數據驅動之鋼筋混凝土構架機率式地震風險評估

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：99

、訪客IP：18.225.55.103

姓名

李坤展(Kun-Chan Lee) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

數據驅動之鋼筋混凝土構架機率式地震風險評估
(Data-driven probabilistic seismic assessment for reinforced concrete frames)

★ 鋼筋混凝土構架含填充磚牆機率式地震風險分析

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2024-12-31以後開放)

摘要(中)

台灣社會發展至今，土木建築物仍然持續開發建設，其中鋼筋混凝土構造建築盈千累萬且坐落區域廣泛，因此本研究預期開發一款能有效快速評估鋼筋混凝土構造且提供給使用者簡易操作之軟體，將來可應用於大規模區域性城市監測評估。由於我國位於環太平洋地震帶，板塊運動造成地震頻繁，經由過往地震災損觀察，柱構件在抗震上極其重要，因此本研究藉由國內外學術研究文獻中所收集總共430組鋼筋混凝土柱反覆載重試驗之遲滯迴圈，使用OpenSees模擬分析將實驗數據與模型分析結果進行校準，以主動式學習架構解決繁瑣的過程。
此軟體開發藉由Python將OpenSees有限元素軟體導入，針對鋼筋混凝土構造物採用集中塑性模型模擬，並且結合基於樹之機器學習預測柱試體遲滯迴圈非線性參數，以數據驅動進行自動化建模，由門型構架模型進行預測分析成果驗證，供以得知遲滯迴圈以人工智慧預測之參考價值。
最後本研究採用機率式建物倒塌耐震評估構架進行非線性增量式動力分析，倒塌判定準則參考PEER-TBI技術報告之整體RC結構倒塌準則，統計回歸成性能等級易損曲線計算建築物之倒塌機率風險，此以性能為導向之評估法可量化震後建物倒榻或受損所造成之災損，包含人員傷亡、修復金額等，提供耐震評估與未來防災政策規劃之需求。

摘要(英)

Reinforced concrete is one of the most common structures. Therefore, this study is expected to develop a software that can effectively and quickly evaluate reinforced concrete structures and provide users with easy operation, which can be applied to large scale regional urban monitoring and evaluation in the future. Since our country is located in the Pacific Rim seismic zone, where plate motions cause frequent earthquakes, the observation of past earthquake damage indicates that column members are extremely important in seismic resistance. The tedious process was solved by using the OpenSees simulation analysis to calibrate the experimental data with the model analysis results, using an active learning framework.
This software was developed by importing OpenSees finite element software in Python, using a centralized plasticity model for reinforced concrete structures, and combining tree-based machine learning to predict the non-linear parameters of the hysteresis loops of the column specimens, using data-driven automated modeling, and validating the results of the predictive analysis by the gantry model, in order to know the reference value of the hysteresis loops predicted by human intelligence.
Finally, this study adopts a probabilistic building collapse seismic evaluation framework to conduct non-linear incremental dynamic analysis, and the collapse determination criteria refer to the overall RC structural collapse criteria of PEER-TBI technical report. This performance-oriented assessment method can quantify the damage caused by the collapse or damage of a building after an earthquake, including casualties, repair costs, etc., and provide the needs for seismic evaluation and future disaster prevention policy planning.

關鍵字(中)

★ OpenSees
★ 機器學習
★ 機率式評估法
★ 增量動力分析
★ 鋼筋混凝土柱
★ 遲滯迴圈

關鍵字(英)

★ OpenSees
★ Machine Learning
★ Probabilistic Assessment Method
★ Incremental Dynamics Analysis
★ Reinforced Concrete Columns
★ Hysteresis Loops

