Modal Identification for High‐Rise Building Structures Using Orthogonality of Filtered Response Vectors

Kim, Doyoung; Oh, Byung Kwan; Park, Hyo Seon; Shim, Hak Bo; Kim, Jiyoung

doi:10.1111/mice.12310

Cited by 36 publications

(27 citation statements)

References 44 publications

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“…Classical system‐identification methods have been reviewed in previous papers (Reyders, ; Sirca & Adeli, ). The literature is also replete with recent developments on system identification (Amezquita‐Sanchez, Park, & Adeli, ; Huang & Beck, ; Kim, Oh, Park, Shim, & Kim, ; Li, Park, & Adeli, ; Oh, Kim, & Park, ; Perez‐Ramirez et al., ; Yao et al., ; Yin, Yuen, Lam, & Zhu, ). Vibration‐based SDD methods with system identification have explored the correlations between the vibration features (natural frequency, mode shape, and their derivatives) and the structural damage information (Chang & Kim, ; Dutta & Talukdar, ; Ndambi, Vantomme, & Harri, ; Pandey et al., ; Ratcliffe, ; Reynders et al., ; Spiridonakos & Chatzi, ; Yan, Kerschen, De Boe, & Golinval, ).…”

Section: Introductionmentioning

confidence: 99%

Vibration‐based structural state identification by a 1‐dimensional convolutional neural network

Zhang

Miyamori

Mikami

et al. 2019

Computer aided Civil Eng

219

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Deep learning has ushered in many breakthroughs in vision‐based detection via convolutional neural networks (CNNs), but the vibration‐based structural damage detection by CNN remains being refined. Thus, this study proposes a simple one‐dimensional CNN that detects tiny local structural stiffness and mass changes, and validates the proposed CNN on actual structures. Three independent acceleration databases are established based on a T‐shaped steel beam, a short steel girder bridge (in test field), and a long steel girder bridge (in service). The raw acceleration data are not pre‐processed and are directly used as the training and validation data. The well‐trained CNN almost perfectly identifies the locations of small local changes in the structural mass and stiffness, demonstrating the high sensitivity of the proposed simple CNN to tiny structural state changes in actual structures. The convolutional kernels and outputs of the convolutional and max pooling layers are visualized and discussed as well.

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Section: Introductionmentioning

confidence: 99%

Vibration‐based structural state identification by a 1‐dimensional convolutional neural network

Zhang

Miyamori

Mikami

et al. 2019

Computer aided Civil Eng

219

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“…Peak picking associated with frequency domain approaches has also been used to carry out automated modal analysis . Peak picking on the frequency domain representation is considered as an intuitive way for operational modal analysis, as natural modes generally have salient peaks.…”

Section: Introductionmentioning

confidence: 99%

Automated peak picking using region‐based convolutional neural network for operational modal analysis

Kim

Sim

2019

Struct Control Health Monit

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Peak picking is used to determine locations of salient peaks in a graphical representation of a physical quantity. It is often used to extract possible natural frequencies from the frequency domain representation of structural responses.One of the challenges in peak picking is to establish a method to automatically distinguish peaks from data containing noise peaks. As selecting peaks intrinsically depends on human perception, algorithms for automated peak picking have exhibited only partial success to date. This paper presents an automated peak picking method that utilizes a region-based convolutional neural network with possible object locations to accurately identify the peaks. A tailored deep learning architecture is proposed, which is subsequently trained using only the peaks numerically generated by computer software. Laboratory and field tests are conducted using acceleration responses obtained from three test structures of beam, truss, and stay cable to verify the identification performance of the proposed peak detector. The proposed peak detector is shown to successfully identify most of the salient peaks with high accuracy.parameters of the physical modes are consistently found regardless of the provided system orders. 15 The modal parameters calculated from SSI or ERA with different system orders are grouped based on their similarity to each other, from which the frequently obtained are selected as the physical modes. [16][17][18][19] Neu et al. 20 and Cabboi et al. 21 further improved the approach in terms of automation by minimizing user-defined parameters. In addition, Afshar and Khodaygan 22 presented an enhanced stabilization chart for automated modal analysis. However, the identification performance is still highly dependent on the maximum system order, of which an optimal value should be provided as a priori information.Peak picking associated with frequency domain approaches has also been used to carry out automated modal analysis. [23][24][25] Peak picking on the frequency domain representation is considered as an intuitive way for operational modal analysis, as natural modes generally have salient peaks. A general approach for automated peak picking in the literature is to select maximum values of frequency domain representations of the structural responses (e.g., Fourier transform and power spectrum) from each of the user-defined subfrequency ranges, which are believed to include peaks. 26,27 This method is valid only when the subfrequency ranges are known; changes in peak frequencies due to structural damage or environmental effects can lead to the selection of undesired peaks. A user-defined threshold value was additionally introduced to filter out less important peaks in each subfrequency range. [28][29][30][31] This approach ignores peaks that are lower than the user-defined threshold and thus reduces the number of false positives; however, the imposition of thresholds for the peak amplitude and subfrequency range critically limits the practical applications of the approach to different ...

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“…At the core of these technologies is monitoring of the performance of the rolling machinery by vibration sensors mounted on the compactor. This is part of the emerging and expanding vibration‐based health monitoring of infrastructure systems, a key high technology in the civil engineering field (Kim, Oh, Park, Shim, & Kim, ; Tsogka, Daskalakis, Comanducci, & Ubertini, ; Yin, Yuen, Lam, & Zhu, ). Numerous different indicators such as Compaction Meter Value (CMV), Resonance Meter Value, Continuous Compaction Value, Omega value, and machine drive power have been proposed to gauge the compaction state of the soil through the spectrum analysis of the vibration signal (White & Thompson, ; Xu & Chang, ).…”

Section: Introductionmentioning

confidence: 99%

Smart bacteria‐foraging algorithm‐based customized kernel support vector regression and enhanced probabilistic neural network for compaction quality assessment and control of earth‐rock dam

et al. 2018

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Compaction quality assessment and control for an earth‐rock dam is the key measure to ensure dam safety. However, to date, the compaction quality assessment model has not been accurate enough, and no effective feedback control measures have been developed. Hybrid data mining algorithms have great potential for solving this problem. In this study, smart bacteria‐foraging algorithm‐based customized kernel support vector regression (SBFA‐CKSVR) is proposed for compaction quality assessment, whereas an enhanced probabilistic neural network (EPNN) is adopted for compaction quality control. SBFA integrates a bacteria‐foraging algorithm, chaos mapping, and adaptive and quantum computing to solve the high‐dimensional complex problem effectively. CKSVR is proposed to approximate a function in quadratic continuous integral space L2(R) where its hyperparameters are optimized by SBFA. Finally, SBFA‐CKSVR is used to establish a high‐precision compaction quality assessment model whereas the EPNN is adopted to realize the compaction quality feedback control. A three‐dimensional real‐time monitoring system for the earth‐rock dam is also developed based on SBFA‐CKSVR and EPNN. A large‐scale hydraulic engineering application proves the effectiveness and superiority of this research compared with the previous work.

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Modal Identification for High‐Rise Building Structures Using Orthogonality of Filtered Response Vectors

Cited by 36 publications

References 44 publications

Vibration‐based structural state identification by a 1‐dimensional convolutional neural network

Vibration‐based structural state identification by a 1‐dimensional convolutional neural network

Automated peak picking using region‐based convolutional neural network for operational modal analysis

Smart bacteria‐foraging algorithm‐based customized kernel support vector regression and enhanced probabilistic neural network for compaction quality assessment and control of earth‐rock dam

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