以人工智慧理論探討臺中市中小學校舍耐震因子及耐震能力

To Interpret the Seismic Factors and Seismic Abilities of School Buildings in Taichung City using Artificial Intelligent Theories

10.3966/101632122017060100006

陳清山(Ching-Shan Chen)

中小學校舍 ； 耐震能力 ； 資料探勘 ； 灰色理論 ； 倒傳遞類神經網路 ； School Building ； Seismic Ability ； Data Mining ； Grey Theory ； Back Propagation Neural Network

建築學報

100期（2017 / 06 / 30）

95 - 116

繁體中文

臺灣位於環太平洋地震火環帶，地震發生機率極高。臺中市位於臺灣強震區，人口密集，歷年來所發生的大地震，造成多棟中小學校舍破壞及生命財產損失。由於中小學校舍常做為地震侵襲後之避難所，地位極為重要，故本論文以校舍之耐震因子及耐震能力為研究主題。所使用的研究方法，包括：主成份分析法、資料探勘、灰色理論以及倒傳遞類神經網路。主成份分析法乃利用特徵值的觀念來歸納出耐震因子；資料探勘則應用於校舍的分群；灰色理論可分析校舍耐震因子與崩塌地表加速度之灰關聯係數，了解耐震因子與崩塌地表加速度之關聯程度；再透過倒傳遞類神經網路之學習、回想、歸納推演三項主要功能，訓練及測試中小學校舍之耐震能力評估模式。實證案例則以國家地震工程研究中心所提供的中小學耐震資料庫為研究樣本，並且實際調查測繪臺中市市中心區326棟校舍，透過上述的研究方法，探討臺中市中小學校舍重要耐震課題。從結果中可知，各分群之耐震因子與崩塌地表加速度之灰關聯度排序並不相同；各分群經類神經推論後的誤差均方根值介於0.0086至0.0829之間；判定係數R^2則介於0.6847至0.9966之間，上述推論並與支持向量機推論做比較，結果頗佳，可提供給建築師規劃設計校舍時使用，亦可供後續學術研究之參考。

Taiwan, located along the seismic belt that fringes the western Pacific at the junction between the Eurasian and Philippines Sea Plates, sees frequent seismic activity. Taichung City, a densely populated area of central Taiwan, had many school buildings damaged after earthquakes. Owing to school buildings serve important dual roles as places of education and as public shelters following earthquakes. This paper probed into the seismic factors and seismic abilities of school buildings in Taichung. The main research methods of this paper include the principal component analysis, data mining, grey theory and back propagation neural network. The concept of principal component analysis was used to generalize the seismic factors by Eigen-values. Data mining was adopted for school buildings' clustering. Grey theory was utilized for the grey relationship between seismic factor and collapse ground acceleration. Back propagation neural network was used to deduce the seismic assessment models of the school buildings. National Earthquake Engineering Research Centre provided some seismic data of school building for this paper. This paper used these data for research specimen and investigated the seismic factors of school buildings in Taichung City. From the results know that the sequence of the grey relationship grades differs in every clustering; the RMSE values range from 0.0086 to 0.0829; the R^2 values are between 0.6847 and 0.9966. The results also compared with the deduction of support vector machine. The fruitful results can provide for architects to use, also can provide academics for references.

