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Cited by 262

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Constructing the mode shapes of a bridge from a passing vehicle: a theoretical study

Yang, Y.B., Li, Y.C., Chang, K.C.·2014·Smart Structures and Systems

Abstract

This paper presents a theoretical algorithm for constructing the mode shapes of a bridge from the dynamic responses of a test vehicle moving over the bridge. In comparison with those approaches that utilize a limited number of sensors deployed on the bridge, the present approach can offer much more spatial information, as well as higher resolution in mode shapes, since the test vehicle can receive the vibration characteristics of each point during its passage on the bridge. Basically only one or few sensors are required to be installed on the test vehicle. Factors that affect the accuracy of the present approach for constructing the bridge mode shapes are studied, including the vehicle speed, random traffic, and road surface roughness. Through numerical simulations, the present approach is verified to be feasible under the condition of constant and low vehicle speeds.

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Crossref · 10.12989/sss.2014.13.5.797

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Metadata

Authors
Yang, Y. B., Li, Y. C., & Chang, K. C.
Venue
Smart Structures and Systems
Publisher
Techno-Press
Published
2014-05-25
Volume
13
Issue
5
Pages
797-819
ISSN
1738-1584 (ISSN-L)
OpenAlex
W1976122853
Cited by
262
References
3
FWCI
17.26
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Constructing the mode shapes of a bridge from a passing vehicle: a theoretical study. https://app.cochat.ai/papers/10.12989/sss.2014.13.5.797
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Yang, Y. B., Li, Y. C., & Chang, K. C. (2014). Constructing the mode shapes of a bridge from a passing vehicle: a theoretical study. Smart Structures and Systems. https://doi.org/10.12989/sss.2014.13.5.797

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.