Automated health condition diagnosis of in situ wood utility poles using an intelligent non-destructive evaluation (NDE) framework
- Yu, Yang, Subhani, Mahbube, Hoshyar, Azadeh, Li, Jianchun, Li, Huan
- Authors: Yu, Yang , Subhani, Mahbube , Hoshyar, Azadeh , Li, Jianchun , Li, Huan
- Date: 2020
- Type: Text , Journal article
- Relation: International Journal of Structural Stability and Dynamics Vol. 20, no. 10 (2020), p.
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- Description: Wood utility poles are widely applied in power transmission and telecommunication systems in Australia. Because of a variety of external influence factors, such as fungi, termite and environmental conditions, failure of poles due to the wood degradation with time is of common occurrence with high degree uncertainty. The pole failure may result in serious consequences including both economic and public safety. Therefore, accurately and timely identifying the health condition of the utility poles is of great significance for economic and safe operation of electricity and communication networks. In this paper, a novel non-destructive evaluation (NDE) framework with advanced signal processing and artificial intelligence (AI) techniques is developed to diagnose the condition of utility pole in field. To begin with, the guided waves (GWs) generated within the pole is measured using multi-sensing technique, avoiding difficult interpretation of various wave modes which cannot be detected by only one sensor. Then, empirical mode decomposition (EMD) and principal component analysis (PCA) are employed to extract and select damage-sensitive features from the captured GW signals. Additionally, the up-to-date machine learning (ML) techniques are adopted to diagnose the health condition of the pole based on selected signal patterns. Eventually, the performance of the developed NDE framework is evaluated using the field testing data from 15 new and 24 decommissioned utility poles at the pole yard in Sydney. © 2020 World Scientific Publishing Company.
- Description: This research is supported by Australian Research Council via Linkage Project (LP110200162) and Industrial Transforming Research Hub for Nanoscience Based Construction Materials Manufacturing (IH150100006) as well as Ausgrid. The authors greatly appreciate the ¯nancial and technical supports from the funding bodies.
- Authors: Yu, Yang , Subhani, Mahbube , Hoshyar, Azadeh , Li, Jianchun , Li, Huan
- Date: 2020
- Type: Text , Journal article
- Relation: International Journal of Structural Stability and Dynamics Vol. 20, no. 10 (2020), p.
- Full Text:
- Reviewed:
- Description: Wood utility poles are widely applied in power transmission and telecommunication systems in Australia. Because of a variety of external influence factors, such as fungi, termite and environmental conditions, failure of poles due to the wood degradation with time is of common occurrence with high degree uncertainty. The pole failure may result in serious consequences including both economic and public safety. Therefore, accurately and timely identifying the health condition of the utility poles is of great significance for economic and safe operation of electricity and communication networks. In this paper, a novel non-destructive evaluation (NDE) framework with advanced signal processing and artificial intelligence (AI) techniques is developed to diagnose the condition of utility pole in field. To begin with, the guided waves (GWs) generated within the pole is measured using multi-sensing technique, avoiding difficult interpretation of various wave modes which cannot be detected by only one sensor. Then, empirical mode decomposition (EMD) and principal component analysis (PCA) are employed to extract and select damage-sensitive features from the captured GW signals. Additionally, the up-to-date machine learning (ML) techniques are adopted to diagnose the health condition of the pole based on selected signal patterns. Eventually, the performance of the developed NDE framework is evaluated using the field testing data from 15 new and 24 decommissioned utility poles at the pole yard in Sydney. © 2020 World Scientific Publishing Company.
- Description: This research is supported by Australian Research Council via Linkage Project (LP110200162) and Industrial Transforming Research Hub for Nanoscience Based Construction Materials Manufacturing (IH150100006) as well as Ausgrid. The authors greatly appreciate the ¯nancial and technical supports from the funding bodies.
Nonlinear characterization of magnetorheological elastomer-based smart device for structural seismic mitigation
- Yu, Yang, Hoshyar, Azadeh, Li, Huan, Zhang, Guang, Wang, Weiqiang
- Authors: Yu, Yang , Hoshyar, Azadeh , Li, Huan , Zhang, Guang , Wang, Weiqiang
- Date: 2021
- Type: Text , Journal article
- Relation: International Journal of Smart and Nano Materials Vol. 12, no. 4 (2021), p. 390-428
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- Reviewed:
- Description: Magnetorheological elastomer (MRE) has been demonstrated to be effective in structural vibration control because of controllable stiffness and damping properties with the effect of external magnetic fields. To achieve a high performance of MRE device-based vibration control, a robust and accurate model is necessary to describe nonlinear dynamics of MRE device. This article aims at realising this target via nonlinear modeling of an innovative MRE device, i.e. MRE vibration isolator. First, the field-dependent properties of MRE isolator were analysed based on experimental data of the isolator in various dynamic tests. Then, a phenomenal model was developed to account for these unique characteristics of MRE-based device. Moreover, an improved PSO algorithm was designed to estimate model parameters. Based on identification results, a generalised model was proposed to clarify the field-dependent properties of the isolator due to varied currents, which was then validated by random and earthquake-excited test data. Based on the proposed model, a frequency control strategy was designed for semi-active control of MRE devices-incorporated smart structure for vibration suppression. Finally, using a three-storey frame model and four benchmark earthquakes, a numerical study was conducted to validate the performance of control strategy based on the generalised current-dependent model with satisfactory results. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
- Authors: Yu, Yang , Hoshyar, Azadeh , Li, Huan , Zhang, Guang , Wang, Weiqiang
- Date: 2021
- Type: Text , Journal article
- Relation: International Journal of Smart and Nano Materials Vol. 12, no. 4 (2021), p. 390-428
- Full Text:
- Reviewed:
- Description: Magnetorheological elastomer (MRE) has been demonstrated to be effective in structural vibration control because of controllable stiffness and damping properties with the effect of external magnetic fields. To achieve a high performance of MRE device-based vibration control, a robust and accurate model is necessary to describe nonlinear dynamics of MRE device. This article aims at realising this target via nonlinear modeling of an innovative MRE device, i.e. MRE vibration isolator. First, the field-dependent properties of MRE isolator were analysed based on experimental data of the isolator in various dynamic tests. Then, a phenomenal model was developed to account for these unique characteristics of MRE-based device. Moreover, an improved PSO algorithm was designed to estimate model parameters. Based on identification results, a generalised model was proposed to clarify the field-dependent properties of the isolator due to varied currents, which was then validated by random and earthquake-excited test data. Based on the proposed model, a frequency control strategy was designed for semi-active control of MRE devices-incorporated smart structure for vibration suppression. Finally, using a three-storey frame model and four benchmark earthquakes, a numerical study was conducted to validate the performance of control strategy based on the generalised current-dependent model with satisfactory results. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
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