• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.5
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• pp.477-484
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• 2012

Recently, as earthquake is more frequently taking place around the world due to diastrophism, the importance of diastrophism and disaster detection is becoming more important. In this study, to analyze the interpretation of seismic displacement by the Japanese earthquake in March, 2011, and monitor the diastrophism of plates in Japan and surrounding Eurasia, Pacific, and Philippines before and after the earthquake, the observational data from IGS observatories in Japan and Asian regions were processed by precise point positioning. The displacement was biggest in MIZU, which was the closest to the epicenter, and the earthquake-affected region was in inverse proportion to the distance from the epicenter. The result of calculating the diastrophism speed before and after the earthquake, based on precise point positioning of IGS observatories located in the 4 plates around Japan, showed that the displacement speed changed and different plates showed different results. The comparison with the plate fate model allowed to analyze the change in diastrophism by earthquake, and to understand the characteristics of the displacement of the plates around Japan. Later, a continuous diastrophism monitoring based on GPS is needed for earthquake prediction and diastrophism research, and the data gained by continuous GPS-based monitoring of diastrophism will be fully used as basic data for relevant research and earthquake disaster management.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2005.03a
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• pp.68-73
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• 2005

In succession of the previous works, seismo-acoustic analysis was conducted to collect ground truth events and to discriminate surface explosions from natural earthquakes in the Korean Peninsula for 2004. In this period, total 510 seismo-acoustic events corresponding to 10.8 percent of total seismic events occurred in and near the Korean Peninsula were analyzed and discriminated as artificial surface explosions. Events distribution of the seismo-acoustic events in 2004 is similar to the previous results of 1999-2003. And newly determined seismo-acoustic events were added to the surface explosions database. To extend infrasound detection capability, Korea Institute of Geoscience and Mineral Resources (KIGAM) and Southern Methodist University (SMU) installed new seismo-acoustic array (BRDAR) in Baekryoung Island last November, 2004. The array configuration and design is nearly same to previous seismo-acoustic arrays CHNAR, KSGAR, a triangular 1 km aperture. BRDAR consists of 5 short period vertical seismometers (GS-13) in seismic vaults and 13 microbarometers (Chaparral Model 2). Preliminary analysis using data collected from BRDAR shows an extension of infrasound detection capability to western part of the Korean Peninsula. Also, multiple observations of infrasound at BRDAR and other arrays gave an opportunity to localize sound source regions.

• Smart Structures and Systems
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• v.5 no.1
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• pp.95-117
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• 2009

In this paper, the time series based damage detection algorithms developed by Nair, et al. (2006) and Nair and Kiremidjian (2007) are applied to the benchmark experimental data from the National Center for Research on Earthquake Engineering (NCREE) in Taipei, Taiwan. Both acceleration and strain data are analyzed. The data are modeled as autoregressive (AR) processes, and damage sensitive features (DSF) and feature vectors are defined in terms of the first three AR coefficients. In the first algorithm developed by Nair, et al. (2006), hypothesis tests using the t-statistic are applied to evaluate the damaged state. A damage measure (DM) is defined to measure the damage extent. The results show that the DSF's from the acceleration data can detect damage while the DSF from the strain data can be used to localize the damage. The DM can be used for damage quantification. In the second algorithm developed by Nair and Kiremidjian (2007) a Gaussian Mixture Model (GMM) is used to model the feature vector, and the Mahalanobis distance is defined to measure damage extent. Additional distance measures are defined and applied in this paper to quantify damage. The results show that damage measures can be used to detect, quantify, and localize the damage for the high intensity and the bidirectional loading cases.

