Steel and Composite Structures

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Development of ensemble machine learning models for evaluating seismic demands of steel moment frames

  • Nguyen, Hoang D. (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST)) ;
  • Kim, JunHee (Department of Architecture and Architectural Engineering, Yonsei University) ;
  • Shin, Myoungsu (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST))
  • Received : 2021.11.15
  • Accepted : 2022.06.27
  • Published : 2022.07.10
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Abstract

This study aims to develop ensemble machine learning (ML) models for estimating the peak floor acceleration and maximum top drift of steel moment frames. For this purpose, random forest, adaptive boosting, gradient boosting regression tree (GBRT), and extreme gradient boosting (XGBoost) models were considered. A total of 621 steel moment frames were analyzed under 240 ground motions using OpenSees software to generate the dataset for ML models. From the results, the GBRT and XGBoost models exhibited the highest performance for predicting peak floor acceleration and maximum top drift, respectively. The significance of each input variable on the prediction was examined using the best-performing models and Shapley additive explanations approach (SHAP). It turned out that the peak ground acceleration had the most significant impact on the peak floor acceleration prediction. Meanwhile, the spectral accelerations at 1 and 2 s had the most considerable influence on the maximum top drift prediction. Finally, a graphical user interface module was created that places a pioneering step for the application of ML to estimate the seismic demands of building structures in practical design.

Keywords

Acknowledgement

This research was supported by the Mid-Career Research Program through the National Research Foundation of Korea, funded by the Ministry of Science and ICT (Grant No. NRF-2018R1A2B6004546) and the A.I. Innovation Project Fund (Grant No. 1.210089) of UNIST (Ulsan national Institute of Science and Technology).

