• Structural Engineering and Mechanics
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• v.68 no.6
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• pp.677-689
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• 2018

Residual deformation is a crucial index that should be paid special attention in the performance-based seismic analyses of reinforced concrete (RC) structures. Owing to their superior re-centering capacity under earthquake excitations, the post-tensioned self-centering (PTSC) RC frames have been proposed and developed for engineering application during the past few decades. This paper presents a comprehensive assessment on the seismic fragility of a PTSC frame by simultaneously considering maximum and residual deformations. Bivariate limit states are defined according to the pushover analyses for maximum deformations and empirical judgments for residual deformations. Incremental Dynamic Analyses (IDA) are conducted to derive the probability of exceeding predefined limit states at specific ground motion intensities. Seismic performance of the PTSC frame is compared with that of a conventional monolithic RC frame. The results show that, taking a synthetical consideration of maximum and residual deformations, the PTSC frame surpasses the monolithic frame in resisting most damage states, but is more vulnerable to ground motions with large intensities.

• Earthquakes and Structures
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• v.22 no.1
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• pp.65-82
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• 2022

This paper presents a structural performance assessment of a historical masonry clock tower both using numerical and experimental process. The numerical assessment includes developing of finite element model with considering different types of soil-structure interaction systems, identifying the numerical dynamic characteristics, finite element model updating procedure, nonlinear time-history analysis and evaluation of seismic performance level. The experimental study involves determining experimental dynamic characteristics using operational modal analysis test method. Through the numerical and experimental processes, the current structural behavior of the masonry clock tower was evaluated. The first five experimental natural frequencies were obtained within 1.479-9.991 Hz. Maximum difference between numerical and experimental natural frequencies, obtained as 20.26%, was reduced to 4.90% by means of the use of updating procedure. According to the results of the nonlinear time-history analysis, maximum displacement was calculated as 0.213 m. The maximum and minimum principal stresses were calculated as 0.20 MPa and 1.40 MPa. In terms of displacement control, the clock tower showed only controlled damage level during the applied earthquake record.

• Smart Structures and Systems
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• v.31 no.4
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• pp.365-381
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• 2023

The existing vision-based techniques for inspection and condition assessment of civil infrastructure are mostly manual and consequently time-consuming, expensive, subjective, and risky. As a viable alternative, researchers in the past resorted to deep learning-based autonomous damage detection algorithms for expedited post-disaster reconnaissance of structures. Although a number of automatic damage detection algorithms have been proposed, the scarcity of labeled training data remains a major concern. To address this issue, this study proposed a semi-supervised learning (SSL) framework based on consistency regularization and cross-supervision. Image data from post-earthquake reconnaissance, that contains cracks, spalling, and exposed rebars are used to evaluate the proposed solution. Experiments are carried out under different data partition protocols, and it is shown that the proposed SSL method can make use of unlabeled images to enhance the segmentation performance when limited amount of ground truth labels are provided. This study also proposes DeepLab-AASPP and modified versions of U-Net++ based on channel-wise attention mechanism to better segment the components and damage areas from images of reinforced concrete buildings. The channel-wise attention mechanism can effectively improve the performance of the network by dynamically scaling the feature maps so that the networks can focus on more informative feature maps in the concatenation layer. The proposed DeepLab-AASPP achieves the best performance on component segmentation and damage state segmentation tasks with mIoU scores of 0.9850 and 0.7032, respectively. For crack, spalling, and rebar segmentation tasks, modified U-Net++ obtains the best performance with Igou scores (excluding the background pixels) of 0.5449, 0.9375, and 0.5018, respectively. The proposed architectures win the second place in IC-SHM2021 competition in all five tasks of Project 2.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.3
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• pp.205-216
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• 2024

Recently, seismic Probabilistic Safety Assessment (PSA) methods have been developed for process plants, such as gas plants, oil refineries, and chemical plants. The framework originated from the PSA of nuclear power plants, which aims to assess the risk of reactor core damage. The original PSA method was modified to adopt the characteristics of a process plant whose purpose is continuous operation without shutdown. Therefore, a fault tree, whose top event is shut down, was constructed and transformed into a Bayesian Network (BN), a probabilistic graph model, for efficient risk-informed decision-making. In this research, the fault tree-based BN from the previous research is further developed to consider the multi-hazard of earthquake-induced fire and explosion (EQ-induced F&E). For this purpose, an event tree describing the occurrence of fire and explosion from a release is first constructed and transformed into a BN. And then, this BN is connected to the previous BN model developed for seismic PSA. A virtual plot plan of a gas plant is introduced as a basis for the construction of the specific EQ-induced F&E BN to test the proposed BN framework. The paper demonstrates the method through two examples of risk-informed decision-making. In particular, the second example verifies how the proposed method can establish a repair and retrofit strategy when a shutdown occurs in a process plant.