論文目次

一、緒論 1
1-1 研究動機與目的 1
1-2 文獻探討 2
1-3 論文架構 4
二、 OpenSees簡介與模擬驗證 6
2-1 OpenSees簡介 6
2-2 TCL程式語言簡介 7
2-2-1 基本指令語法 7
2-3 OpenSees系統架構 8
2-3-1 主要物件(Domain Object) 8
2-3-2 單位物件(Units Object) 8
2-3-3 模型物件(ModelBuilder Object) 8
2-3-4 紀錄物件(Recorder Object) 8
2-3-5 分析物件(Analysis Object) 9
2-4 非線性行為模型介紹 10
2-4-1 集中塑性模型(lumped plasticity model) 10
2-4-2 分佈塑性模型 13
2-4-3 纖維截面模型 13
2-4-4 OpenSees語法介紹 14
2-5 塑性鉸定義 24
2-5-1 RC柱塑鉸參數 24
2-5-2 RC梁塑鉸參數 29
2-6 試體資料庫建立 30
2-7 RC柱試體模型分析與驗證 33
2-7-1 模型分析與驗證成果 33
2-8 小結 43
三、機器學習簡介與模型訓練 44
3-1 機器學習簡介 45
3-1-1 基於樹之機器學習 47
3-2 本研究採用之隨機森林模型 48
3-3 模型訓練 51
3-3-1 研究變數之說明 52
3-3-2 數據標準化 55
3-3-3 特徵選取與重要性 56
3-3-4 超參數最佳化 58
3-4 主動式學習構架應用 60
3-4-1 主動式學習基礎概念 60
3-5 預測精確度校正與結果對比 63
3-5-1 預測精確度偏差優化 65
3-5-2 R2模型性能分數結果比對 70
四、數據驅動之自動化建模 73
4-1 房屋模型自動化編程 73
4-2 數據驅動之門型構架結果驗證 75
4-2-1 BMDF 構架分析驗證 76
4-2-2 F01 構架分析驗證 78
4-3 驗證結論 80
五、機率式地震風險評估 81
5-1 數據驅動之鋼筋混凝土門型構架模型自動化建立 84
5-2 地震歷時震波之挑選 86
5-3 定義倒塌判定準則 87
5-4 增量動力分析 88
5-5 機率地震需求模型與易損曲線之建立 90
5-5-1 機率地震需求模型(PSDM)建立 91
5-5-2 易損曲線之建立 93
5-6 判定耐震性能指標 95
六、結論與未來展望 98
6-1 結論 98
6-2 未來展望 99

參考文獻

[1] ACT-40, Seismic evaluation and retrofit of concrete buildings. Report No. SSC 96-01, Applied Technology Council, 1996.
[2] FEMA 273, NEHRP Guidelines for the seismic rehabilitation of buildings, Federal Emergency Management Agency, Washington, D.C., 1997.
[3] 鍾立來、葉勇凱、簡文郁、柴駿甫、蕭輔沛、沈文成、邱聰智、周德光、趙宜峰、楊耀昇、黃世建，（2008），「校舍結構耐震評估與補強技術手冊」，國家地震工程研究中心報告， NCREE-08-023，台北。
[4] Vision, S. E. A. O. C. (1995). Performance based seismic engineering of buildings. Structural Engineers Association of California, Sacramento, Calif.
[5] FEMA 356, F. E. (2000). Prestandard and commentary for the seismic rehabilitation of buildings. Federal Emergency Management Agency, Washington, DC.
[6] Porter, K. A. (2003, July). An overview of PEER’s performance-based earthquake engineering methodology. In Proceedings of ninth international conference on applications of statistics and probability in civil engineering (pp. 1-8).
[7] Moehle, J., & Deierlein, G. G. (2004, August). A framework methodology for performance-based earthquake engineering. In 13th world conference on earthquake engineering (Vol. 679, p. 12). WCEE Vancouver.
[8] FEMA P-58. (2012). Federal Emergency Management Agency: Seismic Performance Assessment of Buildings.
[9] Vicente, R., Ferreira, T., & Maio, R. (2014). Seismic risk at the urban scale: assessment, mapping and planning. Procedia Economics and Finance, 18, 71-80.
[10] Pelà, L. (2018). New trends and challenges in large-scale and urban assessment of seismic risk in historical centres. International Journal of Architectural Heritage, 12(7-8), 1051-1054.
[11] Ahmed, S., Abarca, A., Perrone, D., & Monteiro, R. (2022). Large-scale seismic assessment of RC buildings through rapid visual screening. International Journal of Disaster Risk Reduction, 80, 103219.
[12] Elwood, K. J., & Moehle, J. P. (2005). Axial capacity model for shear-damaged columns. ACI Structural Journal, 102(4), 578.
[13] Rayjada, S. P., Raghunandan, M., & Ghosh, J. (2023). Machine learning-based RC beam-column model parameter estimation and uncertainty quantification for seismic fragility assessment. Engineering Structures, 278, 115111.
[14] Luo, H., & Paal, S. G. (2018). Machine learning–based backbone curve model of reinforced concrete columns subjected to cyclic loading reversals. Journal of Computing in Civil Engineering, 32(5), 04018042.
[15] Huang, C., Li, Y., Gu, Q., & Liu, J. (2022). Machine learning–based hysteretic lateral force-displacement models of reinforced concrete columns. Journal of Structural Engineering, 148(3), 04021291.
[16] Feng, D. C., Cetiner, B., Azadi Kakavand, M. R., & Taciroglu, E. (2021). Data-driven approach to predict the plastic hinge length of reinforced concrete columns and its application. Journal of Structural Engineering, 147(2), 04020332.

