Author: Thaleia Travasarou
Publisher:
ISBN:
Category :
Languages : en
Pages : 782
Book Description
Optimal Ground Motion Intensity Measures for Probabilistic Assessment of Seismic Slope Displacements
Author: Thaleia Travasarou
Publisher:
ISBN:
Category :
Languages : en
Pages : 782
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 782
Book Description
Optimal Ground Motion Intensity Measures for Probabilistic Assessment of Seismic Slope Displacements
Author: Thaleia Travasarou
Publisher:
ISBN:
Category :
Languages : en
Pages : 646
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 646
Book Description
Vector-valued Ground Motion Intensity Measures for Probabilistic Seismic Demand Analysis
Author: Jack W. Baker
Publisher:
ISBN:
Category :
Languages : en
Pages : 321
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 321
Book Description
Ground Motion Intensity Measures for Seismic Probabilistic Risk Analysis
Author: Marco De Biasio
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
A fundamental issue that arises in the framework of Probabilistic Seismic Risk Analysis is the choice of groundmotion Intensity Measures (IMs). In addition to reducing record-to-record variability, an improved IM (i.e. one able tobetter capture the damaging features of a record, as well as the site hazard) provides criteria for selecting input groundmotions to loosen restrictions.Two new structure-specific IMs are proposed in this study: the first, namely ASAR (i.e. Relative Average SpectralAcceleration), is conceived for Structural demand prediction, the second namely, E-ASAR (i.e. Equipment-RelativeAverage Spectral Acceleration), aims to predict Non-Structural components acceleration demand. The performance ofthe proposed IMs are compared with the ones of current IMs, based on: a) a large dataset of thousands recordedearthquake ground motions; b) numerical analyses conducted with state-of-the-art FE models, representing actualload-bearing walls and frame structures, and validated against experimental tests; and c) systematic statistical analysesof the results. According to the comparative study, the introduced IMs prove to be considerably more “efficient” withrespect to the IMs currently used. Likewise, both ASAR and E-ASAR have shown to own the characteristic of“sufficiency” with respect to magnitude, source-to-site distance and soil-type (Vs30). Furthermore, both the introducedIMs possess the valuable characteristics to need (in order to be computed) merely the knowledge of the building'sfundamental frequency, exactly as it is for the wide-spread spectral acceleration Spa(f1). This key characteristic makesboth ASAR and E-ASAR easily exploitable in Probabilistic Seismic Hazard Analysis.Therefore, due to their proven efficiency, sufficiency, robustness and applicable formulation, both ASAR and EASARcan be considered as worthy candidates for defining seismic hazard within the frameworks of both Probabilisticand Deterministic Seismic Risk Analysis.
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
A fundamental issue that arises in the framework of Probabilistic Seismic Risk Analysis is the choice of groundmotion Intensity Measures (IMs). In addition to reducing record-to-record variability, an improved IM (i.e. one able tobetter capture the damaging features of a record, as well as the site hazard) provides criteria for selecting input groundmotions to loosen restrictions.Two new structure-specific IMs are proposed in this study: the first, namely ASAR (i.e. Relative Average SpectralAcceleration), is conceived for Structural demand prediction, the second namely, E-ASAR (i.e. Equipment-RelativeAverage Spectral Acceleration), aims to predict Non-Structural components acceleration demand. The performance ofthe proposed IMs are compared with the ones of current IMs, based on: a) a large dataset of thousands recordedearthquake ground motions; b) numerical analyses conducted with state-of-the-art FE models, representing actualload-bearing walls and frame structures, and validated against experimental tests; and c) systematic statistical analysesof the results. According to the comparative study, the introduced IMs prove to be considerably more “efficient” withrespect to the IMs currently used. Likewise, both ASAR and E-ASAR have shown to own the characteristic of“sufficiency” with respect to magnitude, source-to-site distance and soil-type (Vs30). Furthermore, both the introducedIMs possess the valuable characteristics to need (in order to be computed) merely the knowledge of the building'sfundamental frequency, exactly as it is for the wide-spread spectral acceleration Spa(f1). This key characteristic makesboth ASAR and E-ASAR easily exploitable in Probabilistic Seismic Hazard Analysis.Therefore, due to their proven efficiency, sufficiency, robustness and applicable formulation, both ASAR and EASARcan be considered as worthy candidates for defining seismic hazard within the frameworks of both Probabilisticand Deterministic Seismic Risk Analysis.
