Data‐Physical Fusion Deep Learning for Site Seismic Response Using KiK‐Net Records DOI Open Access
Su Chen, Xiaolin Hu, Weiping Jiang

и другие.

Earthquake Engineering & Structural Dynamics, Год журнала: 2024, Номер unknown

Опубликована: Дек. 23, 2024

ABSTRACT In the realm of earthquake engineering, response spectra play a crucial role in characterizing effects site dynamic characteristics under seismic activity. Consequently, accurately predicting is paramount importance. We have developed physics‐guided bidirectional long short‐term memory neural network model (Phy‐BiLSTM) that proficient based on bedrock records. The core principle Phy‐BiLSTM to improve alignment between solution space and ground truth by integrating physics knowledge obtained from physical model. introduced this study utilized 5%‐damped spectra, which were derived strong motion records collected at KiK‐net downhole array. results substantiate performance enhancement comparison data‐driven BiLSTM Furthermore, we conduct comparative analysis against traditional methods (EQ, SBSR) as well other architectures (CNN LSTM). result highlights advantages response.

Язык: Английский

A deep learning method for the prediction of ship fuel consumption in real operational conditions DOI Creative Commons
Mingyang Zhang,

Nikolaos Tsoulakos,

Pentti Kujala

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2023, Номер 130, С. 107425 - 107425

Опубликована: Дек. 22, 2023

In recent years, the European Commission and International Maritime Organization (IMO) implemented various operational measures policies to reduce ship fuel consumption related emissions. The effectiveness of these relies upon developing accurate predictive models encompassing influence real conditions. This paper presents a deep learning method for prediction consumption. utilizes big data analytics from sensors, voyage reporting hydrometeorological data, comprising 266 variables made available following sea trials Kamsarmax bulk carrier Laskaridis Shipping Co. Ltd. A variable importance estimation model using Decision Tree (DT) is used understand underlying relationships in dataset. Consequently, developed sailing speed, heading, displacement/draft, trim, weather, conditions, etc. on (SFC). achieved by incorporating attention mechanism into Bi-directional Long Short-Term Memory (Bi-LSTM) network. potential new demonstrated training streams corresponding rates as well internal external comprehensive comparison with existing methods indicates that Bi-LSTM best fit when high frequency data. It concluded subject further testing validation could be development decision support systems monitoring environmentally sustainable operations.

Язык: Английский

Процитировано

63

A rapid analysis framework for seismic response prediction and running safety assessment of train-bridge coupled systems DOI Open Access
Peng Zhang, Han Zhao, Zhanjun Shao

и другие.

Soil Dynamics and Earthquake Engineering, Год журнала: 2023, Номер 177, С. 108386 - 108386

Опубликована: Дек. 7, 2023

Язык: Английский

Процитировано

26

A new machine learning approach for estimating shear wave velocity profile using borelog data DOI
Anushka Joshi, Balasubramanian Raman, C. Krishna Mohan

и другие.

Soil Dynamics and Earthquake Engineering, Год журнала: 2023, Номер 177, С. 108424 - 108424

Опубликована: Дек. 29, 2023

Язык: Английский

Процитировано

17

Probabilistic analysis of tunnel convergence in spatially variable soil based on Gaussian process regression DOI

Houle Zhang,

Yongxin Wu,

Shangchuan Yang

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 131, С. 107840 - 107840

Опубликована: Янв. 9, 2024

Язык: Английский

Процитировано

5

Prediction for underground seismic intensity measures using conditional generative adversarial networks DOI
Shuqian Duan,

Zebin Song,

Jiaxu Shen

и другие.

Soil Dynamics and Earthquake Engineering, Год журнала: 2024, Номер 180, С. 108619 - 108619

Опубликована: Март 26, 2024

Язык: Английский

Процитировано

5

Neural network prediction model for site response analysis based on the KiK-net database DOI
Zilan Zhong,

Bo Ni,

Jiaxu Shen

и другие.

Computers and Geotechnics, Год журнала: 2024, Номер 171, С. 106366 - 106366

Опубликована: Апрель 23, 2024

Язык: Английский

Процитировано

5

Deep learning application for nonlinear seismic ground response prediction based on centrifuge test and numerical analysis DOI

Dong Van Nguyen,

Yun Wook Choo, Dookie Kim

и другие.

Soil Dynamics and Earthquake Engineering, Год журнала: 2024, Номер 182, С. 108733 - 108733

Опубликована: Май 27, 2024

Язык: Английский

Процитировано

5

An LSTM RNN proposal for surrogate modeling the dynamic response of buried structures to earthquake plane waves in soil half-spaces DOI

Hamid Taghavi Ganji,

Elnaz Seylabi

Computers and Geotechnics, Год журнала: 2023, Номер 164, С. 105796 - 105796

Опубликована: Сен. 26, 2023

Язык: Английский

Процитировано

10

Sequence Deep Learning for Seismic Ground Response Modeling: 1D-CNN, LSTM, and Transformer Approach DOI Creative Commons
Yong-Jin Choi, Huyen-Tram Nguyen,

Taek Hee Han

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(15), С. 6658 - 6658

Опубликована: Июль 30, 2024

Accurate seismic ground response analysis is crucial for the design and safety of civil infrastructure establishing effective mitigation measures against risks hazards. This a complex process due to nonlinear soil properties complicated underground geometries. As simplified approach, one-dimensional wave propagation model, which assumes that waves travel vertically through horizontally layered medium, widely adopted its reasonable performance in many practical applications. study explores potential sequence deep learning models, specifically 1D convolutional neural networks (1D-CNNs), long short-term memory (LSTM) networks, transformers, as an alternative modeling. Utilizing motion data from Kiban Kyoshin Network (KiK-net), we train these models predict surface acceleration spectra based on bedrock motions. The data-driven compared with conventional equivalent-linear SHAKE2000. results demonstrate outperform physics-based model across various sites, transformer exhibiting smallest average prediction error ability capture long-range dependencies. 1D-CNN also shows promising performance, albeit occasional higher errors than other models. All exhibit efficient computation times less 0.4 s estimation. These findings highlight approaches

Язык: Английский

Процитировано

3

A Textual and Temporal Data Fused Seismic Displacement Time Histories Prediction Method of Base-Isolated Frame Structures Using Deep Learning DOI
Qingle Cheng, Haotian Ren, Hongyu Zhao

и другие.

Journal of Earthquake Engineering, Год журнала: 2025, Номер unknown, С. 1 - 17

Опубликована: Фев. 24, 2025

Язык: Английский

Процитировано

0