• Smart Structures and Systems
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• v.31 no.4
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• pp.383-392
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• 2023

Post-earthquake building condition assessment is crucial for subsequent rescue and remediation and can be automated by emerging computer vision and deep learning technologies. This study is based on an endeavour for the 2nd International Competition of Structural Health Monitoring (IC-SHM 2021). The task package includes five image segmentation objectives - defects (crack/spall/rebar exposure), structural component, and damage state. The structural component and damage state tasks are identified as the priority that can form actionable decisions. A multi-task Convolutional Neural Network (CNN) is proposed to conduct the two major tasks simultaneously. The rest 3 sub-tasks (spall/crack/rebar exposure) were incorporated as auxiliary tasks. By synchronously learning defect information (spall/crack/rebar exposure), the multi-task CNN model outperforms the counterpart single-task models in recognizing structural components and estimating damage states. Particularly, the pixel-level damage state estimation witnesses a mIoU (mean intersection over union) improvement from 0.5855 to 0.6374. For the defect detection tasks, rebar exposure is omitted due to the extremely biased sample distribution. The segmentations of crack and spall are automated by single-task U-Net but with extra efforts to resample the provided data. The segmentation of small objects (spall and crack) benefits from the resampling method, with a substantial IoU increment of nearly 10%.

• Smart Structures and Systems
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• v.21 no.1
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• pp.53-64
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• 2018

Nondestructive testing methods are required to assess the condition of civil structures and formulate their maintenance programs. Axial force identification is required for several structural members of truss bridges, pipe racks, and space roof trusses. An accurate evaluation of in situ axial forces supports the safety assessment of the entire truss. A considerable redistribution of internal forces may indicate structural damage. In this paper, a novel compressive force identification method for prismatic members implemented using static deflections is applied to steel beams. The procedure uses the Euler-Bernoulli beam model and estimates the compressive load by using the measured displacement along the beam's length. Knowledge of flexural rigidity of the member under investigation is required. In this study, the deflected shape of a compressed steel beam is subjected to an additional vertical load that was short-term measured in several laboratory tests by using fiber Bragg grating-differential settlement measurement (FBG-DSM) sensors at specific cross sections along the beam's length. The accuracy of midspan deflections offered by the FBG-DSM sensors provided excellent force estimations. Compressive load detection accuracy can be improved if substantial second-order effects are induced in the tests. In conclusion, the proposed method can be successfully applied to steel beams with low slenderness under real conditions.

• Journal of Environmental Science International
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• v.12 no.6
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• pp.677-688
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• 2003

The purpose of this study was to predict occurrence of earthquakes in Korea by measuring the concentration of radon radioactivity in the air and in the underground water. Two monitoring systems of radon concentration detection in the air were installed in Seoul, East Coast area, whereas of radon concentration in the underground water in Kyungju area during December, 1999 to June, 2001. The distribution of radon concentration in the air in Seoul is as follows Winter(10.10 $\pm$ 2.81 Bq/㎥), autumn(8.41 $\pm$ 1.35 Bq/㎥), summer(5.83 $\pm$ 0.05 Bq/㎥) and spring (5.34 $\pm$ 0.44 Bq/㎥), whereas the distribution of radon in the air in the East Coast area showed some difference as follows : autumn (14.08 $\pm$ 5.75 Bq/㎥), Summer (12.04 $\pm$ 0.53 Bq/㎥), Winter (12.02 $\pm$ 1.40 Bq/㎥) and spring (8.93 $\pm$ 0.91 Bq/㎥). In the meanwhile, the distribution of radon in the water is as follows : spring (123.59 $\pm$ 16.36count/10min), Winter (93.95 $\pm$ 79.69counter/10min), autumn (68.96 $\pm$ 37.53counter/10min) and spring (34.45 $\pm$ 9.69counter/10min). The daily range of the density of radon concentration in Seoul and East Coast area was between 5.51 Bq/㎥ - 9.44 Bq/㎥, 7.15 Bq/㎥ - 15.27 Bq/㎥, respectively. Correlation of the distributions of radon concentrations in the air and in underground water with earthquake showed considerable variations of radon concentration before the occurrence of the earthquake. The results suggested that radon radioactivity seemed to be helpful for the prediction of the occurrence of earthquake.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.2
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• pp.195-206
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• 2016

