ROC and Cost Curves for SHM Performance Characterization in a Multilevel Damage Classification Framework: Application to Impact Damage in Aircraft Composites Structures

2015 ◽  
Author(s):  
ALFONSO APICELLA ◽  
GIULIO COTTONE ◽  
LUCA DE MARCHI ◽  
ULRIKE HECKENBERGER ◽  
ALESSANDRO MARZANI
Keyword(s):  
Impact Damage ◽  
Damage Classification ◽  
Performance Characterization ◽  
Classification Framework ◽  
Cost Curves ◽  
Aircraft Composites

Impact Damage Detection in Aircraft Composites Using Knowledge-based Reasoning

2008 ◽  
Vol 7 (3) ◽  
pp. 215-230 ◽  
Author(s):  
L.E. Mujica ◽  
J. Vehí ◽  
W. Staszewski ◽  
K. Worden
Keyword(s):  
Damage Detection ◽  
Impact Damage ◽  
Knowledge Based ◽  
Aircraft Composites

Damage Classification in CFRP Laminates Using Principal Component Analysis (PCA) Approach

2012 ◽  
Vol 225 ◽  
pp. 189-194
Author(s):  
Mohamed Thariq Hameed Sultan ◽  
Azmin Shakrine M. Rafie ◽  
Noorfaizal Yidris ◽  
Faizal Mustapha ◽  
Dayang Laila Majid
Keyword(s):  
Signal Processing ◽  
Damage Detection ◽  
Impact Damage ◽  
Research Work ◽  
Principal Component ◽  
Damage Classification ◽  
Cfrp Laminates ◽  
Work Related ◽  
Detection And Identification ◽  
The Impact

Signal processing is an important element used for identifying damage in any SHM-related application. The method here is used to extract features from the use of different types of sensors, of which there are many. The responses from the sensors are also interpreted to classify the location and severity of the damage. This paper describes the signal processing approaches used for detecting the impact locations and monitoring the responses of impact damage. Further explanations are also given on the most widely-used software tools for damage detection and identification implemented throughout this research work. A brief introduction to these signal processing tools, together with some previous work related to impact damage detection, are presented and discussed in this paper.

scholarly journals Multi-Source Data Fusion Based on Ensemble Learning for Rapid Building Damage Mapping during the 2018 Sulawesi Earthquake and Tsunami in Palu, Indonesia

2019 ◽  
Vol 11 (7) ◽  
pp. 886 ◽  
Author(s):  
Bruno Adriano ◽  
Junshi Xia ◽  
Gerald Baier ◽  
Naoto Yokoya ◽  
Shunichi Koshimura
Keyword(s):  
Data Fusion ◽  
Ensemble Learning ◽  
Building Damage ◽  
Optical Remote Sensing ◽  
Damage Classification ◽  
Classification Framework ◽  
Source Data ◽  
Four Levels ◽  
Damage Mapping ◽  
2018 Sulawesi Earthquake

This work presents a detailed analysis of building damage recognition, employing multi-source data fusion and ensemble learning algorithms for rapid damage mapping tasks. A damage classification framework is introduced and tested to categorize the building damage following the recent 2018 Sulawesi earthquake and tsunami. Three robust ensemble learning classifiers were investigated for recognizing building damage from Synthetic Aperture Radar (SAR) and optical remote sensing datasets and their derived features. The contribution of each feature dataset was also explored, considering different combinations of sensors as well as their temporal information. SAR scenes acquired by the ALOS-2 PALSAR-2 and Sentinel-1 sensors were used. The optical Sentinel-2 and PlanetScope sensors were also included in this study. A non-local filter in the preprocessing phase was used to enhance the SAR features. Our results demonstrated that the canonical correlation forests classifier performs better in comparison to the other classifiers. In the data fusion analysis, Digital Elevation Model (DEM)- and SAR-derived features contributed the most in the overall damage classification. Our proposed mapping framework successfully classifies four levels of building damage (with overall accuracy >90%, average accuracy >67%). The proposed framework learned the damage patterns from a limited available human-interpreted building damage annotation and expands this information to map a larger affected area. This process including pre- and post-processing phases were completed in about 3 h after acquiring all raw datasets.

Probabilistic Urban Structural Damage Classification Using Bitemporal Satellite Images

2010 ◽  
Vol 26 (1) ◽  
pp. 87-109 ◽  
Author(s):  
ZhiQiang Chen ◽  
Tara C. Hutchinson
Keyword(s):  
Structural Damage ◽  
Satellite Images ◽  
Probabilistic Approach ◽  
Damage Classification ◽  
Classification Framework ◽  
Sensing Technology ◽  
Probabilistic Classification ◽  
Multiclass Classifier ◽  
Post Disaster

Recent research endeavors in civil engineering have attempted to apply remote sensing technology to urban damage assessment as an aid for post-disaster reconnaissance and recovery. In these attempts, urban structural damage is identified based on pre- and post-disaster satellite images with the use of a pattern classification approach. The result is usually presented in a damage map wherein categorical damage levels, such as “fully collapsed,” “partially collapsed,” or “intact,” are assigned to urban subregions or individual structures in images. However, a major limitation in past attempts is the use of deterministic approaches to classify damage levels. In general, these approaches are not able to capture the inherent uncertainties of structural damage and lack scalability when analyzing damage to built urban subregions of different sizes. To address this, a probabilistic classification framework by means of a multiclass classifier is proposed. By applying this probabilistic approach, classification of urban damage provides posterior probabilities, which can be used to quantify decision uncertainties and to obtain regional urban damage classification. Numerical experiments are conducted using satellite images acquired from a recent earthquake and a tsunami event, namely the 2003 Bam, Iran Earthquake, and the 2004 India Ocean Tsunami.

Status Quo of Research on Impact Damage of Composites in Aircraft

2016 ◽  
Vol 719 ◽  
pp. 33-40
Author(s):  
Wen Jun Chen ◽  
Jing Song Chen ◽  
Wen Bo Cheng ◽  
Lu Chun Zhao
Keyword(s):  
Composite Material ◽  
Composite Laminates ◽  
Impact Test ◽  
Impact Damage ◽  
Status Quo ◽  
Low Energy ◽  
Energy Impact ◽  
Aircraft Composites ◽  
Metal Materials ◽  
The Impact

The application of composites in aircraft was introduced. And compare composites with metal materials. The conclusions referring to the impact test on composite laminates and impact damage characteristics of composite laminates, were summarized by referring to a large number of literature. This investigation shows: composite material is more suitable for the preparation of the overall structure; the research of impact damage test on composites mainly concentrated in the layer order; layer direction and low-energy impact test; and there are clear division and judgment method of four kinds of damage body by studying characteristics of impact damage.Keyword: aircraft; composites; impact damage

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