A boosted deep learning-based approach for near real-time response estimation of structures under ground motion excitations

IF 2.6 3区工程技术 Q2 ENGINEERING, CIVIL

Structure and Infrastructure Engineering Pub Date : 2024-09-17 DOI:10.1080/15732479.2024.2396613

Mohammad Javad Kaffashchian, Mojtaba Salkhordeh, Reza Karami Mohammadi

引用次数: 0

Abstract

This article presents an improved Convolutional Neural Network (CNN)-based approach to predict the vibration response of structures under seismic events without installing sensors at different stor...

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基于增强型深度学习的方法，用于对地动激励下的结构进行近实时响应估算

本文介绍了一种基于卷积神经网络（CNN）的改进方法，用于预测地震事件下结构的振动响应，而无需在不同位置安装传感器。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Structure and Infrastructure Engineering 工程技术-工程：机械

CiteScore

9.50

自引率

8.10%

发文量

131

审稿时长

5.3 months

期刊介绍： Structure and Infrastructure Engineering - Maintenance, Management, Life-Cycle Design and Performance is an international Journal dedicated to recent advances in maintenance, management and life-cycle performance of a wide range of infrastructures, such as: buildings, bridges, dams, railways, underground constructions, offshore platforms, pipelines, naval vessels, ocean structures, nuclear power plants, airplanes and other types of structures including aerospace and automotive structures. The Journal presents research and developments on the most advanced technologies for analyzing, predicting and optimizing infrastructure performance. The main gaps to be filled are those between researchers and practitioners in maintenance, management and life-cycle performance of infrastructure systems, and those between professionals working on different types of infrastructures. To this end, the journal will provide a forum for a broad blend of scientific, technical and practical papers. The journal is endorsed by the International Association for Life-Cycle Civil Engineering ( IALCCE) and the International Association for Bridge Maintenance and Safety ( IABMAS).