Academic literature on the topic 'Stochastic ground motion model'
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Journal articles on the topic "Stochastic ground motion model"
JACOB, C., K. SEPAHVAND, V. A. MATSAGAR, and S. MARBURG. "STOCHASTIC SEISMIC RESPONSE OF BASE-ISOLATED BUILDINGS." International Journal of Applied Mechanics 05, no. 01 (March 2013): 1350006. http://dx.doi.org/10.1142/s1758825113500063.
Full textEdwards, Benjamin, and Donat Fäh. "A Stochastic Ground‐Motion Model for Switzerland." Bulletin of the Seismological Society of America 103, no. 1 (February 2013): 78–98. http://dx.doi.org/10.1785/0120110331.
Full textCui, Xi Zhong, Yong Xu Liu, and Han Ping Hong. "A Stochastic Model for Simulating Vertical Pulseless Near-Fault Seismic Ground Motions." Bulletin of the Seismological Society of America 112, no. 2 (December 7, 2021): 961–77. http://dx.doi.org/10.1785/0120210114.
Full textWang, Zhi Hua, and Chong Shi Gu. "A New Non-Stationary Stochastic Seismic Ground Motion Model and its Application." Advanced Materials Research 243-249 (May 2011): 4627–33. http://dx.doi.org/10.4028/www.scientific.net/amr.243-249.4627.
Full textLekshmy, P. R., and S. T. G. Raghukanth. "Stochastic earthquake source model for ground motion simulation." Earthquake Engineering and Engineering Vibration 18, no. 1 (January 2019): 1–34. http://dx.doi.org/10.1007/s11803-019-0487-8.
Full textPoulos, Alan, Eduardo Miranda, and Jack W. Baker. "Evaluation of Earthquake Response Spectra Directionality Using Stochastic Simulations." Bulletin of the Seismological Society of America 112, no. 1 (October 26, 2021): 307–15. http://dx.doi.org/10.1785/0120210101.
Full textAtkinson, Gail M. "A Comparison of Eastern North American ground Motion Observations with Theoretical Predictions." Seismological Research Letters 61, no. 3-4 (July 1, 1990): 171–80. http://dx.doi.org/10.1785/gssrl.61.3-4.171.
Full textSabetta, Fabio, Antonio Pugliese, Gabriele Fiorentino, Giovanni Lanzano, and Lucia Luzi. "Simulation of non-stationary stochastic ground motions based on recent Italian earthquakes." Bulletin of Earthquake Engineering 19, no. 9 (April 7, 2021): 3287–315. http://dx.doi.org/10.1007/s10518-021-01077-1.
Full textKiremidjian, Anne S., and Shigeru Suzuki. "A stochastic model for site ground motions from temporally dependent earthquakes." Bulletin of the Seismological Society of America 77, no. 4 (August 1, 1987): 1110–26. http://dx.doi.org/10.1785/bssa0770041110.
Full textYamamoto, Y., and J. W. Baker. "Stochastic Model for Earthquake Ground Motion Using Wavelet Packets." Bulletin of the Seismological Society of America 103, no. 6 (October 22, 2013): 3044–56. http://dx.doi.org/10.1785/0120120312.
Full textDissertations / Theses on the topic "Stochastic ground motion model"
Yenier, Emrah. "Limitations On Point-source Stochastic Simulations In Terms Of Ground-motion Models." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12610308/index.pdf.
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7.5), source-to-site distance (less than 100 km), faulting style (shallow dipping and strike-slip) and site class (soft, stiff and rock) bins. The simulations are performed in two main stages: (1) the acceleration time series at outcropping very hard rock sites are simulated based on the stochastic method proposed by Boore (1983, 2003) and (2) they are modified through 1-D equivalent linear site response analysis to generate the free-field motions at soft, stiff and rock sites. Thus, as a part of this study, a probability-based soil profile model that considers the random variation of S-wave slowness as a function of depth is derived. The synthetic ground motions are assessed with several recent empirical ground-motion models to constitute the limitations of the simulation procedure. It is believed that the outcomes of this study will realistically describe the limitations of stochastic point-source simulation approach that can be employed further for the studies on improvements of this simulation technique.
