scholarly journals Construction of an Artificial Neural Network-Based Method to Detect Structural Damage

2020 ◽  
Author(s):  
Francisco Casanova-del-Angel ◽  
Daniel Hernández-Galicia ◽  
Xochicale-Rojas Hugo Alberto
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Structural Damage ◽  
Artificial Neural

Influence of Input Parameters on the Performance of an Artificial Neural Network Used to Detect Structural Damage

2010 ◽  
Author(s):  
Jesus Daniel Villalba ◽  
Ivan Dario Gomez ◽  
Jose Elias Laier ◽  
Theodore E. Simos ◽  
George Psihoyios ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Structural Damage ◽  
Input Parameters ◽  
Artificial Neural

Damage Assessment in Structures Using Incomplete Modal Data and Artificial Neural Network

2015 ◽  
Vol 15 (06) ◽  
pp. 1450087 ◽  
Author(s):  
Seyed Sina Kourehli
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Finite Element Model ◽  
Structural Damage ◽  
Element Model ◽  
Mass System ◽  
Modal Data ◽  
Artificial Neural ◽  
Propagation Network

This paper presents a novel approach for structural damage detection and estimation using incomplete noisy modal data and artificial neural network (ANN). A feed-forward back propagation network is proposed for estimating the structural damage location and severity. Incomplete modal data is used in the dynamic analysis of damaged structures by the condensed finite element model and as input parameters to the neural network for damage identification. In all cases, the first two natural modes were used for the training process. The present method is applied to three examples consisting of a simply supported beam, three-story plane frame, and spring-mass system. Also, the effect of the discrepancy in mass and stiffness between the finite element model and the actual tested dynamic system has been investigated. The results demonstrated the accuracy and efficiency of the proposed method using incomplete modal data, which may be noisy or noise-free.

scholarly journals A REVIEW PAPER ON STRUCTURAL DAMAGE IDENTIFICATION OF CANTILEVER BEAM BY USING VIBRATION TECHNIQUE, FINITE ELEMENT ANALYSIS & ARTIFICIAL NEURAL NETWORK

2015 ◽  
Vol 3 (12) ◽  
pp. 125-128
Author(s):  
Aakanksha MohanraoGarud ◽  
V. G. Bhamre
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Artificial Neural Network ◽  
Structural Damage ◽  
Review Paper ◽  
Damage Identification ◽  
Frequency Measurement ◽  
Element Analysis ◽  
Structural Damage Identification ◽  
Artificial Neural

In this review paper structural damage identification work in cantilever beam is done by using the Artificial Neural Network as diagnostic parameter. The study is based on the concept that natural frequency is inversely proportional to the mass of the structure. Thus to regulate the proper condition of structure, periodical frequency measurement is necessary. But in dynamic conditions and in complicated structures frequency measurement is difficult, for the same we reviewed various papers to identify the structural damage using various methods. The factors which affects on the damage of structural parts like crack depth, crack location etc. is also discussed in this work. Natural frequency is measured with the help of fast fourier transform by various authors and artificial neural network is also used for identification of the damage in many papers. So in this review work we studied methods of structural damage identification such as vibrations, finite element analysis and artificial neural network.

scholarly journals Piezoelectric Active Sensor Self-Diagnosis for Electromechanical Impedance Monitoring Using K-Means Clustering Analysis and Artificial Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xie Jiang ◽  
Xin Zhang ◽  
Yuxiang Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Clustering Analysis ◽  
Structural Damage ◽  
Principal Component ◽  
Piezoelectric Sensor ◽  
Sensor Faults ◽  
Ann Model ◽  
Electromechanical Impedance ◽  
Artificial Neural

Piezoelectric sensor is a crucial part of electromechanical impedance technology whose state will directly affect the effectiveness and accuracy of structural health monitoring (SHM). So carrying out sensor self-diagnosis is important and necessary. However, it is still difficult to distinguish sensor faults from structural damage as well as identify the cases and degrees of sensor faults. In the study, three characteristic indexes of admittance which have different indication intervals for damages of structure and sensors were selected from six indexes after comparison. To improve the discrimination effect, three principal components (PC) were extracted by principal component analysis (PCA). And the damage information represented by PCs was clustered by the K-means algorithm to identify the cases of damage. Then, the degrees of sensor damages were classified with the artificial neural network (ANN). The results show that the K-means clustering analysis based on admittance characteristics can accurately distinguish and identify the structural damage and four kinds of sensor damages, namely, pseudosoldering, debonding, wear, and breakage. The trained ANN model has a good recognition effect on the damage degrees and the accuracy of recognition reaches 100%. This study has a certain reference value for piezoelectric sensor self-fault identification.

