109710.3390/civileng4040059Stiffness Moduli Modelling and Prediction in Four-Point Bending of Asphalt Mixtures: A Machine Learning-Based FrameworkNicola Baldo0Fabio Rondinella1Fabiola Daneluz2Pavla Vacková3Jan Valentin4Marcin D. Gajewski5Jan B. Król6Polytechnic Department of Engineering and Architecture (DPIA), University of Udine, Via del Cotonificio 114, 33100 Udine, ItalyPolytechnic Department of Engineering and Architecture (DPIA), University of Udine, Via del Cotonificio 114, 33100 Udine, ItalyPolytechnic Department of Engineering and Architecture (DPIA), University of Udine, Via del Cotonificio 114, 33100 Udine, ItalyFaculty of Civil Engineering, Czech Technical University, Thákurova 7, 166 29 Prague, Czech RepublicFaculty of Civil Engineering, Czech Technical University, Thákurova 7, 166 29 Prague, Czech RepublicFaculty of Civil Engineering, Warsaw University of Technology, 00-637 Warsaw, PolandFaculty of Civil Engineering, Warsaw University of Technology, 00-637 Warsaw, PolandStiffness modulus represents one of the most important parameters for the mechanical characterization of asphalt mixtures (AMs). At the same time, it is a crucial input parameter in the process of designing flexible pavements. In the present study, two selected mixtures were thoroughly investigated in an experimental trial carried out by means of a four-point bending test (4PBT) apparatus. The mixtures were prepared using spilite aggregate, a conventional 50/70 penetration grade bitumen, and limestone filler. Their stiffness moduli (SM) were determined while samples were exposed to 11 loading frequencies (from 0.1 to 50 Hz) and 4 testing temperatures (from 0 to 30 °C). The SM values ranged from 1222 to 24,133 MPa. Observations were recorded and used to develop a machine learning (ML) model. The main scope was the prediction of the stiffness moduli based on the volumetric properties and testing conditions of the corresponding mixtures, which would provide the advantage of reducing the laboratory efforts required to determine them. Two of the main soft computing techniques were investigated to accomplish this task, namely decision trees with the Categorical Boosting algorithm and artificial neural networks. The outcomes suggest that both ML methodologies achieved very good results, with Categorical Boosting showing better performance (MAPE = 3.41% and R<sup>2</sup> = 0.9968) and resulting in more accurate and reliable predictions in terms of the six goodness-of-fit metrics that were implemented.https://www.mdpi.com/2673-4109/4/4/59stiffness modulusasphalt mixturemachine learningcategorical boostingartificial neural network |
| spellingShingle | Nicola Baldo Fabio Rondinella Fabiola Daneluz Pavla Vacková Jan Valentin Marcin D. Gajewski Jan B. Król Stiffness Moduli Modelling and Prediction in Four-Point Bending of Asphalt Mixtures: A Machine Learning-Based Framework CivilEng stiffness modulus asphalt mixture machine learning categorical boosting artificial neural network |
| title | Stiffness Moduli Modelling and Prediction in Four-Point Bending of Asphalt Mixtures: A Machine Learning-Based Framework |
| title_full | Stiffness Moduli Modelling and Prediction in Four-Point Bending of Asphalt Mixtures: A Machine Learning-Based Framework |
| title_fullStr | Stiffness Moduli Modelling and Prediction in Four-Point Bending of Asphalt Mixtures: A Machine Learning-Based Framework |
| title_full_unstemmed | Stiffness Moduli Modelling and Prediction in Four-Point Bending of Asphalt Mixtures: A Machine Learning-Based Framework |
| title_short | Stiffness Moduli Modelling and Prediction in Four-Point Bending of Asphalt Mixtures: A Machine Learning-Based Framework |
| title_sort | stiffness moduli modelling and prediction in four point bending of asphalt mixtures a machine learning based framework |
| topic | stiffness modulus asphalt mixture machine learning categorical boosting artificial neural network |
| url | https://www.mdpi.com/2673-4109/4/4/59 |
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