Adaptive Cross-Scenario Few-Shot Learning Framework for Structural Damage Detection in Civil Infrastructure-Reference-Cited by-同舟云学术

Adaptive Cross-Scenario Few-Shot Learning Framework for Structural Damage Detection in Civil Infrastructure

Published:2023-05 Issue:5 Volume:149 Page:
ISSN:0733-9364
Container-title:Journal of Construction Engineering and Management
language:en
Short-container-title:J. Constr. Eng. Manage.

Author:

Xue Gang¹,Liu Shifeng²,Ren Long³,Gong Daqing⁴

Affiliation:

1. Ph.D. Candidate, School of Economics and Management, Beijing Jiaotong Univ., Beijing 100044, China.

2. Professor, School of Economics and Management, Beijing Jiaotong Univ., Beijing 100044, China.

3. Associate Professor, School of Information Technology and Management, Univ. of International Business and Economics, Beijing 100029, China.

4. Associate Professor, School of Economics and Management, Beijing Jiaotong Univ., Beijing 100044, China (corresponding author).

Publisher

American Society of Civil Engineers (ASCE)

Subject

Strategy and Management,Industrial relations,Building and Construction,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/JCEMD4.COENG-13196

Reference68 articles.

1. Adnan A. 2021. “Railway track fault detection: Dataset2(Fastener).” Kaggle. Accessed November 21 2021. https://www.kaggle.com/datasets/ashikadnan/railway-track-fault-detection-dataset2fastener.

2. Structural crack detection using deep convolutional neural networks

3. Autonomous detection of concrete damage under fire conditions

4. Basily A. 2020. “Road damage.” Kaggle. Accessed August 20 2020. https://www.kaggle.com/datasets/alvarobasily/road-damage.

5. Blundell C. J. Cornebise K. Kavukcuoglu and D. Wierstra. 2015. “Weight uncertainty in neural networks.” In Vol. 37 of Proc. 32nd Int. Conf. on Machine Learning. New York: Association for Computing Machinery.

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