This paper provides an overview of methods to detect, locate, and characterize damage in structural and mechanical systems by examining changes in measured vibration response. Research in vibration-based damage identification has been rapidly expanding over the last few years. The basic idea behind this technology is that modal parameters (notably frequencies, mode shapes, and modal damping) are functions of the physical properties of the structure (mass, damping, and stiffness). Therefore, changes in the physical properties will cause detectable changes in the modal properties. The motivation for the development of this technology is presented. The methods are categorized according to various criteria such as the level of damage detection provided, model-based versus non-model-based methods, and linear versus nonlinear methods. The methods are also described in general terms including difficulties associated with their implementation and their fidelity. Past, current, and future-planned applications of this technology to actual engineering systems are summarized. The paper concludes with a discussion of critical issues for future research in the area of vibration-based damage identification.

A summary review of vibration-based damage identification methods

This tutorial/survey paper: (1) provides a concise point of departure for researchers and practitioners alike wishing to assess the current state of the art in the control and monitoring of civil engineering structures; and (2) provides a link between structural control and other fields of control theory, pointing out both differences and similarities, and points out where future research and application efforts are likely to prove fruitful. The paper consists of the following sections: section 1 is an introduction; section 2 deals with passive energy dissipation; section 3 deals with active control; section 4 deals with hybrid and semiactive control systems; section 5 discusses sensors for structural control; section 6 deals with smart material systems; section 7 deals with health monitoring and damage detection; and section 8 deals with research needs. An extensive list of references is provided in the references section.

Structural control: past, present, and future

Staff members at Los Alamos National Laboratory (LANL) produced a summary of the structural health monitoring literature in 1995. This presentation will summarize the outcome of an updated review covering the years 1996 2001. The updated review follows the LANL statistical pattern recognition paradigm for SHM, which addresses four topics: 1. Operational Evaluation; 2. Data Acquisition and Cleansing; 3. Feature Extraction; and 4. Statistical Modeling for Feature Discrimination. The literature has been reviewed based on how a particular study addresses these four topics. A significant observation from this review is that although there are many more SHM studies being reported, the investigators, in general, have not yet fully embraced the well-developed tools from statistical pattern recognition. As such, the discrimination procedures employed are often lacking the appropriate rigor necessary for this technology to evolve beyond demonstration problems carried out in laboratory setting.

A review of structural health monitoring literature 1996-2001

Vibration based condition monitoring refers to the use of in situ non-destructive sensing and analysis of system characteristics –in the time, frequency or modal domains –for the purpose of detecting changes, which may indicate damage or degradation. In the field of civil engineering, monitoring systems have the potential to facilitate the more economical management and maintenance of modern infrastructure. This paper reviews the state of the art in vibration based condition monitoring with particular emphasis on structural engineering applications.

Vibration Based Condition Monitoring: A Review:

This book focuses on structural health monitoring in the context of machine learning. The authors review the technical literature and include case studies. Chapters include: operational evaluation, sensing and data acquisition, introduction to probability and statistics, machine learning and statistical pattern recognition, and data prognosis.

Structural Health Monitoring: A Machine Learning Perspective

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Dynamic characterization and damage detection in the I-40 bridge over the Rio Grande

Static and dynamic properties of high-density metal honeycombs

Microwave interferometers for non-contact vibration measurements on large structures

Strain-Rate Effects in the Propagation of Torsional Plastic Waves

We developed a split Hopkinson bar technique to evaluate the performance of accelerometers that measure large amplitude pulses. A nondispersive stress pulse propagates in an aluminum bar and interacts with a tungsten or steel disk at the end of the bar. We measure stress at the aluminum bar-disk interface with a quartz gage and measure acceleration at the free end of the disk with an accelerometer. The rise time of the incident stress pulse in the aluminum bar is long enough and the disk length is short enough that the response of the disk can be approximated closely as rigid-body motion; an experimentally verified analytical model supports this assumption. Since the cross-sectional area and mass of the disk are known, we calculate acceleration of the rigid disk from the stress measurement and Newton's Second Law. Comparisons of accelerations calculated from the quartz gage data and measured acceleration data show excellent agreement for acceleration pulses with the peak amplitudes between 20,000 and 120,000 G (1 G = 9.81m/s 2 ), rise times as short as 20 μs, and pulse durations between 40 and 70 μs.

W. E. Baker

Papers

Dynamic characterization and damage detection in the I-40 bridge over the Rio Grande

Static and dynamic properties of high-density metal honeycombs

Microwave interferometers for non-contact vibration measurements on large structures

Strain-Rate Effects in the Propagation of Torsional Plastic Waves

A Split Hopkinson Bar Technique to Evaluate the Performance of Accelerometers