Deep learning-driven pavement crack analysis: Autoencoder-enhanced crack feature extraction and structure classification-Reference-Cited by-同舟云学术

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Deep learning-driven pavement crack analysis: Autoencoder-enhanced crack feature extraction and structure classification

Published:2024-06 Issue: Volume:132 Page:107949
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Zhang Miaomiao,Zhong Jingtao,Zhou Changhong^ORCID,Jia Xiaoyang,Zhu Xingyi,Huang Baoshan

Funder

Tennessee Department of Transportation

Publisher

Elsevier BV

Reference37 articles.

1. Pavement deterioration and its causes;Adlinge;International Journal of Innovative Research and Development,2013

2. Road Survey Equipment. ARRB Systems,2022

3. Random forests;Breiman;Mach. Learn.,2001

4. Automatic Pavement Crack Detection Based on Structured Prediction with the Convolutional Neural Network;Fan,2018

5. FHWA Computation Procedure for the Pavement Condition Measures (No. FHWA-HIF-18-022);Grogg,2018

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1. Multimodal data fusion enhanced deep learning prediction of crack path segmentation in CFRP composites;Composites Science and Technology;2024-10

2. High-dimensional multi-objective optimization of coupled cross-laminated timber walls building using deep learning;Engineering Applications of Artificial Intelligence;2024-10

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