References

  1. AISC 360-16 (2016), Specification for Structural Steel Buildings, ANSI / AISC 360-16, American Institute of Steel Construction; IL, USA.
  2. ASCE (2016), Minimum Design Loads for Buildings and Other Structures, ASCE/SEI 7-16, American Society of Civil Engineers; VA, USA.
  3. Asteris, P.G., Lemonis, M.E., Nguyen, T.A., Van Le, H. and Pham, B.T. (2021), "Soft computing-based estimation of ultimate axial load of rectangular concrete-filled steel tubes", Steel Compos. Struct., 39(1), https://doi.org/10.12989/scs.2021.39.4.471.
  4. Breiman, L. (2001), "Random forests", Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324.
  5. Cao, V. Van and Ronagh, H.R. (2014), "Correlation between seismic parameters of far-fault motions and damage indices of low-rise reinforced concrete frames", Soil Dynam. Earthq. Eng., 66, 102-112. https://doi.org/10.1016/j.soildyn.2014.06.020.
  6. Chen, T. and Guestrin, C. (2016), "XGBoost: A scalable tree boosting system", Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794, San Francisco, USA, August. https://doi.org/10.1145/2939672.2939785.
  7. Elkady, A. and Lignos, D.G. (2014), "Modeling of the composite action in fully restrained beam-to-column connections: Implications in the seismic design and collapse capacity of steel special moment frames", Earthq. Eng. Struct. Dynam., 43(13), 1935-1954. https://doi.org/10.1002/eqe.2430.
  8. Elkady, A. and Lignos, D.G. (2015), "Effect of gravity framing on the overstrength and collapse capacity of steel frame buildings with perimeter special moment frames", Earthq. Eng. Struct. Dynam.s, 44(8), 1289-1307. https://doi.org/10.1002/eqe.2519.
  9. EN 1998-1 (2004), Eurocode 8: Design of structures for earthquake resistance - Part 1 : General rules, seismic actions and rules for buildings, EN 1998-1: 2004, European Committee for Standardization; Brussels, Belgium.
  10. FEMA (2012), Assessing seismic performance of buildings with configuration irregularities: calibrating current standards and practices; Applied Technology Council, Washington D.C., USA. www.ATCouncil.org
  11. Feng, D.C., Wang, W.J., Mangalathu, S., Hu, G. and Wu, T. (2021), "Implementing ensemble learning methods to predict the shear strength of RC deep beams with/without web reinforcements", Eng. Struct., 235, 111979. https://doi.org/10.1016/j.engstruct.2021.111979.
  12. Freund, Y. and Schapire, R.E. (1997), "A decision-theoretic generalization of on-line learning and an application to boosting", J. Comput. Syst. Sci., 55(1), 119-139. https://doi.org/10.1006/jcss.1997.1504.
  13. Friedman, J.H. (2001), "Greedy function approximation: A gradient boosting machine", Annals Statistics, 29(5), 1189-1232. https://doi.org/10.1214/aos/1013203451.
  14. Fu, B. and Feng, D.C. (2021), "A machine learning-based timedependent shear strength model for corroded reinforced concrete beams", J. Build. Eng., 36, 102118. https://doi.org/10.1016/j.jobe.2020.102118.
  15. Gao, X. and Lin, C. (2021), "Prediction model of the failure mode of beam-column joints using machine learning methods", Eng. Failure Analysis, 120, 105072. https://doi.org/10.1016/j.engfailanal.2020.105072
  16. Guan, M., EERI, X., Burton, M., EERI, H. and Shokrabadi, M. (2021), "A database of seismic designs, nonlinear models and seismic responses for steel moment-resisting frame buildings", Earthq. Spectra, 37(2), 1199-1222. https://doi.org/10.1177/8755293020971209.
  17. Guo, K. and Yang, G. (2020), "Load-slip curves of shear connection in composite structures: Prediction based on ANNs", Steel Compos. Struct., 36(5), 493-506. https://doi.org/10.12989/scs.2020.36.5.493.
  18. Gupta, A. and Krawinkler, H. (1999), "Seismic Demands for Performance Evaluation of Steel", John A. Blume Earthquake Engineering Center Technical Report Series (Issue 132), Stanford University, CA, USA.
  19. Huang, H. and Burton, H.V. (2019), "Classification of in-plane failure modes for reinforced concrete frames with infills using machine learning", J. Build. Eng., 25, 100767. https://doi.org/10.1016/j.jobe.2019.100767.
  20. Hwang, S.H., Mangalathu, S., Shin, J. and Jeon, J.S. (2021), "Machine learning-based approaches for seismic demand and collapse of ductile reinforced concrete building frames", J. Build. Eng., 34, 101905. https://doi.org/10.1016/j.jobe.2020.101905.
  21. Ibarra, L.F. and Krawinkler, H. (2005), "Global Collapse of Frame Structures under Seismic Excitations", John A. Blume Earthquake Engineering Center Technical Report Series (Issue 152), Stanford University, CA, USA.
  22. Kiani, J., Camp, C. and Pezeshk, S. (2019), "On the application of machine learning techniques to derive seismic fragility curves", Comput. Struct., 218, 108-122. https://doi.org/10.1016/j.compstruc.2019.03.004.
  23. Kim, S.E., Vu, Q.V., Papazafeiropoulos, G., Kong, Z. and Truong, V.H. (2020), "Comparison of machine learning algorithms for regression and classification of ultimate load-carrying capacity of steel frames", Steel Compos. Struct., 37(2), 193-209. https://doi.org/10.12989/scs.2020.37.2.193.
  24. Krawinkler, H. (1978), "Shear in Beam-Column joints in seismic design of steel frames", Eng. J., 15(3), 82-91.
