Generative adversarial networks review in earthquake-related engineering fields

GC Marano, MM Rosso, A Aloisio… - Bulletin of Earthquake …, 2024 - Springer
Within seismology, geology, civil and structural engineering, deep learning (DL), especially
via generative adversarial networks (GANs), represents an innovative, engaging, and …

Deep Learning for Earthquake Disaster Assessment: Objects, Data, Models, Stages, Challenges, and Opportunities

J Jia, W Ye - Remote Sensing, 2023 - mdpi.com
Earthquake Disaster Assessment (EDA) plays a critical role in earthquake disaster
prevention, evacuation, and rescue efforts. Deep learning (DL), which boasts advantages in …

A CNN-BiLSTM model with attention mechanism for earthquake prediction

P Kavianpour, M Kavianpour, E Jahani… - The Journal of …, 2023 - Springer
Earthquakes, as natural phenomena, have consistently caused damage and loss of human
life throughout history. Earthquake prediction is an essential aspect of any society's plans …

Globally optimized machine-learning framework for CO2-hydrocarbon minimum miscibility pressure calculations

C Huang, L Tian, T Zhang, J Chen, J Wu, H Wang… - Fuel, 2022 - Elsevier
Accurate determination of CO 2-hydrocarbon minimum miscibility pressure (MMP) is
critically important for CO 2 geological storage and utilization in oil and gas reservoirs. Here …

[HTML][HTML] Microstructural image based convolutional neural networks for efficient prediction of full-field stress maps in short fiber polymer composites

S Gupta, T Mukhopadhyay, V Kushvaha - Defence Technology, 2023 - Elsevier
The increased demand for superior materials has highlighted the need of investigating the
mechanical properties of composites to achieve enhanced constitutive relationships. Fiber …

Prediction of lateral spreading displacement using conditional Generative Adversarial Network (cGAN)

H Woldesellasse, S Tesfamariam - Soil Dynamics and Earthquake …, 2022 - Elsevier
Lateral spreading is the most pervasive type of earthquake-induced ground deformation,
which can cause considerable damage to engineered structures and lifelines. There are …

建筑基本周期多因素机器学习预测模型.

陈隽, 宋颖豪, 王泽涛 - Engineering Mechanics/Gongcheng …, 2024 - search.ebscohost.com
建筑物基本周期是其最重要的动力特性参数, 影响因素众多. 受限于曲线拟合的传统建模手段,
目前的基本周期预测模型表达式中仅能包含高度或层数等单一因素, 而忽略其他因素的影响 …

Virtual Scenarios of Earthquake Early Warning to Disaster Management in Smart Cities Based on Auxiliary Classifier Generative Adversarial Networks

JK Ahn, B Kim, B Ku, EH Hwang - Sensors, 2023 - mdpi.com
Effective response strategies to earthquake disasters are crucial for disaster management in
smart cities. However, in regions where earthquakes do not occur frequently, model …

Prediction for underground seismic intensity measures using conditional generative adversarial networks

S Duan, Z Song, J Shen, J Xiong - Soil Dynamics and Earthquake …, 2024 - Elsevier
With the escalating development and utilization of subterranean spaces, the seismic
hazards faced by underground structures are progressively increasing. However, owing to …

Conditional probability modeling of intensity measures for offshore mainshock-aftershock sequences

X Bai, H Jiang, G Song - Soil Dynamics and Earthquake Engineering, 2022 - Elsevier
Offshore structures are typically subjected to offshore mainshock (MS)–aftershock (AS)
sequences. The realistic statistical modeling of intensity measures (IMs) for offshore MS–AS …