• Earthquakes and Structures
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• v.8 no.1
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• pp.1-18
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• 2015

A simple method of assessing the risk of overturning of precast reinforced concrete columns is presented in this paper. The displacement-based methodology introduced herein is distinguished from conventional force-based codified methods of aseismic design of structures. As evidenced by results from field tests precast reinforced concrete columns can be displaced to a generous limit without sustaining damage and then fully recover from most of the displacement afterwards. Realistic predictions of the displacement demand of such (rocking) system in conjunction with the displacement capacity estimates enable fragility curves for overturning to be constructed. The interesting observation from the developed fragility curves is that the probability of failure of the precast soft-storey column decreases with increasing size of the column importantly illustrating the "size effect" phenomenon.

• Journal of the Earthquake Engineering Society of Korea
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• v.7 no.6
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• pp.35-47
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• 2003

Seismic ground motion can vary significantly over distances comparable to the length of a majority of highway bridges on multiple supports. This paper presents results of fragility analysis of two actual highway bridges under ground motion with spatial variation. Ground motion time histories are artificially generated with different amplitudes, phases, as well as frequency contents at different support locations. Monte Carlo simulation is performed to study dynamic responses of the bridges under these ground motions. The effect of spatial variation on the seismic response is systematically examined and the resulting fragility curves are compared with those under identical support ground motion. This study shows that ductility demands for the bridge columns can be underestimated if the bridge is analyzed using identical support ground motions rather than differential support ground motions. Fragility curves are developed as functions of different measures of ground motion intensity including peak ground acceleration(PGA), peak ground velocity(PGV), spectral acceleration(SA), spectral velocity(SV) and spectral intensity(SI). This study represents a first attempt to develop fragility curves under spatially varying ground motion and provides information useful for improvement of the current seismic design codes so as to account for the effects of spatial variation in the seismic design of long-span bridges.

• Journal of the Korean Geotechnical Society
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• v.34 no.7
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• pp.51-58
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• 2018

In this study a series of centrifuge tests were carried out in dry sand to analyze the comparison of lateral pile behavior for static loading and dynamic loading condition. In case of static loading condition, the lateral displacement was applied up to 50% of pile diameter by deflection control method. And the input sine wave of 0.1 g~0.4 g amplitude and 1 Hz frequency was applied at the base of the soil box using shaking table for dynamic loading condition. From comparison of experimental static p-y curve obtained from static loading tests with API p-y curves, API p-y curves can predict well within 20% error the ultimate subgrade reaction force of static loading condition. The ultimate subgrade reaction force of experimental dynamic p-y curve is 5 times larger than that of API p-y curves and experimental static p-y curves. Therefore, pseudo-static analysis applied to existing p-y curve for seismic design could greatly underestimate the soil resistance at non-linear domain and cause overly conservative design.

• Earthquakes and Structures
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• v.12 no.3
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• pp.359-374
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• 2017

The seismic events in Northern Italy, May 2012, have revealed the seismic vulnerability of typical Italian precast industrial buildings. The aim of this paper is to present a seismic fragility model for Italian RC precast buildings, to be used in earthquake loss estimation and seismic risk assessment by comparing two building typologies and three different codes: D.M. 3-03-1975, D.M. 16-01-1996 and current Italian building code that has been released in 2008. Based on geometric characteristics and design procedure applied, ten different building classes were identified. A Monte Carlo simulation was performed for each building class in order to generate the building stock used for the development of fragility curves trough analytical method. The probabilistic distributions of geometry were mainly obtained from data collected from 650 field surveys, while the material properties were deduced from the code in place at the time of construction or from expert opinion. The structures were modelled in 2D frameworks; since the past seismic events have identified the beam-column connection as the weakest element of precast buildings, two different modelling solutions were adopted to develop fragility curves: a simple model with post processing required to detect connection collapse and an innovative modelling solution able to reproduce the real behaviour of the connection during the analysis. Fragility curves were derived using both nonlinear static and dynamic analysis.

• Journal of Korean Society of Steel Construction
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• v.17 no.2 s.75
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• pp.207-216
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• 2005

The maximum strength of the stainless steel square and the circular hollow section columns, which are cold-formed and TIG welded, is experimented on and analyzed. The paper presents centrally compressed experiments, including stub column tests and coupon tests, on stainless steel pipe columns. A total of 24 stainless steel pipe column experiments are conducted, using the slenderness ratios ($L_k/r$ = 20, 30, 40, 50, 60, 70) as parameters. The experimental results were compared with the design standard curves, AIK-LSD and AISC-LRFD, AIJ-LSD, SIJ-ASD curves, and multiple column curves.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.11
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• pp.1786-1795
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• 1997

In order to perform leak-before-break design of nuclear piping systems and integrity evaluation of reactor vessels, full stress-strain curves and fracture resistance (J-R) curves are required. However it is time-consuming and expensive to obtain J-R curves experimentally. The objective of this paper is to modify two J-R curve prediction methods previously proposed by the authors and to propose an additional J-R curve prediction method for nuclear piping materials. In the first method which is based on the elastic-plastic finite element analysis, a blunting region handling procedure is added to the existing method. In the second method which is based on the empirical equation, a revised general equation is proposed to apply to both carbon steel and stainless steel. Finally, in the third method, both full stress-strain curve and finite element analysis results are used for J-R curve prediction. A good agreement between the predicted results based on the proposed methods and the experimental ones is obtained.