SCOZZESE, FABRIZIO. "AN EFFICIENT PROBABILISTIC FRAMEWORK FOR SEISMIC RISK ANALYSIS OF STRUCTURAL SYSTEMS EQUIPPED WITH LINEAR AND NONLINEAR VISCOUS DAMPERS." Doctoral thesis, Università degli Studi di Camerino, 2018. http://hdl.handle.net/11581/429547.
Full textD'Amico, Laura. "Stochastic analysis and design of vibrating barriers under simulated ground motion processes." Thesis, University of Brighton, 2017. https://research.brighton.ac.uk/en/studentTheses/91e41bc5-dbd6-4f79-a133-fcfd5a105f3e.
Full textSiebrits, Eduard. "Three-dimensional elastodynamic shear fracture propagation and ground motion simulation model." Master's thesis, University of Cape Town, 1986. http://hdl.handle.net/11427/26137.
Full textKewlani, Gaurav. "Stochastic approaches to mobility prediction, path planning and motion control for ground vehicles in uncertain environments." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/55270.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 107-111).
The ability of autonomous or semi-autonomous unmanned ground vehicles (UGVs) to rapidly and accurately predict terrain negotiability, generate efficient paths online and have effective motion control is a critical requirement for their safety and use in unstructured environments. Most techniques and algorithms for performing these functions, however, assume precise knowledge of vehicle and/or environmental (i.e. terrain) properties. In practical applications, significant uncertainties are associated with the estimation of the vehicle and/or terrain parameters, and these uncertainties must be considered while performing the above tasks. Here, computationally inexpensive methods based on the polynomial chaos approach are studied that consider imprecise knowledge of vehicle and/or terrain parameters while analyzing UGV dynamics and mobility, evaluating safe, traceable paths to be followed and controlling the vehicle motion. Conventional Monte Carlo methods, that are relatively more computationally expensive, are also briefly studied and used as a reference for evaluating the computational efficiency and accuracy of results from the polynomial chaos-based techniques.
by Gaurav Kewlani.
S.M.
Zadonina, Ekaterina. "Strong ground motion simulations and assessment of influence of model parameters on waveforms." Master's thesis, Universidade de Évora, 2010. http://hdl.handle.net/10174/21222.
Full textUgurhan, Beliz. "Stochastic Strong Ground Motion Simulations On North Anatolian Fault Zone And Central Italy: Validation, Limitation And Sensitivity Analyses." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612413/index.pdf.
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Aquila and Erzincan regions. In Dü
zce study, regional seismic source, propagation and site parameters are determined through validation of the simulations against the records. In L&rsquo
Aquila case study, in addition to study of the regional parameters, the limitations of the method in terms of simulating the directivity effects are also investigated. In Erzincan case study, where there are very few records, the optimum model parameters are determined using a large set of simulations with an error-minimization scheme. Later, a parametric sensitivity study is performed to observe the variations in simulation results to small perturbations in input parameters. Results of this study confirm that stochastic finite-fault simulation method is an effective technique for generating realistic physics-based synthetic records of large earthquakes in near field regions.
Manko, N. N., and I. A. Lyashenko. "Stochastic Oscillations at Stick-Slip Motion in the Boundary Friction Regime." Thesis, Sumy State University, 2013. http://essuir.sumdu.edu.ua/handle/123456789/35148.
Full textKelekele, Liloo Didier Joel. "Mathematical model of performance measurement of defined contribution pension funds." University of the Western Cape, 2015. http://hdl.handle.net/11394/4367.
