Structural health monitoring of civil engineering structures

1998 ◽  
Vol 1 (3) ◽  
pp. 308-315 ◽  
Author(s):  
B Culshaw
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Civil Engineering ◽  
Engineering Structures ◽  
Structural Health ◽  
Civil Engineering Structures

scholarly journals Smart Sensing Technologies for Structural Health Monitoring of Civil Engineering Structures

2010 ◽  
Vol 2010 ◽  
pp. 1-13 ◽  
Author(s):  
M. Sun ◽  
W. J. Staszewski ◽  
R. N. Swamy
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Smart Materials ◽  
Civil Engineering ◽  
Piezoelectric Sensors ◽  
Sensors And Actuators ◽  
Engineering Structures ◽  
Structural Health ◽  
Civil Engineering Structures ◽  
Smart Sensing

Structural Health Monitoring (SHM) aims to develop automated systems for the continuous monitoring, inspection, and damage detection of structures with minimum labour involvement. The first step to set up a SHM system is to incorporate a level of structural sensing capability that is reliable and possesses long term stability. Smart sensing technologies including the applications of fibre optic sensors, piezoelectric sensors, magnetostrictive sensors and self-diagnosing fibre reinforced composites, possess very important capabilities of monitoring various physical or chemical parameters related to the health and therefore, durable service life of structures. In particular, piezoelectric sensors and magnetorestrictive sensors can serve as both sensors and actuators, which make SHM to be an active monitoring system. Thus, smart sensing technologies are now currently available, and can be utilized to the SHM of civil engineering structures. In this paper, the application of smart materials/sensors for the SHM of civil engineering structures is critically reviewed. The major focus is on the evaluations of laboratory and field studies of smart materials/sensors in civil engineering structures.

scholarly journals Parameter Identification for Structural Health Monitoring with Extended Kalman Filter Considering Integration and Noise Effect

2018 ◽  
Vol 8 (12) ◽  
pp. 2480 ◽  
Author(s):  
Liyu Xie ◽  
Zhenwei Zhou ◽  
Lei Zhao ◽  
Chunfeng Wan ◽  
Hesheng Tang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Structural Health Monitoring ◽  
Parameter Identification ◽  
Extended Kalman Filter ◽  
Health Monitoring ◽  
Civil Engineering ◽  
Damage Identification ◽  
Engineering Structures ◽  
Structural Health ◽  
Civil Engineering Structures

Since physical parameters are much more sensitive than modal parameters, structural parameter identification with an extended Kalman filter (EKF) has received extensive attention in structural health monitoring for civil engineering structures. In this paper, EKF-based parameter identification technique is studied with numerical and experimental approaches. A four-degree-of-freedom (4-DOF) system is simulated and analyzed as an example. Different integration methods are examined and their influence to the final identification results of the structural stiffness and damping is also studied. Furthermore, the effect of different kinds of noise is studied as well. Identification results show that the convergence speed and estimation accuracy under Gaussian noises are better than those under non-Gaussian noises. Finally, experiments with a five-story steel frame are conducted to verify the damage identification capacity of the EKF. The results show that stiffness with different damage degrees can be identified effectively, which indicates that the EKF is capable of being applied for damage identification and health monitoring for civil engineering structures.

Optimum wavelet selection for nonparametric analysis toward structural health monitoring for processing big data from sensor network: A comparative study

2021 ◽  
pp. 147592172110102
Author(s):  
Ahmed Silik ◽  
Mohammad Noori ◽  
Wael A Altabey ◽  
Ji Dang ◽  
Ramin Ghiasi ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Civil Engineering ◽  
Engineering Structures ◽  
Structural Health ◽  
Nonstationary Data ◽  
Civil Engineering Structures ◽  
Wavelet Selection ◽  
The Right

A critical problem encountered in structural health monitoring of civil engineering structures, and other structures such as mechanical or aircraft structures, is how to convincingly analyze the nonstationary data that is coming online, how to reduce the high-dimensional features, and how to extract informative features associated with damage to infer structural conditions. Wavelet transform among other techniques has proven to be an effective technique for processing and analyzing nonstationary data due to its unique characteristics. However, the biggest challenge frequently encountered in assuring the effectiveness of wavelet transform in analyzing massive nonstationary data from civil engineering structures, and in structural health diagnosis, is how to select the right wavelet. The question of which wavelet function is appropriate for processing and analyzing the nonstationary data in civil engineering structures has not been clearly addressed, and no clear guidelines or rules have been reported in the literature to show how the right wavelet is chosen. Therefore, this study aims to address an important question in this regard by proposing a new framework for choosing a proper wavelet that can be customized for massive nonstationary data analysis, disturbances separation, and extraction of informative features associated with damage. The proposed method takes into account data type, data and wavelet characteristics, similarity, sharing information, and data recovery accuracy. The novelty of this study lies in integrating multi-criteria which are associated directly with features that correlated well with change in structures due to damage, including common criteria such as energy, entropy, linear correlation index, and variance. Also, it introduces and considers new proposed measures, such as wavelet-based nonlinear correlation such as cosh spectral distance and mutual information, wavelet-based energy fluctuation, measures-based recovery accuracy, such as sensitive feature extraction, noise reduction, and others to evaluate various base wavelets’ function capabilities for appropriate decomposition and reconstruction of structural dynamic responses. The proposed method is verified by experimental and simulated data. The results revealed that the proposed method has a satisfactory performance for base wavelet selection and the small order of Daubechies and Symlet provide the best results, especially order 3. The idea behind our proposed framework can be applied to other structural applications.

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