0
  • DE
  • EN
  • FR
  • Internationale Datenbank und Galerie für Ingenieurbauwerke

Anzeige

A New Ensemble Prediction Method for Reclaimed Asphalt Pavement (RAP) Mixtures Containing Different Constituents

Autor(en): ORCID
ORCID



Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Buildings, , n. 7, v. 13
Seite(n): 1787
DOI: 10.3390/buildings13071787
Abstrakt:

Fatigue and rutting are two common damage types in asphalt pavements. Reclaimed asphalt pavement (RAP), as a sustainable approach in the pavement industry, deals with the foregoing damage. Fatigue and rutting characteristics of asphalt pavement are generally assessed using laboratory tests, taking a long time and consuming significant amounts of raw material. This study aims to propose a novel approach for predicting fatigue and rutting performance of RAP mixtures. A new ensemble prediction method, named COA-KNN, is introduced by combining the coyote optimization algorithm and K-nearest neighbor to increase the accuracy of fatigue and rutting prediction. In order to evaluate the accuracy, the proposed method was compared against robust prediction methods, including random forest (RF), gradient boosting (GB), decision tree regression (DT), and multiple linear regression (MLR). Afterward, the influence of each variable on the mentioned damages is examined, and the variables are ranked based on their relative influence on the mentioned damages. The results suggest that COA-KNN outperformed other prediction techniques when comparing different performance indicators. Total binder content in asphalt mixes and the PG span of the virgin binder added to the recycled asphalt mixture had the highest relative influence on fatigue and rutting performance, respectively.

Copyright: © 2023 by the authors; licensee MDPI, Basel, Switzerland.
Lizenz:

Dieses Werk wurde unter der Creative-Commons-Lizenz Namensnennung 4.0 International (CC-BY 4.0) veröffentlicht und darf unter den Lizenzbedinungen vervielfältigt, verbreitet, öffentlich zugänglich gemacht, sowie abgewandelt und bearbeitet werden. Dabei muss der Urheber bzw. Rechteinhaber genannt und die Lizenzbedingungen eingehalten werden.

  • Über diese
    Datenseite
  • Reference-ID
    10737580
  • Veröffentlicht am:
    03.09.2023
  • Geändert am:
    14.09.2023
 
Structurae kooperiert mit
International Association for Bridge and Structural Engineering (IABSE)
e-mosty Magazine
e-BrIM Magazine