scholarly journals Development of Tactile Imaging for Underwater Structural Damage Detection

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3925
Author(s):  
Chen ◽  
Wu ◽  
Hou ◽  
Fan ◽  
Dang ◽  
...  
Keyword(s):  
Damage Detection ◽  
Data Storage ◽  
Structural Damage ◽  
Imaging System ◽  
Water Environment ◽  
Surface Damage ◽  
Imaging Data ◽  
Artificial Lighting ◽  
Structural Inspection ◽  
Tactile Imaging

Underwater structural damage inspection has mainly relied on diver-based visual inspection, and emerging technologies include the use of remotely operated vehicles (ROVs) for improved efficiency. With the goal of performing an autonomous and robotic underwater inspection, a novel Tactile Imaging System for Underwater Inspection (TISUE) is designed, prototyped, and tested in this paper. The system has two major components, including the imaging subsystem and the manipulation subsystem. The novelty lies in the imaging subsystem, which consists of an elastomer-enabled contact-based optical sensor with specifically designed artificial lighting. The completed TISUE system, including optical imaging, data storage, display analytics, and a mechanical support subsystem, is further tested in a laboratory experiment. The experiment demonstrates that high-resolution and high-quality images of structural surface damage can be obtained using tactile ‘touch-and-sense’ imaging, even in a turbid water environment. A deep learning-based damage detection framework is developed and trained. The detection results demonstrate the similar detectability of five damage types in the obtained tactile images to images obtained from regular (land-based) structural inspection.

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.

Effect of Attribute Reduction on Rough-Probabilistic Neural Network for Structural Damage Detection

2010 ◽  
Vol 163-167 ◽  
pp. 2482-2487
Author(s):  
Shao Fei Jiang ◽  
Zhao Qi Wu
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Data Storage ◽  
Structural Damage ◽  
Probabilistic Neural Network ◽  
Attribute Reduction ◽  
Measurement Data ◽  
Noise Tolerance ◽  
Structural Damage Detection ◽  
Proposed Model

In this paper, a new rough-probabilistic neural network (RSPNN) model, whereby rough set data and a probabilistic neural network (PNN) are integrated, is proposed. This model is used for structural damage detection, particularly for cases where the measurement data has many uncertainties. To verify the proposed method, an example is presented to identify both single and multi-damage case patterns. The effects of measurement noise and attribute reduction on the damage detection results are also discussed. The results show that the proposed model not only has good damage detection capability and noise tolerance, but also reduces data storage memory requirements.

scholarly journals Structural damage detection using hybrid deep learning algorithm

2020 ◽  
Vol 14 (2) ◽  
pp. 53-64
Author(s):  
Dang Viet Hung ◽  
Ha Manh Hung ◽  
Pham Hoang Anh ◽  
Nguyen Truong Thang
Keyword(s):  
Deep Learning ◽  
Damage Detection ◽  
Data Storage ◽  
Structural Damage ◽  
Large Scale ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Vibration Sensor ◽  
Structural Damage Detection ◽  
Deep Learning Algorithm

Timely monitoring the large-scale civil structure is a tedious task demanding expert experience and significant economic resources. Towards a smart monitoring system, this study proposes a hybrid deep learning algorithm aiming for structural damage detection tasks, which not only reduces required resources, including computational complexity, data storage but also has the capability to deal with different damage levels. The technique combines the ability to capture local connectivity of Convolution Neural Network and the well-known performance in accounting for long-term dependencies of Long-Short Term Memory network, into a single end-to-end architecture using directly raw acceleration time-series without requiring any signal preprocessing step. The proposed approach is applied to a series of experimentally measured vibration data from a three-story frame and successful in providing accurate damage identification results. Furthermore, parametric studies are carried out to demonstrate the robustness of this hybrid deep learning method when facing data corrupted by random noises, which is unavoidable in reality. Keywords: structural damage detection; deep learning algorithm; vibration; sensor; signal processing.