Full textThe industry of pension funds has become one of the drivers of today’s economic activity by its important volume of contribution in the financial market and by creating wealth. The increasing importance that pension funds have acquired in today’s economy and financial market, raises special attention from investors, financial actors and pundits in the sector. Regarding this economic weight of pension funds, a thorough analysis of the performance of different pension funds plans in order to optimise benefits need to be undertaken. The research explores criteria and invariants that make it possible to compare the performance of different pension fund products. Pension fund companies currently do measure their performances with those of others. Likewise, the individual investing in a pension plan compares different products available in the market. There exist different ways of measuring the performance of a pension fund according to their different schemes. Generally, there exist two main pension funds plans. The defined benefit (DB) pension funds plan which is mostly preferred by pension members due to his ability to hold the risk to the pension fund manager. The defined contributions (DC) pension fund plan on the other hand, is more popularly preferred by the pension fund managers due to its ability to transfer the risk to the pension fund members. One of the reasons that motivate pension fund members’ choices of entering into a certain programme is that their expectations of maintaining their living lifestyle after retirement are met by the pension fund strategies. This dissertation investigates the various properties and characteristics of the defined contribution pension fund plan with a minimum guarantee and benchmark in order to mitigate the risk that pension fund members are subject to. For the pension fund manager the aim is to find the optimal asset allocation strategy which optimises its retribution which is in fact a part of the surplus (the difference between the pension fund value and the guarantee) (2004) [19] and to analyse the effect of sharing between the contributor and the pension fund. From the pension fund members’ perspective it is to define a optimal guarantee as a solution to the contributor’s optimisation programme. In particular, we consider a case of a pension fund company which invests in a bond, stocks and a money market account. The uncertainty in the financial market is driven by Brownian motions. Numerical simulations were performed to compare the different models.
Händel, Annabel [Verfasser], Frank [Akademischer Betreuer] Scherbaum, and Frank [Akademischer Betreuer] Krüger. "Ground-motion model selection and adjustment for seismic hazard analysis / Annabel Händel ; Frank Scherbaum, Frank Krüger." Potsdam : Universität Potsdam, 2018. http://d-nb.info/121840406X/34.
Full textBooks on the topic "Stochastic ground motion model"
S, Cakmak A., and International Conference on Soil Dynamics and Earthquake Engineering (3rd : 1987 : Princeton University), eds. Ground motion and engineering seismology. Amsterdam: Elsevier, 1987.
Find full textUnited States. National Aeronautics and Space Administration., ed. Stochastic model of the NASA/MSFC ground facility for large space structures with uncertain parameters, report. Tuscaloosa, Ala: Dept. of Mathematics, University of Alabama, 1988.
Find full textEvernden, J. F. Predictive model for important ground motion parameters associated with large and great earthquakes. [Washington]: U.S. G.P.O., 1988.
Find full textEvernden, J. F. Predictive model for important ground motion parameters associated with large and great earthquakes. Washington, DC: U.S. Geological Survey, 1988.
Find full textauthor, Sarich Marco 1985, ed. Metastability and Markov state models in molecular dynamics: Modeling, analysis, algorithmic approaches. Providence, Rhode Island: American Mathematical Society, 2013.
Find full textEcole d'été de probabilités de Saint-Flour (27th 1997). Lectures on probability theory and statistics: Ecole d'eté de probabilités de Saint-Flour XXVII, 1997. Edited by Bertoin Jean, Martinelli F, Peres Y, and Bernard P. 1944-. Berlin: Springer, 1999.
Find full textJean, Bertoin, Martinelli F, Peres Y, Bernard P. 1944-, Bertoin Jean, Martinelli F, and Peres Y, eds. Lectures on probability theory and statistics: Ecole d'été de probabilités de Saint-Flour XXVII, 1997. Berlin: Springer, 2000.
Find full textS, Cakmak A., ed. Ground motion and engineering seismology. Amsterdam: Elsevier, co-published with Computational Mechanics, 1987.
Find full textStochastic model of the NASA/MSFC ground facility for large space structures with uncertain parameters: Part II, the maximum entropy approach. Tuscaloosa, Ala: Dept. of Mathematics, University of Alabama, 1989.
Find full textNational Aeronautics and Space Administration (NASA) Staff. Stochastic Model of the Nasa/Msfc Ground Facility for Large Space Structures with Uncertain Parameters: The Maximum Entropy Approach, Part 2. Independently Published, 2018.
Find full textBook chapters on the topic "Stochastic ground motion model"
Takada, Tsuyoshi, and Tetsuo Shimomura. "Stochastic Prediction of Seismic Ground Motions Using Macro-Spatial Correlation Model." In Probabilistic Safety Assessment and Management, 2926–31. London: Springer London, 2004. http://dx.doi.org/10.1007/978-0-85729-410-4_468.
Full textRezaeian, Sanaz, and Xiaodan Sun. "Stochastic Ground Motion Simulation." In Encyclopedia of Earthquake Engineering, 1–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-36197-5_239-1.
