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Buildings
Special Issues
Structural Health Monitoring and Damage Detection Based on Vibration
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Structural Health Monitoring and Damage Detection Based on Vibration

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Structures".

Deadline for manuscript submissions: 30 May 2025 | Viewed by 1239

Special Issue Editors

Prof. Dr. Ying Lei

E-Mail Website
Guest Editor
School of Architecture and Civil Engineering, Xiamen University, Xiamen 361005, China
Interests: structural health monitoring; structural damage detection; Kalman filter with unknown input; signal processing; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Lijun Liu

E-Mail Website
Guest Editor
Department of Civil Engineering, Xiamen University, Xiamen 361005, China
Interests: structural health monitoring; structural damage detection; structural dynamic load identification; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Vibration-based structural health monitoring and damage detection technologies play a crucial role in ensuring the safety, reliability, and lifespan of engineering structures. As the scale and complexity of buildings, bridges, and other critical infrastructure continue to grow, traditional visual inspections and scheduled maintenance have become insufficient to fully address potential structural damage. Vibration monitoring provides an efficient, real-time, and non-destructive approach by analyzing the structural vibrational responses under internal or external loads, enabling the detection of subtle damage and deterioration, thereby preventing catastrophic failures. This technology not only enhances the automation of structural health monitoring, but also allows for more precise damage identification, condition assessment, and remaining life prediction when combined with big data analytics and machine learning. Although significant progresses have been made in this field, there remain key challenges and frontier areas that need to be addressed to bridge the gap between cutting-edge research and best practices.

This Special Issue, "Structural Health Monitoring and Damage Detection Based on Vibration", aims to promote recent innovations in, progress in, and applications of vibration-based structural health monitoring and damage detection in structural health management by showcasing the latest advancements and successful real-world case studies in this field.

Prof. Dr. Ying Lei
Dr. Lijun Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • structural damage detection
  • signal processing
  • structural identification
  • substructural identification

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Published Papers (1 paper)

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Research

15 pages, 5341 KiB  
Article
Optimization of Covariance Matrices of Kalman Filter with Unknown Input Using Modified Directional Bat Algorithm
by Lijun Liu, Chang Yin, Yonghui Su, Yinghai Lin and Ying Lei
Buildings 2025, 15(2), 196; https://doi.org/10.3390/buildings15020196 - 10 Jan 2025
Viewed by 977
Abstract
The proper selection of the model error covariance matrix and the measurement noise covariance matrix of Kalman filter is an optimization problem. Some scholars have studied this, but there is relatively little research on the selection of the two covariance matrices for Kalman [...] Read more.
The proper selection of the model error covariance matrix and the measurement noise covariance matrix of Kalman filter is an optimization problem. Some scholars have studied this, but there is relatively little research on the selection of the two covariance matrices for Kalman filters with an unknown input. Recently, the authors proposed a modified directed bat algorithm (MDBA) which introduces the best historical location of individuals and the elimination strategy to effectively prevent falling into local optimal solution. So, two methods are proposed in this paper to optimize the model error covariance matrix and measurement noise covariance matrix of Kalman filter with unknown inputs (KF-UI) and extended Kalman filter with unknown inputs (EKF-UI) by MDBA, respectively. The objective functions are constructed using the measurement vectors and the corresponding estimated values, and MDBA is adopted to optimize the two covariance matrices of KF-UI and EKF-UI. To validate the effectiveness of proposed methods, two simple structure examples and a benchmark example are adopted. The influence of structural parameter uncertainties on KF-UI is also considered. The result shows that the MDBA-optimized KF-UI has a strong convergence and can take into account the effect of parameter uncertainties. Then, the effectiveness of the proposed MDBA-optimized EKF-UI method is validated by comparing it with EKF-UI with empirically selected covariance values through trial-and-error. The identification results showed that the proposed methods achieved better identification accuracy and enhanced convergence compared to KF-UI and EKF-UI with empirical covariance values. Full article
(This article belongs to the Special Issue Structural Health Monitoring and Damage Detection Based on Vibration)
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