A System Identification‐Based Damage‐Detection Method for Gravity Dams

Mirtaheri, Masoud; Salkhordeh, Mojtaba; Mohammadgholiha, Masoud

doi:10.1155/2021/6653254

Cited by 10 publications

(3 citation statements)

References 42 publications

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“…The spectral density of white noise which is a random signal is constant. In other words, at various frequencies, white noise has an identical intensity [40].…”

Section: Methodsmentioning

confidence: 99%

Vibration-based damage detection of buildings using a decision-tree-based algorithm

Hosseini Lavasaniا,

mahdipour

2023

Numerical Methods in Civil Engineering

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Previous studies revealed that traditional methods of damage detection (e.g., visual inspection) are time-consuming and require large monetary resources. In the last three decades, machine learning algorithms, sensor technologies, and computer science have progressively advanced, which paved the way for implementing machine learning-based damage detection frameworks. This paper presents a damage detection framework for civil structures using machine learning algorithms. The decision-tree classifier is used to classify the state of damage in the building based on the damage indicators obtained from the output acceleration signals of the building. The braced-frame structure known as the IASC-ASCE structural health monitoring benchmark building was used to verify the presented approach. The total number of 6000 Gaussian white noise signals with 10s length was applied to the case study model as ambient vibrations using the Matlab platform. Five different damage indicators, including the vibration intensity, mean period, mean, variance, and the fundamental frequency of the structure, were used to train the classifier. A Bayesian Optimization algorithm was implemented to tune the hyperparameters of the decision-tree classification learner. The results indicate that the proposed approach could estimate the state of damage in the building with promising accuracy.

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“…The spectral density of white noise which is a random signal is constant. In other words, at various frequencies, white noise has an identical intensity [40].…”

Section: Methodsmentioning

confidence: 99%

Vibration-based damage detection of buildings using a decision-tree-based algorithm

Hosseini Lavasaniا,

mahdipour

2023

Numerical Methods in Civil Engineering

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“…In the former approach, a system identification method should be conducted for identifying the dynamic properties of the structure. Then, by capturing the variation of these dynamic properties, structural health condition will be determined 13–15 . On the other hand, data‐driven methods are based on features obtained from vibration data of the structure through a statistical learning algorithm to predict the damage.…”

Section: Introductionmentioning

confidence: 99%

“…Then, by capturing the variation of these dynamic properties, structural health condition will be determined. [13][14][15] On the other hand, data-driven methods are based on features obtained from vibration data of the structure through a statistical learning algorithm to predict the damage.…”

Section: Introductionmentioning

confidence: 99%

A decision‐tree‐based algorithm for identifying the extent of structural damage in braced‐frame buildings

Salkhordeh

Mirtaheri

Soroushian

2021

Structural Contr & Hlth

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Summary Rapid health assessment of essential buildings such as hospitals, fire stations, and large residential complexes is crucial after damaging earthquakes. The use of advanced technologies such as wireless sensors, learning algorithms, and signal processing methods became more attractive in such fast applications due to their higher reliabilities and efficiencies compared to the conventional visual inspection methods. This paper presents a robust post‐earthquake damage detection framework for predicting the extent and location of damage occurrence in the braced‐frame structures after an earthquake. To do so, features derived from acceleration response of the structure were used along with a classification learner to determine the health condition of the structure. Decision tree classifiers are used for the purpose of damage classification where the Bayesian optimization algorithm is implemented to optimize the architecture of the mentioned classifier. A one‐story chevron steel‐braced frame, a three‐story X‐braced steel‐frame, and a five‐story three‐dimensional building are considered to validate the proposed method. The total number of 3774 and 1887 nonlinear response history analyses were respectively performed for 2D and 3D numerical models under scaled SAC motions, using the OpenSees simulation platform. Furthermore, in order to simulate the field condition, a maximum level of 10% white Gaussian noise is added to the output signals. Results obtained from the three case studies show that the proposed framework is robust and reliable in predicting the extent of damage level in the braced‐frame structures in a short time after an earthquake.

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A Two-Stage Method for Damage Identification in Concrete Gravity Dams by Using a Modal Curvature-Based Index and Pathfinder Algorithm

Mohebian,

Motahari,

Aval

2023

Arab J Sci Eng

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A System Identification‐Based Damage‐Detection Method for Gravity Dams

Cited by 10 publications

References 42 publications

Vibration-based damage detection of buildings using a decision-tree-based algorithm

Vibration-based damage detection of buildings using a decision-tree-based algorithm

A decision‐tree‐based algorithm for identifying the extent of structural damage in braced‐frame buildings

A Two-Stage Method for Damage Identification in Concrete Gravity Dams by Using a Modal Curvature-Based Index and Pathfinder Algorithm

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