Correlation of Pore Structure and Permeability by SEM-Image Analysis

1990 ◽  
Vol 48 (1) ◽  
pp. 428-429
Author(s):  
C. A. Callender ◽  
Wm. C. Dawson ◽  
J. J. Funk
Keyword(s):  
Image Analysis ◽  
Pore Structure ◽  
Imaging System ◽  
Pore Space ◽  
Simulation Models ◽  
Image Analyzer ◽  
Imaging Data ◽  
Sem Images ◽  
Thin Sections ◽  
Rock Types

The geometric structure of pore space in some carbonate rocks can be correlated with petrophysical measurements by quantitatively analyzing binaries generated from SEM images. Reservoirs with similar porosities can have markedly different permeabilities. Image analysis identifies which characteristics of a rock are responsible for the permeability differences. Imaging data can explain unusual fluid flow patterns which, in turn, can improve production simulation models.Analytical SchemeOur sample suite consists of 30 Middle East carbonates having porosities ranging from 21 to 28% and permeabilities from 92 to 2153 md. Engineering tests reveal the lack of a consistent (predictable) relationship between porosity and permeability (Fig. 1). Finely polished thin sections were studied petrographically to determine rock texture. The studied thin sections represent four petrographically distinct carbonate rock types ranging from compacted, poorly-sorted, dolomitized, intraclastic grainstones to well-sorted, foraminiferal,ooid, peloidal grainstones. The samples were analyzed for pore structure by a Tracor Northern 5500 IPP 5B/80 image analyzer and a 80386 microprocessor-based imaging system. Between 30 and 50 SEM-generated backscattered electron images (frames) were collected per thin section. Binaries were created from the gray level that represents the pore space. Calculated values were averaged and the data analyzed to determine which geological pore structure characteristics actually affect permeability.

EXPRESS: Design Considerations for Discrete Frequency Infrared Microscopy Systems

2021 ◽  
pp. 000370282110133
Author(s):  
Rohit Bhargava ◽  
Yamuna Dilip Phal ◽  
Kevin Yeh
Keyword(s):  
Imaging System ◽  
Chemical Imaging ◽  
Optical Component ◽  
Imaging Systems ◽  
Imaging Data ◽  
Discrete Frequency ◽  
Figures Of Merit ◽  
Design Considerations ◽  
Hardware Implementations ◽  
Measurement Capabilities

Discrete frequency infrared (DFIR) chemical imaging is transforming the practice of microspectroscopy by enabling a diversity of instrumentation and new measurement capabilities. While a variety of hardware implementations have been realized, considerations in the design of all-IR microscopes have not yet been compiled. Here we describe the evolution of IR microscopes, provide rationales for design choices, and the major considerations for each optical component that together comprise an imaging system. We analyze design choices in illustrative examples that use these components to optimize performance, under their particular constraints. We then summarize a framework to assess the factors that determine an instrument’s performance mathematically. Finally, we summarize the design and analysis approach by enumerating performance figures of merit for spectroscopic imaging data that can be used to evaluate the capabilities of imaging systems or suitability for specific intended applications. Together, the presented concepts and examples should aid in understanding available instrument configurations, while guiding innovations in design of the next generation of IR chemical imaging spectrometers.

Sparse damage detection via the elastic net method using modal data

2021 ◽  
pp. 147592172110219
Author(s):  
Rongrong Hou ◽  
Xiaoyou Wang ◽  
Yong Xia
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Damage Identification ◽  
Measurement Data ◽  
Elastic Net ◽  
Sensitivity Matrix ◽  
Regularization Technique ◽  
Modal Data ◽  
Regularization Techniques ◽  
Net Method

The l1 regularization technique has been developed for damage detection by utilizing the sparsity feature of structural damage. However, the sensitivity matrix in the damage identification exhibits a strong correlation structure, which does not suffice the independency criteria of the l1 regularization technique. This study employs the elastic net method to solve the problem by combining the l1 and l2 regularization techniques. Moreover, the proposed method enables the grouped structural damage being identified simultaneously, whereas the l1 regularization cannot. A numerical cantilever beam and an experimental three-story frame are utilized to demonstrate the effectiveness of the proposed method. The results showed that the proposed method is able to accurately locate and quantify the single and multiple damages, even when the number of measurement data is much less than the number of elements. In particular, the present elastic net technique can detect the grouped damaged elements accurately, whilst the l1 regularization method cannot.

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