• Journal of the Earthquake Engineering Society of Korea
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• v.13 no.4
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• pp.1-13
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• 2009

Liquefaction is a significant threat to structures on loose saturated sandy soil deposits in the event of an earthquake, and can often cause catastrophic damage, economic loss, and loss of life. Nevertheless, the Korean peninsula has for a long time been recognized as a safe region with respect to the hazard of liquefaction, as the peninsula is located in a moderate seismicity region, and there have been no reports of liquefaction, with the exception of references in some historical documents. However, some earthquakes that have recently occurred in different parts of the world have led to liquefaction in non-plastic silty soils, a soil type that can be found in many of the western coastal areas of Korea. In this study, we first present procedures for evaluating the liquefaction potential, and calculate the liquefaction potential index (LPI) distribution at two western coastal sites using both piezocone penetration test (CPTu) data and standard penetration test (SPT) data. The LPI is computed by integrating liquefaction potential over a depth of 20m, and provides an estimate of liquefaction-related surface damage. In addition, we compared the LPI values obtained from CPTu and SPT, respectively. Our research found that the CRR values from CPTu were lower than those from the SPT, particularly in the range between 40 and 120 for the corrected tip resistance, (qc1N)CS, from the CPTu, or in the range of CRR less than 0.23, resulting in relatively high LPI values. Moreover, it was observed that the differences in the CRR between the two methods were relatively higher for soils with high fine contents.

• Economic and Environmental Geology
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• v.52 no.6
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• pp.573-586
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• 2019

A seismic hazard map based on spatial analysis of various sources of geologic seismic information was developed and assessed for regional seismic vulnerability in South Korea. The indicators for assessment were selected in consideration of the geological characteristics affecting the seismic damage. Probabilistic seismic hazard and fault information were used to be associated with the seismic activity hazard and bedrock depth related with the seismic damage hazard was also included. Each indicator was constructed of spatial information using GIS and geostatistical techniques such as ordinary kriging, line density mapping and simple kriging with local varying means. Three spatial information constructed were integrated by assigning weights according to the research purpose, data resolution and accuracy. In the case of probabilistic seismic hazard and fault line density, since the data uncertainty was relatively high, only the trend was intended to be reflected firstly. Finally, the seismic activity hazard was calculated and then integrated with the bedrock depth distribution as seismic damage hazard indicator. As a result, a seismic hazard map was proposed based on the analysis of three spatial data and the southeast and northwest regions of South Korea were assessed as having high seismic hazard. The results of this study are expected to be used as basic data for constructing seismic risk management systems to minimize earthquake disasters.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.11a
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• pp.101-104
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• 2008

Recently, the serious damage by earthquakes is increased around the world. SOC fo city is established to minimize the loss of lives and assets by earthquakes, which an objective standard is required. Generally, bridges damage by earthquakes occurred the inelastic hinge under the column. Nonlinear element model of inelastic hinge have been used to Bilinear model, but Takeda model for material characterization of concrete is a little. In this study, railway bridge was performed seismic fragility analysis for Takeda model and Bilinear model comparatively. This analysis shows that damage probability of Takeda model is larger than Bilinear model. And analysis of Takeda model in longitudinal direction and transverse direction are different. Therefore developed analysis for concrete column of bridge is expected to apply to material characterization.

