BWIM aided damage detection in bridges using machine learning-Reference-Cited by-同舟云学术

BWIM aided damage detection in bridges using machine learning

Published:2015-08-29 Issue:5 Volume:5 Page:715-725
ISSN:2190-5452
Container-title:Journal of Civil Structural Health Monitoring
language:en
Short-container-title:J Civil Struct Health Monit

Author:

Gonzalez Ignacio,Karoumi Raid

Publisher

Springer Science and Business Media LLC

Subject

Safety, Risk, Reliability and Quality,Civil and Structural Engineering

Link

http://link.springer.com/content/pdf/10.1007/s13349-015-0137-4.pdf

Reference25 articles.

1. Cavadas F, Smith FC, Figueiras J (2013) Damage detection using data-driven methods applied to moving-load responses. Mech Syst Signal Process 39(1):409–425

2. An Y et al (2014) A damage localization method based on the ‘jerk energy’. Smart Mater Struct 23(2):025020

3. Dackermann U, Smith WA, Randall RB (2013) Application of cepstrum analysis and artificial neural networks for the damage identification of a two-storey framed structure based on response-only measurements. Proceedings for the 6th international conference on structural health monitoring of intelligent infrastructure, Hong Kong, China, 9–11

4. Figueiredo E et al (2014) A Bayesian approach based on a Markov-chain Monte Carlo method for damage detection under unknown sources of variability. Eng Struct 80:1–10

5. Laory I et al (2013) ‘Combined model-free data-interpretation methodologies for damage detection during continuous monitoring of structures. J Comput Civil Eng 27(6):657–666

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