Research Topic

Vibration-Based Structural Health Monitoring

About this Research Topic

Identifying damage through Structural Health Monitoring (SHM) methods is increasingly attracting attention due to multiple maintenance and failure prevention applications. Contemporary applications that span the fields of structural dynamics, earthquake, civil, mechanical, aerospace and related engineering fields, call for adoption of advanced damage detection and prognosis schemes for operating engineering systems, guiding the scheduling of maintenance actions and diminishing production costs. Vibration based Structural Health Monitoring has been increasingly used to detect changes in a system’s behavior and is a robust, efficient, and straightforward implementation. During system operation, the condition of a structure and as a result, its performance and reliability against various modes of structural failure may deteriorate due to damage induced by sudden severe loading events or due to long-term deterioration from fatigue and corrosion induced by normal operational loads. Monitoring data from sensor systems, optimally deployed within the structure, contain valuable information that can be used to continually track the health of the structure and rapidly predict, identify and locate the onset of structural damage.

The aim of this Research Topic is to discuss recent advances in the field, focusing on critical issues and successful applications of Vibration-based SHM. The Research Topic welcomes contributions that cover, but are not limited to, theoretical, computational, experimental and practical aspects of SHM.

Topics relevant to the Research Topic include, among others:
• FE Model Updating Techniques and Verification
• Prediction of Fatigue Damage Accumulation using Monitoring Data
• Fault Detection and Identification
• Damage Detection and Localization
• Uncertainty Quantification and Parameter Estimation
• Optimal Sensor Location
• Linear and Nonlinear System Identification
• Machine Learning Techniques

Papers dealing with experimental-field investigations and results of long-term monitoring deployments are especially welcomed.


Keywords: Structural Health Monitoring, System Identification, Model Updating, Machine Learning, Damage Detection


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Identifying damage through Structural Health Monitoring (SHM) methods is increasingly attracting attention due to multiple maintenance and failure prevention applications. Contemporary applications that span the fields of structural dynamics, earthquake, civil, mechanical, aerospace and related engineering fields, call for adoption of advanced damage detection and prognosis schemes for operating engineering systems, guiding the scheduling of maintenance actions and diminishing production costs. Vibration based Structural Health Monitoring has been increasingly used to detect changes in a system’s behavior and is a robust, efficient, and straightforward implementation. During system operation, the condition of a structure and as a result, its performance and reliability against various modes of structural failure may deteriorate due to damage induced by sudden severe loading events or due to long-term deterioration from fatigue and corrosion induced by normal operational loads. Monitoring data from sensor systems, optimally deployed within the structure, contain valuable information that can be used to continually track the health of the structure and rapidly predict, identify and locate the onset of structural damage.

The aim of this Research Topic is to discuss recent advances in the field, focusing on critical issues and successful applications of Vibration-based SHM. The Research Topic welcomes contributions that cover, but are not limited to, theoretical, computational, experimental and practical aspects of SHM.

Topics relevant to the Research Topic include, among others:
• FE Model Updating Techniques and Verification
• Prediction of Fatigue Damage Accumulation using Monitoring Data
• Fault Detection and Identification
• Damage Detection and Localization
• Uncertainty Quantification and Parameter Estimation
• Optimal Sensor Location
• Linear and Nonlinear System Identification
• Machine Learning Techniques

Papers dealing with experimental-field investigations and results of long-term monitoring deployments are especially welcomed.


Keywords: Structural Health Monitoring, System Identification, Model Updating, Machine Learning, Damage Detection


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

About Frontiers Research Topics

With their unique mixes of varied contributions from Original Research to Review Articles, Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author.

Topic Editors

Loading..

Submission Deadlines

03 June 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

Loading..

Topic Editors

Loading..

Submission Deadlines

03 June 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

Loading..
Loading..

total views article views article downloads topic views

}
 
Top countries
Top referring sites
Loading..