Structural Engineering and Mechanics

  • 제83권3호
  • /
  • Pages.327-340
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  • 2022
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Calculating the collapse margin ratio of RC frames using soft computing models

  • Sadeghpour, Ali (Department of Civil Engineering, Eastern Mediterranean University) ;
  • Ozay, Giray (Department of Civil Engineering, Eastern Mediterranean University)
  • 투고 : 2021.06.05
  • 심사 : 2022.05.22
  • 발행 : 2022.08.10
⟨ 이전 논문 다음 논문 ⟩

초록

The Collapse Margin Ratio (CMR) is a notable index used for seismic assessment of the structures. As proposed by FEMA P695, a set of analyses including the Nonlinear Static Analysis (NSA), Incremental Dynamic Analysis (IDA), together with Fragility Analysis, which are typically time-taking and computationally unaffordable, need to be conducted, so that the CMR could be obtained. To address this issue and to achieve a quick and efficient method to estimate the CMR, the Artificial Neural Network (ANN), Response Surface Method (RSM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) will be introduced in the current research. Accordingly, using the NSA results, an attempt was made to find a fast and efficient approach to derive the CMR. To this end, 5016 IDA analyses based on FEMA P695 methodology on 114 various Reinforced Concrete (RC) frames with 1 to 12 stories have been carried out. In this respect, five parameters have been used as the independent and desired inputs of the systems. On the other hand, the CMR is regarded as the output of the systems. Accordingly, a double hidden layer neural network with Levenberg-Marquardt training and learning algorithm was taken into account. Moreover, in the RSM approach, the quadratic system incorporating 20 parameters was implemented. Correspondingly, the Analysis of Variance (ANOVA) has been employed to discuss the results taken from the developed model. Additionally, the essential parameters and interactions are extracted, and input parameters are sorted according to their importance. Moreover, the ANFIS using Takagi-Sugeno fuzzy system was employed. Finally, all methods were compared, and the effective parameters and associated relationships were extracted. In contrast to the other approaches, the ANFIS provided the best efficiency and high accuracy with the minimum desired errors. Comparatively, it was obtained that the ANN method is more effective than the RSM and has a higher regression coefficient and lower statistical errors.

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참고문헌

  1. Abolfathi, M. and Nia, A.A. (2018), "Optimization of energy absorption properties of thin-walled tubes with combined deformation of folding and circumferential expansion under axial load", Thin Wall. Struct., 130, 57-70. https://doi.org/10.1016/j.tws.2018.05.011.
  2. Ahmad, S., Pilakoutas, K., Rafi, M.M. and Zaman, Q.U. (2018), "Bond strength prediction of steel bars in low strength concrete by using ANN", Comput. Concrete, 22(2), 249-259. https://doi.org/ 10.12989/cac.2018.22.2.249.
  3. Ali, L., Wajahat, I., Golilarz, N.A., Keshtkar, F. and Bukhari, S. A.C. (2021), "LDA-GA-SVM: Improved hepatocellular carcinoma prediction through dimensionality reduction and genetically optimized support vector machine", Neur. Comput. Appl., 33(7), 2783-2792. https://doi.org/10.1007/s00521-020-05157-2
  4. Anjneya, K. and Roy, K. (2021), "Response surface-based structural damage identification using dynamic responses", Struct., 29, 1047-1058. https://doi.org/10.1016/j.istruc.2020.11.033.
  5. Applied Technology Council, and United States. Federal Emergency Management Agency (2009), Quantification of Building Seismic Performance Factors, US Department of Homeland Security, FEMA.
  6. ASEC/SEI 7-16 (2017), Minimum Design Loads and Associated Criteria for Buildings and Other Structures, American Society of Civil Engineers.
  7. Asen, F. and Dehestani, M. (2021), "Influence of concrete mix proportions on lifetime flexural load-bearing capacity of RC beams under chloride corrosion of rebars", Struct., 29, 2017-2027. https://doi.org/10.1016/j.istruc.2021.01.009.
