scholarly journals Damage Localization of Cable-Supported Bridges Using Modal Frequency Data and Probabilistic Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
X. T. Zhou ◽  
Y. Q. Ni ◽  
F. L. Zhang
Keyword(s):  
Neural Network ◽  
Structural Damage ◽  
Probabilistic Neural Network ◽  
Random Noise ◽  
Measurement Data ◽  
Frequency Change ◽  
Damage Localization ◽  
Pattern Class ◽  
Modal Frequency ◽  
Training Samples

This paper presents an investigation on using the probabilistic neural network (PNN) for damage localization in the suspension Tsing Ma Bridge (TMB) and the cable-stayed Ting Kau Bridge (TKB) from simulated noisy modal data. Because the PNN approach describes measurement data in a Bayesian probabilistic framework, it is promising for structural damage detection in noisy conditions. For locating damage on the TMB deck, the main span of the TMB is divided into a number of segments, and damage to the deck members in a segment is classified as one pattern class. The characteristic ensembles (training samples) for each pattern class are obtained by computing the modal frequency change ratios from a 3D finite element model (FEM) when incurring damage at different members of the same segment and then corrupting the analytical results with random noise. The testing samples for damage localization are obtained in a similar way except that damage is generated at locations different from the training samples. For damage region/type identification of the TKB, a series of pattern classes are defined to depict different scenarios with damage occurring at different portions/components. Research efforts have been focused on evaluating the influence of measurement noise level on the identification accuracy.

Effect of Attribute Reduction on Rough-Probabilistic Neural Network for Structural Damage Detection

2010 ◽  
Vol 163-167 ◽  
pp. 2482-2487
Author(s):  
Shao Fei Jiang ◽  
Zhao Qi Wu
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Data Storage ◽  
Structural Damage ◽  
Probabilistic Neural Network ◽  
Attribute Reduction ◽  
Measurement Data ◽  
Noise Tolerance ◽  
Structural Damage Detection ◽  
Proposed Model

In this paper, a new rough-probabilistic neural network (RSPNN) model, whereby rough set data and a probabilistic neural network (PNN) are integrated, is proposed. This model is used for structural damage detection, particularly for cases where the measurement data has many uncertainties. To verify the proposed method, an example is presented to identify both single and multi-damage case patterns. The effects of measurement noise and attribute reduction on the damage detection results are also discussed. The results show that the proposed model not only has good damage detection capability and noise tolerance, but also reduces data storage memory requirements.

Application of Probabilistic Neural Network in Pattern Classification

2013 ◽  
Vol 441 ◽  
pp. 738-741 ◽  
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Classification ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Networks ◽  
Training Samples ◽  
The One ◽  
Speed Accuracy

The network model of probabilistic neural network and its method of pattern classification and discrimination are first introduced in this paper. Then probabilistic neural network and three usually used back propagation neural networks are established through MATLAB7.0. The pattern classification of dots on a two-dimensional plane is taken as an example. Probabilistic neural network and improved back propagation neural networks are used to classify these dots respectively. Their classification results are compared with each other. The simulation results show that compared with back propagation neural networks, probabilistic neural network has simpler learning rules, faster training speed and it needs fewer training samples; the pattern classification method based on probabilistic neural network is very effective, and it is superior to the one based on back propagation neural networks in classifying speed, accuracy as well as generalization ability.

Structural Damage Localization Based on AR Model and BP Neural Network

2011 ◽  
Vol 94-96 ◽  
pp. 1211-1215
Author(s):  
Yan Song Diao ◽  
Fei Yu ◽  
Dong Mei Meng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Structural Damage ◽  
Ar Model ◽  
Model Parameters ◽  
Damage Localization ◽  
Modal Parameter ◽  
Damage Location ◽  
Simulation And Experiment ◽  
Damage Characteristic

