Data-Driven Methodologies for Structural Damage Detection Based on Machine Learning Applications

Vitola, Jaime; Anaya, Maribel; Burgos, Diego Alexander Tibaduiza; Pozo, Francesc

doi:10.5772/65867

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…In [39], the authors applied the proper orthogonal decomposition (POD) to track the structural behavior. In [40], the authors proposed a data-driven methodology for the detection and classification of damage by using multivariate data-driven approaches and PCA. A support vector machine (SVM) was used for damage detection in [41].…”

Section: Non-localized Behaviour: Platementioning

confidence: 99%

Data Completion, Model Correction and Enrichment Based on Sparse Identification and Data Assimilation

et al. 2022

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Many models assumed to be able to predict the response of structural systems fail to efficiently accomplish that purpose because of two main reasons. First, some structures in operation undergo localized damage that degrades their mechanical performances. To reflect this local loss of performance, the stiffness matrix associated with the structure should be locally corrected. Second, the nominal model is sometimes too coarse grained for reflecting all structural details, and consequently, the predictions are expected to deviate from the measurements. In that case, there is no small region of the model that needs to be repaired, but the entire domain needs to be repaired; therefore, the entire structure-stiffness matrix should be corrected. In the present work, we propose a methodology for locally correcting or globally enriching the models from collected data, which is, upon its turn, completed beyond the sensor’s location. The proposed techniques consist in the first case of an L1-minimization procedure that, with the support of data, aims at the same time period to detect the damaged zone in the structure and to predict the correct solution. For the global enrichment, instead, the methodology consists of an L2-minimization procedure with the support of measurements. The results obtained showed, for the local problem, a correction up to 90% with respect to the initially incorrectly predicted displacement of the structure, and for the global one, a correction up to 60% was observed (this results concern the problems considered in the present study, but they depend on different factors, such as the number of data used, the geometry or the intensity of the damage). The benefits and potential of such techniques are illustrated on four different problems, showing the large generality and adaptability of the methodology.

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Section: Non-localized Behaviour: Platementioning

confidence: 99%

Data Completion, Model Correction and Enrichment Based on Sparse Identification and Data Assimilation

et al. 2022

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“…Machine-learning approaches have also been studied, on their own or in combination with different feature extraction methods [184], as damage detection and classification methodologies. In this kind of approach, in general, PCA is used as a feature-extraction method to define the feature vector to train the machine using several states of the structure.…”

Section: Development Of Statistical Modelsmentioning

confidence: 99%

Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications

Burgos

Vargas

Pedraza

et al. 2020

Sensors

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The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.

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“…As far as current state-of-the-art solutions for damage identification are concerned, there are mostly two adopted philosophies: physics-based and data-driven methods [1][2][3]. The purpose of this study is to propose a Deep Learning methodology with multi-classification damage capabilities, with respect to the research proposed by the authors' previous work [4][5].…”

Section: Introductionmentioning

confidence: 99%

Data-driven deep neural network for structural damage detection in composite solar arrays on flexible spacecraft

Angeletti

2023

Materials Research Proceedings

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Abstract. A data-driven approach based on Deep Neural Network (DNN) techniques is here proposed for Structural Health Monitoring of large in-orbit flexible systems. Damage scenarios are generated via a Finite Element commercial code to train and test the machine learning model, by considering equivalent properties of the composite material of the solar panels. The fully coupled 3D equations for the flexible spacecraft are integrated to test typical profiles of attitude manoeuvres in case of different damages. The DNN model is trained using sensor-measured time series responses, with each response associated with the label of the corresponding damage scenario, and tested via k-folding approach. This methodology offers a promising approach to detect structural damage in solar arrays on spacecraft using machine learning techniques.

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Data-Driven Methodologies for Structural Damage Detection Based on Machine Learning Applications

Cited by 7 publications

References 20 publications

Data Completion, Model Correction and Enrichment Based on Sparse Identification and Data Assimilation

Data Completion, Model Correction and Enrichment Based on Sparse Identification and Data Assimilation

Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications

Data-driven deep neural network for structural damage detection in composite solar arrays on flexible spacecraft

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