1. 王文清、廖佳俊(2016)。學校建築耐震能力資料探勘模型評估與系統建置。災害防救科技與管理，5(1)，1-25。
   連結：
2. 王瀅翠、張嘉祥、陳嘉基、吳建邦(2006)。既存中小學老舊校舍結構調查研究─以台南縣為例。建築學報，58，1-18。
   連結：
3. 陳清山、鄭明淵、吳育偉(2014)。中小學校舍耐震評估─以演化式支持向量機推論系統為評估方法。建築學報，87，103-119。
   連結：
4. 黃宏謀、魏秀凌(2007)。台北市國小建築物地震災損評估。建築學報，59，45-62。
   連結：
5. Pearson, K. (1901). On lines and planes of closest fit to systems of points in space.Philosophical Magazine, 2(6), 559-572.
6. Anbazhagan, P.,Murali Krishna, A.(2016).Forensic investigation of earthquake induced failures during Sikkim 2011 earthquake, India.Japanese Geotechnical Society Special Publication,2(3),201-206.
7. Assaleh, K.,AlHamaydeh, M.,Choudhary, I.(2015).Modeling nonlinear behavior of buckling-restrained braces via different artificial intelligence methods.Applied Soft Computing,37,923-938.
8. Asteris, P. G.,Tsaris, A. K.,Cavaleri, L.,Repapis, C. C.,Papalou, A.,Di Trapani, F.,Karypidis, D. F.(2016).Prediction of the fundamental period of infilled RC frame structures using artificial neural networks.Computational Intelligence and Neuroscience,2016,1-12.
9. Bilgin, H.(2015).Seismic performance evaluation of an existing school building in Turkey.Challenge Journal of Structural Mechanics,1(4),161-167.
10. Chakraverty, S.,Sahoo, D. M.(2014).Interval response data based system identification of multi storey shear buildings using interval neural network modelling.Computer Assisted Methods in Engineering and Science,21(2),123-140.
11. Chao, Y. F.,Chen, H. M.(2016).Automatic modeling method for regional seismic damage assessment of school buildings in Taiwan.Journal of the Chinese Institute of Engineers,39(5),606-614.
12. Chen, H. M.,Kao, W. K.,Tsai, H. C.(2012).Genetic programming for predicting aseismic abilities of school buildings.Engineering Application of Artificial Intelligence,25(6),1103-1113.
13. Cheng, M. Y.,Chen, C. S.(2011).Optimal planning model for school buildings considering the tradeoff of seismic resistance and cost effectiveness: A Taiwan case study.Structural and Multidisciplinary Optimization,43(6),863-879.
14. Cheng, M. Y.,Chen, C. S.(2014).Preliminary planning efficiency evaluation for school buildings considering the tradeoffs of MOOP and planning preferences.Journal of Civil Engineering and Management,20(2),211-222.
15. Chiu, C. K.,Chen, M. R.,Chiu, C. H.(2013).Financial and environmental payback periods of seismic retrofit investments for reinforced concrete buildings estimated using a novel method.Journal of Architectural Engineering,19(2),112-118.
16. Chiu, C. K.,Lin, Y. F.(2014).Multi-objective decision-making supporting system of maintenance strategies for deteriorating reinforced concrete buildings.Automation in Construction,39,15-31.
17. Deng, J. L.(1982).Control problem of grey system.Systems & Control Letters,1(5),288-294.
18. Frascadore, R.,Di Lud.ovico, M.,Prota, A.,Verderame, G. M.,Manfredi, G.,Dolce, M.,Cosenza, E.(2015).Local strengthening of RC structures as a strategy for seismic risk mitigation at regional scale.Earthquake Spectra,31(2),1083-1102.
19. García-Pérez, J.,Riaño, R.(2016).Optimum seismic zoning using an artificial neural network.Earthquake Spectra,32(2),1187-1207.
20. Gordan, B.,Armaghani, D. J.,Hajihassani, M.,Monjezi, M.(2016).Prediction of seismic slope stability through combination of particle swarm optimization and neural network.Engineering with Computers,32(1),85-97.
21. Hu, J. L.,Tang, X. W.,Qiu, J. N.(2016).Assessment of seismic liquefaction potential based on Bayesian network constructed from domain knowledge and history data.Soil Dynamics and Earthquake Engineering,89,49-60.
