Experimental Evaluation of Convolutional Neural Networks in Asphalt Concrete Computed Tomography Scan Image Analysis-Reference-Cited by-同舟云学术

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Experimental Evaluation of Convolutional Neural Networks in Asphalt Concrete Computed Tomography Scan Image Analysis

Published:2024-03-18 Issue: Volume:0 Page:506-515
ISSN:
Container-title:Construction Research Congress 2024
language:
Short-container-title:

Author:

Han Sisi¹²³,Chen Jeffrey¹²³,Jiang Yuhan¹²³

Affiliation:

1. Ph.D. Candidate, Dept. of Civil, Construction, and Environmental Engineering, Marquette Univ., Milwaukee, WI. ORCID: .

2. Marvin Ridge High School, Waxhaw, NC.

3. Assistant Professor, Dept. of Built Environment, North Carolina A&T State Univ., Greensboro, NC. ORCID: .

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784485262.052

Reference11 articles.

1. Chattopadhay A. A. Sarkar P. Howlader and V. N. Balasubramanian. 2018. “Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks.” 2018 IEEE Winter Conf. Appl. Comput. Vis. 839–847. IEEE.

2. Improved Image Unevenness Reduction and Thresholding Methods for Effective Asphalt X-Ray CT Image Segmentation

3. Chiarelli A. 2016. “Asphalt Core CT Scan Images.” figshare. Accessed June 3 2021. https://figshare.com/collections/Asphalt_Core_CT_Scan_Images/3468273.

4. Determination of the air void content of asphalt concrete mixtures using artificial intelligence techniques to segment micro-CT images;Enríquez-León A. J.;Int. J. Pavement Eng,2021

5. Scan2Drawing: Use of Deep Learning for As-Built Model Landscape Architecture;Han S.;J. Constr. Eng. Manag,2023

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