Application of the Baseline Stiffness Method for Damage Detection of an Rc Building Without Baseline Modal Parameters Utilizing the Independent Component Analysis for Modal Extraction

Rodriguez-Rocha, Ramses; Rivero-Angeles, F.J.; Gómez-Ramı́rez, E.

doi:10.1142/s0219455412500745

Cited by 6 publications

(6 citation statements)

References 13 publications

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“…However, a scalar indicator for each structural element is needed. One approach used by many researchers to describe a matrix that contains damage information is eigenvalue decomposition [11,28,34,38,39]. Applying this approach to each matrix [x s ] i we obtain…”

Section: Advances In Civil Engineeringmentioning

confidence: 99%

“…In general, a damage index is defined as the change of a system characteristic that allows a quantitative comparison between the undamaged state and the damaged one. Some authors define the damage index as a loss of stiffness [11][12][13], as an increase of flexibility [14,15], or as modal strain energy changes [16,17], among other definitions [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

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Damaged Flexibility Matrix Method for Damage Detection of Frame Buildings

Oyola¹,

Fonseca²,

Rocha³

2020

Advances in Civil Engineering

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is study presents the damaged flexibility matrix method (DFM) to identify and determine the magnitude of damage in structural elements of plane frame buildings. Damage is expressed as the increment in flexibility along the damaged structural element. is method uses a new approach to assemble the flexibility matrix of the structure through an iterative process, and it adjusts the eigenvalues of the damaged flexibility matrices of each system element. e DFM was calibrated using numerical models of plane frames of buildings studied by other authors. e advantage of the DFM, with respect to other flexibility-based methods, is that DFM minimizes the adverse effect of modal truncation. e DFM demonstrated excellent accuracy with complete modal information, even when it was applied to a more realistic scenario, considering frequencies and modal shapes measured from the recorded accelerations of buildings stories. e DFM also presents a new approach to simulate the effects of noise by perturbing matrices of flexibilities. is approach can be useful for research on realistic damage detection. e combined effects of incomplete modal information and noise were studied in a ten-story four-bay building model taken from the literature. e ability of the DFM to assess structural damage was corroborated. Application of the proposed method to a ten-story four-bay building model demonstrates its efficiency to identify the flexibility increment in damaged structural elements.

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Section: Advances In Civil Engineeringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Damaged Flexibility Matrix Method for Damage Detection of Frame Buildings

Oyola¹,

Fonseca²,

Rocha³

2020

Advances in Civil Engineering

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“…After extracting the signal feature, we need to select features sensitive to damage patterns and remove the features with useless information, thus decreasing the dimension of features for the purpose of calculation efficiency. The feature-selection methods for structural DI include local linear embedding (LLE), 10–12 independent component analysis (ICA), 13–15 principal component analysis (PCA), 16–18 isometric mapping algorithm 19 and so on. With optimal features chosen from vibration signals, the final step is to identify the signal patterns via machine learning (ML) methods, such as artificial neural networks (ANNs), 20–22 support vector machine (SVM), 23–25 extreme learning machine (ELM), 26 adaptive neuro-fuzzy inference system (ANFIS) 27 and so on.…”

Section: Introductionmentioning

confidence: 99%

A novel deep learning-based method for damage identification of smart building structures

Wang

et al. 2018

Structural Health Monitoring

237

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In the past few years, intelligent structural damage identification algorithms based on machine learning techniques have been developed and obtained considerable attentions worldwide, due to the advantages of reliable analysis and high efficiency. However, the performances of existing machine learning–based damage identification methods are heavily dependent on the selected signatures from raw signals. This will cause the fact that the damage identification method, which is the optimal solution for a specific application, may fail to provide the similar performance on other cases. Besides, the feature extraction is a time-consuming task, which may affect the real-time performance in practical applications. To address these problems, this article proposes a novel method based on deep convolutional neural networks to identify and localise damages of building structures equipped with smart control devices. The proposed deep convolutional neural network is capable of automatically extracting high-level features from raw signals or low-level features and optimally selecting the combination of extracted features via a multi-layer fusion to satisfy any damage identification objective. To evaluate the performance of the proposed deep convolutional neural network method, a five-level benchmark building equipped with adaptive smart isolators subjected to the seismic loading is investigated. The result shows that the proposed method has outstanding generalisation capacity and higher identification accuracy than other commonly used machine learning methods. Accordingly, it is deemed as an ideal and effective method for damage identification of smart structures.

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“…Hyvärinen [11,12] proposed a very famous ICA algorithm called fast-fixed algorithm for independent component analysis, which is very effective and efficient for linear systems. Since then, independent component analysis has attracted a worldwide attention and has been widely used in many engineering applications, such as image processing [13], EEG/MEG analysis [14], communications [15], radar [16], and modal parameter estimation [17]. In recent years, independent component analysis has been applied to mechanical signal processing, and some practical algorithms have been constructed.…”

Section: Introductionmentioning

confidence: 99%

Acoustical Source Tracing Using Independent Component Analysis and Correlation Analysis

Cheng

Zhang

et al. 2015

Shock and Vibration

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Acoustical signals from mechanical systems reveal the operating conditions of mechanical components and thus benefit for machinery condition monitoring and fault diagnosis. However, the acoustical signals directly measured by the sensors in essential are the mixed signals of all the sources, and normally it is very difficult to be used for source identification or operating feature extraction. Therefore, this paper studies the acoustical source tracing problem using independent component analysis (ICA) and identifies the sources using correlation analysis: the measured acoustical signals are separated into independent components by independent component analysis method, and thus all the independent information of all the sources is obtained; these independent components are identified based on the prior information of the sources and correlation analysis. Therefore, all the source information contained in the measured acoustical signals can be independently separated and traced, which can provide more purer source information for condition monitoring and fault diagnosis.

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Application of the Baseline Stiffness Method for Damage Detection of an Rc Building Without Baseline Modal Parameters Utilizing the Independent Component Analysis for Modal Extraction

Cited by 6 publications

References 13 publications

Damaged Flexibility Matrix Method for Damage Detection of Frame Buildings

Damaged Flexibility Matrix Method for Damage Detection of Frame Buildings

A novel deep learning-based method for damage identification of smart building structures

Acoustical Source Tracing Using Independent Component Analysis and Correlation Analysis

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