  8. Asteris, P.G., Armaghani, D.J., Hatzigeorgiou, G.D., Karayannis, C.G. and Pilakoutas, K. (2019), "Predicting the shear strength of reinforced concrete beams using Artificial Neural Networks", Comput. Concrete, 24(5), 469-488. https://doi.org/10.12989/cac.2019.24.5.469.
  9. ATC (2007), "Recommended methodology for quantification of building system performance and response parameters", ATC-63 (90% draft).
  10. Banik, A., Dutta, S., Bandyopadhyay, T.K. and Biswal, S.K. (2019), "Prediction of maximum permeate flux (%) of disc membrane using response surface methodology (RSM)", Can. J. Civil Eng., 46(6), 299-307. https://doi.org/10.1139/cjce-2018-0007.
  11. Birzhandi, M.S. and Halabian, A.M. (2017), "Application of 2DMPA method in develpoing fragility curves of plan-asymmetric structures", Eng. Struct., 153, 540-549. https://doi.org/10.1016/j.engstruct.2017.10.038.
  12. Chojaczyk, A.A., Teixeira, A.P., Neves, L.C., Cardoso, J.B. and Soares, C.G. (2015), "Review and application of artificial neural networks models in reliability analysis of steel structures", Struct. Saf., 52, 78-89. https://doi.org/10.1016/j.strusafe.2014.09.002.
  13. Das, S. and Choudhury, S. (2019), "Influence of effective stiffness on the performance of RC frame buildings designed using displacement-based method and evaluation of column effective stiffness using ANN", Eng. Struct., 197, 109354. https://doi.org/10.1016/j.engstruct.2019.109354.
  14. Daouadji, T.H., Hadji, L., Meziane, M.A.A. and Bekki, H. (2016), "Elastic analysis effect of adhesive layer characteristics in steel beam strengthened with a fiber-reinforced polymer plates", Struct. Eng. Mech., 59(1), 83-100. https://doi.org/10.12989/sem.2016.59.1.083.
  15. De Luca, F., Vamvatsikos, D. and Iervolino, I. (2013), "Near- optimal piecewise linear fits of static pushover capacity curves for equivalent SDOF analysis", Earthq. Eng. Struct. Dyn., 42(4), 523-543. https://doi.org/10.1002/eqe.2225.
  16. Ghafari, E., Costa, H. and Julio, E. (2014), "RSM-based model to predict the performance of self-compacting UHPC reinforced with hybrid steel micro-fibers", Constr. Build. Mater., 66, 375-383. https://doi.org/10.1016/j.conbuildmat.2014.05.064.
  17. Gholamzadeh-Chitgar, A. and Berenjian, J. (2019), "Elman ANNs along with two different sets of inputs for predicting the properties of SCCs", Comput. Concrete, 24(5), 399-412. https://doi.org/10.12989/cac.2019.24.5.399.
  18. Hadji, L., Daouadji, T.H., Meziane, M. and Bedia, E.A. (2016), "Analyze of the interfacial stress in reinforced concrete beams strengthened with externally bonded CFRP plate", Steel Compos. Struct., 20(2), 413-429. https://doi.org/10.12989/scs.2016.20.2.413.
  19. Hakim, S.J.S., Razak, H.A. and Ravanfar, S.A. (2015), "Fault diagnosis on beam-like structures from modal parameters using artificial neural networks", Measure., 76, 45-61. https://doi.org/10.1016/j.measurement.2015.08.021.
  20. Hamidia, M. (2013) "Simplified seismic collapse capacity-based evaluation and design of frame buildings with and without supplemental damping systems", State University of New York at Buffalo.
  21. Hamidia, M., Filiatrault, A. and Aref, A. (2014a), "Simplified seismic sidesway collapse capacity-based evaluation and design of frame buildings with linear viscous dampers", J. Earthq. Eng., 18(4), 528-552. https://doi.org/10.1080/13632469.2013.876948.
