A cost-effective neural network–based damage detection procedure for cylindrical equipment

et al. 2020

Sensors

Self Cite

This study presents a technique to identify the vibration characteristics in power transmission towers and to detect the potential structural damages. This method is based on the curvature of the mode shapes coupled with a continuous wavelet transform. The elaborated numerical method is based on signal processing of the output that resulted from ambient vibration. This technique benefits from a limited number of sensors, which makes it a cost-effective approach compared to others. The optimal spatial location for these sensors is obtained by the minimization of the non-diagonal entries in the modal assurance criterion (MAC) matrix. The Hilbert–Huang transform was also used to identify the dynamic anatomy of the structure. In order to simulate the realistic condition of the measured structural response in the field condition, a 10% noise is added to the response of the numerical model. Four damage scenarios were considered, and the potential damages were identified using wavelet transform on the difference of mode shapes curvature in the intact and damaged towers. Results show a promising accuracy considering the small number of applied sensors. This study proposes a low-cost and feasible technique for structural health monitoring.

Section: Structural Damage Detectionmentioning

confidence: 99%

Vibration Anatomy and Damage Detection in Power Transmission Towers with Limited Sensors

Mohammadi

et al. 2020

Sensors

Self Cite

“…Therefore, estimating these parameters can be significantly efficient in determining the extent of damage in different building types [19]. Machine Learning (ML) is a scientific discipline that investigates the study and development of mathematical algorithms that can construct functional relationship between quantities in terms of known information and rules [20,21]. ML algorithms are widely used for estimating the behavior as well as structural damage of a system under different types of excitations [22].…”

Section: Introductionmentioning

confidence: 99%

“…These regressive algorithms are an alternative approach for time-consuming response estimation methods. Karami et al proposed a cost-effective neural network-based algorithm to identify the structural damage of cylindrical equipment [20]. Weinstein et al investigated the application of ANNs using the operational response data as input features to develop a probabilistic model of bridge behavior.…”

Section: Introductionmentioning

confidence: 99%

A swift neural network-based algorithm for demand estimation in concrete moment-resisting buildings

2022

NMCE

Rapid evaluation of demand parameters of different types of buildings is crucial for social restoration after damaging earthquakes. Previous studies proposed numerous methodologies to measure the performance of buildings for assessing the potential risk under the seismic hazard. However, time-consuming Nonlinear Response History Analysis (NRHA) barricaded implementing a prompt loss estimation for emergency confronting actions. The present study proposes a swift framework for demand estimation in concrete moment-resisting buildings using artificial neural networks. For this purpose, a simplified model is developed based on the HAZUS method. To eliminate the required time-consuming NRHA from the post-earthquake actions, Artificial Neural Networks (ANNs) are used. Before the event, ANNs are studied to estimate the demand parameters using a set of time-history analyses. This study applies to a suite of 111 earthquake events, originally developed in the SAC project and uniformly scaled from 0.1 g to 1.5 g , to achieve a generalized prediction model. Bayesian Optimization (BO) algorithm is carried out to tune the architecture of the NNs. Results reveal that the presented approach is reliable for predicting the structural response, and is cost-effective compared to the conventional NRHA. This framework can be implemented in the body of a risk assessment platform to expedite the postearthquake actions required for crisis management.

“…8 It should be noted that the process of detecting and tracking the structural damage is known as the SHM. [9][10][11] SHM methods could be divided into two general categories, modelbased and data-driven methods. 12 In the former approach, a system identification method should be conducted for identifying the dynamic properties of the structure.…”

Section: Introductionmentioning

confidence: 99%

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

Structural Contr & Hlth

Soroushian

2021

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.