• Current Optics and Photonics
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• v.7 no.5
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• pp.511-517
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• 2023

Pipelines play an important role in urban water supply and drainage, oil and gas transmission, etc. This paper presents a technique for pattern recognition of fiber optic vibration signals collected by a distributed vibration sensing (DVS) system using a deep learning residual network (ResNet). The optical fiber is laid on the pipeline, and the signal is collected by the DVS system and converted into a 64 × 64 single-channel grayscale image. The grayscale image is input into the ResNet to extract features, and finally the K-nearest-neighbors (KNN) algorithm is used to achieve the classification and recognition of pipeline damage.

• Journal of the Korea institute for structural maintenance and inspection
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• v.15 no.3
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• pp.134-141
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• 2011

There have been increased economic and societal demands to continuously monitor the integrity and long-term deterioration of civil infrastructures to ensure their safety and adequate performance throughout their life span. However, it is very difficult to continuously monitor the structural condition of the pipeline structures because those are placed underground and connected each other complexly, although pipeline structures are core underground infrastructures which transport primary sources. Moreover, damage can occur at several scales from micro-cracking to buckling or loose bolts in the pipeline structures. In this study, guided wave measurement can be achieved with a self-sensing circuit using a piezoelectric active sensor. In this self sensing system, a specific frequency-induced structural wavelet response is obtained from the self-sensed guided wave measurement. To classify the multiple types of structural damage, supervised learning-based statistical pattern recognition was implemented using the damage indices extracted from the guided wave features. Different types of structural damage artificially inflicted on a pipeline system were investigated to verify the effectiveness of the proposed SHM approach.

• Smart Structures and Systems
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• v.9 no.3
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• pp.253-271
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• 2012

Finite element (FE) model updating is a useful tool for global damage detection technique, which identifies the damage of the structure using measured vibration data. This paper presents the application of a finite element model updating method to detect the damage of a small-scale reinforced concrete building structure using measured acceleration data from shaking table tests. An iterative FE model updating strategy using the least-squares solution based on sensitivity of frequency response functions and natural frequencies was provided. In addition, a side constraint to mitigate numerical difficulties associated with ill-conditioning was described. The test structure was subjected to six El Centro 1942 ground motion histories with different Peak Ground Accelerations (PGA) ranging from 0.06 g to 0.5 g, and analytical models corresponding to each stage of the shaking were obtained using the model updating method. Flexural stiffness values of the structural members were chosen as the updating parameters. In model updating at each stage of shaking, the initial values of the parameter were set to those obtained from the previous stage. Severity of damage at each stage of shaking was determined from the change of the updated stiffness values. Results indicated that larger reductions in stiffness values occurred at the slab members than at the wall members, and this was consistent with the observed damage pattern of the test structure.

• Journal of the Korean GEO-environmental Society
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• v.18 no.1
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• pp.49-58
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• 2017

Due to low annual average temperature, road tunnel lining in domestic cold region (Gangwon province) experiences durability problems. The financial and human damage due to cracks, breakout, exfoliation and water leakage increases every year. However, domestic research on effect of temperature on road tunnel lining damage is insufficient. Thus, this research has investigated 70 tunnels located in cold region (Gangwon-do) to analyze damage status. Furthermore, by contrasting damage on tunnels in relatively warm Gangneung area with those in relatively cold Hongcheon area, the effect of temperature on road tunnel lining damage was analyzed.

• Journal of the Korean Society of Clothing and Textiles
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• v.25 no.1
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• pp.124-131
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• 2001

To find out the effect of burn out print finishing for better quality of fabric, examined processing which could make less damages on the fiber because the biggest problem is remained fibers damage after burn out print finishing. Fiber damage examined to the condition of finishing material NaHSO$_4$and H$_2$SO$_4$, 3~10min., 100~13$0^{\circ}C$, glycerin. The fiber damages evaluated the break strength and the surface condition by SEM. Among satin, pile fabric which remained fiber is silk, warp knitted fabric which remained fiber is polyester, the fibers damage level were warp knitted fabric$0^{\circ}C$, glycerin and for 6 minutes by NaHSO$_4$. When carbonized by 20%. 50% and 70% to express textile design, carbonizing rate was not effect on the fiber damage very much. There was almost no damages with glycerine, and almost no damages during 3~6minutes fixation time, 10$0^{\circ}C$ steaming heat fixation by NaHSO$_4$and H$_2$SO$_4$. Without glycerine, there were damage by hydrolysis on polyesters surface and the fiver was broken by fixation time.

