Using Artificial Intelligence to Estimate Nonlinear Resilient Modulus Parameters from Common Index Properties
| Autor: | Laura Camarena |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Transportation Research Record: Journal of the Transportation Research Board. 2675:1054-1061 |
| ISSN: | 2169-4052 0361-1981 |
| DOI: | 10.1177/03611981211023766 |
| Popis: | The Mechanistic–Empirical Pavement Design Guide (MEPDG) considers a hierarchical approach to determine the input values necessary for most design parameters. Level 1 requires site-specific measurement of the material properties from laboratory testing, whereas other levels make use of equations developed from regression models to estimate the material properties. Resilient modulus is a mechanical property that characterizes the unbound and subgrade materials under loading that is essential for the mechanistic design of pavements. The MEPDG resilient modulus model makes use of a three-parameter constitutive model to characterize the nonlinear behavior of the geomaterials. As the resilient modulus tests are complex, expensive, and require lengthy preparation time, most state highway agencies are unlikely to implement them as routine daily applications. Therefore, it is imperative to make use of models to calculate these nonlinear parameters. Existing models to determine these parameters are frequently based on linear regression. With the development of machine learning techniques, it is feasible to develop simpler equations that can be used to estimate the nonlinear parameters more accurately. This study makes use of the Long-Term Pavement Performance database and machine learning techniques to improve the equations utilized to determine the nonlinear parameters crucial to estimate the resilient modulus of unbound base and subgrade materials. |
| Databáze: | OpenAIRE |
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