FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4364375626> ?p ?o ?g. }

• W4364375626 endingPage "116083" @default.
• W4364375626 startingPage "116083" @default.
• W4364375626 abstract "Predicting the nonlinear time-history responses of civil engineering structures under seismic loading remains an essential task in earthquake engineering. This paper explores the promise of developing three deep learning (DL) models, i.e., long short-term memory (LSTM), WaveNet, and 2D convolution neural network (CNN), to predict seismic response time histories of three benchmark structures, including a single degree-of-freedom (SDOF) system, a steel building frame, and a multi-component bridge structure. LSTM has been previously developed and is utilized to serve as a reference model, while WaveNet and 2D CNN (i.e., it deals with the data in coupled time–frequency dimensions) are newly developed in the current study. One other novel contribution is to replace the final layer of the WaveNet with an LSTM layer, which significantly improves the model performance. Methodological backgrounds of these DL models are introduced, followed by discussions on model architectures and hyperparameters, the list of evaluation metrics, and the ground motion (GM) suite selected for data generation. High-fidelity numerical models are developed for conducting nonlinear time history analyses that generate numerous motion-response pairs for training, validating, and testing the DL models. The LSTM and WaveNet directly use time series GM inputs and seismic response outputs to train the models, whereas the CNN makes inferences on time–frequency spectrogram images converted through the short-time Fourier transform (STFT). These three DL models are investigated by comparing deterministic predictions under one testing GM and probabilistic distributions of six evaluation metrics. The models’ accuracy, efficiency, and robustness are further examined using a sensitivity study under different training samples and model architectures. Research findings from this study provide a sound reference for the community to leverage these three DL models to achieve reliable and efficient time history response predictions, which are crucial for fulfilling cutting-edge research/practical tasks such as regional seismic risk assessment and performance-based seismic design." @default.
• W4364375626 created "2023-04-12" @default.
• W4364375626 creator A5041516110 @default.
• W4364375626 creator A5077794780 @default.
• W4364375626 creator A5089073667 @default.
• W4364375626 date "2023-07-01" @default.
• W4364375626 modified "2023-09-30" @default.
• W4364375626 title "LSTM, WaveNet, and 2D CNN for nonlinear time history prediction of seismic responses" @default.
• W4364375626 cites W1500974069 @default.
• W4364375626 cites W179875071 @default.
• W4364375626 cites W1893890348 @default.
• W4364375626 cites W1978713621 @default.
• W4364375626 cites W2014712737 @default.
• W4364375626 cites W2033839559 @default.
• W4364375626 cites W2039537697 @default.
• W4364375626 cites W2075315687 @default.
• W4364375626 cites W2076063813 @default.
• W4364375626 cites W2077179900 @default.
• W4364375626 cites W2087904591 @default.
• W4364375626 cites W2103482032 @default.
• W4364375626 cites W2110893990 @default.
• W4364375626 cites W2120847449 @default.
• W4364375626 cites W2131774270 @default.
• W4364375626 cites W2135370187 @default.
• W4364375626 cites W2152977846 @default.
• W4364375626 cites W2167996923 @default.
• W4364375626 cites W2191779130 @default.
• W4364375626 cites W2613985117 @default.
• W4364375626 cites W2728030886 @default.
• W4364375626 cites W2748988943 @default.
• W4364375626 cites W2789384841 @default.
• W4364375626 cites W2807042118 @default.
• W4364375626 cites W2898456550 @default.
• W4364375626 cites W2924904487 @default.
• W4364375626 cites W2946752227 @default.
• W4364375626 cites W2963103134 @default.
• W4364375626 cites W2976308430 @default.
• W4364375626 cites W3022953487 @default.
• W4364375626 cites W3033652307 @default.
• W4364375626 cites W3037134996 @default.
• W4364375626 cites W3039209232 @default.
• W4364375626 cites W3135694489 @default.
• W4364375626 cites W3136631225 @default.
• W4364375626 cites W3138776657 @default.
• W4364375626 cites W3196184381 @default.
• W4364375626 cites W3217240125 @default.
• W4364375626 cites W4200434888 @default.
• W4364375626 cites W4205947740 @default.
• W4364375626 cites W4210696059 @default.
• W4364375626 cites W4220953858 @default.
• W4364375626 cites W4242688646 @default.
• W4364375626 cites W4283787648 @default.
• W4364375626 cites W4288032561 @default.
• W4364375626 cites W4380077532 @default.
• W4364375626 doi "https://doi.org/10.1016/j.engstruct.2023.116083" @default.
• W4364375626 hasPublicationYear "2023" @default.
• W4364375626 type Work @default.
• W4364375626 citedByCount "2" @default.
• W4364375626 countsByYear W43643756262023 @default.
• W4364375626 crossrefType "journal-article" @default.
• W4364375626 hasAuthorship W4364375626A5041516110 @default.
• W4364375626 hasAuthorship W4364375626A5077794780 @default.
• W4364375626 hasAuthorship W4364375626A5089073667 @default.
• W4364375626 hasConcept C102519508 @default.
• W4364375626 hasConcept C104317684 @default.
• W4364375626 hasConcept C108583219 @default.
• W4364375626 hasConcept C113364801 @default.
• W4364375626 hasConcept C11413529 @default.
• W4364375626 hasConcept C119599485 @default.
• W4364375626 hasConcept C119857082 @default.
• W4364375626 hasConcept C121332964 @default.
• W4364375626 hasConcept C127413603 @default.
• W4364375626 hasConcept C13280743 @default.
• W4364375626 hasConcept C134306372 @default.
• W4364375626 hasConcept C153180895 @default.
• W4364375626 hasConcept C154945302 @default.
• W4364375626 hasConcept C158622935 @default.
• W4364375626 hasConcept C166386157 @default.
• W4364375626 hasConcept C185592680 @default.
• W4364375626 hasConcept C185798385 @default.
• W4364375626 hasConcept C203024314 @default.
• W4364375626 hasConcept C205649164 @default.
• W4364375626 hasConcept C33923547 @default.
• W4364375626 hasConcept C41008148 @default.
• W4364375626 hasConcept C45273575 @default.
• W4364375626 hasConcept C50644808 @default.
• W4364375626 hasConcept C55493867 @default.
• W4364375626 hasConcept C62520636 @default.
• W4364375626 hasConcept C63479239 @default.
• W4364375626 hasConcept C81363708 @default.
• W4364375626 hasConcept C8642999 @default.
• W4364375626 hasConceptScore W4364375626C102519508 @default.
• W4364375626 hasConceptScore W4364375626C104317684 @default.
• W4364375626 hasConceptScore W4364375626C108583219 @default.
• W4364375626 hasConceptScore W4364375626C113364801 @default.
• W4364375626 hasConceptScore W4364375626C11413529 @default.
• W4364375626 hasConceptScore W4364375626C119599485 @default.
• W4364375626 hasConceptScore W4364375626C119857082 @default.

• next