Analysis of Seismic Demand Attenuation of steel Moment-Resisting Frames According to the Distance from the Fault

Subject Areas : Analysis of Structure and Earthquake

ayoub mehri dehno ¹ , hasan aghabarati ² , Mehdi Mahdavi Adeli ³

1 -
2 - Department of Civil Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
3 - Department of Civil Engineering, West Tehran Branch, Islamic Azad University, Tehran, Iran

Received: 2024-09-26 Accepted : 2024-11-05 Published : 2024-11-10

Keywords: Nonlinear Dynamic Analysis, Bayesian Statistics, Near and Far Fields, Seismic Demand, ,

Abstract :

There are many uncertainties in the estimation of the seismic demand of structures, and the distance to the fault is one of the most important ones. But it seems that due to the different nature of the area near the fault, this effect of distance on the seismic demand will be different in the area near and far from the fault. Evaluating this issue and determining the difference between the distance between the fault and the building on the seismic demand of steel bending frames in the area near and far from the fault is the main goal of this research. In the current research and in order to achieve the desired goals, in two similar steel bending frames with three and fifteen floors, after their nonlinear modeling in the OpenSsees software, under the effect of five groups of 40 accelerometers, except for the distance to their fault Other characteristics of these records were chosen the same, nonlinear dynamic analysis was added and the results were used to determine their seismic demand, considering that the only variable in this analysis is the distance to the fault, the difference in the results can be attributed to ratio variable. Based on the results of this research, from a statistical point of view, there is a difference between the effect of distance changes on the seismic demand in the far and near fault areas, and this difference will depend on variables such as the behavior of the frame itself and its performance level.

References:

