Thermal features prediction in asphalt pavements using ANFIS-based regression
Abstract This paper aims to develop predictive models for the thermal properties of laboratory-prepared hot mix asphalt (HMA) specimens using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The thermal properties investigated include thermal conductivity, thermal diffusivity, and specific heat. T...
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| Format: | Article |
| Language: | English |
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Springer
2025-05-01
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| Series: | Discover Civil Engineering |
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| Online Access: | https://doi.org/10.1007/s44290-025-00257-1 |
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| author | Mohammad Ali Khasawneh Hiren Mewada Ahmad Ali Khasawneh Ansam Adnan Sawalha |
| author_facet | Mohammad Ali Khasawneh Hiren Mewada Ahmad Ali Khasawneh Ansam Adnan Sawalha |
| author_sort | Mohammad Ali Khasawneh |
| collection | DOAJ |
| description | Abstract This paper aims to develop predictive models for the thermal properties of laboratory-prepared hot mix asphalt (HMA) specimens using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The thermal properties investigated include thermal conductivity, thermal diffusivity, and specific heat. Thirty specimens were prepared by varying the mixture’s nominal maximum aggregate size and gradation coarseness, using a single asphalt binder. The transient plane source method was employed to determine the thermal properties, and volumetric parameters such as air void volume and effective binder volume were calculated. Surface characteristics, including friction and texture, were also considered. A total of 150 data points were generated through three aggregate sizes, two gradation levels, five replicates, and five measurement locations to ensure accuracy and repeatability. The model development process was structured in three phases: (1) feature reduction to detect multicollinearity, (2) outlier detection and removal, and (3) application of the ANFIS regression model. Model performance was evaluated using mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R2). Results indicated that outliers significantly increased MSE and reduced prediction accuracy, with RMSE improving from 0.4752 to 0.1765 after outlier removal for thermal conductivity. R2 scores for thermal diffusivity and specific heat improved from 0.55 and 0.72 to 0.866 and 0.91, respectively, after addressing multicollinearity and outliers. The study demonstrates that the proposed ANFIS model effectively predicts the thermal properties of HMA specimens and highlights the potential for even greater predictive accuracy by integrating other advanced regression methods. |
| format | Article |
| id | doaj-art-6e89ea4d759948e6b8cece5642c26063 |
| institution | Kabale University |
| issn | 2948-1546 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Civil Engineering |
| spelling | doaj-art-6e89ea4d759948e6b8cece5642c260632025-08-20T03:48:18ZengSpringerDiscover Civil Engineering2948-15462025-05-012111810.1007/s44290-025-00257-1Thermal features prediction in asphalt pavements using ANFIS-based regressionMohammad Ali Khasawneh0Hiren Mewada1Ahmad Ali Khasawneh2Ansam Adnan Sawalha3Civil Engineering Department, Prince Mohammad Bin Fahd UniversityElectrical Engineering Department, Prince Mohammad Bin Fahd UniversityManagement Engineering and Process Improvement Department/Integrated Systems Engineering Department, Ohio State University Wexner Medical Center &, Ohio State UniversityDepartment of Civil Engineering, Jordan University of Science and TechnologyAbstract This paper aims to develop predictive models for the thermal properties of laboratory-prepared hot mix asphalt (HMA) specimens using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The thermal properties investigated include thermal conductivity, thermal diffusivity, and specific heat. Thirty specimens were prepared by varying the mixture’s nominal maximum aggregate size and gradation coarseness, using a single asphalt binder. The transient plane source method was employed to determine the thermal properties, and volumetric parameters such as air void volume and effective binder volume were calculated. Surface characteristics, including friction and texture, were also considered. A total of 150 data points were generated through three aggregate sizes, two gradation levels, five replicates, and five measurement locations to ensure accuracy and repeatability. The model development process was structured in three phases: (1) feature reduction to detect multicollinearity, (2) outlier detection and removal, and (3) application of the ANFIS regression model. Model performance was evaluated using mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R2). Results indicated that outliers significantly increased MSE and reduced prediction accuracy, with RMSE improving from 0.4752 to 0.1765 after outlier removal for thermal conductivity. R2 scores for thermal diffusivity and specific heat improved from 0.55 and 0.72 to 0.866 and 0.91, respectively, after addressing multicollinearity and outliers. The study demonstrates that the proposed ANFIS model effectively predicts the thermal properties of HMA specimens and highlights the potential for even greater predictive accuracy by integrating other advanced regression methods.https://doi.org/10.1007/s44290-025-00257-1Thermal conductivityThermal diffusivitySpecific heatAggregate gradationAggregate SkeletonAsphalt mixture volumetrics |
| spellingShingle | Mohammad Ali Khasawneh Hiren Mewada Ahmad Ali Khasawneh Ansam Adnan Sawalha Thermal features prediction in asphalt pavements using ANFIS-based regression Discover Civil Engineering Thermal conductivity Thermal diffusivity Specific heat Aggregate gradation Aggregate Skeleton Asphalt mixture volumetrics |
| title | Thermal features prediction in asphalt pavements using ANFIS-based regression |
| title_full | Thermal features prediction in asphalt pavements using ANFIS-based regression |
| title_fullStr | Thermal features prediction in asphalt pavements using ANFIS-based regression |
| title_full_unstemmed | Thermal features prediction in asphalt pavements using ANFIS-based regression |
| title_short | Thermal features prediction in asphalt pavements using ANFIS-based regression |
| title_sort | thermal features prediction in asphalt pavements using anfis based regression |
| topic | Thermal conductivity Thermal diffusivity Specific heat Aggregate gradation Aggregate Skeleton Asphalt mixture volumetrics |
| url | https://doi.org/10.1007/s44290-025-00257-1 |
| work_keys_str_mv | AT mohammadalikhasawneh thermalfeaturespredictioninasphaltpavementsusinganfisbasedregression AT hirenmewada thermalfeaturespredictioninasphaltpavementsusinganfisbasedregression AT ahmadalikhasawneh thermalfeaturespredictioninasphaltpavementsusinganfisbasedregression AT ansamadnansawalha thermalfeaturespredictioninasphaltpavementsusinganfisbasedregression |