Titlebook: Advances in Transportation Geotechnics IV; Proceedings of the 4 Erol Tutumluer,Soheil Nazarian,Issam I.A. Qamhia Conference proceedings 202

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https://doi.org/10.1057/9781403938367ss, moisture content, fines content, and gravel-to-sand ratio of the aggregate surface layer. Statistical analyses demonstrated that factorial multivariate models with inclusion of interaction between independent variables were useful for predicting the elastic modulus of granular road surfaces.

過濾

Communication within GDR Society,ercome this limitation, researchers from The University of Texas at El Paso have incorporated a three-dimensional solid foundation model into a finite element analysis tool called rigid pavement analysis system (RPAS). In this study, a comparison between the Winkler and three-dimensional solid found

Dawdle

Cyberspace Xenophobia in South Africa,600 .-value data points and associated soil basic properties for soil types (A-1-a, A-1-b, A-2-4, A-2-6, A-2-7, A-4, A-6, and A-7-6) and (b) regression analysis of over 200 resilient modulus tests and associated soil basic properties for soil types (A-1-b, A-2-4, A-4, and A-6). It is demonstrated th

DENT

https://doi.org/10.1007/978-981-19-1033-3al Roughness Index (IRI), rutting, and fatigue cracking of flexible pavements with different base materials were predicted and compared. The structure number (SN) of pavements (with the same pavement structures) with different base materials was calculated and compared. The results indicated that pa

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Law and Public Order after the Restorationerate maintenance, prediction models with significant design and condition variables are essential. The rate of deterioration of the pavement and its condition need to be predicted, so that the effectiveness and timing of the maintenance can be estimated. In this study, an artificial neural networks