[1] P. Tothong, N. Luco, Probabilistic seismic demand analysis using advanced ground motion intensity measures. Earthquake Engineering and Structural Dynamic, 36 (13) (2007) 1837–1860.
[2] A. Mehri Dehno, H. Aghabarti, M. Mahdavi Adeli, Bayesian approach for the accuracy evaluating of the seismic demand estimation of SMRF. Earthquakes and Structures, 26 (2) 2024 117-130.
[3] T. T. Liu, D. G. Lu, X. H. Yu, Development of a compound intensity measure using partial least-squares regression and its statistical evaluation based on probabilistic seismic demand analysis, 125 (2019). doi.org/10.1016/j.soildyn.2019.105725
[4] B. R. Ellingwood, Earthquake risk assessment of building structures, Reliability Engineering & System Safety, 74 (3) (2001) 251-262, doi.org/10.1016/S0951-8320(01)00105-3.
[5] S. Ghosh, S. Ghosh, S. Chakraborty, Seismic fragility analysis in the probabilistic performance-based earthquake engineering framework: an overview. International Journal of Advances in Engineering Sciences and Applied Mathematics, (2017). https://doi.org/10.1007/s12572-017-0200-y
[6] G. J. O'Reilly, G. M. Calvi, Conceptual seismic design in performance‐based earthquake engineering, Earthquake Engineering and Structural Dynamic, 48 (4) (20187) 389-411, doi.org/10.1002/eqe.3141
[7] A. Chaudhuri, S. Chakraborty, Reliability of linear structures with parameter uncertainty under non-stationary earthquake, Structural Safety, 28 (3) (2006)
[8]Soraghi A (2021) Probabilistic characterization of bond behavior at rebar-concrete interface in corroded RC structures: experiment, modeling, and implementation. PhD Thesis, University of Akron, Ohio, United States of America.
[9]SoleimaniBabakamali MH, Zaker Esteghamati M (2022) Estimating seismic demand models of a building inventory from nonlinear static analysis using deep learning methods. Engineering Structures 266: 114576, Doi:10.1016/j.engstruct.2022.114576
[10] X. X. Liu, ZY. Wu, F. Liang, Multidimensional performance limit state for probabilistic seismic demand analysis. Bull Earthquake Engineering, 14 (2016) 3389–3408, doi.org/10.1007/s10518-016-0013-6
[11]Taslimi A, Tehranizadeh M The effect of vertical near-field ground motions on the collapse risk of high-rise reinforced concrete frame-core wall structures. Advances in Structural Engineering (2022)25(2):410–25,Doi:10.1177/136943322110561
[12] M. Maleki, R. Ahmady Jazany, M. S. Ghobadi, Probabilistic Seismic Assessment of SMFs with Drilled Flange Connections Subjected to Near-Field Ground Motions. International Journal of Steel Structures, 19 (2019) 224–240. https://doi.org/10.1007/s13296-018-0112-0
[13]Nguyen DD, Lee TH, Phan VT (2021) Optimal Earthquake Intensity Measures for Probabilistic Seismic Demand Models of Base-Isolated Nuclear Power Plant Structures. Energies 14(16):5163, Doi: 10.3390/en14165163
[14] M. Mahdavi Adeli, A. Deylami, M. Banazadeh, M. M. Alinia, A Bayesian approach to construction of probabilistic seismic demand models for steel moment-resisting frames. Sharif University of Technology ScientiaIranica, 18 (4) (2011) 885–894.
[15] M. Mahdavi Adeli, M. Banazadeh, A. Deylami, M.M. Alinia, Introducing a New Spectral Intensity Measure Parameter to Estimate the Seismic Demand of Steel Moment-Resisting Frames Using Bayesian Statistics, Advances in Structural Engineering , 15 (2) (2016) 231-247.
[16] Sh. Shahbazi, I. Mansouri, J. W. Hu, N. Sam Daliri, A. Karami, Seismic Response of Steel SMFs Subjected to Vertical Components of Far- and Near-Field Earthquakes with Forward Directivity Effects, Advances in Civil Engineering, (2019). doi.org/10.1155/2019/2647387
[17] J. Hou, Y. An, S. Wang, Zh. Wang, Ł. Jankowski, and J. Ou, Structural Damage Localization and Quantification Based on Additional Virtual Masses and Bayesian Theory, Journal of Engineering Mechanics, 144 (10) (2018).
[18] F. Jalayer, R. De Risi, G. Manfredi, Bayesian Cloud Analysis: efficient structural fragility assessment using linear regression. Bull Earthquake Engineering 13 (4) (2015) 1183–1203.
[19] A. Brezger, S. Lang, Generalized structured additive regression based on Bayesian P-Splines, Computational Statistics and Data Analysis, 50 (2006) 967-991.
[20] A. Brezger, S. Lang, Simultaneous Probability statements for Bayesian P-Splines, Statistical Modeling, 8 (2008) 141-168.
231-246. doi.org/10.1016/j.strusafe.2005.07.001.
[21] M. Onvani, A. Yahyaabadi, Probabilistic seismic demand analysis of steel moment frames by utilizing Bayesian statistics, European Journal of Environmental and Civil Engineering, (2018), DOI: 10.1080/19648189.2018.1538905
[22] Sh. Kwag, A. Gupta, Probabilistic risk assessment framework for structural systems under multiple hazards using Bayesian statistics, Nuclear Engineering and Design, 315 (2017) 20-34, doi.org/10.1016/j.nucengdes.2017.02.009.
[23] Sh. Kwag, J. Oh, J. M. Lee, Application of Bayesian statistics to seismic probabilistic safety assessment for research reactor, Nuclear Engineering and Design, 328 (2018) 166-181. doi.org/10.1016/j.nucengdes.2018.01.022
[24] M. Ansari, M. Ansari, A. Safiey, Evaluation of seismic performance of mid-rise reinforced concrete frames subjected to far-field and near-field ground motions, 15 (8) (2018) 453-462, doi.org/10.12989/eas.2018.15.5.453 453
[25] R. A. Medina, H. Krawinkler, Evaluation of drift demands for the seismic performance assessment of frames, Journal of Structural Engineering, 131 (7) 2005, 1003-1013.
[26] A. Kaveh, R. Mahdipou Moghanni, S.M. Javadi, Ground Motion Record Selection Using Multi-objective Optimization Algorithms: A Comparative Study. Periodica Polytechnica Civil Engineering. 63 (3) (2019), 812-822. https://doi.org/10.3311/PPci.14354.
[27] D. Bindi, M. Picozzi, D. Spallarossa, F. Cotton, S. R. Kotha, Impact of Magnitude Selection on Aleatory Variability Associated with Ground‐Motion Prediction Equations: Part II—Analysis of the Between‐Event Distribution in Central Italy, Bulletin of the Seismological Society of America, 109 (1) (2019) 251–262. doi.org/10.1785/0120180239
[28] L. Macedo, J.M. Castro, SelEQ: An advanced ground motion record selection and scaling framework, Advances in Engineering Software, 114 (2017)32-47. doi.org/10.1016/j.advengsoft.2017.05.005.
[29] L. Tian, H. Pan, R. Ma, Probabilistic seismic demand model and fragility analysis of transmission tower subjected to near-field ground motions, Journal of Constructional Steel Research, 156 (2019) 266-275, doi.org/10.1016/j.jcsr.2019.02.011.
[30] D. Vamvatsikos, M. Fragiadakis, Incremental dynamic analysis for estimating seismic performance sensitivity and uncertainty. Earthquake Engineering and Structural Dynamic, 39 (2) (2010) 141–163.
[31] M. Mahdavi Adeli, M. Banazadeh, A. Deylami, Bayesian approach for determination of drift hazard curves for generic steel moment-resisting frames in territory of Tehran, 9 (3) 2011 145-154.