Citation: Ruan, Z.-G.; Ying, Z.-G.
Comparative Study of Structural
Anomaly Diagnosis Based on ANN
Model Using Random Displacement
and Acceleration Responses with
Incomplete Measurements. Sensors
2022, 22, 4128. https://doi.org/
10.3390/s22114128
Academic Editors: Wenjun
(Chris) Zhang, Dhanjoo N. Ghista,
Kelvin K.L. Wong and Andrew
W.H. Ip
Received: 4 April 2022
Accepted: 27 May 2022
Published: 29 May 2022
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Article
Comparative Study of Structural Anomaly Diagnosis Based on
ANN Model Using Random Displacement and Acceleration
Responses with Incomplete Measurements
Zhi-Gang Ruan and Zu-Guang Ying *
Department of Mechanics, School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China;
ruanzg@zju.edu.cn
* Correspondence: yingzg@zju.edu.cn
Abstract:
Structural anomaly diagnosis, such as damage identification, is a continuously interesting
issue. Artificial neural networks have an excellent ability to model complex structure dynamics. In
this paper, an artificial neural network model is used to describe the relationship between structural
responses and anomalies such as stiffness reduction due to damages. Random acceleration and
displacement responses as generally measured data are used as the input to the artificial neural
network, and the output of the artificial neural network is the anomaly severity. The artificial neural
network model is set up by training and then validated using random vibration responses with
different structural anomalies. The structural anomaly diagnosis method based on the artificial neural
network model using random acceleration and displacement responses is applied to a five-story
building structure under random base excitations (seismic loading). Anomalies in the structure are
denoted by stiffness reduction. Structural anomaly diagnosis using random acceleration responses is
compared with that using random displacement responses. The numerical results show the effects
of different random vibration responses used on the accuracy of predicting stiffness reduction. The
actual incomplete measurements include intensive noise, finite sampling time length, and limited
measurement points. The effects of the incomplete measurements on the accuracy of predicting
results are also discussed.
Keywords:
structural anomaly diagnosis; artificial neural network; random response; five-story
building; incomplete measurements
1. Introduction
Structural anomaly diagnosis (SAD) or structural damage identification (SDI) is very
significant for reducing catastrophic failures and prolonging the service life of structures.
Typical SAD or SDI methods, proposed by analyzing dynamic responses of engineering
structures, include the local non-destructive testing-based method and the globe vibration-
based method [
1
–
3
]. The vibration-based SAD/SDI method has not limitations such as
certain detection regions and, thus, is increasingly studied. This method is based on the
theory that variations in structural physical parameters such as stiffness cause variations
in the modal parameters (i.e., modal frequencies, damping, and shapes) and vibration re-
sponses, and structural anomalies or damages result in variations in the structural physical
parameters. Changes in the modal parameters, including the modal frequency [
4
–
8
], modal
shape curvatures [
9
–
11
], modal strain energy [
12
–
15
], and modal flexibility [
16
], have
been used to identify structural damages. However, an SDI method using an individual
modal parameter may result in a mistake [
17
]. With the rapid development of computer
technologies, a comprehensive SDI method based on artificial intelligence technology is
developing, which has the advantage of using combined modal parameters for damage
identification.
Sensors 2022, 22, 4128. https://doi.org/10.3390/s22114128 https://www.mdpi.com/journal/sensors