• Structural Engineering and Mechanics
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• v.74 no.4
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• pp.481-494
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• 2020

In this paper, a probability-based procedure to evaluate the performance of existing RC structures exposed to seismic and fire actions is presented. The procedure is demonstrated with reference to an existing old school building, located in Italy. The vulnerability assessment of the building highlights deficiencies under both static and seismic loads. Retrofit operations are designed to achieve the seismic safety. The idea of the work consists in assessing the performance of the existing and retrofitted building in terms of both the seismic and fire resistance. The seismic retrofit and fire resistance upgrading follow different paths, depending on the specific configuration of the building. A good seismic retrofit does not entail an improving of the fire resistance and vice versa. The goal of the current work is to study the variation of response due to the uncertainties considered in records/fire curves selection and to carry out the assessment of the studied RC structure by obtaining fragility curves under the effect of different records/temperature. The results show the fragility curves before and after retrofit operations and both in terms of seismic performance and fire resistance performance, measuring the percent improving for the different limit states.

• Earthquakes and Structures
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• v.11 no.3
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• pp.445-459
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• 2016

Quantitative estimation of seismic response of various structural systems at the collapse limit state is one of the most significant objectives in Performance-Based Earthquake Engineering (PBEE). Assessing the effects of uncertainties, due to variability in ground motion characteristics and random nature of earthquakes, on nonlinear structural response is a pivotal issue regarding collapse safety prediction. Incremental Dynamic Analysis (IDA) and fragility curves are utilized to estimate demand parameters and seismic performance levels of structures. Since producing these curves based on a large number of nonlinear dynamic analyses would be time-consuming, selection of appropriate earthquake ground motion records resulting in reliable responses with sufficient accuracy seems to be quite essential. The aim of this research study is to propose a methodology to assess the seismic behavior of reinforced concrete frames at collapse limit state via accurate estimation of seismic fragility curves for different Engineering Demand Parameters (EDPs) by using a limited number of ground motion records. Research results demonstrate that accurate estimating of structural collapse capacity is feasible through applying the proposed method offering an appropriate suite of limited ground motion records.

• Proceedings of the Korean Geotechical Society Conference
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• 2005.03a
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• pp.1205-1210
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• 2005

The behavior of rock mass and solute(e.g. groundwater, radioactivity) flow in fractured rock can be directly influenced by joint roughness. The characteristics of joint roughness is also a main factor for the rock classification(e.g. RMR, Q system) which is usually used in tunnel design. Nevertheless, most of JRC estimation has been carried out only by the examination with the naked eye. This JRC estimation has a lack of objectivity because each investigator judges JRC by his subjective opinion. Therefore, it will be desirable that the assessment of JRC is performed by a numerical analysis which can give a quantitative value corresponding to the characteristics of a roughness curve. Meanwhile, roughness curves for joint surfaces which are observed in drill cores have been obtained only along linear profiles. Although roughness curves are measured in the same joint surface, they can frequently show diverse aspects in a standpoint of roughness characteristics. If roughness curves can be measured along the elliptical circumferences of joint surfaces from core scanning images or Televiewer images, they will certainly be more comprehensive than those measured along linear profiles for roughness characteristics of joint surfaces. This study is focus on dealing with (1) extracting automatically roughness curves from core scan image or Televiewer image, (2) improving the accuracy of quantitative assessment of JRC using fractal dimension concept.

• Steel and Composite Structures
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• v.36 no.5
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• pp.493-506
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• 2020

The load-slip relationship of the shear connection is an important parameter in design and analysis of composite structures. In this paper, a load-slip curve prediction method of the shear connection based on the artificial neural networks (ANNs) is proposed. The factors which are significantly related to the structural and deformation performance of the connection are selected, and the shear stiffness of shear connections and the transverse coordinate slip value of the load-slip curve are taken as the input parameters of the network. Load values corresponding to the slip values are used as the output parameter. A twolayer hidden layer network with 15 nodes and 10 nodes is designed. The test data of two different forms of shear connections, the stud shear connection and the perforated shear connection with flange heads, are collected from the previous literatures, and the data of six specimens are selected as the two prediction data sets, while the data of other specimens are used to train the neural networks. Two trained networks are used to predict the load-slip curves of their corresponding prediction data sets, and the ratio method is used to study the proximity between the prediction loads and the test loads. Results show that the load-slip curves predicted by the networks agree well with the test curves.