• Proceedings of the KSRS Conference
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• v.2
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• pp.827-830
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• 2006

The tsunami from the megathrust earthquake magnitude 9.3 on 26 December 2004 is the largest tsunami the world has known in over forty years. This tsunami destructively attacked 13 countries around Indian Ocean with at least 230,000 fatalities, displaced people 2,089,883 and 1.5 million people who lost their livelihoods. The ratio of women and children killed to men is 3 to 1. The total damage costs US$ 10.73 billion and rebuilding costs US$ 10.375 billion. The tsunami's death toll could have been drastically reduced, if the warning was disseminated quickly and effectively to the coastal dwellers along the Indian Ocean rim. With a warning system in Indian Ocean similar to that operating in the Pacific Ocean since 1965, it would have been possible to warn, evacuate and save countless lives. The best tribute we can pay to all who perished or suffered in this disaster is to heed its powerful lessons. UNESCO/IOC have put their tremendous effort on better disaster preparedness, functional early warning systems and realistic arrangements to cope with tsunami disaster. They organized ICG/IOTWS (Indian Ocean Tsunami Warning System) and the third of this meeting is held in Bali, Indonesia during $31^{st}$ July to $4^{th}$ August 2006. A US$ 53 million interim warning system using tidal gauges and undersea sensors is nearing completion in the Indian Ocean with the assistance from IOC. The tsunami warning depends strictly on an early detection of a tsunami (wave) perturbation in the ocean itself. It does not and cannot depend on seismological information alone. In the case of 26 December 2004 tsunami when the NOAA/PMEL DART (Deep-ocean Assessment and Reporting of Tsunami) system has not been deployed, the initialized input of sea surface perturbation for the MOST (Method Of Splitting Tsunami) model was from the tsunamigenic-earthquake source model. It is the first time that the satellite altimeters can detect the signal of tsunami wave in the Bay of Bengal and was used to validate the output from the MOST model in the deep ocean. In the case of Thailand, the inundation part of the MOST model was run from Sumatra 2004 for inundation mapping purposes. The medium and high resolution satellite data were used to assess the degree of the damage from Indian Ocean tsunami of 2004 with NDVI classification at 6 provinces on the Andaman seacoast of Thailand. With the tide-gauge station data, run-up surveys, bathymetry and coastal topography data and land-use classification from satellite imageries, we can use these information for coastal zone management on evacuation plan and construction code.

• Smart Structures and Systems
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• v.26 no.2
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• pp.175-184
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• 2020

Conventional Monte Carlo simulation-based methods for seismic risk assessment of water networks often require excessive computational time costs due to the hydraulic analysis. In this study, an Artificial Neural Network-based surrogate model was proposed to efficiently evaluate the flow-based system reliability of water distribution networks. The surrogate model was constructed with appropriate training parameters through trial-and-error procedures. Furthermore, a deep neural network with hidden layers and neurons was composed for the high-dimensional network. For network training, the input of the neural network was defined as the damage states of the k-dimensional network facilities, and the output was defined as the network system performance. To generate training data, random sampling was performed between earthquake magnitudes of 5.0 and 7.5, and hydraulic analyses were conducted to evaluate network performance. For a hydraulic simulation, EPANET-based MATLAB code was developed, and a pressure-driven analysis approach was adopted to represent an unsteady-state network. To demonstrate the constructed surrogate model, the actual water distribution network of A-city, South Korea, was adopted, and the network map was reconstructed from the geographic information system data. The surrogate model was able to predict network performance within a 3% relative error at trained epicenters in drastically reduced time. In addition, the accuracy of the surrogate model was estimated to within 3% relative error (5% for network performance lower than 0.2) at different epicenters to verify the robustness of the epicenter location. Therefore, it is concluded that ANN-based surrogate model can be utilized as an alternative model for efficient seismic risk assessment to within 5% of relative error.

• Journal of the Earthquake Engineering Society of Korea
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• v.8 no.6 s.40
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• pp.73-80
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• 2004

A nonlinear time-domain system identification algorithm using measured acceleration data is developed for structural damage assessment. To take account of nonlinear behavior of structural systems, an output error between measured and computed acceleration increments has been defined and a constrained nonlinear optimization problem is solved for optimal structural parameters. The algorithm estimates time-varying properties of stiffness and damping parameters. Nonlinear response of restoring force of a structural system is recovered by using the estimated time-varying structural properties and computed displacement by Newmark-$\beta$ method. In the recovery, no pre-defined model for inelastic behavior has been assumed. In developing the algorithm, noise and incomplete measurement in space and state have been considered. To examine the developed algorithm, numerical simulation and laboratory experimental studies on a three-story shear building have been carried out.