Shock and Vibration

Structural Health Monitoring through Vibration-Based Approaches


Publishing date
01 Oct 2018
Status
Published
Submission deadline
25 May 2018

Lead Editor

1IUAV University of Venice, Venice, Italy

2Cranfield University, Cranfield, UK

3University of Rome, Rome, Italy


Structural Health Monitoring through Vibration-Based Approaches

Description

Historic masonry structures are among the most vulnerable human constructions when natural disasters—hurricanes, tornadoes, floods, landslides, and earthquakes—strike a territory. The potential of these events to cause large-scale destruction and the intrinsic susceptibility of heritage structures make their structural health assessment of significant importance.

A promising means of assessing the safety of vulnerable structures is the application of vibration-based monitoring (VBM) systems. These allow the observation of the global response for a structure, including damage detection, classification, and progression. In fact, dynamic monitoring systems have proven to be particularly suited for cultural heritage structures such as palaces, churches, and towers, whose structural behaviors are strongly influenced by their geometric complexity and the nonlinearity and inhomogeneity of the masonry material. Moreover, because of its nondestructive and noninvasive nature, vibration-based monitoring can be safely applied to damaged structures, which are potentially dangerous under other test conditions. Thus, analysis of modal behavior can reveal structural weaknesses or deficiencies induced by a seismic event. In fact, monitoring of a set of appropriately chosen features and capturing the local and global structural weaknesses may reveal the effectiveness of safety and retrofitting interventions—as well as any progression in structural damage. Thanks to their noninvasive characteristics, VBM procedures are widely used in historic masonry structures, and several studies have addressed structural identification of cultural heritage using ambient vibration data.

This special issue aims to explore structural health monitoring via vibration-based approaches, especially for complex systems such as historic structures. In welcoming interdisciplinary studies from across several scientific communities—including engineering, numerical modeling, seismology, and geotechnics—we hope to present studies from the breadth of the field and provide insights into its future development.

We invite researchers to contribute original research, case studies, and review articles in the following main areas: (1) dynamic identification for model and data-driven approaches; (2) local and global damage detection; and (3) numerical, experimental, and physical modeling.

Potential topics include but are not limited to the following:

  • Model-driven approaches in structural health monitoring
  • Data-driven approaches in structural health monitoring
  • Machine learning based structural health monitoring
  • Damage detection, classification, or localization
  • Operative approaches and case studies for data acquisition and signal processing
  • Operational and experimental modal analysis
  • Uncertainty quantification in structural health monitoring data (sensor accuracy, environmental and geometrical effects, etc.)
  • Finite element procedures for structural health monitoring
  • Soil-structure interaction in vibration-based monitoring

Special Issue Editors in Chief


Articles

  • Special Issue
  • - Volume 2019
  • - Article ID 2380616
  • - Editorial

Structural Health Monitoring through Vibration-Based Approaches

Giosuè Boscato | Luca Zanotti Fragonara | ... | Daniele Baraldi
  • Special Issue
  • - Volume 2019
  • - Article ID 4971695
  • - Research Article

Experimental Study on Acoustic Emission Characteristics of Dry and Saturated Basalt Columnar Joints under Uniaxial Compression and Tensile Damage

Liu Gang | Xiao Fu-kun | ... | Qin Tao
  • Special Issue
  • - Volume 2018
  • - Article ID 5384358
  • - Research Article

Development of an Indicator for the Assessment of Damage Level in Rolling Element Bearings Based on Blind Deconvolution Methods

Marco Buzzoni | Elia Soave | ... | Giorgio Dalpiaz
  • Special Issue
  • - Volume 2018
  • - Article ID 6863925
  • - Research Article

Rock Stability Assessment Based on the Chronological Order of the Characteristic Acoustic Emission Phenomena

Penghai Zhang | Tianhong Yang | ... | Wancheng Zhu
  • Special Issue
  • - Volume 2018
  • - Article ID 1737219
  • - Research Article

Alpha-Stable Distribution and Multifractal Detrended Fluctuation Analysis-Based Fault Diagnosis Method Application for Axle Box Bearings

Qing Xiong | Weihua Zhang | ... | Pengyi Deng
  • Special Issue
  • - Volume 2018
  • - Article ID 3435249
  • - Research Article

Numerical and Experimental Modal Analysis Applied to an Optical Test System Designed for the Form Measurements of Metre-Scale Optics

P. G. Golanó | L. Zanotti Fragonara | ... | R. Jourdain
  • Special Issue
  • - Volume 2018
  • - Article ID 5106370
  • - Research Article

Guided Wave Energy Transfer in Composite Sandwich Structures and Application to Defect Detection

Siavash Shoja | Viktor Berbyuk | Anders Boström
  • Special Issue
  • - Volume 2018
  • - Article ID 8076085
  • - Research Article

Shape Sensing of Plate and Shell Structures Undergoing Large Displacements Using the Inverse Finite Element Method

Alexander Tessler | Rinto Roy | ... | Marco Gherlone
  • Special Issue
  • - Volume 2018
  • - Article ID 8452509
  • - Research Article

Fault Diagnosis of Rotating Machinery Based on Stochastic Resonance with a Bistable Confining Potential

Zhixing Li | Boqiang Shi
  • Special Issue
  • - Volume 2018
  • - Article ID 9869561
  • - Research Article

Joint Amplitude and Frequency Demodulation Analysis Based on Variational Mode Decomposition for Multifault Diagnosis of a Multistage Reducer

Feng Li | Xinyu Pang | Zhaojian Yang
Shock and Vibration
 Journal metrics
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Acceptance rate25%
Submission to final decision95 days
Acceptance to publication17 days
CiteScore2.800
Journal Citation Indicator0.400
Impact Factor1.6
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