Vision-based algorithms for damage detection and localization in structural health monitoring

2015 ◽  
Vol 23 (1) ◽  
pp. 35-50 ◽  
Author(s):  
Ziemowit Dworakowski ◽  
Piotr Kohut ◽  
Alberto Gallina ◽  
Krzysztof Holak ◽  
Tadeusz Uhl
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Structural Health ◽  
Detection And Localization

scholarly journals A Combined Model-Order Reduction and Deep Learning Approach for Structural Health Monitoring under Varying Operational and Environmental Conditions

2020 ◽  
Vol 2 (1) ◽  
pp. 94
Author(s):  
Matteo Torzoni ◽  
Luca Rosafalco ◽  
Andrea Manzoni
Keyword(s):  
Deep Learning ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Environmental Conditions ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Model Order ◽  
Structural Health ◽  
Detection And Localization

Nowadays, the aging, deterioration, and failure of civil structures are challenges of paramount importance, increasingly motivating the search of advanced Structural Health Monitoring (SHM) tools. In this work, we propose a SHM strategy for online structural damage detection and localization, combining Deep Learning (DL) and Model-Order Reduction (MOR). The developed data-based procedure is driven by the analysis of vibration and temperature recordings, shaped as multivariate time series and collected on the fly through pervasive sensor networks. Damage detection and localization are treated as a supervised classification task considering a finite number of predefined damage scenarios. During a preliminary offline phase, for each damage scenario, a collection of synthetic structural responses and temperature distributions, is numerically generated through a physics-based model. Several loading and thermal conditions are considered, thanks to a suitable parametrization of the problem, which controls the dependency of the model on operational and environmental conditions. Because of the huge amount of model evaluations, MOR techniques are employed in order to relieve the computational burden that is associated to the dataset construction. Finally, a deep neural network, featuring a stack of convolutional layers, is trained by assimilating both vibrational and thermal data. During the online phase, the trained DL network processes new incoming recordings in order to classify the actual state of the structure, thus providing information regarding the presence and localization of the damage, if any. Numerical performances of the proposed approach are assessed on the monitoring of a two-storey frame under low intensity seismic excitation.

scholarly journals Stayed-Cable Bridge Damage Detection and Localization Based on Accelerometer Health Monitoring Measurements

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Mosbeh R. Kaloop ◽  
Jong Wan Hu
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Model Identification ◽  
Main Idea ◽  
Response Spectra ◽  
Structural Health ◽  
Damage States ◽  
Bridge Damage ◽  
Detection And Localization

In situ damage detection and localization using real acceleration structural health monitoring technique are the main idea of this study. The statistical and model identification time series, the response spectra, and the power density of the frequency domain are used to detect the behavior of Yonghe cable-stayed bridge during the healthy and damage states. The benchmark problem is used to detect the damage localization of the bridge during its working time. The assessment of the structural health monitoring and damage analysis concluded that (1) the kurtosis statistical moment can be used as an indicator for damage especially with increasing its percentage of change as the damage should occur; (2) the percentage of change of the Kernel density probability for the model identification error estimation can detect and localize the damage; (3) the simplified spectrum of the acceleration-displacement responses and frequencies probability changes are good tools for detection and localization of the one-line bridge damage.

scholarly journals Magnetostrictive Sensors for Composite Damage Detection and Wireless Structural Health Monitoring

2019 ◽  
Vol 55 (7) ◽  
pp. 1-6
Author(s):  
Zhaoyuan Leong ◽  
William Holmes ◽  
James Clarke ◽  
Akshay Padki ◽  
Simon Hayes ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Structural Health ◽  
Composite Damage

Time–frequency and time–scale analyses for structural health monitoring

2006 ◽  
Vol 365 (1851) ◽  
pp. 449-477 ◽  
Author(s):  
Wiesław J Staszewski ◽  
Amy N Robertson
Keyword(s):  
Signal Processing ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Time Scale ◽  
Time Frequency ◽  
Structural Health ◽  
Variant Analysis

Signal processing is one of the most important elements of structural health monitoring. This paper documents applications of time-variant analysis for damage detection. Two main approaches, the time–frequency and the time–scale analyses are discussed. The discussion is illustrated by application examples relevant to damage detection.

Radar-based structural health monitoring of wind turbine blades: The case of damage detection

2017 ◽  
Vol 17 (4) ◽  
pp. 815-822 ◽  
Author(s):  
Jochen Moll ◽  
Philip Arnold ◽  
Moritz Mälzer ◽  
Viktor Krozer ◽  
Dimitry Pozdniakov ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Turbine Blades ◽  
Heterogeneous Material ◽  
Material System ◽  
Wind Turbine Blades ◽  
Millimetre Wave ◽  
Structural Health

Structural health monitoring of wind turbine blades is challenging due to its large dimensions, as well as the complex and heterogeneous material system. In this article, we will introduce a radically new structural health monitoring approach that uses permanently installed radar sensors in the microwave and millimetre-wave frequency range for remote and in-service inspection of wind turbine blades. The radar sensor is placed at the tower of the wind turbine and irradiates the electromagnetic waves in the direction of the rotating blades. Experimental results for damage detection of complex structures will be presented in a laboratory environment for the case of a 10-mm-thick glass-fibre-reinforced plastic plate, as well as a real blade-tip sample.

scholarly journals Application of Machine Learning with Impedance Based Techniques for Structural Health Monitoring of Civil Infrastructure

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1139-1148
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Soft Computing ◽  
Health Monitoring ◽  
Multiple Cracks ◽  
Civil Infrastructure ◽  
Structural Health ◽  
Soft Computing Techniques

Increased attentiveness on the environmental and effects of aging, deterioration and extreme events on civil infrastructure has created the need for more advanced damage detection tools and structural health monitoring (SHM). Today, these tasks are performed by signal processing, visual inspection techniques along with traditional well known impedance based health monitoring EMI technique. New research areas have been explored that improves damage detection at incipient stage and when the damage is substantial. Addressing these issues at early age prevents catastrophe situation for the safety of human lives. To improve the existing damage detection newly developed techniques in conjugation with EMI innovative new sensors, signal processing and soft computing techniques are discussed in details this paper. The advanced techniques (soft computing, signal processing, visual based, embedded IOT) are employed as a global method in prediction, to identify, locate, optimize, the damage area and deterioration. The amount and severity, multiple cracks on civil infrastructure like concrete and RC structures (beams and bridges) using above techniques along with EMI technique and use of PZT transducer. In addition to survey advanced innovative signal processing, machine learning techniques civil infrastructure connected to IOT that can make infrastructure smart and increases its efficiency that is aimed at socioeconomic, environmental and sustainable development.

High-performance fiber optic systems for damage detection and structural health monitoring

2004 ◽  
Author(s):  
Mark E. Seaver ◽  
Stephen T. Trickey ◽  
Jonathan M. Nichols ◽  
Linda Moniz ◽  
Lou Pecora ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
High Performance ◽  
Fiber Optic ◽  
Structural Health ◽  
High Performance Fiber ◽  
Optic Systems
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