  22. Hamidia, M., Filiatrault, A. and Aref, A. (2014b), "Simplified seismic sidesway collapse analysis of frame buildings", Earthq. Eng. Struct. Dyn., 43(3), 429-448. https://doi.org/10.1002/eqe.2353.
  23. Hammoudi, A., Moussaceb, K., Belebchouche, C. and Dahmoune, F. (2019), "Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled concrete aggregates", Constr. Build. Mater., 209, 425-436. https://doi.org/10.1016/j.conbuildmat.2019.03.119.
  24. Han, S.W., Moon, K.H. and Chopra, A.K. (2010), "Application of MPA to estimate probability of collapse of structures", Earthq. Eng. Struct. Dyn., 39(11), 1259-1278. https://doi.org/10.1002/eqe.992.
  25. Jiang, Y., Zhao, L., Beer, M., Patelli, E., Broggi, M., Luo, J., ... & Zhang, J. (2017), "Multiple response surfaces method with advanced classification of samples for structural failure function fitting", Struct. Saf., 64, 87-97. https://doi.org/10.1016/j.strusafe.2016.10.002.
  26. Kotsovou, G.M., Cotsovos, D.M. and Lagaros, N.D. (2017), "Assessment of RC exterior beam-column Joints based on artificial neural networks and other methods", Eng. Struct., 144, 1-18. https://doi.org/10.1016/j.engstruct.2017.04.048.
  27. Manafpour, A.R. and Jalilkhani, M. (2019), "A rapid analysis procedure for estimating the seismic collapse capacity of moment resisting frames", J. Earthq. Eng., 25(8), 1513-1532. https://doi.org/10.1080/13632469.2019.1583144.
  28. Mashrei, M.A., Abdulrazzaq, N., Abdalla, T.Y. and Rahman, M.S. (2010), "Neural networks model and adaptive neuro-fuzzy inference system for predicting the moment capacity of ferrocement members", Eng. Struct., 32(6), 1723-1734. https://doi.org/10.1016/j.engstruct.2010.02.024.
  29. Njomo, W.W. and Ozay, G. (2014), "Minimization of differential column shortening and sequential analysis of RC 3D-frames using ANN", Struct. Eng. Mech., 51(6), 989-1003. https://doi.org/10.12989/sem.2014.51.6.989.
  30. Pathirage, C.S.N., Li, J., Li, L., Hao, H., Liu, W. and Ni, P. (2018), "Structural damage identification based on autoencoder neural networks and deep learning", Eng. Struct., 172, 13-28. https://doi.org/10.1016/j.engstruct.2018.05.109.
  31. Perus, I., Klinc, R., Dolenc, M. and Dolsek, M. (2013), "A web- based methodology for the prediction of approximate IDA curves", Eng. Struct., 42(1), 43-60. https://doi.org/10.1002/eqe.2192.
  32. Sadeghpour, A. and Ozay, G. (2020), "Evaluation of reinforced concrete frames designed based on previous Iranian seismic codes", Arab. J. Sci. Eng., 45, 8069-8085. https://doi.org/10.1007/s13369-020-04548-w.
  33. Safa, M., Shariati, M., Ibrahim, Z., Toghroli, A., Baharom, S.B., Nor, N.M. and Petkovic, D. (2016), "Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam's shear strength", Steel Compos. Struct, 21(3), 679-688. https://doi.org/10.12989/scs.2016.21.3.679.
  34. Sedghi, Y., Zandi, Y., Shariati, M., Ahmadi, E., Azar, V.M., Toghroli, A., ... & Wakil, K. (2018), "Application of ANFIS technique on performance of C and L shaped angle shear connectors", Smart Struct. Syst., 22(3), 335-340. https://doi.org/10.12989/sss.2018.22.3.335.
  35. Shafei, B., Zareian, F. and Lignos, D.G. (2011), "A simplified method for collapse capacity assessment of moment-resisting frame and shear wall structural systems", Eng. Struct., 33(4), 1107-1116. https://doi.org/10.1016/j.engstruct.2010.12.028.