Full textRezaeian, Sanaz, and Xiaodan Sun. "Stochastic Ground Motion Simulation." In Encyclopedia of Earthquake Engineering, 3483–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-35344-4_239.
Full textLi, Jie, and Wei Liu. "Seismic Ground Motion Model." In Lifeline Engineering Systems, 25–44. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9101-3_3.
Full textBoore, David M. "Simulation of Ground Motion Using the Stochastic Method." In Seismic Motion, Lithospheric Structures, Earthquake and Volcanic Sources: The Keiiti Aki Volume, 635–76. Basel: Birkhäuser Basel, 2003. http://dx.doi.org/10.1007/978-3-0348-8010-7_10.
Full textPapageorgiou, Apostolos S. "The Barrier Model and Strong Ground Motion." In Seismic Motion, Lithospheric Structures, Earthquake and Volcanic Sources: The Keiiti Aki Volume, 603–34. Basel: Birkhäuser Basel, 2003. http://dx.doi.org/10.1007/978-3-0348-8010-7_9.
Full textPetters, Arlie O., and Xiaoying Dong. "Stochastic Calculus and Geometric Brownian Motion Model." In An Introduction to Mathematical Finance with Applications, 253–327. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3783-7_6.
Full textHolley, Richard. "The One Dimensional Stochastic X-Y Model." In Random Walks, Brownian Motion, and Interacting Particle Systems, 295–307. Boston, MA: Birkhäuser Boston, 1991. http://dx.doi.org/10.1007/978-1-4612-0459-6_16.
Full textShiryaev, Albert N. "Multi-stage Quickest Detection of Breakdown of a Stationary Regime. Model with Brownian Motion." In Stochastic Disorder Problems, 217–37. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-01526-8_7.
Full textPayyappilly, Leanda J., and Surendra Nadh Somala. "Risk Uncertainty Quantification for Various Occupancy Classes Using Stochastic Ground Motion." In Lecture Notes in Civil Engineering, 751–58. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80312-4_64.
Full textConference papers on the topic "Stochastic ground motion model"
Cacciola, P., and A. Tombari. "A Ground Motion Model in Proximity of Vibrating Buildings." In Proceedings of the 8th International Conference on Computational Stochastic Mechanics (CSM 8). Singapore: Research Publishing Services, 2018. http://dx.doi.org/10.3850/978-981-11-2723-6_11-cd.
Full textAi, X. Q., and J. Li. "Random Model of Earthquake Ground Motion for Engineering Site Basing on Stochastic Physical Process." In Seventh China-Japan-US Trilateral Symposium on Lifeline Earthquake Engineering. Reston, VA: American Society of Civil Engineers, 2017. http://dx.doi.org/10.1061/9780784480342.053.
Full textFnais, M. S. "Ground-motion simulation for the eastern province of Saudi Arabia using a stochastic model." In ERES 2011. Southampton, UK: WIT Press, 2011. http://dx.doi.org/10.2495/eres110121.
Full textTeng, Tsung-Jen, Pei-Ting Chen, Ting-Wei Chang, Yuan-Sen Yang, Chien-Kuo Chiu, and Wen-I. Liao. "The Simulation of Strong Ground Motion Using Empirical Green Function and Stochastic Method for Southern Taiwan Area." In ASME 2018 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/pvp2018-84670.
Full textNardin, Chiara, Igor Lanese, Rocco di Filippo, Roberto Endrizzi, Oreste S. Bursi, and Fabrizio Paolacci. "Ground Motion Model for Seismic Vulnerability Assessment of Prototype Industrial Plants." In ASME 2020 Pressure Vessels & Piping Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/pvp2020-21190.
Full textdi Filippo, Rocco, Giuseppe Abbiati, Osman Sayginer, Patrick Covi, Oreste S. Bursi, and Fabrizio Paolacci. "Numerical Surrogate Model of a Coupled Tank-Piping System for Seismic Fragility Analysis With Synthetic Ground Motions." In ASME 2019 Pressure Vessels & Piping Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/pvp2019-93685.
Full textBrahimi, Malek. "A Stochastic Approach to Nonlinear Seismic Design Spectra." In 18th International Conference on Nuclear Engineering. ASMEDC, 2010. http://dx.doi.org/10.1115/icone18-30146.
