Multisensor-based hybrid empirical mode decomposition method towards system identification of structures

Barbosh, Mohamed; Sadhu, Ayan; Vogrig, Mike

doi:10.1002/stc.2147

Cited by 28 publications

(24 citation statements)

References 33 publications

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“…Thus, it is assumed that such multiple EQs do not change the system of the structure. System changes can be detected through the system identification method of the building under ambient excitation after occurrence of an EQ. The seismic responses recorded before the system changes are accumulated and used for CNN training.…”

Section: Seismic Response Prediction Methodsmentioning

confidence: 99%

Seismic response prediction method for building structures using convolutional neural network

Park

2020

Struct Control Health Monit

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Summary In this study, a method of predicting the seismic responses of building structures based on a convolutional neural network (CNN) is proposed. In the method, the time histories of acceleration responses previously measured in a building during earthquakes are used in the CNN input layer, with the corresponding time histories of the displacement responses being used in the CNN output layer. The correlations between the features automatically extracted from the acceleration responses by the convolution and pooling operations in the various CNN layers and the displacement responses in the CNN output layer are established through CNN training. The trained CNN predicts the displacement responses for a future earthquake event using only the measured acceleration responses. To verify the validity of the proposed method, a numerical study on the ASCE benchmark model and an experimental study on a reinforced concrete frame structure are conducted. In the numerical study, the structural responses of the ASCE model subject to artificial earthquakes are utilized for CNN training, and the performance of the trained CNN is verified through seismic response prediction. In the experimental study, shaking table tests are performed for various earthquakes to measure the seismic responses of the reinforced concrete test structure. The seismic response prediction performance of the proposed method is examined using CNN trained with the measured seismic responses. Furthermore, the performances of CNNs trained with different numbers of datasets generated by the proposed data generation method based on data overlapping with the same data pool are discussed with related to the number of CNN training iteration number.

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Section: Seismic Response Prediction Methodsmentioning

confidence: 99%

Seismic response prediction method for building structures using convolutional neural network

Park

2020

Struct Control Health Monit

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“…The CR is for Forward Error Correction (FEC), which is combined with the spread spectrum technique to further increase the receiver sensitivity and correction. The SF affects the rate of data transmission, while the LoRa supports multiple spreading factors (between [7][8][9][10][11][12] to decide the tradeoff between the range and data rate. A lower SF results in a higher data transmission rate but also a lower range of transmission due to the reduced immunity to interference [15].…”

Section: Lora Data Transmissionmentioning

confidence: 99%

“…Therefore, features must be pre-coded onto the edge sensor before deployment. Univariate, single-channel features extracted from the recorded vibrations can be categorised in time, frequency and time-frequency domains, with the latter two being associated with significant computational complexity, which results in processing requirements at the edge and therefore leads to high power consumption [12]. Frequency and time-frequency features also produce high-dimensional feature vectors, while time domain features are usually computationally efficient and suitable for edge feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Edge Structural Health Monitoring (E-SHM) Using Low-Power Wireless Sensing

Buckley

Ghosh

Pakrashi

2021

Sensors

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Effective Structural Health Monitoring (SHM) often requires continuous monitoring to capture changes of features of interest in structures, which are often located far from power sources. A key challenge lies in continuous low-power data transmission from sensors. Despite significant developments in long-range, low-power telecommunication (e.g., LoRa NB-IoT), there are inadequate demonstrative benchmarks for low-power SHM. Damage detection is often based on monitoring features computed from acceleration signals where data are extensive due to the frequency of sampling (~100–500 Hz). Low-power, long-range telecommunications are restricted in both the size and frequency of data packets. However, microcontrollers are becoming more efficient, enabling local computing of damage-sensitive features. This paper demonstrates the implementation of an Edge-SHM framework through low-power, long-range, wireless, low-cost and off-the-shelf components. A bespoke setup is developed with a low-power MEM accelerometer and a microcontroller where frequency and time domain features are computed over set time intervals before sending them to a cloud platform. A cantilever beam excited by an electrodynamic shaker is monitored, where damage is introduced through the controlled loosening of bolts at the fixed boundary, thereby introducing rotation at its fixed end. The results demonstrate how an IoT-driven edge platform can benefit continuous monitoring.

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“…Predetermined basis functions and function orthogonality are not used for component extraction, so HHT can provide more accurate instantaneous frequencies of extracted IMFs. Moreover, Mahato et al., 34 Barbosh et al., 35 Shi et al., 36 and Feldman 37 discussed a revised EMD and HHT method and its application in signal processing. However, HHT seriously suffers from the edge effect.…”

Section: Introductionmentioning

confidence: 99%

An instantaneous frequency analysis method of stay cables

Zhong

Pai

2019

Journal of Low Frequency Noise, Vibration and Active Control

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This paper presents and evaluates in detail a conjugate-pair decomposition method for tracking the instantaneous frequency of a vibration signal by processing only three (or more) most recent data points, and Probability Box theory was employed to obtain the uncertain probability distribution of instantaneous frequency. A composite cablestayed bridge with large span, named as Guanhe Bridge, was selected as the engineering background; the proposed conjugate-pair decomposition method is verified and compared with Hilbert-Huang transform method using the measured dynamical data of structural health monitoring system. Moreover, the uncertain probability distribution of instantaneous frequency was discussed; the Probability Box also can be obtained. Results show that the identify results are close to each other using conjugate-pair decomposition and Hilbert-Huang transform, respectively; but the variance of conjugate-pair decomposition is less than Hilbert-Huang transform, and conjugate-pair decomposition requires only a few data points for sliding-window fitting to extract the instantaneous frequency, and the Probability Box of instantaneous frequency will be useful to evaluate safety and potential fatigue problems in the bridge cables.

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Multisensor-based hybrid empirical mode decomposition method towards system identification of structures

Cited by 28 publications

References 33 publications

Seismic response prediction method for building structures using convolutional neural network

Seismic response prediction method for building structures using convolutional neural network

Edge Structural Health Monitoring (E-SHM) Using Low-Power Wireless Sensing

An instantaneous frequency analysis method of stay cables

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