Structural health monitoring

About: Structural health monitoring is a research topic. Over the lifetime, 11727 publications have been published within this topic receiving 186231 citations.

A new algorithm for acoustic emission localization and flexural group velocity determination in anisotropic structures

Abstract: This paper presents a new in situ Structural Health Monitoring (SHM) system able to identify the location of acoustic emission (AE) sources due to low-velocity impacts and to determine the group velocity in complex composite structures with unknown lay-up and thickness. The proposed algorithm is based on the differences of stress waves measured by six piezoelectric sensors surface bonded. The magnitude of the Continuous Wavelet Transform (CWT) squared modulus was employed for the identification of the time of arrivals (TOA) of the flexural Lamb mode ( A 0 ). Then, the coordinates of the impact location and the flexural wave velocity were obtained by solving a set of non-linear equations through a combination of global Line Search and backtracking techniques associated to a local Newton’s iterative method. To validate this algorithm, experimental tests were conducted on two different composite structures, a quasi-isotropic CFRP and a sandwich panel. The results showed that the impact source location and the group speed were predicted with reasonable accuracy (maximum error in estimation of the impact location was approximately 2% for quasi-isotropic CFRP panel and nearly 1% for sandwich plate), requiring little computational time (less than 2 s).

Electro-Mechanical Impedance-Based Wireless Structural Health Monitoring Using PCA-Data Compression and k-means Clustering Algorithms:

Abstract: This article presents a practical method for an electro-mechanical impedance-based wireless structural health monitoring (SHM), which incorporates the principal component analysis (PCA)-based data ...

Practical implementation of optical fiber sensors in civil structural health monitoring

Abstract: Implementation of successful civil structural health monitoring strategies requires selection and placement of sensors suitable for measurement of key parameters that influence the performance and health of the structural system. Optical fiber sensors have been successfully implemented in aeronautics, mechanical systems, and medical applications. Civil structures pose further challenges in monitoring mainly due to their large dimensions, diversity as well as heterogeneity of materials involved, and hostile construction environment. This article provides a summary of basic principles pertaining to practical health monitoring of civil engineering structures with optical fiber sensors. The issues discussed include basic sensor principles, strain transfer mechanism, sensor packaging, sensor placement in construction environment, and reliability and survivability of the sensors.

Efficient model updating and health monitoring methodology using incomplete modal data without mode matching

Abstract: A methodology is presented for Bayesian structural model updating using noisy incomplete modal data corresponding to natural frequencies and partial mode shapes of some of the modes of a structural system. The procedure can be used to find the most probable model within a specified class of structural models, based on the incomplete modal data, as well as the most probable values of the system natural frequencies and the full system mode shapes. The method does not require matching measured modes with corresponding modes from the structural model, which is in contrast to many existing methods. To find the most probable values of the structural model parameters and system modal parameters, the method uses an iterative scheme involving a series of coupled linear optimization problems. Furthermore, it does not require solving the eigenvalue problem of any structural model; instead, the eigenvalue equations appear in the prior probability distribution to provide soft constraints. The method appears to be computationally efficient and robust, judging from its successful application to noisy simulated data for a ten-storey building model and for a three-dimensional braced-frame model. This latter example is also used to demonstrate an application to structural health monitoring.