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Hashemi, Seyed Amir Hossein, Ghodrati Amiri, Gholamreza, Hamedi, Farzaneh. (1393). Steel Buildings Damage Classification by damage spectrum and Decision Tree Algorithm. هنرهای کاربردی, 3(1), 24-42. doi: 10.22075/jrce.2015.356
Seyed Amir Hossein Hashemi; Gholamreza Ghodrati Amiri; Farzaneh Hamedi. "Steel Buildings Damage Classification by damage spectrum and Decision Tree Algorithm". هنرهای کاربردی, 3, 1, 1393, 24-42. doi: 10.22075/jrce.2015.356
Hashemi, Seyed Amir Hossein, Ghodrati Amiri, Gholamreza, Hamedi, Farzaneh. (1393). 'Steel Buildings Damage Classification by damage spectrum and Decision Tree Algorithm', هنرهای کاربردی, 3(1), pp. 24-42. doi: 10.22075/jrce.2015.356
Hashemi, Seyed Amir Hossein, Ghodrati Amiri, Gholamreza, Hamedi, Farzaneh. Steel Buildings Damage Classification by damage spectrum and Decision Tree Algorithm. هنرهای کاربردی, 1393; 3(1): 24-42. doi: 10.22075/jrce.2015.356

Steel Buildings Damage Classification by damage spectrum and Decision Tree Algorithm

Journal of Rehabilitation in Civil Engineering
مقاله 3، دوره 3، شماره 1 - شماره پیاپی 5، اردیبهشت 2015، صفحه 24-42    اصل مقاله (1.07 M)
نوع مقاله: Regular Paper
شناسه دیجیتال (DOI): 10.22075/jrce.2015.356
نویسندگان
Seyed Amir Hossein Hashemi1؛ Gholamreza Ghodrati Amiri* 2؛ Farzaneh Hamedi3
1Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2Professor, School of Civil Engineering Iran University of Science & Technology
3Assistant Professor, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran
تاریخ دریافت: 06 بهمن 1393،  تاریخ بازنگری: 23 اردیبهشت 1394،  تاریخ پذیرش: 19 خرداد 1394 
چکیده
Results of damage prediction in buildings can be used as a useful tool for managing and decreasing seismic risk of earthquakes. In this study, damage spectrum and C4.5 decision tree algorithm were utilized for damage prediction in steel buildings during earthquakes. In order to prepare the damage spectrum, steel buildings were modeled as a single-degree-of-freedom (SDOF) system and time-history nonlinear analysis was carried out to develop a set of SDOF structures. Then, damage index was used to prepare the damage spectrum. Data parameters required for training and evaluating the C4.5 decision tree algorithm were obtained from the results of damage spectra for steel structures and using Krawinkler damage index Also, two decision trees were trained based on quantitative indices. The first decision tree determined whether damage occurred in buildings or not and the second predicted severity of damage as repairable, beyond repair, or collapse. decision tree classification algorithm was used to predict damage to steel structures.
کلیدواژه‌ها
Damage prediction؛ Damage Index؛ Steel buildings؛ Decision tree algorithm
مراجع

[1] Riddell, R.Garcia, JE.Garces, E.( 2002)."Inelastic deformation response of SDOF systems subjected to earthquakes". Earthquake Eng Struct Dyn, 515,pp.31–38.

[2] Karbassi, A. Mohebi, B. Rezaee, S. Lestuzzi, P. (2014). "Damage prediction for regular reinforced  concrete buildings using the decision tree algorithm". Computers and Structures ,130, 46–56.

[3] Amziane, S., Dube, JF.(2008)." Global RC structural damage index based on the assessment of local material damage". J Adv Concr Technol,6,459–68.

[4] Wahalthantri, BL.Thambiratnam, DP. Chan, THT. Fawzia, S.(2012)."An improved method to detect damage using modal strain energy based damage index". Adv Struct Eng,15,727–42.

[5] Benavent-Climent,. A.( 2011)."A seismic index method for vulnerability assessment of existing frames: application to RC structures with wide beams in Spain". Bull Earthquake Eng,9,491–517.

[6] Bozorgnia,Y. Bertero,V.(2003)."Damage Spectra: Characteristics and Applications to SeismicRisk Reduction". Journal of Structural Engineering, 129, no. 10,1330-1340.

[7] Elenas. A. Meskouris, K. (2001)." Correlation study between seismic acceleration parameters and damage indices of structures". Eng Struct,23,698–704.

[8] Ghobarah, A. and Osman, A.( 1995)." Seismic Damage Assesment in Low-Rise Steel Moment Resisting Frames". 10th European Conference on Earthquake Engineering.

[9] DiPasquale, E. Cakmak, AS. (1987)."Detection and assessment of seismic structural damage".State University of New York at Buffalo, National Center for Earthquake Engineering Research.

[10] McCabe, SL.Hall ,WJ. (1989)."Assessment of seismic structural damage". J Struct Eng ASCE  115,2166–2183.

[11] Krawinkler, H. Zohrei, M. (1983)."Cumulative Damage in Steel Structures Subjected to Earthquake Ground Motions. Computers and Structures".,16, 531-541.

[12] Building and Housing Research Center (BHRC), http://www.bhrc.ir/.

[13] Hashemi, S. A. H. Ghodrati Amiri ,G. Mohebi, B. Hamedi, F. (2014)."Developing the attenuation relation for damage spectrum in X-braced steel structures with neural network. JVE. Internationalltd".Journal of Vibroengineering  , 16, 8. 3879-3900

[14] Witten, IH. Frank, E. Hall, MA. (2011).,"Data mining: practical machine learning tools andtechniques". 3rd ed. Burlington: Morgan Kaufmann.

[15] Quinlan JR. C4.5: programs for machine learning. Morgan KaufmannPublishers; 1993.

[16] Hall ,M. Frank, E. Holmes, G. Pfahringer, B. Reutemann, P. Witten, IH. The WEKA data mining software: an update. SIGKDD Explor 2009;11(1).

[17] Matlab (7.6.0.324(R2008a)).[The Language of Technical Computing].USA:Math Works, Inc,U.S. patents.

[18] Opensees (2.4)[Open system for earthquake engineering simulation]. University of California, Berkeley :Pacifc Earthquake Engineering Research Center.

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