Isomap-based damage classification of cantilevered beam using modal frequency changes

2013 ◽  
Vol 21 (4) ◽  
pp. 590-602 ◽  
Author(s):  
Minjoong Jeong ◽  
Jong-Hun Choi ◽  
Bong-Hwan Koh
Keyword(s):  
Damage Classification ◽  
Modal Frequency ◽  
Cantilevered Beam ◽  
Frequency Changes

scholarly journals Comparative analysis of damage to sewer rehabilitation coatings

2018 ◽  
Vol 45 ◽  
pp. 00041
Author(s):  
Andrzej Kuliczkowski ◽  
Stanisław Nogaj
Keyword(s):  
Damage Assessment ◽  
Technical Condition ◽  
Classification Methods ◽  
Damage Classification ◽  
Reliable Assessment ◽  
Sewer Rehabilitation ◽  
Underground Infrastructure ◽  
Original Part ◽  
Near Future

Technologies for the trenchless rehabilitation of pipelines using various types of coatings have been used for almost half a century. Considering that the assumed life expectancy of such renewed pipelines is 50 years, it will be necessary to assess their technical condition in the near future. The aim of this article is to attempt to answer the question "Do existing damage classification methods allow for the full and reliable assessment of the sewers already renewed with rehabilitation coatings?". The scope of the article, and its original part, is to describe how the problem of damage assessment of rehabilitation coatings has been included in various methods of classification of underground infrastructure pipelines, and conducting a comparison that compares these methods in terms of the damages described. An interpretation of the results of the research on rehabilitation coatings operated in various time periods, starting from those recently applied to those operating for over 30 years, was also made. The result of the analysis is to present the differences and deficiencies in the damage classification methods discussed.

Damage Classification of Composites Using Machine Learning

2019 ◽  
Author(s):  
Shweta Dabetwar ◽  
Stephen Ekwaro-Osire ◽  
João Paulo Dias
Keyword(s):  
Machine Learning ◽  
Composite Materials ◽  
Random Forest ◽  
Condition Monitoring ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Damage Classification ◽  
Combining Data ◽  
Ultrasonic Measurements

Abstract Composite materials have tremendous and ever-increasing applications in complex engineering systems; thus, it is important to develop non-destructive and efficient condition monitoring methods to improve damage prediction, thereby avoiding catastrophic failures and reducing standby time. Nondestructive condition monitoring techniques when combined with machine learning applications can contribute towards the stated improvements. Thus, the research question taken into consideration for this paper is “Can machine learning techniques provide efficient damage classification of composite materials to improve condition monitoring using features extracted from acousto-ultrasonic measurements?” In order to answer this question, acoustic-ultrasonic signals in Carbon Fiber Reinforced Polymer (CFRP) composites for distinct damage levels were taken from NASA Ames prognostics data repository. Statistical condition indicators of the signals were used as features to train and test four traditional machine learning algorithms such as K-nearest neighbors, support vector machine, Decision Tree and Random Forest, and their performance was compared and discussed. Results showed higher accuracy for Random Forest with a strong dependency on the feature extraction/selection techniques employed. By combining data analysis from acoustic-ultrasonic measurements in composite materials with machine learning tools, this work contributes to the development of intelligent damage classification algorithms that can be applied to advanced online diagnostics and health management strategies of composite materials, operating under more complex working conditions.

scholarly journals Classification of Earthquake-Induced Damage forR/CSlab Column Frames Using Multiclass SVM and Its Combination with MLP Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Ali Kia ◽  
Serhan Sensoy
Keyword(s):  
Neural Network ◽  
Seismic Vulnerability ◽  
Gaussian Function ◽  
Damage Index ◽  
Time History ◽  
Nonlinear Time History Analysis ◽  
Support Vector ◽  
Damage Classification ◽  
Ground Acceleration

Nonlinear time history analysis (NTHA) is an important engineering method in order to evaluate the seismic vulnerability of buildings under earthquake loads. However, it is time consuming and requires complex calculations and a high memory machine. In this study, two networks were used for damage classification: multiclass support vector machine (M-SVM) and combination of multilayer perceptron neural network with M-SVM (MM-SVM). In order to collect data, three frames ofR/Cslab column frame buildings with wide beams in slab were considered. For NTHA, twenty different ground motion records were selected and scaled to ten different levels of peak ground acceleration (PGA). Thus, 600 obtained data from the numerical simulations were applied to M-SVM and MM-SVM in order to predict the global damage classification of samples based on park and Ang damage index. Amongst the four different kernel tricks, the Gaussian function was determined as an efficient kernel trick using the maximum total accuracy method of test data. By comparing the obtained results from M-SVM and MM-SVM, the total classification accuracy of MM-SVM is more than M-SVM and it is accurate and reliable for global damage classification ofR/Cslab column frames. Furthermore, the proposed combined model is able to classify the classes with low members.

scholarly journals AUTOMATED BUILDING SEGMENTATION AND DAMAGE ASSESSMENT FROM SATELLITE IMAGES FOR DISASTER RELIEF

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 741-748
Author(s):  
X. Yuan ◽  
S. M. Azimi ◽  
C. Henry ◽  
V. Gstaiger ◽  
M. Codastefano ◽  
...  
Keyword(s):  
Damage Assessment ◽  
Building Damage ◽  
Accurate Information ◽  
Generalization Capability ◽  
Damage Classification ◽  
Performance Impact ◽  
Humanitarian Crisis ◽  
Disaster Data ◽  
The Impact

Abstract. After a natural disaster or humanitarian crisis, rescue forces and relief organisations are dependent on fast, area-wide and accurate information on the damage caused to infrastructure and the situation on the ground. This study focuses on the assessment of building damage levels on optical satellite imagery with a two-step ensemble model performing building segmentation and damage classification trained on a public dataset. We provide an extensive generalization study on pre- and post-disaster data from the passage of the cyclone Idai over Beira, Mozambique, in 2019 and the explosion in Beirut, Lebanon, in 2020. Critical challenges are addressed, including the detection of clustered buildings with uncommon visual appearances, the classification of damage levels by both humans and deep learning models, and the impact of varying imagery acquisition conditions. We show promising building damage assessment results and highlight the strong performance impact of data pre-processing on the generalization capability of deep convolutional models.

Organoleptic damage classification of potatoes with the use of image analysis in production process

2014 ◽  
Author(s):  
K. Przybyl ◽  
M. Zaborowicz ◽  
K. Koszela ◽  
P. Boniecki ◽  
W. Mueller ◽  
...  
Keyword(s):  
Image Analysis ◽  
Production Process ◽  
Damage Classification
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