The remote sensing technique using SAR data have many advantages when applied to the disaster site due to its wide coverage and all-weather acquisition availability. Although a single-pol (polarimetric) SAR image cannot represent the land surface better than a quad-pol SAR image can, single-pol SAR data are worth using for disaster-induced change detection. In this paper, an automatic change detection method based on a mixture of GGDs (generalized Gaussian distribution) is proposed, and usability of the textural features and intensity is evaluated by using the proposed method. Three ALOS/PALSAR images were used in the experiments, and the study site was Norita City, which was affected by the 2011 Tohoku earthquake. The experiment results showed that the proposed automatic change detection method is practical for disaster sites where the large areas change. The intensity information is useful for detecting disaster-induced changes with a 68.3% g-mean, but the texture information is not. The autocorrelation and correlation show the interesting implication that they tend not to extract agricultural areas in the change detection map. Therefore, the final tsunami-induced change map is produced by the combination of three maps: one is derived from the intensity information and used as an initial map, and the others are derived from the textural information and used as auxiliary data.

• Smart Structures and Systems
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• v.4 no.6
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• pp.759-777
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• 2008

A wireless sensing system is designed for application to structural monitoring and damage detection applications. Embedded in the wireless monitoring module is a two-tier prediction model, the auto-regressive (AR) and the autoregressive model with exogenous inputs (ARX), used to obtain damage sensitive features of a structure. To validate the performance of the proposed wireless monitoring and damage detection system, two near full scale single-story RC-frames, with and without brick wall system, are instrumented with the wireless monitoring system for real time damage detection during shaking table tests. White noise and seismic ground motion records are applied to the base of the structure using a shaking table. Pattern classification methods are then adopted to classify the structure as damaged or undamaged using time series coefficients as entities of a damage-sensitive feature vector. The demonstration of the damage detection methodology is shown to be capable of identifying damage using a wireless structural monitoring system. The accuracy and sensitivity of the MEMS-based wireless sensors employed are also verified through comparison to data recorded using a traditional wired monitoring system.

• Smart Structures and Systems
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• v.33 no.6
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• pp.449-463
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• 2024

Automated crack detection is crucial for structural health monitoring and post-earthquake rapid damage detection. However, realizing high precision automatic crack detection in the absence of corresponding manual labeling presents a formidable challenge. This paper presents a novel crack segmentation transfer learning method and a novel crack segmentation model called Swin-CrackFormer. The proposed method facilitates efficient crack image style transfer through a meticulously designed data preprocessing technique, followed by the utilization of a GAN model for image style transfer. Moreover, the proposed Swin-CrackFormer combines the advantages of Transformer and convolution operations to achieve effective local and global feature extraction. To verify the effectiveness of the proposed method, this study validates the proposed method on three unlabeled crack datasets and evaluates the Swin-CrackFormer model on the METU dataset. Experimental results demonstrate that the crack transfer learning method significantly improves the crack segmentation performance on unlabeled crack datasets. Moreover, the Swin-CrackFormer model achieved the best detection result on the METU dataset, surpassing existing crack segmentation models.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2006.03a
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• pp.64-69
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• 2006

At starting point, 1D velocity models were inverted by using 430 events with P-wave 5147, S-wave 3729 from KIGAM, KMA, KEPRI, and KINS's seismic networks. A minimum 1D model shows that P-wave velocities are around $6.0{\pm}0.5\;km/s$ slowly increasing with depth between surface and 15 km. The velocities are about $6.4{\pm}0.2\;km/s$ below 15km to 35km. The earthquake data number for 3D tomography was 630 adding to previous 430 events with limitation of more than 6 station detection and relocation stability of location. The checkerboard test shows that only upper curst part from surface to 17 km have reliable resolution. The results of upper crust part present that the boundary of Gyeong-sang basin and Youngnam massif is mach well velocity variation pattern. The western part of the basin is shown as lower velocity and south-eastern part as higher. This is because that sedimentary rocks are widely located around western part of the basin and volcanic origin rocks are distributed around south-eastern part.