• Smart Structures and Systems
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• v.15 no.3
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• pp.699-715
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• 2015

A modal parameter based damage sensitive feature (DSF) is defined to mimic the relative change in any diagonal element of the stiffness matrix of a model of a structure. The damage assessment is performed in a statistical pattern recognition framework using empirical complementary cumulative distribution functions (ECCDFs) of the DSFs extracted from measured operational vibration response data. Methods are discussed to perform probabilistic structural health assessment with respect to the following questions: (a) "Is there a change in the current state of the structure compared to the baseline state?", (b) "Does the change indicate a localized stiffness reduction or increase?", with the latter representing a situation of retrofitting operations, and (c) "What is the severity of the change in a probabilistic sense?". To identify a range of normal structural variations due to environmental and operational conditions, lower and upper bound ECCDFs are used to define the baseline structural state. Such an approach attempts to decouple "non-damage" related variations from damage induced changes, and account for the unknown environmental/operational conditions of the current state. The damage assessment procedure is discussed using numerical simulations of ambient vibration testing of a bridge deck system, as well as shake table experimental data from a 4-story steel frame.

• Smart Structures and Systems
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• v.14 no.2
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• pp.159-189
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• 2014

One of the most important requirements in the evaluation of existing structural systems and ensuring a safe performance during their service life is damage assessment. Damage can be defined as a weakening of the structure that adversely affects its current or future performance which may cause undesirable displacements, stresses or vibrations to the structure. The mass and stiffness of a structure will change due to the damage, which in turn changes the measured dynamic response of the system. Damage detection can increase safety, reduce maintenance costs and increase serviceability of the structures. Artificial Neural Networks (ANNs) are simplified models of the human brain and evolved as one of the most useful mathematical concepts used in almost all branches of science and engineering. ANNs have been applied increasingly due to its powerful computational and excellent pattern recognition ability for detecting damage in structural engineering. This paper presents and reviews the technical literature for past two decades on structural damage detection using ANNs with modal parameters such as natural frequencies and mode shapes as inputs.

• Smart Structures and Systems
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• v.18 no.3
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• pp.525-540
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• 2016

The CFRP-confined circular concrete-filled steel tubular column is composed of concrete, steel, and CFRP. Its failure mechanics are complex. The most important difficulties are lack of an available method to establish a relationship between a specific damage mechanism and its acoustic emission (AE) characteristic parameter. In this study, AE technique was used to monitor the evolution of damage in CFRP-confined circular concrete-filled steel tubular columns. A fuzzy c-means method was developed to determine the relationship between the AE signal and failure mechanisms. Cluster analysis results indicate that the main AE sources include five types: matrix cracking, debonding, fiber fracture, steel buckling, and concrete crushing. This technology can not only totally separate five types of damage sources, but also make it easier to judge the damage evolution process. Furthermore, typical damage waveforms were analyzed through wavelet analysis based on the cluster results, and the damage modes were determined according to the frequency distribution of AE signals.

• Smart Structures and Systems
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• v.23 no.5
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• pp.507-520
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• 2019

In the structural health monitoring field, damage detection has been commonly carried out based on the structural model and the engineering features related to the model. However, the extracted features are often subjected to various errors, which makes the pattern recognition for damage detection still challenging. In this study, an automated damage identification method is presented for hanger cables in a tied-arch bridge using a convolutional neural network (CNN). Raw measurement data for Fourier amplitude spectra (FAS) of acceleration responses are used without a complex data pre-processing for modal identification. A CNN is a kind of deep neural network that typically consists of convolution, pooling, and fully-connected layers. A numerical simulation study was performed for multiple damage detection in the hangers using ambient wind vibration data on the bridge deck. The results show that the current CNN using FAS data performs better under various damage states than the CNN using time-history data and the traditional neural network using FAS. Robustness of the present CNN has been proven under various observational noise levels and wind speeds.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.13 no.4
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• pp.65-74
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• 2010

This study is for Hanbuk Mountain Range within Gyeonggi province which is to propose the conservation plan by each damage pattern through site survey of the mountain range. The damage patterns are classified by siding, pointing and lining. The total damaged area is 103 areas: The siding pattern is damaged by developing farmland, mineral and quarry mining, dam, large scale development complex and cemetery park; The pointing pattern is including the development of road, transmission tower and way and mountaineering trail; The construction of electricity and communication facility, military facility, mobile communication station, heliport and shelter. The damages by developing road and large scale development complex are the most cause, and military facility, dam and reservoir, and residential area are the main causes, respectively. One of the compromised situation Hanbuk-Mountain Range usage as per section 7 section (18.45%), 12 section (18.45%) is the largest number of compromised has been surveyed, undermine the situation if you look at the usage by the road 25 locations (24.22%), military facilities and dam and reservoir to undermine this 11 established respectively (10.68%) were the most undermine. Therefore, this research propose the conservation plan as follow: first, need to understand, educate and publicize on Hanbuk-Mounatin Range; second, manage through the regulations and ordinance of Gyeonggi province; third build and expand the law for protecting Baekdu-Great Mountain Range.