Full textYokoyama, Haruka, Hajime Iwai, and Masayuki Kohiyama. "Phase Similarity Model Between Element Waves of Adjacent Element Faults for Simulated Ground Motion Based on the Stochastic Green’s Function Method." In Proceedings of the 29th European Safety and Reliability Conference (ESREL). Singapore: Research Publishing Services, 2019. http://dx.doi.org/10.3850/978-981-11-2724-3_0444-cd.
Full textAbaid, Nicole, and Maurizio Porfiri. "Influence of Leaders on Mean Square Consentability in Biologically-Inspired Stochastic Networks." In ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control. ASMEDC, 2011. http://dx.doi.org/10.1115/dscc2011-6051.
Full textChoi, Byunghyun, Akemi Nishida, Ken Muramatsu, Tatsuya Itoi, and Tsuyoshi Takada. "Uncertainty Quantification of Seismic Response of Reactor Building Considering Different Modeling Methods." In 2020 International Conference on Nuclear Engineering collocated with the ASME 2020 Power Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/icone2020-16862.
Full textReports on the topic "Stochastic ground motion model"
Seryi, Andrei. Ground Motion Model of the SLAC Site. Office of Scientific and Technical Information (OSTI), August 2000. http://dx.doi.org/10.2172/764992.
Full textM. Gross. Sampling of Stochastic Input Parameters for Rockfall Calculations and for Structural Response Calculations Under Vibratory Ground Motion. Office of Scientific and Technical Information (OSTI), September 2004. http://dx.doi.org/10.2172/838659.
Full textGregor, Nicholas, Kofi Addo, Linda Al Atik, Gail Atkinson, David Boore, Yousef Bozorgnia, Kenneth Campbell, et al. Comparison of NGA-Sub Ground-Motion Models. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, November 2020. http://dx.doi.org/10.55461/ubdv7944.
Full textEvenson, W. E., J. A. Gardner, Ruiping Wang, Han-Tzong Su, and A. G. McKale. PAC (perturbed angular correlation) analysis of defect motion by Blume's stochastic model for I = 5/2 electric quadrupole interactions. Office of Scientific and Technical Information (OSTI), January 1990. http://dx.doi.org/10.2172/6135930.
Full textRodgers, A., and N. Petersson. Evaluating Ground Motion Predictions of USGS 3D Seismic Model of the San Francisco Bay Area with Broadband Seismograms. Office of Scientific and Technical Information (OSTI), May 2010. http://dx.doi.org/10.2172/1129987.
Full textJAMES N. BRUNE AND ABDOLRASOOL ANOOSHEHPOOR. A PHYSICAL MODEL OF THE EFFECT OF A SHALLOW WEAK LAYER ON STRONG GROUND MOTION FOR STRIKE-SLIP RUPTURES. Office of Scientific and Technical Information (OSTI), February 1998. http://dx.doi.org/10.2172/776519.
Full textPitarka, A. Performance of Irikura's Recipe Rupture Model Generator in Earthquake Ground Motion Simulations as Implemented in the Graves and Pitarka Hybrid Approach. Office of Scientific and Technical Information (OSTI), November 2016. http://dx.doi.org/10.2172/1335790.
Full textSi, Hongjun, Saburoh Midorikawa, and Tadahiro Kishida. Development of NGA-Sub Ground-Motion Model of 5%-Damped Pseudo-Spectral Acceleration Based on Database for Subduction Earthquakes in Japan. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, December 2020. http://dx.doi.org/10.55461/lien3652.
Full textAnderson, David P., Brian W. Stump, and Meredith Ness. Utilization of Near-Source Video and Ground Motion in the Assessment of Seismic Source Functions from Mining Explosions. Velocity Model and Depth Model of the Grefco Perlite Mine. Fort Belvoir, VA: Defense Technical Information Center, April 1995. http://dx.doi.org/10.21236/ada286839.
Full textMazzoni, Silvia, Nicholas Gregor, Linda Al Atik, Yousef Bozorgnia, David Welch, and Gregory Deierlein. Probabilistic Seismic Hazard Analysis and Selecting and Scaling of Ground-Motion Records (PEER-CEA Project). Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, November 2020. http://dx.doi.org/10.55461/zjdn7385.
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