scholarly journals GP-ARX-Based Structural Damage Detection and Localization under Varying Environmental Conditions

2020 ◽  
Vol 9 (3) ◽  
pp. 41 ◽  
Author(s):  
Konstantinos Tatsis ◽  
Vasilis Dertimanis ◽  
Yaowen Ou ◽  
Eleni Chatzi
Keyword(s):  
Damage Detection ◽  
Time Scale ◽  
Structural Damage ◽  
Gaussian Process Regression ◽  
Parametric Models ◽  
Arx Model ◽  
Single Input Single Output ◽  
Operational Conditions ◽  
Long Time ◽  
Detection And Localization

The representation of structural dynamics in the absence of physics-based models, is often accomplished through the identification of parametric models, such as the autoregressive with exogenous inputs, e.g. ARX models. When the structure is amenable to environmental variations, parameter-varying extensions of the original ARX model can be implemented, allowing for tracking of the operational variability. Yet, the latter occurs in sufficiently longer time-scales (days, weeks, months), as compared to system dynamics. For inferring a “global”, long time-scale varying ARX model, data from a full operational cycle has to typically become available. In addition, when the sensor network comprises multiple nodes, the identification of long time-scale varying, vector ARX models grow in complexity. We address these issues by proposing a distributed framework for structural identification, damage detection and localization. Its main features are: (i) the individual estimation of local, single-input-single-output ARX models at every operational point; (ii) the long time-scale representation of each individual ARX coefficient via a Gaussian process regression, which captures dependency on varying Environmental and Operational Conditions (EOCs); (iii) the establishment of a distributed residual generation algorithm for damage detection, which produces time-series of well-defined stationary statistics, with detected discrepancies used for damage diagnosis; and, (iv) exploitation of ARX-inferred mode shape curvatures, obtained via ARX-inferred global state-space models, of the healthy and damaged states, for damage localization. The method is assessed via application on two numerical case studies of different complexity, with the results confirming its efficacy for diagnostics under varying EOCs.

scholarly journals DEEP CASCADED NEURAL NETWORKS FOR AUTOMATIC DETECTION OF STRUCTURAL DAMAGE AND CRACKS FROM IMAGES

2020 ◽  
Vol V-2-2020 ◽  
pp. 411-417
Author(s):  
Y. Bai ◽  
B. Zha ◽  
H. Sezen ◽  
A. Yilmaz
Keyword(s):  
Neural Networks ◽  
Damage Detection ◽  
Structural Damage ◽  
Second Step ◽  
Structural Level ◽  
Long Time ◽  
Public Datasets ◽  
Detection And Localization ◽  
Structure Engineering ◽  
Object Level

Abstract. In this paper, two different convolutional neural networks (CNNs) are applied on images for automated structural damage detection (SDD) in earthquake damaged structures and cracking localization (e.g., detection of cracks, their widths and distributions) at various scales, such as pixel level, object level, and structural level. The proposed method has two main steps: 1) diagnosis, and 2) localization of cracking or other damage. At first a residual CNN with transfer learning is employed to classify the damage in the structures and structural components. This step performs damage detection using two public datasets. The second step uses another CNN with U-Net structure to locate the cracking on low resolution images. The implementations using public and self-collected datasets show promising performance for a problem that had remained a challenge in the structure engineering field for a long time and indicate that the proposed approach can perform detection and localization of structural damage with an acceptable accuracy.

scholarly journals Structural Building Damage Detection with Deep Learning: Assessment of a State-of-the-Art CNN in Operational Conditions

2019 ◽  
Vol 11 (23) ◽  
pp. 2765 ◽  
Author(s):  
Francesco Nex ◽  
Diogo Duarte ◽  
Fabio Giulio Tonolo ◽  
Norman Kerle
Keyword(s):  
Deep Learning ◽  
Damage Detection ◽  
Structural Damage ◽  
Relevant Information ◽  
Building Damage ◽  
Computational Time ◽  
Learning Networks ◽  
Remotely Sensed Data ◽  
Operational Conditions ◽  
Damage Maps

Remotely sensed data can provide the basis for timely and efficient building damage maps that are of fundamental importance to support the response activities following disaster events. However, the generation of these maps continues to be mainly based on the manual extraction of relevant information in operational frameworks. Considering the identification of visible structural damages caused by earthquakes and explosions, several recent works have shown that Convolutional Neural Networks (CNN) outperform traditional methods. However, the limited availability of publicly available image datasets depicting structural disaster damages, and the wide variety of sensors and spatial resolution used for these acquisitions (from space, aerial and UAV platforms), have limited the clarity of how these networks can effectively serve First Responder needs and emergency mapping service requirements. In this paper, an advanced CNN for visible structural damage detection is tested to shed some light on what deep learning networks can currently deliver, and its adoption in realistic operational conditions after earthquakes and explosions is critically discussed. The heterogeneous and large datasets collected by the authors covering different locations, spatial resolutions and platforms were used to assess the network performances in terms of transfer learning with specific regard to geographical transferability of the trained network to imagery acquired in different locations. The computational time needed to deliver these maps is also assessed. Results show that quality metrics are influenced by the composition of training samples used in the network. To promote their wider use, three pre-trained networks—optimized for satellite, airborne and UAV image spatial resolutions and viewing angles—are made freely available to the scientific community.

Compressive Sensing for Structural Damage Detection of Reinforced Concrete Structures

2013 ◽  
Vol 569-570 ◽  
pp. 742-750 ◽  
Author(s):  
Madhuka Jayawardhana ◽  
Xin Qun Zhu ◽  
Ranjith Liyanapathirana ◽  
Upul Gunawardana
Keyword(s):  
Energy Consumption ◽  
Reinforced Concrete ◽  
Damage Detection ◽  
Compressive Sensing ◽  
Data Storage ◽  
Structural Damage ◽  
Structural Response ◽  
High Energy ◽  
Reconstructed Signal ◽  
Detection And Localization

High energy consumption, excessive data storage and transfer requirements are prevailing issues associated with structural health monitoring (SHM) systems, especially with those employing wireless sensors. Data compression is one of the techniques being explored to mitigate the effects of these issues. Compressive sensing (CS) introduces a means of reproducing a signal with a much less number of samples than the Nyquist's rate, reducing the energy consumption, data storage and transfer cost. This paper explores the applicability of CS for SHM, in particular for damage detection and localization. CS is implemented in a simulated environment to compress SHM data. The reconstructed signal is verified for accuracy using structural response data obtained from a series of tests carried out on a reinforced concrete (RC) slab. Results show that the reconstruction was close, but not exact as a consequence of the noise associated with the responses. However, further analysis using the reconstructed signal provided successful damage detection and localization results, showing that although the reconstruction using CS is not exact, it is sufficient to provide the crucial information of the existence and location of damage.

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