Review of Bridge Structural Health Monitoring Aided by Big Data and Artificial Intelligence: From Condition Assessment to Damage Detection-Reference-Cited by-同舟云学术

Review of Bridge Structural Health Monitoring Aided by Big Data and Artificial Intelligence: From Condition Assessment to Damage Detection

Published:2020-05 Issue:5 Volume:146 Page:04020073
ISSN:0733-9445
Container-title:Journal of Structural Engineering
language:en
Short-container-title:J. Struct. Eng.

Author:

Sun Limin¹,Shang Zhiqiang²,Xia Ye³,Bhowmick Sutanu⁴,Nagarajaiah Satish⁵^ORCID

Affiliation:

1. Professor, State Key Lab for Disaster Reduction in Civil Engineering, Dept. of Bridge Engineering, College of Civil Engineering, Tongji Univ., Shanghai 200092, China.

2. Ph.D. Candidate, Dept. of Bridge Engineering, College of Civil Engineering, Tongji Univ., Shanghai 200092, China.

3. Associate Professor, Dept. of Bridge Engineering, College of Civil Engineering, Tongji Univ., Shanghai 200092, China.

4. Ph.D. Candidate, Dept. of Civil and Environmental Engineering, Rice Univ., Houston, TX 77005.

5. Professor, Dept. of Civil and Environmental Engineering, Rice Univ., Houston, TX 77005 (corresponding author). ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Mechanical Engineering,Mechanics of Materials,General Materials Science,Building and Construction,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29ST.1943-541X.0002535

Reference184 articles.

1. 1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data

2. Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

3. Condition Assessment for Bridge Management

4. A spectral-based clustering for structural health monitoring of the Sydney Harbour Bridge

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