Vector-valued Ground Motion Intensity Measures for Probabilistic Seismic Demand Analysis
Author: Jack W. Baker
Publisher:
ISBN:
Category : Earthquake engineering
Languages : en
Pages : 346
Book Description
Publisher:
ISBN:
Category : Earthquake engineering
Languages : en
Pages : 346
Book Description
Probabilistic Assessment of the Seismic Performance of Earth Slopes Using Computational Simulation
Author: Youngkyu Cho
Publisher:
ISBN:
Category :
Languages : en
Pages : 228
Book Description
Earthquake-induced permanent slope displacement has been the main damage measure used in evaluating the seismic performance of earth slopes and various predictive models for this displacement have been proposed. However, these predictive models are mostly based on displacements computed using sliding block analysis, although nonlinear finite element or finite difference simulations are becoming the preferred method to evaluate the performance of slopes. This research aims at developing predictive models for slope displacement based on nonlinear finite element simulations, and demonstrating how these predictive models can be used in probabilistic assessments of slope displacement. These methodological developments are demonstrated first using a single slope geometry representative of a site-specific analysis and then generic predictive models are established using a range of slope geometries. These generic displacement models are developed through both classical and artificial neural network (ANN) regression. Toward these goals, this research comprises the following three sections. Nonlinear finite element analyses are performed for a soft clay slope using a suite of 105 input motions and the computed displacements are used to develop slope-specific displacement prediction models that utilize different ground motion intensity measures. The efficiency and proficiency of the displacement models using different combinations of intensity measures are assessed. These displacement models are used to compute probabilistic hazard curves of the permanent displacement, which represent the annual frequency of exceedance for a range of displacement levels. The computed hazard curves provide insight into the range of epistemic uncertainty associated with different displacement models. A large set of nonlinear finite element simulations are performed on 40 slope models each subjected to more than 1000 input motions. A generic predictive model for displacement is derived from the computed displacements using classical regression techniques. The predictive model characterizes the slope in terms of its yield acceleration (ky) and the natural period of the sliding mass (Ts), and characterizes the input motion in terms of its peak ground velocity (PGV). The displacement variability is partitioned into the between-slope component, which represents the variability associated different slope models, and the within-slope component, which represents the variability due to different input ground motions. Lastly, the database of slope displacements used in the classical regression are used to develop an artificial neural network (ANN) predictive model for displacement. ANN models allow researchers to investigate complex interactions between independent and dependent variables without specifying any restrictions on the functional form. The developed ANN moderately improves the displacement prediction relative to the classical regression model, although without the need of a complex functional form. The ANN displacement model is presented as a simplified mathematical expression that can be easily implemented into deterministic or probabilistic assessments of slope performance
Publisher:
ISBN:
Category :
Languages : en
Pages : 228
Book Description
Earthquake-induced permanent slope displacement has been the main damage measure used in evaluating the seismic performance of earth slopes and various predictive models for this displacement have been proposed. However, these predictive models are mostly based on displacements computed using sliding block analysis, although nonlinear finite element or finite difference simulations are becoming the preferred method to evaluate the performance of slopes. This research aims at developing predictive models for slope displacement based on nonlinear finite element simulations, and demonstrating how these predictive models can be used in probabilistic assessments of slope displacement. These methodological developments are demonstrated first using a single slope geometry representative of a site-specific analysis and then generic predictive models are established using a range of slope geometries. These generic displacement models are developed through both classical and artificial neural network (ANN) regression. Toward these goals, this research comprises the following three sections. Nonlinear finite element analyses are performed for a soft clay slope using a suite of 105 input motions and the computed displacements are used to develop slope-specific displacement prediction models that utilize different ground motion intensity measures. The efficiency and proficiency of the displacement models using different combinations of intensity measures are assessed. These displacement models are used to compute probabilistic hazard curves of the permanent displacement, which represent the annual frequency of exceedance for a range of displacement levels. The computed hazard curves provide insight into the range of epistemic uncertainty associated with different displacement models. A large set of nonlinear finite element simulations are performed on 40 slope models each subjected to more than 1000 input motions. A generic predictive model for displacement is derived from the computed displacements using classical regression techniques. The predictive model characterizes the slope in terms of its yield acceleration (ky) and the natural period of the sliding mass (Ts), and characterizes the input motion in terms of its peak ground velocity (PGV). The displacement variability is partitioned into the between-slope component, which represents the variability associated different slope models, and the within-slope component, which represents the variability due to different input ground motions. Lastly, the database of slope displacements used in the classical regression are used to develop an artificial neural network (ANN) predictive model for displacement. ANN models allow researchers to investigate complex interactions between independent and dependent variables without specifying any restrictions on the functional form. The developed ANN moderately improves the displacement prediction relative to the classical regression model, although without the need of a complex functional form. The ANN displacement model is presented as a simplified mathematical expression that can be easily implemented into deterministic or probabilistic assessments of slope performance
Probabilistic Seismic Demand Analysis Using Advanced Ground Motion Intensity Measures, Attenuation Relationships, and Near-fault Effects
Author: Polsak Tothong
Publisher:
ISBN:
Category : Earthquake engineering
Languages : en
Pages : 0
Book Description
Publisher:
ISBN:
Category : Earthquake engineering
Languages : en
Pages : 0
Book Description
Earthquake Geotechnical Engineering
Author: Kyriazis D. Pitilakis
Publisher: Springer Science & Business Media
ISBN: 1402058934
Category : Technology & Engineering
Languages : en
Pages : 497
Book Description
This book contains the full papers on which the invited lectures of the 4th International Conference on Geotechnical Earthquake Engineering (4ICEGE) were based. The conference was held in Thessaloniki, Greece, from 25 to 28 June, 2007. The papers offer a comprehensive overview of the progress achieved in soil dynamics and geotechnical earthquake engineering, examine ongoing and unresolved issues, and discuss ideas for the future.
Publisher: Springer Science & Business Media
ISBN: 1402058934
Category : Technology & Engineering
Languages : en
Pages : 497
Book Description
This book contains the full papers on which the invited lectures of the 4th International Conference on Geotechnical Earthquake Engineering (4ICEGE) were based. The conference was held in Thessaloniki, Greece, from 25 to 28 June, 2007. The papers offer a comprehensive overview of the progress achieved in soil dynamics and geotechnical earthquake engineering, examine ongoing and unresolved issues, and discuss ideas for the future.
Probabilistic Seismic Demand Analysis for the Near-fault Zone
Author: Reza Sehhati
Publisher:
ISBN:
Category : Earthquake engineering
Languages : en
Pages : 171
Book Description
Publisher:
ISBN:
Category : Earthquake engineering
Languages : en
Pages : 171
Book Description
Probabilistic Seismic Demand Analysis of Nonlinear Structures
Author: Nilesh Shome
Publisher:
ISBN:
Category : Structural analysis (Engineering)
Languages : en
Pages : 640
Book Description
Publisher:
ISBN:
Category : Structural analysis (Engineering)
Languages : en
Pages : 640
Book Description