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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 吳日騰 | zh_TW |
| dc.contributor.advisor | Rih-Teng Wu | en |
| dc.contributor.author | 林廷謙 | zh_TW |
| dc.contributor.author | Ting-Chien Lin | en |
| dc.date.accessioned | 2024-03-07T16:19:07Z | - |
| dc.date.available | 2024-03-08 | - |
| dc.date.copyright | 2024-03-07 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-02-17 | - |
| dc.identifier.citation | [1] D. Abrams and M. Sozen. Experimental study of frame-wall interaction in reinforced concrete structures subjected to strong earthquake motions. Number 460 in Civil Engineering Studies, Structural Research Series. University of Illinois Urbana-Champaign, United States, 460 edition, May 1979.
[2] J. D. Aristizabal-Ochoa and M. A. Sozen. Behavior of ten-story reinforced concrete walls subjected to earthquake motions., 1976. https://nehrpsearch.nist.gov/static/files/NSF/PB262950.pdf,. [3] J. A. Blume and Associates. Holiday inn. in san fernando california earthquake of february 9, 1971. national oceanic and atmospheric administration., 1973. [4] J. F. Bonacci. Experiments to study seismic drift of reinforced concrete structures., 1989. http://hdl.handle.net/2142/22959,. [5] S. L. Brunton, J. L. Proctor, and J. N. Kutz. Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proceedings of the National Academy of Sciences, 113(15):3932–3937, 2016. [6] H. Cecen. Response of ten story, reinforced concrete model frames to simulated earthquakes., 1979. http://hdl.handle.net/2142/66859,. [7] H. Chiroma, S. Abdulkareem, A. Abubakar, A. Zeki, A. Y. Gital, and M. J. Usman. Correlation study of genetic algorithm operators: crossover and mutation probabilities. In Proceedings of the International Symposium on Mathematical Sciences and Computing Research, pages 6–7, 2013. [8] M. O. Eberhard. Experiments and analyses to study the seismic response of reinforced concrete frame wall structures with yielding columns. PhD thesis, 1989. 著作權- Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works; 最後更新- 2023-02-20. [9] Federal Emergency Management Agency. Washington D.C. Prestandard and commentary for the seismic rehabilitation of bulidings., 2000. https://www.nehrp.gov/pdf/fema356.pdf,. [10] Federal Emergency Management Agency. Washington D.C. Improvement of nonlinear static seismic procedures, 2005. [11] T. Healey and M. Sozen. Experimental study of the dynamic response of a ten-story reinforced concrete frame with a tall first story., 1978. http://hdl.handle.net/2142/13911,. [12] K. Kajiwara, Y. Tosauchi, E. Sato, K. Fukuyama, T. Inoue, H. Shiohara, T. Kabeyasawa, T. Nagae, H. Fukuyama, T. Kabeyasawa, and T. Mukai. 2015 threedimensional shaking table test of a 10-story reinforced concrete building on the edefense, part 1: Overview and specimen design of the base slip and base fixed tests. In 16th World Conference on Earthquake Engineering. Earthquake Engineering Research Institute, 2017. https://www.wcee.nicee.org/wcee/article/16WCEE/WCEE2017-4012.pdf. [13] Lucas Laughery. Response of high-strength steel reinforced concrete structures to simulated earthquakes, 2016. [14] M. Maslyaev, A. Hvatov, and A. Kalyuzhnaya. Data-driven partial derivative equations discovery with evolutionary approach. In J. M. F. Rodrigues, P. J. S. Cardoso, J. Monteiro, R. Lam, V. V. Krzhizhanovskaya, M. H. Lees, J. J. Dongarra, and P. M. Sloot, editors, Computational Science – ICCS 2019, pages 635–641, Cham, 2019. Springer International Publishing. [15] F. McKenna, M. H. Scott, and G. L. Fenves. Nonlinear finite-element analysis software architecture using object composition. Journal of Computing in Civil Engineering, 24(1):95–107, 2010. [16] J. Moehle and M. Sozen. Earthquake simulation tests of a ten-story reinforced concrete frame with a discontinued first-level beam., 1978. http://hdl.handle.net/2142/13912,. [17] J. Moehle and M. Sozen. Experiments to study earthquake response of r/c structures with stiffness interruptions., 1980. http://hdl.handle.net/2142/14089,. [18] N. M. Newmark and W. J. Hall. Earthquake spectra and designs, 1982. [19] Newmark, N. M., Hall, W. J., Mohraz, B. A study of vertical and horizontal earthquake spectra., 1973. [20] S. H. Rudy, S. L. Brunton, J. L. Proctor, and J. N. Kutz. Data-driven discovery of partial differential equations. Science Advances, 3(4):e1602614, 2017. [21] H. Schaeffer. Learning partial differential equations via data discovery and sparse optimization. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 473(2197):20160446, 2017. [22] H. Schaeffer and S. G. McCalla. Sparse model selection via integral terms. Phys. Rev. E, 96:023302, Aug 2017. [23] M. J. Schoettler, J. I. Restrepo, G. Guerrini, and F. Duck, D. E.and Carrea. A full-scale, single-column bridge bent tested by shake-table excitation., 2015. https://peer.berkeley.edu/sites/default/files/webpeer-2015-02-schoettler.pdf,. [24] A. E. SCHULTZ. AN EXPERIMENTAL AND ANALYTICAL STUDY OF THE EARTHQUAKE RESPONSE OF R/C FRAMES WITH YIELDING COLUMNS (CYCLIC, DYNAMIC, HYSTERESIS, NONLINEAR). PhD thesis, 1986. 著作權- Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works;最後更新- 2023-02-19. [25] P. P. Shah. Seismic Drift Demands. 7 2021. [26] B. M. Shahrooz and J. P. Moehle. Experimental study of seismic response of RC setback buildings. Earthquake Engineering Research Center, College of Engineering, University …, 1987. [27] A. Shibata and M. A. Sozen. Substitute-structure method for seismic design in r/c. Journal of the Structural Division, 102(1):1–18, 1976. [28] Shimazaki, K., Sozen, M. A. Seismic drift of reinforced concrete structures., 1984. [29] M. Sozen. Review of earthquake response of reinforced concrete buildings with a view to drift control. State-of-the-Art in Earthquake Engineering, pages 383–418, 01 1981. [30] M. A. Sozen. The velocity of displacement. In S. T. Wasti and G. Ozcebe, editors, Seismic Assessment and Rehabilitation of Existing Buildings, pages 11–28, Dordrecht, 2003. Springer Netherlands. [31] Tsung-Chih Chiou. Development and verification on the rapid seismic evaluation of low rise rc residential buildings, 2015. [32] C. E. WOLFGRAM. EXPERIMENTAL MODELLING AND ANALYSIS OF THREE ONE-TENTH-SCALE REINFORCED CONCRETE FRAME-WALL STRUCTURES. PhD thesis, 1984. 著作權- Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works; 最後更新-2023-02-19. [33] S. L. Wood. Experiments to study the earthquake response of reinforced concrete frames with setbacks., 1985. http://hdl.handle.net/2142/69962,. [34] H. Xu, H. Chang, and D. Zhang. Dlga-pde: Discovery of pdes with incomplete candidate library via combination of deep learning and genetic algorithm. Journal of Computational Physics, 418:109584, 2020. [35] S. Zhang and G. Lin. Robust data-driven discovery of governing physical laws with error bars. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 474(2217):20180305, 2018. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/92151 | - |
| dc.description.abstract | 本研究採用基因演算法，結合了Rudy、Samuel H.等人(2017)提出的稀疏回歸算法，以發現結構數據中的方程式。結構位移估算方程中的參數被重組成多個候選方程式，利用基因演算法中基因的排列組合形成候選方程式，並且加入稀疏回歸進行重要方程式的搜索，以發現位移估算的方程。旨在使用此方法發現新的位移估算方程。
新的位移估算方程中的參數數據來自Shah(2021)中整理計算而得的數值。這些參數被使用於計算Sozen，M. A.(2003)提出的位移估算方程和FEMA 440(2005)中的系數法提出的位移估算方程。利用這些參數構建多元的方程式組合(基因組合)，並且經過演算法後發現方程式組合中的重要方程式，並找到新的位移估算方程。 基因演算法可以利用演化的方式發現多元的基因組合(方程式組合)，但在此多元的組合中並不一定所有的組合都適用於目標函數，因此在透過稀疏回歸的演算後，進而找到新的漂移估算方程式，並對於發現的方程與先前提出的方程進行討論與比較。 | zh_TW |
| dc.description.abstract | This study adopts a genetic algorithm combined with the sparse regression algorithm proposed by Rudy, Samuel H., et al.(2017) to discover equations in structural data. The parameters in the structural displacement estimation equations are rearranged into multiple candidate equations using the permutation and combination of genes in the genetic algorithm. Sparse regression is integrated to search for important equations within the candidate equations, aiming to discover new displacement estimation equations.