Prediction of Vibration Velocity due to Blasting Using Artificial Neural Network

2012 ◽  
Vol 170-173 ◽  
pp. 3063-3067
Author(s):  
Fu Qiang Gao ◽  
Ai Jun Hou
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Velocity ◽  
Structural Damage ◽  
Mining Industry ◽  
Coefficient Of Determination ◽  
Vibration Velocity ◽  
Monitoring Point ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Vibration induced by blasting is one of the most hazardous events in the mining industry and may cause structural damage in country areas. Therefore, mitigating the possible hazard and predicting the vibration velocity is important. In this paper, an attempt has been made to predict the peak particle velocity using artificial neural network (ANN) by taking into consideration of maximum explosive charge used per delay and distance between blast face to monitoring point. To achieve the classic framework of this approach, the prediction results by artificial neural network were compared with measured values by coefficient of determination (CoD) and sum of squares due to error (SSE).

Comparison between a Linear Regression and an Artificial Neural Network Model to Detect and Localize Damage in the Powder Mill Bridge

2020 ◽  
Vol 2674 (8) ◽  
pp. 394-404
Author(s):  
Kathryn Kaspar ◽  
Erin Santini-Bell ◽  
Marek Petrik ◽  
Masoud Sanayei
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Structural Damage ◽  
Data Driven ◽  
Strain Gauges ◽  
Average Error ◽  
Ann Model ◽  
Bridge Superstructure ◽  
Artificial Neural

This paper evaluates the ability of two different data-driven models to detect and localize simulated structural damage in an in-service bridge for long-term structural health monitoring (SHM). Strain gauge data collected over 4 years is used to characterize the undamaged state of the bridge. The Powder Mill Bridge in Barre, Massachusetts, U.S., which has been instrumented with strain gauges since its opening in 2009, is used as a case study, and the strain gauges used in this study are located at 26 different stations throughout the bridge superstructure. A linear regression (LR) model and an artificial neural network (ANN) model are evaluated based on the following criteria: (a) the ability to accurately predict the strain at each location in the undamaged state of the bridge; (b) the ability to detect simulated structural damage to the bridge superstructure; and (c) the ability to localize simulated structural damage. Both the LR and the ANN models were able to predict the strain at the 26 stations with an average error of less than 5%, indicating that both methodologies were effective in characterizing the undamaged state of the bridge. A calibrated finite element model was then used to simulate damage to the Powder Mill Bridge for three damage scenarios: fascia girder corrosion, girder fracture, and deck delamination. The LR model proved to be just as effective as the ANN model at detecting and localizing damage. A recommended protocol is thus presented for integrating data-driven models into bridge asset management systems.

scholarly journals Structural Damage Identification Based on Rough Sets and Artificial Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Chengyin Liu ◽  
Xiang Wu ◽  
Ning Wu ◽  
Chunyu Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Damage Detection ◽  
Rough Sets ◽  
Structural Damage ◽  
Damage Identification ◽  
Measurement Data ◽  
Structural Damage Detection ◽  
Element Analysis ◽  
Artificial Neural

This paper investigates potential applications of the rough sets (RS) theory and artificial neural network (ANN) method on structural damage detection. An information entropy based discretization algorithm in RS is applied for dimension reduction of the original damage database obtained from finite element analysis (FEA). The proposed approach is tested with a 14-bay steel truss model for structural damage detection. The experimental results show that the damage features can be extracted efficiently from the combined utilization of RS and ANN methods even the volume of measurement data is enormous and with uncertainties.

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