Damage detection of 3D structures using nearest neighbor search method

Abasi, Ali; Harsij, Vahid; Soraghi, Ahmad

doi:10.1007/s11803-021-2048-1

Cited by 14 publications

(3 citation statements)

References 56 publications

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“…Khatir et al [30] indicated one specific approach that has two steps in order to assess damage in beam-like structures using two dimensions, namely, isogeometric analysis (IGA) and finite element method besides optimization techniques. Abasi et al [31] presented a damage identification method using the nearest neighbor search method to evaluate structures. Consequently, the nearest neighbor search method was rather strong for noisy data in comparison to the artificial neural network.…”

Section: Introductionmentioning

confidence: 99%

Improved Structural Health Monitoring Using Mode Shapes: An Enhanced Framework for Damage Detection in 2D and 3D Structures

Zamani Kouhpangi,

Yaghoubi,

Torabipour

2023

Eng

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Structural health monitoring (SHM) is crucial for ensuring the safety and performance of offshore platforms. SHM uses advanced sensor systems to detect and respond to negative changes in structures, improving their reliability and extending their life cycle. Model updating methods are also useful for sensitivity analysis. It is feasible to discuss and introduce established techniques for detecting damage in structures by utilizing their mode shapes. In this research, by considering reducing the stiffness of elements in the damage scenarios, we conducted simulations of the models in MATLAB, including both two-dimensional and three-dimensional structures, to update the method suggested by Wang. Wang’s method was improved to produce a sensitivity equation for the damaged structures. The sensitivity equation solution using a subset of mode shapes data was found to evaluate structural parameter changes. Comparing the updated results for Wang’s method and the suggested method in the two- and three-dimensional frames showed a noticeable modification in damage recognition. Furthermore, the suggested method can update a model containing measurement errors. Since Wang’s damage detection formulation is suitable only for 2D structures, this modified framework provides a more accurate decision-making tool for damage detection of structures, regardless of whether a 2D or 3D formulation is used.

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Section: Introductionmentioning

confidence: 99%

Improved Structural Health Monitoring Using Mode Shapes: An Enhanced Framework for Damage Detection in 2D and 3D Structures

Zamani Kouhpangi,

Yaghoubi,

Torabipour

2023

Eng

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“…Hence, it is possible to analyse a broader range of models and systems irrespective of the nature of the problem. In particular, ML-DL modelling is an active field of research in other engineering fields such as structural and earthquake engineering (Abasi et al, 2021;M S Barkhordari & Es-haghi, 2021;Mohammad Sadegh Barkhordari & Tehranizadeh, 2021;Esteghamati & Flint, 2021;Hariri-Ardebili & Salazar, 2020;Pourkamali-Anaraki et al, 2020;Soraghi & Huang, 2021), biomedical engineering (Alizadehsani et al, 2021;Ayoobi et al, 2021), etc. Other applications of ML-DL techniques can also be found (Aswin et al, 2018;Athira et al, 2018;Selvin et al, 2017;Vinayakumar et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Accurate discharge coefficient prediction of streamlined weirs by coupling linear regression and deep convolutional gated recurrent unit

Chen

Sharifrazi

Liang

et al. 2022

Engineering Applications of Computational Fluid Mechanics

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Streamlined weirs, as a nature-inspired type of weirs, has gained tremendous attention among hydraulic engineers mainly due to their well-known performance with high discharge coefficient. Computational fluid dynamic (CDF) is considered as a robust tool to predict discharge coefficient. To bypass the computational cost of CFD-based assessment, the present study proposes data-driven modelling techniques, as an alternative to CFD simulation, to predict discharge coefficient based on an experimental dataset. To this end, after splitting the dataset by k-fold cross-validation technique, the performance assessment of classical and hybrid machine-deep learning (ML-DL) algorithms is undertaken. Amongst ML techniques, linear regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbours (KNN), and decision tree (DT) algorithms are studied . In the context of DL, long short-term memory (LSTM), convolutional neural network (CNN), gated recurrent unit (GRU) and their hybrid forms such as LSTM-GRU, CNN-LSTM and CNN-GRU techniques are compared by different error metrics. It is found that the proposed three-layer hierarchical DL algorithm consisting of a convolutional layer coupled with two subsequent GRU levels, which is also hybridized by LR method (i.e., LR-CGRU), leads to lower error metrics. This paper paves the way for data-driven modelling of streamlined weirs.

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“…Thus, it could be utilized to detect damage in complex 3-D structures. [11] Moreover, machine learning algorithms are a novel approach for the damage detection. Ghiasi et al used seven artificial intelligence (AI) methods to locate and quantify the damage to handle the current shortcomings and compare the methods.…”

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confidence: 99%

Seismic damage identification of moment frames based on random forest algorithm and enhanced gray wolf optimization

Nourizadeh

Seyedpoor

2023

Structural Design Tall Build

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The present study aims to identify damage in two-dimensional (2-D) moment frames using seismic responses by combining the random forest (RF) machine classifier and the enhanced gray wolf optimizer (EGWO) metaheuristic algorithm. First, a 2-D moment frame for the dynamic analysis is simulated using the finite element method (FEM). Then, the placement of sensors is optimized using a proposed optimal sensor placement (POSP) method, which is a combination of the iterated improved reduced system (IIRS) and the binary differential evolution (BDE) optimization algorithm. The acceleration responses of the moment frame having damaged elements under 1995 Kobe earthquake are measured at the optimal sensor placement. Then, the natural frequencies and mode shapes of the structure are extracted using the auto-regressive model with exogenous input method (ARX) as a system identification method. The natural frequencies are exploited to train an RF machine learning network that can find the damaged story of the moment frame. Then, EGWO is employed to accurately locate and quantify the damaged elements of the structure. The efficiency of the proposed method is assessed through considering a six-story frame with 18 elements, a seven-story frame with 49 elements, and the experimental data of an eightstory frame for various conditions. The results show that the RF algorithm has an outstanding performance to correctly find a damaged story. Furthermore, the location and severity of damaged elements are precisely determined by EGWO algorithm.As a final outcome, it is demonstrated that the two-step proposed method is very effective in seismically identifying damage to such structures.

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Damage detection of 3D structures using nearest neighbor search method

Cited by 14 publications

References 56 publications

Improved Structural Health Monitoring Using Mode Shapes: An Enhanced Framework for Damage Detection in 2D and 3D Structures

Improved Structural Health Monitoring Using Mode Shapes: An Enhanced Framework for Damage Detection in 2D and 3D Structures

Accurate discharge coefficient prediction of streamlined weirs by coupling linear regression and deep convolutional gated recurrent unit

Seismic damage identification of moment frames based on random forest algorithm and enhanced gray wolf optimization

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