The parameter data for the new displacement estimation equation is compiled from Shah (2021). These parameters are used to compute the displacement estimation equations proposed by Sozen, M. A. (2003) and the Coefficient Method presented in FEMA 440 (2005). By utilizing these parameters to construct a multivariate set of equations (genetic combination), and through algorithmic analysis, significant equations within the combination are identified, leading to the discovery of a new displacement estimation equation. Genetic algorithms can utilize evolution to discover diverse sets of gene combinations (equation combinations). However, not all combinations within this diversity are necessarily suitable for the objective function. Therefore, through the algorithm of sparse regression, new drift estimation equations are identified. Subsequently, a discussion and comparison are carried out between the discovered equations and the previously proposed equations. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-03-07T16:19:07Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-03-07T16:19:07Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Verification Letter from the Oral Examination Committee . . . . . . . . . . . i
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii 摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii Denotation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xix Chapter 1 INTRODUCTION 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Research Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Chapter 2 LITERATURE REVIEW 5 2.1 Method for estimating drift demands . . . . . . . . . . . . . . . . . . 5 2.1.1 Sozen, M. A. (2003).[30] . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.2 Coefficient Method . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Method for data-driven discovery . . . . . . . . . . . . . . . . . . . 7 Chapter 3 BASELINE EQUATIONS 9 3.1 Sozen, M. A. (2003).[30] . . . . . . . . . . . . . . . . . . . . . . . . 9 3.1.1 Velocity Amplification Factor . . . . . . . . . . . . . . . . . . . . . 11 3.1.2 Effective Period . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.2 Coefficient Method . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.2.1 C0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2.2 C1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2.3 C2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Chapter 4 METHODOLOGY 17 4.1 Genetic algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.1.1 Customized genes . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.1.2 Fitness function . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.1.3 Crossover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.1.4 Mutation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.1.5 Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.2 Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.2.1 Sparse regression . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.2.2 Ridge regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.2.3 STRidge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.3 Algorithm description . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.4 Selection of parameters and candidate functions . . . . . . . . . . . . 27 4.4.1 Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.4.2 Selection of candidate functions . . . . . . . . . . . . . . . . . . . 28 Chapter 5 RESULTS 31 5.1 Hyperparameter in GA . . . . . . . . . . . . . . . . . . . . . . . . . 31 5.2 Discovered equation . . . . . . . . . . . . . . . . . . . . . . . . . . 32 5.2.1 Effective period . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 5.2.2 Peak ground acceleration (PGA) . . . . . . . . . . . . . . . . . . . 35 5.2.3 Spectral acceleration . . . . . . . . . . . . . . . . . . . . . . . . . 35 5.2.4 Discussion about discovered equation . . . . . . . . . . . . . . . . 36 5.2.5 Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 38 5.3 Comparison with baseline equations . . . . . . . . . . . . . . . . . . 40 5.3.1 Roof drift ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 5.3.2 Maximum story drift ratio . . . . . . . . . . . . . . . . . . . . . . . 44 5.4 Performance in low-rise and high-rise structures . . . . . . . . . . . 45 5.4.1 High-rise structures . . . . . . . . . . . . . . . . . . . . . . . . . . 46 5.4.2 Low-rise structures . . . . . . . . . . . . . . . . . . . . . . . . . . 48 5.4.3 Conclusion of high-rise and low-rise structures . . . . . . . . . . . 50 Chapter 6 Conclusions 69 6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 6.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 References 73 Appendix A — Algorithm 79 Appendix B — Database 83 B.1 Aristizabal and Sozen, 1976 . . . . . . . . . . . . . . . . . . . . . . 83 B.2 Healey and Sozen, 1978 . . . . . . . . . . . . . . . . . . . . . . . . 84 B.3 Moehle and Sozen, 1978 . . . . . . . . . . . . . . . . . . . . . . . . 84 B.4 Abrams and Sozen, 1979 . . . . . . . . . . . . . . . . . . . . . . . . 85 B.5 Cecen, 1979 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 B.6 Moehle and Sozen, 1980 . . . . . . . . . . . . . . . . . . . . . . . . 86 B.7 Wolfgram, 1984 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 B.8 Wood, 1985 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 B.9 Schultz, 1985 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 B.10 Shahrooz and Moehle, 1987 . . . . . . . . . . . . . . . . . . . . . . 89 B.11 Bonacci, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 B.12 Eberhard and Sozen, 1989 . . . . . . . . . . . . . . . . . . . . . . . 90 B.13 Van Nuys Holiday Inn, 1994 . . . . . . . . . . . . . . . . . . . . . . 91 B.14 UCSD Bridge Column, 2012 . . . . . . . . . . . . . . . . . . . . . . 91 B.15 Laughery, 2016 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 B.16 10F E-Defense RC Building, 2015/2018 . . . . . . . . . . . . . . . . 93 | - |
| dc.language.iso | en | - |
| dc.subject | 稀疏回歸 | zh_TW |
| dc.subject | 基因演算法 | zh_TW |
| dc.subject | 數據驅動 | zh_TW |
| dc.subject | 方程探索 | zh_TW |
| dc.subject | 變位角估計 | zh_TW |
| dc.subject | Drift estimation | en |
| dc.subject | Equation discovery | en |
| dc.subject | Data-driven | en |
| dc.subject | Sparse regression | en |
| dc.subject | Genetic algorithm | en |
| dc.title | 使用基因演算法結合稀疏回歸進行結構變位角估計的方程式探索 | zh_TW |
| dc.title | Equation Discovery for Structural Drift Estimation Using Genetic Algorithm with Sparse Regression | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 朴艾雪;張國鎮;歐昱辰 | zh_TW |
| dc.contributor.oralexamcommittee | Aishwarya Puranam;Kuo-Chun Chang;Yu-Chen Ou | en |
| dc.subject.keyword | 基因演算法,稀疏回歸,變位角估計,方程探索,數據驅動, | zh_TW |
| dc.subject.keyword | Genetic algorithm,Sparse regression,Drift estimation,Equation discovery,Data-driven, | en |
| dc.relation.page | 111 | - |
| dc.identifier.doi | 10.6342/NTU202400575 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-02-18 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| 顯示於系所單位： | 土木工程學系 | |
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