首页/文章/ 详情

Earthq Eng Struct Dyn 人工智能特刊

1年前浏览2489
  

在国内外众多专家和朋友的支持下,Earthquake Engineering & Structural Dynamics AI特刊(EESD special issue: AI and data-driven methods in earthquake engineering)已经正式出版。本次特刊分为Part1和Part2两卷,详细信息请参见以下链接。


   
   

Part 1

https://onlinelibrary.wiley.com/toc/10969845/2023/52/8

   

Part 2

https://onlinelibrary.wiley.com/toc/10969845/2023/52/11

   


为更好的分享和传播地震工程领域与AI交叉的最新学科成果,EESD主编Masayoshi Nakashima教授与Wiley出版社商议后,本次特刊到2023年11月中旬前提供免费下载服务,欢迎大家阅读和交流。

Part 1论文目录

A hybrid non-parametric ground motion model for shallow crustal earthquakes in Europe. Vemula Sreenath, Bhargavi Podili, S. T. G. Raghukanth


Site classification using deep-learning-based image recognition techniques. Kun Ji, Chuanbin Zhu, Saman Yaghmaei-Sabegh, Jianqi Lu, Yefei Ren, Ruizhi Wen


Assessment of ground motion amplitude scaling using interpretable Gaussian process regression: Application to steel moment frames. Jawad Fayaz, Pablo Torres-Rodas, Miguel Medalla, Farzad Naeim


An unsupervised machine learning based ground motion selection method for computationally efficient estimation of seismic fragility. Jinjun Hu, Bali Liu, Lili Xie


Deep learning based seismic response prediction of hysteretic systems having degradation and pinching. Taeyong Kim, Oh-Sung Kwon, Junho Song


Surrogate modeling of structural seismic response using probabilistic learning on manifolds. Kuanshi Zhong, Javier G. Navarro, Sanjay Govindjee, Gregory G. Deierlein


Combination of physics-based and data-driven modeling for nonlinear structural seismic response prediction through deep residual learning. Jia Guo, Ryuta Enokida, Dawei Li, Kohju Ikago


The automated collapse data constructor technique and the data-driven methodology for seismic collapse risk assessment. Nenad Bijelić, Dimitrios G. Lignos, Alexandre Alahi


Reliability analysis of structures using probability density evolution method and stochastic spectral embedding surrogate model. Sourav Das, Solomon Tesfamariam


Deep learning-based evaluation for mechanical property degradation of seismically damaged RC columns. Zenghui Miao, Xiaodong Ji, Minghui Wu, Xiang Gao


Instance segmentation of soft-story buildings from street-view images with semiautomatic annotation. Chaofeng Wang, Sascha Hornauer, Stella X. Yu, Frank McKenna, Kincho H. Law


Three-dimensional fragility surface for reinforced concrete shear walls using image-based damage features. Amir Hossein Asjodi, Kiarash M. Dolatshahi, Henry V. Burton


Story drift and damage level estimation of buildings using relative acceleration responses with multi-target deep learning models under seismic excitation. Jau-Yu Chou, Chieh-Yu Liu, Chia-Ming Chang


Part 2论文目录

Unsupervised machine learning for detecting soil layer boundaries from cone penetration test data. Kenneth S. Hudson, Kristin J. Ulmer, Paolo Zimmaro, Steven L. Kramer, Jonathan P. Stewart, Scott J. Brandenberg


A continuous Bayesian network regression model for estimating seismic liquefaction-induced settlement of the free-field ground. Jilei Hu, Bin Xiong, Zheng Zhang, Jing Wang


Machine learning-based prediction of the seismic response of fault-crossing natural gas pipelines. Wenyang Zhang, Francois Ayello, Doug Honegger, Yousef Bozorgnia, Ertugrul Taciroglu


Machine-learning-based optimization framework to support recovery-based design. Omar Issa, Rodrigo Silva-Lopez, Jack W. Baker, Henry V. Burton


Base-isolation design of shear wall structures using physics-rule-co-guided self-supervised generative adversarial networks. Wenjie Liao, Xinyu Wang, Yifan Fei, Yuli Huang, Linlin Xie, Xinzheng Lu


A deep learning method to monitor axial pressure and shear deformation of rubber bearings under coupled compression and shear loading. Yi Zeng, Zhizhou He, Peng Pan


Collaborative filtering-based collapse fragility assessment. Xingquan Guan, Henry V. Burton


Uncertainty-aware structural damage warning system using deep variational composite neural networks. Kareem A. Eltouny, Xiao Liang


Multi-channel response reconstruction using transformer based generative adversarial network. Wenhao Zheng, Jun Li, Qilin Li, Hong Hao


Geometry-guided semantic segmentation for post-earthquake buildings using optical remote sensing images. Yu Wang, Xin Jing, Yang Xu, Liangyi Cui, Qiangqiang Zhang, Hui Li


Deep neural network-based regional seismic loss assessment considering correlation between EDP residuals of building structures. Chulyoung Kang, Taeyong Kim, Oh-Sung Kwon, Junho Song


Efficient regional seismic risk assessment via deep generative learning of surrogate models. Shanwu Li, Charles Farrar, Yongchao Yang. 


Simulation-based methodology to identify damage indicators and safety thresholds for post-earthquake evaluation of structures. Francisco A. Galvis, Anne M. Hulsey, Jack W. Baker, Gregory G. Deierlein


Generalized stacked LSTM for the seismic damage evaluation of ductile reinforced concrete buildings. Bilal Ahmed, Sujith Mangalathu, Jong-Su Jeon


Seismic damage prediction of RC buildings using machine learning. Sanjeev Bhatta, Ji Dang


Guest Editors

   

陆新征

清华大学土木工程系教授


   

Henry Burton

加州大学洛杉矶分校(UCLA)

土木与环境工程学院副教授


EESD简介

Earthquake Engineering & Structural Dynamics是世界地震工程学会会刊、土木工程领域JCR Q1期刊(最新影响因子4.500),主编为中岛正爱(Masayoshi Nakashima)教授。该刊在地震工程领域享有盛誉。主要发表与地震工程和结构动力学相关的研究工作,包括以下方向:


1. Ground motions for analysis and design

2. Geotechnical earthquake engineering, including wave propagation, dynamic interaction, and underground structures

3. Probabilistic and deterministic methods of dynamic analysis

4. Experimental and computational simulation of dynamic effects on structures, including validation and verification in seismic simulation

5. Seismic protective systems, including seismic isolation systems, damping systems, new structural systems, adaptive and active systems

6. Earthquake health monitoring and sensors

7. Seismic code requirements, development, and evaluation

8. Methods for earthquake‐resistant design and retrofit of structures

9. Seismic risk assessment, quantification of resilience, and lifetime considerations for buildings, other structures, and community systems

10. Multi-hazard engineering that includes earthquakes and other hazards (wind, tsunami, storm surge, fire, blast, impact, etc.)

11. Enhanced seismic design emphasizing damage minimization, repairability, functional recovery, etc.

12. Seismic behavior, modelling and dynamic analysis of non-structural elements

13. Data-driven methods in earthquake engineering

---End---



来源:陆新征课题组
振动非线性建筑BIM科普数字孪生控制试验人工智能无人机
著作权归作者所有,欢迎分享,未经许可,不得转载
首次发布时间:2023-08-21
最近编辑:1年前
地震那些事
博士 抗震防灾数值模拟仿真
获赞 42粉丝 56文章 558课程 0
点赞
收藏
未登录
还没有评论
课程
培训
服务
行家
VIP会员 学习 福利任务 兑换礼品
下载APP
联系我们
帮助与反馈