scholarly journals Application of artificial neural network on vibration test data for damage identification in bridge girder

2011 ◽  
Vol 6 (35) ◽  
Author(s):  
S. J. S. Hakim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Test Data ◽  
Damage Identification ◽  
Vibration Test ◽  
Bridge Girder ◽  
Artificial Neural

Prediction of Resilient Modulus for Hot Mix Asphalt Based on Artificial Neural Network

2011 ◽  
Vol 304 ◽  
pp. 18-23
Author(s):  
Chun Hua Hu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Test Data ◽  
Hot Mix Asphalt ◽  
Resilient Modulus ◽  
Structure Design ◽  
Forecast Precision ◽  
Long Time ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Resilient modulus of material is an important parameter for pavement structure design and analysis. However it is very tedious to get this parameter for hot mixture asphalt in laboratory. Moreover it takes long time to do experiments. In this paper, artificial neural network (ANN) is applied to predict to resilient modulus for hot mixture asphalt. A neural network model is constructed and trained plenty of times with selected test data until precision meets requirement. Then the model is used to predict resilient modulus for hot mix asphalt. Result of contrast prediction with test data shows that forecast precision is high. This provides a new method to predict resilient modulus for hot mixture asphalt.

Hierarchical development of training database for artificial neural network-based damage identification

2006 ◽  
Vol 76 (3) ◽  
pp. 224-233 ◽  
Author(s):  
Lin Ye ◽  
Zhongqing Su ◽  
Chunhui Yang ◽  
Zhihao He ◽  
Xiaoming Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Damage Identification ◽  
Artificial Neural

Simulation of Performance Deterioration of a Microturbine and Application of Neural Network to Its Performance Diagnosis

2008 ◽  
Author(s):  
Jae Eun Yoon ◽  
Jong Joon Lee ◽  
Tong Seop Kim ◽  
Jeong Lak Sohn
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Test Data ◽  
Input Data ◽  
Data Sets ◽  
Characteristic Parameters ◽  
Performance Diagnosis ◽  
The Neural Network ◽  
Performance Deterioration ◽  
Artificial Neural

This study aims to simulate performance deterioration of a microturbine and apply artificial neural network to its performance diagnosis. As it is hard to obtain test data with degraded component performance, the degraded engine data have been acquired through simulation. Artificial neural network is adopted as the diagnosis tool. First, the microturbine has been tested to get reference operation data, assumed to be degradation free. Then, a simulation program was set up to regenerate the performance test data. Deterioration of each component (compressor, turbine and recuperator) was modeled by changes in the component characteristic parameters such as compressor and turbine efficiency, their flow capacities and recuperator effectiveness and pressure drop. Single and double faults (deterioration of single and two components) were simulated to generate fault data. The neural network was trained with majority of the data sets. Then, the remaining data sets were used to check the predictability of the neural network. Given measurable performance parameters (power, temperatures, pressures) as inputs to the neural network, characteristic parameters of each component were predicted as outputs and compared with original data. The neural network produced sufficiently accurate prediction. Reducing the number of input data decreased prediction accuracy. However, excluding up to a couple of input data still produced acceptable accuracy.

Identifying Damage of the Benchmark Structure by Using Artificial Neural Network Methods

2011 ◽  
Vol 219-220 ◽  
pp. 312-317 ◽  
Author(s):  
Bai Sheng Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Damage Identification ◽  
Damage Localization ◽  
Building Structures ◽  
Bp Network ◽  
Network Methods ◽  
Damage Extent ◽  
Artificial Neural ◽  
Set Up

This paper discusses the damage identification using artificial neural network methods for the benchmark problem set up by IASC-ASCE Task Group on Health Monitoring. A three-stage damage identification strategy for building structures is proposed. The BP network and PNN are employed for damage localization and BP network for damage extent identification. Four damage patterns (patterns i~iv) in Cases 1-6 are discussed. The comparison between BP network and PNN are carried out. The results show that PNN performs better than BP network in damage localization. The damage extent identification using BPN is successful even in Cases 2 and 5&6 in which the modeling error is quite large.

Application of an artificial neural network as a flight test data estimator

1995 ◽  
Vol 32 (5) ◽  
pp. 1088-1094 ◽  
Author(s):  
Roger L. McMillen ◽  
James E. Steck ◽  
Kamran Rokhsaz
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Test Data ◽  
Flight Test ◽  
Artificial Neural
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