On model-based damage detection by an enhanced sensitivity function of modal flexibility and LSMR-Tikhonov method under incomplete noisy modal data

Sarmadi, Hassan; Entezami, Alireza; Ghalehnovi, Mansour

doi:10.1007/s00366-020-01041-8

Cited by 35 publications

(18 citation statements)

References 37 publications

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“…Due to recent advances in sensing and data acquisition systems, the strategies in the SHM realm have been shifted from model-driven techniques under the concept of finite element model updating [ 10 , 11 , 12 , 13 ] to data-driven or data-based methods based on statistical pattern recognition and machine learning [ 1 , 14 , 15 , 16 , 17 ]. In contrast to model-based techniques that require elaborate numerical models of real-life structures, data-driven methods are only based on raw measurements with no requirement for numerical modeling and model updating strategies.…”

Section: Introductionmentioning

confidence: 99%

Damage Detection in Largely Unobserved Structures under Varying Environmental Conditions: An AutoRegressive Spectrum and Multi-Level Machine Learning Methodology

Entezami

Mariani

Shariatmadar

2022

Sensors

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Vibration-based damage detection in civil structures using data-driven methods requires sufficient vibration responses acquired with a sensor network. Due to technical and economic reasons, it is not always possible to deploy a large number of sensors. This limitation may lead to partial information being handled for damage detection purposes, under environmental variability. To address this challenge, this article proposes an innovative multi-level machine learning method by employing the autoregressive spectrum as the main damage-sensitive feature. The proposed method consists of three levels: (i) distance calculation by the log-spectral distance, to increase damage detectability and generate distance-based training and test samples; (ii) feature normalization by an improved factor analysis, to remove environmental variations; and (iii) decision-making for damage localization by means of the Jensen–Shannon divergence. The major contributions of this research are represented by the development of the aforementioned multi-level machine learning method, and by the proposal of the new factor analysis for feature normalization. Limited vibration datasets relevant to a truss structure and consisting of acceleration time histories induced by shaker excitation in a passive system, have been used to validate the proposed method and to compare it with alternate, state-of-the-art strategies.

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Section: Introductionmentioning

confidence: 99%

Damage Detection in Largely Unobserved Structures under Varying Environmental Conditions: An AutoRegressive Spectrum and Multi-Level Machine Learning Methodology

Entezami

Mariani

Shariatmadar

2022

Sensors

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“…In general, there are two main types of methods for SHM, namely model‐based and data‐based approaches (Barthorpe, 2010). A model‐based technique relies on constructing an elaborate finite element model of a structure (referred to as the normal/reference condition), solving an inverse problem, and a semianalytical approach (a surrogate model) for detection, localization, and quantification of damage based on the concept of model updating (Jin & Jung, 2016; Moaveni, Conte, & Hemez, 2009; Sarmadi, Entezami, & Ghalehnovi, 2020; Yin, Jiang, & Yuen, 2017). Due to advances in sensing and data acquisition systems, a data‐based method focuses on using raw measured data through the concept of statistical pattern recognition (Entezami et al., 2019; Entezami, Sarmadi, Behkamal, & Mariani, 2020; Huang, Yi, & Li, 2020; Kuok, Yuen, Roberts, & Girolami, 2020; Ni, Zhang, & Noori, 2020; Nigro, Pakzad, & Dorvash, 2014).…”

Section: Introductionmentioning

confidence: 99%

Early damage detection by an innovative unsupervised learning method based on kernel null space and peak‐over‐threshold

Sarmadi

Yuen

2021

Computer aided Civil Eng

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This article proposes an innovative unsupervised learning method for early damage detection and long‐term structural health monitoring of civil structures under environmental variability. This method consists of three main parts including a novelty detector based on kernel null Foley–Sammon transform (KNFST), a practical approach to choosing an optimal Gaussian kernel parameter, and a probabilistic method for the threshold estimation. The crux of KNFST is to map all original samples to a kernel feature space and project the kernelized features into a single point in a null space. The proposed threshold estimation method exploits the extreme value theory, the generalized Pareto distribution, and the peak‐over‐threshold. The major contribution of this article is to propose an innovative novelty detection method by a one‐class kernel null space algorithm and a probabilistic threshold estimation approach. Dealing with the problem of environmental variations and estimating a reliable alarming threshold are the main advantages of the proposed method. The effectiveness and reliability of the proposed method are validated by the Wooden Bridge in a laboratory environment and the full‐scale Z24 Bridge. Results demonstrate that the proposed unsupervised learning method highly succeeds in detecting damage even under strong environmental variations.

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“…A model-based method needs an accurate numerical (e.g., finite element) model of the real structure in order to define the physical properties and/or modal parameters as information regarding the relevant virgin or undamaged state. By exploiting the experimentally measured dynamic characteristics obtained via a sensor network deployed over the structure, it is possible to detect, locate and quantify possible damage patterns by means of model updating procedures [ 4 , 5 ]. These procedures attempt to reduce the discrepancy between the outputs of the numerical model and the real-life data [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Health Monitoring of Large-Scale Civil Structures: An Approach Based on Data Partitioning and Classical Multidimensional Scaling

Entezami

Sarmadi

Behkamal

et al. 2021

Sensors

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A major challenge in structural health monitoring (SHM) is the efficient handling of big data, namely of high-dimensional datasets, when damage detection under environmental variability is being assessed. To address this issue, a novel data-driven approach to early damage detection is proposed here. The approach is based on an efficient partitioning of the dataset, gathering the sensor recordings, and on classical multidimensional scaling (CMDS). The partitioning procedure aims at moving towards a low-dimensional feature space; the CMDS algorithm is instead exploited to set the coordinates in the mentioned low-dimensional space, and define damage indices through norms of the said coordinates. The proposed approach is shown to efficiently and robustly address the challenges linked to high-dimensional datasets and environmental variability. Results related to two large-scale test cases are reported: the ASCE structure, and the Z24 bridge. A high sensitivity to damage and a limited (if any) number of false alarms and false detections are reported, testifying the efficacy of the proposed data-driven approach.

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On model-based damage detection by an enhanced sensitivity function of modal flexibility and LSMR-Tikhonov method under incomplete noisy modal data

Cited by 35 publications

References 37 publications

Damage Detection in Largely Unobserved Structures under Varying Environmental Conditions: An AutoRegressive Spectrum and Multi-Level Machine Learning Methodology

Damage Detection in Largely Unobserved Structures under Varying Environmental Conditions: An AutoRegressive Spectrum and Multi-Level Machine Learning Methodology

Early damage detection by an innovative unsupervised learning method based on kernel null space and peak‐over‐threshold

Health Monitoring of Large-Scale Civil Structures: An Approach Based on Data Partitioning and Classical Multidimensional Scaling

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