Data‐driven rapid damage evaluation for life‐cycle seismic assessment of regional reinforced concrete bridges

Rapid and accurate post‐earthquake damage evaluation of regional reinforced concrete (RC) bridges is a key issue for assessing the seismic resilience of cities and communities. Especially, RC bridges are susceptible to the aggressive environment, which can induce time‐dependent aging effects such as...

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Vydáno v:Earthquake engineering & structural dynamics Ročník 51; číslo 11; s. 2730 - 2751
Hlavní autoři: Xu, Ji‐Gang, Feng, De‐Cheng, Mangalathu, Sujith, Jeon, Jong‐Su
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bognor Regis Wiley Subscription Services, Inc 01.09.2022
Témata:
Ageing
Aging
Aging (artificial)
aging corrosion effects
Concrete bridges
Corrosion
Corrosion effects
Damage assessment
damage state
Earthquake damage
Earthquake prediction
Earthquake resistance
Earthquakes
Learning algorithms
Machine learning
Marking and tracking techniques
Mathematical models
Modelling
Numerical models
Performance assessment
Performance testing
RC bridges
Regional analysis
Reinforced concrete
Seismic activity
seismic assessment
Seismic response
tagging
ISSN:0098-8847, 1096-9845
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Shrnutí:Rapid and accurate post‐earthquake damage evaluation of regional reinforced concrete (RC) bridges is a key issue for assessing the seismic resilience of cities and communities. Especially, RC bridges are susceptible to the aggressive environment, which can induce time‐dependent aging effects such as corrosion, and thus, it should be considered in the assessment. This paper presents an approach for regional seismic performance assessment of RC bridges in a life‐cycle context based on machine‐learning techniques. The life‐cycle seismic demand and capacity of the bridges are, firstly, obtained by the elaborated numerical model, which includes the deterioration induced by the aging corrosion effect. Then, the tagging‐based damage state (green, yellow, or red) is easily obtained by comparing the pairs of demand and capacity through machine learning. Four hundred and eighty bridge models are generated to develop the machine‐learning models and the performance of the machine learning models is evaluated. Results show that the extreme gradient boosting (XGBoost) model has the best performance, which has an accuracy of 81% in predicting the damage states. The proposed approach is demonstrated with a single bridge example and bridges in a sample region. It is shown that the machine learning model can accurately predict the post‐earthquake damage states of the single bridge, and it can also rapidly assess the damage states of the bridges in the sample region. Approximately 30% bridges in the sample region will experience damage states shift after 100 years, which highlights the importance of considering the aging effects on the post‐earthquake damage assessment of bridges.
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ISSN:0098-8847
1096-9845
DOI:10.1002/eqe.3699