22. Johan, A.(2000).Technical ReportTechnical Report,Linkoping, Sweden:Department of Mechanical Engineering, Linkoping University.
23. Kaiser, H. F.(1958).The varimax criterion for analytic rotation in factor analysis.Psychometrika,23(3),187-200.
24. Kantardzic, M.(2011).Data Mining: Concepts, Models, Methods, and Algorithms.NY, USA:John Wiley & Sons.
25. Kao, W. K.,Chen, H. M.,Chou, J. S.(2011).Aseismic ability estimation of school building using predictive data mining models.Expert Systems with Applications,38(8),10252-10263.
26. Kerh, T.,Su, Y. H.,Mosallam, A.(2017).Incorporating global search capability of a genetic algorithm into neural computing to model seismic records and soil test data.Neural Computing and Applications,28(3),437-448.
27. Luperto, M.,Amigoni, F.(2016).Exploiting structural properties of buildings towards general semantic mapping systems.Intelligent Autonomous Systems,Padova, Italy:
28. Mahrenholtz, C.,Lin, P. C.,Wu, A. C.,Tsai, K. C.,Hwang, S. J.,Lin, R. Y.,Bhayusukma, M. Y.(2015).Retrofit of reinforced concrete frames with bucklingrestrained braces.Earthquake Engineering & Structural Dynamics,44(1),59-78.
29. Porco, F.,Fiore, A.,Uva, G.,Raffaele, D.(2015).The influence of infilled panels in retrofitting interventions of existing reinforced concrete buildings: A case study.Structure and Infrastructure Engineering,11(2),162-175.
30. Richard, A. J.,Dean, W. W.(2002).Applied Multivariate Statistical Analysis (International Edition).NJ, USA:Pearson, Prentice Hall.
31. Shiwaku, K.,Ueda, Y.,Oikawa, Y.,Shaw, R.(2016).School disaster resilience assessment in the affected areas of 2011 East Japan earthquake and tsunami.Natural Hazards,82(1),333-365.
32. Tan, K. T.,Razak, H. A.,Lu, D.,Li, Y.(2015).Seismic response of a four-storey RC school building with masonry-infilled walls.Natural Hazards,78(1),141-153.
33. Vahdat, K.,Smith, N. J.(2014).A risk-based system for managing the retrofitting of school buildings in seismic prone areas: A case study from Iran.International Journal of Risk Assessment and Management,17(4),311-331.
34. Wang, N.,Adeli, H.(2015).Self-constructing wavelet neural network algorithm for nonlinear control of large structures.Engineering Applications of Artificial Intelligence,41,249-258.
35. Yakut, A.,Tonguç, Y.,Gülkan, P.(2008).A comparative seismic performance assessment and rehabilitation of existing school buildings.Proceeding of the 14th World Conference on Earthquake Engineering,Beijing, China:
36. 張嘉祥、賴宗吾、林益民(1995)。鋼筋混凝土校舍結構系統耐震行為分析及比較。建築學報，12，53-69。
37. 黃世建、鍾立來、簡文郁、葉勇凱、王翊光、余建維、張撼軍、陳永蒼、周德光、許丁友、邱建國、邱聰智(2005)。全國中小學校耐震評估與補強施行計畫。台北市=Taipei:國家地震工程研究中心=National Center for Research on Earthquake Engineering。
38. 廖文義、柴駿甫(2000)。學校建築耐震能力之簡易評估法及震譜容量法。台北市=Taipei:國家地震工程研究中心=National Center for Research on Earthquake Engineering。
39. 鍾立來、簡文郁、葉勇凱、黃世建、余建維、張撼軍、陳永蒼、王翊光、周德光、許丁友、邱建國、邱聰智(2005)。國民中小學典型校舍耐震能力之簡易調查。台北市=Taipei:國家地震工程研究中心=National Center for Research on Earthquake Engineering。
40. 鍾立來、黃鈞鼎、楊耀昇、林聖學、賴勇安、吳賴雲(2015)。以現地試驗驗證口湖國小含磚牆校舍之簡易側推分析。中國土木水利工程學刊，27(1)，59-68。
41. 蘇進國(2008)。台北市=Taipei，國立臺北科技大學工程科技研究所=Graduate Institution of Engineering Technology, National Taipei University of Science and Technology。

1. 陳清山(2020)。規劃設計階段考量中小學體育館之耐震因子及耐震能力－以多變量及人工智慧理論為研究方法。建築學報，111，55-75。
2. 陳清山(2021)。中小學校舍耐震評估模式之優化－以敏感度分析及人工智慧理論為研究方法。建築學報，115，1-20。
3. 陳清山(2022)。街屋耐震評估模型之研究－以人工智慧及敏感度分析理論為研究方法。建築學報，120，17-38。