Raw Data Library
About
Aims and ScopeAdvisory Board Members
More
Who We Are?
User Guide
Green Science
​
​
Sign inGet started
​
​

About
Aims and ScopeAdvisory Board Members
More
Who We Are?
User GuideGreen Science

Sign inGet started
RDL logo

Verified research datasets. Instant access. Built for collaboration.

Navigation

About

Aims and Scope

Advisory Board Members

More

Who We Are?

Add Raw Data

User Guide

Legal

Privacy Policy

Terms of Service

Support

Got an issue? Email us directly.

Email: info@rawdatalibrary.netOpen Mail App
​
​

© 2025 Raw Data Library. All rights reserved.
PrivacyTerms
  1. Raw Data Library
  2. /
  3. Publications
  4. /
  5. Joint approximate diagonalization technique for modal identification of the Donghai Bridge

Verified authors • Institutional access • DOI aware
50,000+ researchers120,000+ datasets90% satisfaction
Article
en
2025

Joint approximate diagonalization technique for modal identification of the Donghai Bridge

0 Datasets

0 Files

en
2025
Vol 6 (1)
Vol. 6
DOI: 10.1186/s43251-024-00148-y

Get instant academic access to this publication’s datasets.

Create free accountHow it works

Frequently asked questions

Is access really free for academics and students?

Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.

How is my data protected?

Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.

Can I request additional materials?

Yes, message the author after sign-up to request supplementary files or replication code.

Advance your research today

Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.

Get free academic accessLearn more
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaboration
Access Research Data

Join our academic network to download verified datasets and collaborate with researchers worldwide.

Get Free Access
Institutional SSO
Secure
This PDF is not available in different languages.
No localized PDFs are currently available.
Satish Nagarajaiah
Satish Nagarajaiah

Institution not specified

Verified
Ben Li
Lin Chen
Satish Nagarajaiah
+1 more

Abstract

Abstract The second-order blind identification (SOBI) and its variants have been extensively explored for output-only modal identification of civil structures under varied excitations. At the core of these methods is the matrix joint approximate diagonalization (JAD) technique, while their efficiency and accuracy are largely determined by how the target-matrices for JAD are constructed from multi-channel structural responses. This study first formulates the JAD framework for structural identification, where different techniques in formulating the target-matrices are summarized and mathematical tools to conduct JAD are also presented. Then two novel ways stemming from conventional identification methods are presented as alternatives to construct the target-matrices for ambient identification, to maintain a low-order formulation and even avoiding the formation of covariance matrix. Subsequently, in view of the large number of candidate target-matrices which are analytically usable, a guiding principle is proposed for selecting reliable target-matrices, where the closeness of the eigenvectors of the target-matrices are compared beforehand, therefore eliminating of distorted target-matrices and also improving the efficiency of the subsequent JAD. The proposed techniques are applied to modal identification of the Donghai Bridge from monitoring data and the proposed JAD-based methods are compared in this context. The results suggest the effectiveness of the proposed techniques and also provide a performance evaluation of these methods.

How to cite this publication

Ben Li, Lin Chen, Satish Nagarajaiah, Limin Sun (2025). Joint approximate diagonalization technique for modal identification of the Donghai Bridge. , 6(1), DOI: https://doi.org/10.1186/s43251-024-00148-y.

Related publications

Why join Raw Data Library?

Quality

Datasets shared by verified academics with rich metadata and previews.

Control

Authors choose access levels; downloads are logged for transparency.

Free for Academia

Students and faculty get instant access after verification.

Publication Details

Type

Article

Year

2025

Authors

4

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1186/s43251-024-00148-y

Join Research Community

Access datasets from 50,000+ researchers worldwide with institutional verification.

Get Free Access