[17] Berry, M., Parrish, M., & Eberhard, M. (2004). PEER structural performance database user’s manual (version 1.0). University of California, Berkeley.
[18] 陳瑩瑄. (2012). 鋼筋混凝土柱受撓曲變形參數之研究.
[19] 游雅喬（2012）.鋼筋混凝土柱之極限破壞研究.
[20] 黃冠傑. (2013). 鋼筋混凝土柱耐震圍束之研究.
[21] 王禹琁（2013）。RC柱性能曲線分析模型之驗證與改進。
[22] 吳秉誠（2017）.典型鋼筋混凝土柱構件震後性能研究.
[23] Fei, Y., X. Lu. (2020) "RC column backbone curve dataset."
[24] 張宗豪. (2021). 高軸力下高強度鋼筋混凝土柱撓曲主控之側力位移曲線.
[25] Settles, B. (2009). Active learning literature survey.
[26] Xu, L., & Grierson, D. E. (1993). Computer-automated design of semirigid steel frameworks. Journal of Structural Engineering, 119(6), 1740-1760.
[27] Kavlie, D., & Moe, J. (1971). Automated design of frame structures. Journal of the Structural Division, 97(1), 33-62.
[28] Moharrami, H., & Grierson, D. E. (1993). Computer-automated design of reinforced concrete frameworks. Journal of Structural Engineering, 119(7), 2036-2058.
[29] Guan, X., Burton, H., & Sabol, T. (2020). Python-based computational platform to automate seismic design, nonlinear structural model construction and analysis of steel moment resisting frames. Engineering Structures, 224, 111199.
[30] Ibarra L.F., Medina R. A., and Krawinkler H. (2005). “Hysteretic models that incorporate strength and stiffness deterioration”, Earthquake Engineering and Structural Dynamics, 34(12), 1489-1511.
[31] Karavasilis T.L., Ricles J.M., Sause R. (2009). "Implementation of deterioration elements in OpenSEES for collapse simulations" ATLSS Engineering Research Center, Rep. No. 09-11.
[32] Lignos, D.G., and Krawinkler, H. (2011). “Deterioration modeling of steel components in support of collapse prediction of steel moment frames under earthquake loading”, Journal of Structural Engineering, ASCE, Vol. 137 (11), 1291-1302.
[33] Lignos, D.G., Krawinkler, H. (2012). “Development and Utilization of Structural Component Databases for Performance-Based Earthquake Engineering", Journal of Structural Engineering, ASCE, doi: 10.1061/(ASCE)ST.1943-541X.0000646.
[34] Mazzoni, S., McKenna, F., Scott, M. H., & Fenves, G. L. (2006). OpenSees command language manual. Pacific Earthquake Engineering Research (PEER) Center, 264(1), 137-158.
[35] Ibarra, L. F. (2004). Global collapse of frame structures under seismic excitations. Stanford University.
[36] 中國土木水利工程學會 (2021)，「混凝土工程設計規範與解說(土木401-110)」，台北。
[37] American Society of Civil Engineers. (2014, May). Seismic evaluation and retrofit of existing buildings. American Society of Civil Engineers.
[38] ACI Committee. (2005). Building code requirements for structural concrete (ACI 318-05) and commentary (ACI 318R-05). American Concrete Institute.
