Prediction of asphalt concrete flexibility index and rut depth utilising deep learning and Monte Carlo Dropout simulation-Reference-Cited by-同舟云学术

Prediction of asphalt concrete flexibility index and rut depth utilising deep learning and Monte Carlo Dropout simulation

Published:2023-10-06 Issue:1 Volume:24 Page:
ISSN:1029-8436
Container-title:International Journal of Pavement Engineering
language:en
Short-container-title:International Journal of Pavement Engineering

Author:

Rivera-Pérez José¹^ORCID,Talebpour Alireza²^ORCID,Al-Qadi Imad L.²^ORCID

Affiliation:

1. Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA

2. Illinois Center for Transportation, Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA

Publisher

Informa UK Limited

Subject

Mechanics of Materials,Civil and Structural Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/10298436.2023.2253964

Reference47 articles.

1. Agarap A. F. 2018. Deep learning using rectified linear units (relu). ArXiv preprint arXi 1803.08375 .

2. Al-Qadi, I. L., et al., 2015. Testing protocols to ensure performance of high asphalt binder replacement mixes using RAP and RAS. Rantoul, IL: Illinois Center for Transportation/Illinois Department of Transportation.

3. Development of Long-Term Aging Protocol for Implementation of the Illinois Flexibility Index Test (I-FIT)

4. Cracking prediction of asphalt concrete using fracture and strength tests;Al-Qadi I. L.;International Journal of Pavement Engineering,2021

5. Branco P. Ribeiro R. P. and Torgo L. 2016. UBL: an R package for utility-based learning. arXiv preprint arXiv:1604.08079 .

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