When the AR model is used to identify the structural damage, one problem is often met, that is the method can only make a decision whether the structure is damaged, however, the damage location can not be identified exactly. A structural damage localization method based on AR model in combination with BP neural network is proposed in this paper. The AR time series models are used to describe the acceleration responses. The changes of the first 3-order AR model parameters are extracted and composed as damage characteristic vectors which are put into BP neural network to identify the damage location. The effectiveness of the method is validated by the results of numerical simulation and experiment for a four-layer offshore platform. Only the acceleration responses can be used adequately to localize the structural damage, without the usage of modal parameter and excitation force. Thus the dependence on the modal parameter and excitation can be avoided in this method.

scholarly journals Structural damage detection based on improved frequency change ratio

2019 ◽  
Vol 272 ◽  
pp. 01010
Author(s):  
Jian WANG ◽  
Huan JIN ◽  
Xiao MA ◽  
Bin ZHAO ◽  
Zhi YANG ◽  
...  
Keyword(s):  
Damage Detection ◽  
Health Monitoring ◽  
Structural Damage ◽  
Detection Method ◽  
Mode Shapes ◽  
Frequency Change ◽  
Damage Localization ◽  
Structural Damage Detection ◽  
Practical Application ◽  
Slight Damage

Frequency Change Ratio (FCR) based damage detection methodology for structural health monitoring (SHM) is analyzed in detail. The effectiveness of damage localization using FCR for some slight damage cases and worse ones are studied on an asymmetric planar truss numerically. Disadvantages of damage detection using FCR in practical application are found and the reasons for the cases are discussed. To conquer the disadvantages of FCR, an Improved Frequency Change Ratio (IFCR) based damage detection method which takes the changes of mode shapes into account is proposed. Verification is done in some damage cases and the results reveal that IFCR can identify the damage more efficiently. Noisy cases are considered to assess the robustness of IFCR and results indicate that the proposed method can work well when the noise is not severe.

Damage Severity Assessment Using Modified BP Neural Network

2011 ◽  
Vol 179-180 ◽  
pp. 1016-1020
Author(s):  
Xiao Ma Dong ◽  
Zhong Hui Wang
Keyword(s):  
Neural Network ◽  
Composite Material ◽  
Bp Neural Network ◽  
Structural Damage ◽  
Simulation Experiment ◽  
Damage Identification ◽  
Severity Assessment ◽  
Improved Method ◽  
Modal Frequency ◽  
Structural Damage Identification

Damage severity identification is an important content among structural damage identification. In order to avoid the disadvantages of conventional BPNN, a modified BP neural network was proposed to identify structural damage severity in this paper. The modified BPNN was trained by using structural modal frequency qua BPNN input, and then used to forecast structural damage severity. Finally, the results of simulation experiment of composite material cantilever girder show that the improved method is very effective for damage severity identification and possess great applied foreground.

scholarly journals Condition Assessment of Joints in Steel Truss Bridges Using a Probabilistic Neural Network and Finite Element Model Updating

2021 ◽  
Vol 13 (3) ◽  
pp. 1474
Author(s):  
Jiawang Zhan ◽  
Chuang Wang ◽  
Zhiheng Fang
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Model Updating ◽  
Probabilistic Neural Network ◽  
Fe Model ◽  
Damage Localization ◽  
Truss Bridges ◽  
Steel Truss ◽  
Fe Model Updating ◽  
Steel Truss Bridges

The condition of joints in steel truss bridges is critical to railway operational safety. The available methods for the quantitative assessment of different types of joint damage are, however, very limited. This paper numerically investigates the feasibility of using a probabilistic neural network (PNN) and a finite element (FE) model updating technique to assess the condition of joints in steel truss bridges. A two-step identification procedure is developed to achieve damage localization and severity assessment. A series of FE models with single or multiple damages are simulated to generate the training and testing data samples and validate the effectiveness of the proposed approach. The influence of noise on the identification accuracy is also evaluated. The results show that the change rate of modal curvature (CRMC) can be used as a damage-sensitive input of the PNN and the accuracy of preliminary damage localization can exceed 90% when suitable training patterns are utilized. Damaged members can be localized in the correct substructure even with noise contamination. The FE model updating method used can effectively quantify the joint deterioration severity and is robust to noise.

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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