[39] Building Seismic Safety Council (US), & Applied Technology Council. (1997). NEHRP guidelines for the seismic rehabilitation of buildings (Vol. 1). Federal Emergency Management Agency.
[40] Ibarra, L. F. (2004). Global collapse of frame structures under seismic excitations. Stanford University.
[41] Sezen, H., & Moehle, J. P. (2004). Shear strength model for lightly reinforced concrete columns. Journal of structural engineering, 130(11), 1692-1703.
[42] American Society of Civil Engineering. (2007, May). Seismic rehabilitation of existing buildings. American Society of civil engineers.
[43] Cohn, D., Ghahramani, Z., & Jordan, M. (1996). Active learning with statistical models. Journal of Artificial Intelligence Research, 4, 129-145.
[44] Tong, S., & Koller, D. (2001). Support vector machine active learning with applications to text classification. Journal of machine learning research, 2(Nov), 45-66.
[45] Settles, B. (2009). Active learning literature survey.
[46] Nagelkerke, N. J. (1991). A note on a general definition of the coefficient of determination. Biometrika, 78(3), 691-692.
[47] Freund, Y., & Schapire, R. E. (1995). A desicion-theoretic generalization of on-line learning and an application to boosting. In Computational Learning Theory: Second European Conference, EuroCOLT′95 Barcelona, Spain, March 13–15, 1995 Proceedings 2 (pp. 23-37). Springer Berlin Heidelberg.
[48] 曾至堅（2007）。低矮型校舍耐震能力詳細評估方法之研究。
[49] 黃世建,陳力平,陳俊宏(2003).含開口RC牆非韌性構架之耐震行為研究,報告編號：NCREE-03-010
[50] 陳奕信（2003）。含磚牆RC建築結構之耐震診斷。
[51] 鄒季峯（2017）。ETABS內建塑鉸之檢核，技師報。
[52] Mazzoni, S., McKenna, F., Scott, M. H., & Fenves, G. L. (2003). OpenSees Example Manual. PEER, Berkeley: University of California, 2003: 52.
[53] 內政部營建署(2022)。建築物耐震設計規範及解說，中華民國內政部營建署，台北，台灣。
[54] Dagang, L., Xiaohui, Y., Feng, P., & Guangyuan, W. (2010). Probabilistic seismic demand analysis of structures based on an improved cloud method. World Earthquake Engineering, 26(1), 7-15.
[55] PEER-TBI Task7 (2010) Modeling and acceptance criteria for seismic design and analysis of tall buildings. PEER Report No. 2010/111, University of California at Berkeley.
[56] ASCE 41-13 (2014) “Seismic rehabilitation of existing building.” American Society of Civil Engineers.
[57] Chen, P. Y., & Guan, X. (2023). A multi-source data-driven approach for evaluating the seismic response of non-ductile reinforced concrete moment frames. Engineering Structures, 278, 115452.
[58] Kircher, C., Deierlein, G., Hooper, J., Krawinkler, H., Mahin, S., Shing, B., & Wallace, J. (2010). Evaluation of the FEMA P-695 methodology for quantification of building seismic performance factors.

指導教授

陳鵬宇(Peng-Yu Chen)

審核日期

2023-7-25

推文