WeChat Mini Program
Old Version Features

A Model‐driven Method Based on Multisource Data for Reproducing Displacement Time History

EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS(2023)

Tsinghua Univ

Cited 2|Views8
Abstract
The seismic displacement time history of a structure is very important for damage assessment. This paper proposes a displacement estimation method based on multisource measurement data. In this method, the structure is simplified to a mass-spring shear model, where a set of tunable parameters are assigned. The particle swarm optimization algorithm is used to search for the optimal values of the parameters, and the objective of the optimization problem is to minimize the difference between the measured roof response and that calculated by the model. A refined finite element model was established in ABAQUS, and the numerical calculation results were used to verify this proposed method. Results show that the estimation error of the maximum inter-story drift ratio among all floors is 8.9%, and the average error of the maximum displacement of each floor is 7.0%, representing a satisfactory estimation effectiveness. The source of the errors is discussed, and we find that this method is more suitable for low-rise buildings. Shaking table tests of a scaled four-story reinforced concrete (RC) frame were conducted, and the estimation error of the maximum inter-story drift ratio among all floors was 6.1%, indicating the feasibility of this method in practice.
More
Translated text
Key words
displacement estimation,model-driven,multisource data,optimization algorithm,shaking table test
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined