학위논문 (석사)-- 서울대학교 대학원 : 건설환경공학부, 2017. 2. 송준호. ; Structural collapse is the dominant cause of deaths and injuries under seismic excitation. Thus, collapse prevention of building during strong earthquake is the most important design objective of modern seismic design provisions to promote life-safety and to prevent socio-economic losses. In order to ensure an acceptably small likelihood of structural collapse under the earthquake load, nonlinear dynamic analysis coupled with probabilistic seismic hazard analysis is needed. However, nonlinear structural responses under seismic excitation vary greatly even if ground motions are scaled to get the same level of intensity measure (e.g., ground motions are scaled to get the same spectral acceleration at first mode period of structure). Furthermore, a large set of ground motions are needed for comprehensive reflection of hazard characteristics at a given site, which incurs high computational cost during dynamic analyses. To reduce the variability of structural responses as well as the number of ground motion time series used in nonlinear stochastic analyses, the study aims to develop a new seismic intensity measure by combining a cumulative IM, e.g. Arias intensity (Arias 1970) and a peak IM, e.g. spectral acceleration, and a new algorithm about selecting ground motion time series for IDA. To this end, various techniques of statistical methods such as linear regression, clustering analysis, and best subset selection method are employed. In order to demonstrate the proposed intensity measure (IM) and algorithm, nonlinear dynamic analyses are performed using a validated computational model of ductile steel frame structure and one of the reinforced concrete (RC) structural frames modeled by Haselton et al. (2011). It is found that using a developed IM and ground motion selection algorithm, one can obtain a reliable estimation on the collapse potential of structure using far less number of ground motion time histories with uncertainty reduced. ; Chapter 1. Introduction 1 1.1. Study Background 1 1.2. Objectives, Framework and Importance of the Research 2 1.3. Organization of the Study 4 Chapter 2. Incremental Dynamic Analysis and Collapse Fragilities 6 2.1. Incremental Dynamic Analysis 6 2.2. Statistical Procedure for Fitting Fragility Functions to Structural Analysis Data 8 2.2.1. Maximum Likelihood Estimate (MLE) Formulation 9 2.2.2. Fragility Function based on Probabilistic Seismic Demand Model 10 Chapter 3. New Seismic Intensity Measure for Collapse Prediction Combining Cumulative and Peak Indices 13 3.1. A Four-Story Ductile Structural Frame Collapse Case Study 13 3.2. Existing IMs for Ground Motions 15 3.2.1. Basic Index 16 3.2.2. Peak Index 16 3.2.3. Cumulative Index 17 3.3. Energy-based Collapse Criteria and Descriptor 18 3.3.1. Energy-based Collapse Criteria 18 3.3.2. Energy-based Collapse Descriptor 20 3.4. Development of a New Intensity Measure 22 3.4.1. Seismic Input Energy 22 3.4.2. Dissipated Hysteretic Energy 25 3.4.3. A New Intensity Measure 27 3.4.4. Application of New IM to Reinforced Concrete Structural Frame 29 3.5. Influence of Energy Balance Ratio Between EI and EDegrading on Structural Collapse Capacity 32 Chapter 4. A Ground Motions Selection Procedure Using Clustering-based Adaptive Sampling 35 4.1. Ground Motion Selection Algorithm 35 4.2. Identification of Critical Features for Incremental Dynamic Analysis 40 4.3. Euclidian Metric Distance 41 4.4. Numerical Examples 43 4.4.1. Numerical Example 1 43 4.4.2. Numerical Example 2 46 4.4.3. Numerical Example 3 48 4.4.4. Numerical Example 4 50 Chapter 5. Conclusions 53 Appendix A 55 References 57 Abstract in Korean 63 ; Master


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    Titel :

    Enhancing Seismic Fragility Analysis of Structural System: Developing Intensity Measure and Ground Motion Selection Algorithm ; 구조물의 지진 취약도 해석 고도화: 지진강도척도 및 지진동 선택 알고리즘 개발



    Erscheinungsdatum :

    2017-01-01


    Medientyp :

    Hochschulschrift


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



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