Improvement of GPS displacement measurement accuracy for high-rise buildings by machine learning-Reference-Cited by-同舟云学术

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Improvement of GPS displacement measurement accuracy for high-rise buildings by machine learning

Published:2023-11 Issue: Volume:78 Page:107581
ISSN:2352-7102
Container-title:Journal of Building Engineering
language:en
Short-container-title:Journal of Building Engineering

Author:

Zhou Qi,Li Qiu-Sheng,Han Xu-Liang^ORCID,Lu Bin,Wan Jun-Wen,Xu Kang

Funder

Research Grants Council, University Grants Committee

Science, Technology and Innovation Commission of Shenzhen Municipality

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Mechanics of Materials,Safety, Risk, Reliability and Quality,Building and Construction,Architecture,Civil and Structural Engineering

Reference26 articles.

1. Wind tunnel and full-scale study of wind effects on a super-tall building;Yi;J. Fluid Struct.,2015

2. An interferometric radar for non-contact measurement of deflections on civil engineering structures: laboratory and full-scale tests;Gentile;Struct. Infrastruct. Eng.,2010

3. Monitoring of displacements with ground-based microwave interferometry: IBIS-S and IBIS-L;Rödelsperger;J. Appl. Geodesy,2010

4. Design of an FIR filter for the displacement reconstruction using measured acceleration in low-frequency dominant structures;Lee;Int. J. Numer. Methods Eng.,2010

5. Deformation monitoring of a super-tall structure using real-time strain data;Xia;Eng. Struct.,2014

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1. Data-driven prediction of natural period for existing RC high-rise buildings using probabilistic machine learning methods;Journal of Building Engineering;2024-08

2. Prediction of non-uniform shrinkage of steel-concrete composite slabs based on explainable ensemble machine learning model;Journal of Building Engineering;2024-07

3. Dynamic response prediction of high-speed train on cable-stayed bridge based on genetic algorithm and fused neural networks;Engineering Structures;2024-05

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