  36. Shakeel, S., Landolfo, R. and Fiorino, L. (2019), "Behaviour factor evaluation of CFS shear walls with gypsum board sheathing according to FEMA P695 for Eurocodes", Thin Wall. Struct., 141, 194-207. https://doi.org/10.1016/j.tws.2019.04.017.
  37. Shariati, M., Mafipour, M.S., Haido, J.H., Yousif, S.T., Toghroli, A., Trung, N.T. and Shariati, A. (2020), "Identification of the most influencing parameters on the properties of corroded concrete beams using an Adaptive Neuro-Fuzzy Inference System (ANFIS)", Steel Compos. Struct., 34(1), 155-170. https://doi.org/10.12989/scs.2020.34.1.155.
  38. Shin, J., Scott, D.W., Stewart, L.K. and Jeon, J.S. (2020), "Multi-hazard assessment and mitigation for seismically-deficient RC building frames using artificial neural network models", Eng. Struct., 207, 110204. https://doi.org/10.1016/j.engstruct.2020.110204.
  39. Su, G., Jiang, J., Yu, B. and Xiao, Y. (2015), "A Gaussian process-based response surface method for structural reliability analysis", Struct. Eng. Mech., 56(4), 549-567. https://doi.org/10.12989/sem.2015.56.4.549.
  40. Tesfamariam, S., Skandalos, K., Goda, K., Bezabeh, M.A., Bitsuamlak, G. and Popovski, M. (2021), "Quantifying the ductility-related force modification factor for 10-Story Timber-RC hybrid building using FEMA P695 procedure and considering the 2015 NBC seismic hazard", J. Struct. Eng., 147(5), 04021052. https://doi.org/10.1061/(ASCE)ST.1943-541X.0003007.
  41. Vahidi, E.K., Malekabadi, M.M., Rezaei, A., Roshani, M.M. and Roshani, G.H. (2017), "Modelling of mechanical properties of roller compacted concrete containing RHA using ANFIS", Comput. Concrete, 19(4), 435-442. https://doi.org/10.12989/cac.2017.19.4.435.
  42. Vamvatsikos, D. and Allin Cornell, C. (2006), "Direct estimation of the seismic demand and capacity of oscillators with multi-linear static pushovers through IDA", Earthq. Eng. Struct. Dyn., 35(9), 1097-1117. https://doi.org/10.1002/eqe.573.
  43. Wang, B., Man, T. and Jin, H. (2015), "Prediction of expansion behavior of self-stressing concrete by artificial neural networks and fuzzy inference systems", Constr. Build. Mater., 84, 184-191. https://doi.org/10.1016/j.conbuildmat.2015.03.059.
  44. Zarringol, M., Thai, H.T., Thai, S. and Patel, V. (2020), "Application of ANN to the design of CFST columns", Struct., 28, 2203-2220. https://doi.org/10.1016/j.istruc.2020.10.048.
  45. Zhang, X., Shahnewaz, M. and Tannert, T. (2018), "Seismic reliability analysis of a timber steel hybrid system", Eng. Struct., 167, 629-638. https://doi.org/10.1016/j.engstruct.2018.04.051.
  46. Zhou, Q., Wang, F. and Zhu, F. (2016), "Estimation of compressive strength of hollow concrete masonry prisms using artificial neural networks and adaptive neuro-fuzzy inference systems", Constr. Build. Mater., 125, 417-426. https://doi.org/10.1016/j.conbuildmat.2016.08.064.
  47. Zhou, Q., Zhu, F., Yang, X., Wang, F., Chi, B. and Zhang, Z. (2017), "Shear capacity estimation of fully grouted reinforced concrete masonry walls using neural network and adaptive neuro-fuzzy inference system models", Constr. Build. Mater., 153, 937-947. https://doi.org/10.1016/j.conbuildmat.2017.07.171.