Image Retraining Using TensorFlow Implementation of the Pretrained Inception-v3 Model for Evaluating Gravel Road Dust-Reference-Cited by-同舟云学术

Image Retraining Using TensorFlow Implementation of the Pretrained Inception-v3 Model for Evaluating Gravel Road Dust

Published:2020-06 Issue:2 Volume:26 Page:04020014
ISSN:1076-0342
Container-title:Journal of Infrastructure Systems
language:en
Short-container-title:J. Infrastruct. Syst.

Author:

Albatayneh Omar¹^ORCID,Forslöf Lars²,Ksaibati Khaled³

Affiliation:

1. Graduate Research Assistant, Dept. of Civil and Architectural Engineering, Univ. of Wyoming, 1000 E University Ave., Laramie, WY 82070 (corresponding author). ORCID: .

2. Roadroid Chief Executive Officer and Inventor, Egnahemsgatan 5, 82735 Ljusdal, Sweden.

3. Professor of Civil Engineering and Director of the Wyoming Technology Transfer Center, Dept. of Civil and Architectural Engineering, Univ. of Wyoming, 1000 E University Ave., Dept. 3295, Laramie, WY 82071.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29IS.1943-555X.0000545

Reference37 articles.

1. Abadi M. et al. 2016. “Tensorflow: A system for large-scale machine learning.” In Proc. 12th {USENIX} Symp. on Operating Systems Design and Implementation ({OSDI} 16) 265–283. Savannah GA: USENIX Association.

2. A comprehensive approach for quantifying environmental costs associated with unpaved roads dust

3. An optimisation tool to select gravel roads for dust chemical treatment projects using genetic algorithms

4. Estimation of Gravel Roads Ride Quality Through an Android-Based Smartphone

5. Development of Performance Prediction Models for Gravel Roads Using Markov Chains

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