Finite element analysis-aided seismic behavior examination of modular underground arch bridge

Nguyen, Toan Van; Seo, Junwon; Ahn, Jin Hee; Haldar, Achintya; Huh, Jungwon

doi:10.1016/j.tust.2021.104166

Cited by 6 publications

(2 citation statements)

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“…Skolnik et al 1 developed an FE model to forecast the earthquake performance of a 15-story building frame; a substantial amount of computing power was required to process the model. Van Nguyen et al 2 investigated the seismic performance of bridges affected by ground vibrations using three-dimensional FE analysis. The commonly employed autoregressive integrated moving average model 3 was created by combining autoregressive models, 4 moving average models, 5 and autoregressive moving average models.…”

Section: Introductionmentioning

confidence: 99%

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Generalized stacked LSTM for the seismic damage evaluation of ductile reinforced concrete buildings

Ahmed

Mangalathu²,

Jeon

2023

Earthq Engng Struct Dyn

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To organize accurate and effective emergency responses after an earthquake, it is vital to conduct an early and precise assessment of damage to structures. The use of fragility/vulnerability curves is an advanced evaluation approach for structural damage assessments. However, the analysis based on fragility curves significantly varies depending on soil conditions, ground motion, and structural characteristics. To overcome this issue, a stacked long short‐term memory network was proposed in this research. Unlike previous studies, two input features (acceleration time history in the form of vector and the number of stories in the scalar) are utilized to generalize the results for the same plan building frames with different stories. Three different approaches are presented in this work to link the ground motion time history with the number of stories (2, 4, 8, 12, and 20 stories) in the reinforced concrete building frame, and the networks were tested for unknown ground motions. Of the three approaches, those providing good results were selected for further analysis. For the approaches chosen, the network architectures were changed to a diamond shape and an autoencoder‐like shape with more hidden units (to obtain higher accuracy), which were tested for unknown same plan layout frames. The accuracy obtained using these approaches was significantly high (80%–90%) with a low training time. The proposed model is compared with other techniques and shows significant accuracy. The suggested networks exhibited a number of scenarios for estimating the damage state for unknown ground motions, as well as for unknown frames with various stories. Moreover, the capability of the networks to handle more scalar input features is examined by adding them probabilistically; with additional input variables, the networks predicted the damage state with higher accuracy.

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Section: Introductionmentioning

confidence: 99%

“…developed an FE model to forecast the earthquake performance of a 15‐story building frame; a substantial amount of computing power was required to process the model. Van Nguyen et al 2 . investigated the seismic performance of bridges affected by ground vibrations using three‐dimensional FE analysis.…”

Section: Introductionmentioning

confidence: 99%