  25. Kuhn, M. and Johnson, K. (2013), "Applied predictive modeling", Applied Predictive Modeling, Berlin, Springer. https://doi.org/10.1007/978-1-4614-6849-3
  26. Lignos, D.G., Krawinkler, H. and Whittaker, A.S. (2011), "Prediction and validation of sidesway collapse of two scale models of a 4-story steel moment frame", Earthq. Eng. Struct. Dynam., 40(7), 807-825. https://doi.org/10.1002/eqe.1061.
  27. Lignos Dimitrios, G., Hikino, T., Matsuoka, Y. and Nakashima, M. (2013), "Collapse assessment of steel moment frames based on e-defense full-scale shake table collapse tests", J. Struct. Eng., 139(1), 120-132. https://doi.org/10.1061/(asce)st.1943-541x.0000608.
  28. Lignos, D.G. and Krawinkler, H. (2011), "Deterioration modeling of steel components in support of collapse prediction of steel moment frames under earthquake loading", J. Struct. Eng., 137(11), 1291-1302. https://doi.org/10.1061/(asce)st.1943-541x.0000376.
  29. Lim, S. and Chi, S. (2019), "Xgboost application on bridge management systems for proactive damage estimation", Adv. Eng. Informatics, 41, 100922. https://doi.org/10.1016/j.aei.2019.100922.
  30. Lundberg, S.M., Erion, G.G. and Lee, S.I. (2018), "Consistent individualized feature attribution for tree ensembles", arXiv preprint arXiv:1802.03888, https://doi.org/10.48550/arXiv.1802.03888.
  31. Lundberg, S.M. and Lee, S.I. (2017), "A unified approach to interpreting model predictions", Adv. Neural Info Process. Syst., 30, 4766-4775.
  32. Mangalathu, S., Jang, H., Hwang, S.H. and Jeon, J.S. (2020), "Data-driven machine-learning-based seismic failure mode identification of reinforced concrete shear walls", Eng. Struct., 208, 110331. https://doi.org/10.1016/j.engstruct.2020.110331
  33. Mangalathu, S., Shin, H., Choi, E. and Jeon, J.S. (2021), "Explainable machine learning models for punching shear strength estimation of flat slabs without transverse reinforcement", J. Build. Eng., 39, 102300. https://doi.org/10.1016/j.jobe.2021.102300.
  34. Mangalathu, S., Sun, H., Nweke, C. C., Yi, Z. and Burton, H. V. (2020), "Classifying earthquake damage to buildings using machine learning", Earthq. Spectra, 36(1), 183-208. https://doi.org/10.1177/8755293019878137.
  35. Mazzoni, S., McKenna, F., Scott, M.H. and Fenves, G.L. (2006), "OpenSees command language manual", Pacific Earthquake Engineering Research (PEER) Center, 264(1), 137-158.
  36. Miranda, E. (1999), "Approximate Seismic Lateral Deformation Demands in Multistory Buildings", J. Struct. Eng., 125(4), 417-425. https://doi.org/10.1061/(asce)0733-9445(1999)125:4(417).
  37. Morfidis, K. and Kostinakis, K. (2018), "Approaches to the rapid seismic damage prediction of r/c buildings using artificial neural networks", Eng. Struct., 165, 120-141. https://doi.org/10.1016/j.engstruct.2018.03.028.
  38. Nguyen-Sy, T., Wakim, J., To, Q.D., Vu, M.N., Nguyen, T.D. and Nguyen, T.T. (2020), "Predicting the compressive strength of concrete from its compositions and age using the extreme gradient boosting method", Construct. Build. Mater., 260, 119757. https://doi.org/10.1016/j.conbuildmat.2020.119757
  39. Nguyen, H.D., Dao, N.D. and Shin, M. (2021), "Prediction of seismic drift responses of planar steel moment frames using artificial neural network and extreme gradient boosting", Eng. Struct., 242, 112518. https://doi.org/10.1016/j.engstruct.2021.112518.
  40. Nguyen, H.D., Dao, N.D. and Shin, M. (2022), "Machine learning-based prediction for maximum displacement of seismic isolation systems", J. Build. Eng., 51, 104251. https://doi.org/10.1016/j.jobe.2022.104251.
  41. Nguyen, H.D., LaFave, J.M., Lee, Y.-J. and Shin, M. (2022), "Rapid seismic damage-state assessment of steel moment frames using machine learning", Eng. Struct., 252, 113737. https://doi.org/10.1016/j.engstruct.2021.113737.
  42. Nguyen, H.D. and Shin, M. (2021), "Effects of soil-structure interaction on seismic performance of a low-rise R/C moment frame considering material uncertainties", J. Build. Eng., 44, 102713. https://doi.org/10.1016/j.jobe.2021.102713.
  43. Nguyen, H.D., Torbol, M. and Shin, M. (2020), "Reliability assessment of a planar steel frame subjected to earthquakes in case of an implicit limit-state function", J. Build. Eng., 32, 101782. https://doi.org/10.1016/j.jobe.2020.101782.
  44. Nguyen, H.D., Truong, G.T. and Shin, M. (2021), "Development of extreme gradient boosting model for prediction of punching shear resistance of r/c interior slabs", Eng. Struct., 235, 112067. https://doi.org/10.1016/j.engstruct.2021.112067.
  45. Nguyen, M.S.T., Thai, D.K. and Kim, S.E. (2020), "Predicting the axial compressive capacity of circular concrete filled steel tube columns using an artificial neural network", Steel Compos. Struct., 35(3), 415-437. https://doi.org/10.12989/SCS.2020.35.3.415.
  46. Nguyen, N.V., Nguyen, H.D. and Dao, N.D. (2022), "Machine learning models for predicting maximum displacement of triple pendulum isolation systems", Structures, 36, 404-415. https://doi.org/10.1016/j.istruc.2021.12.024.
  47. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E. (2011), "Scikit-learn: Machine learning in Python", J. Machine Learning Res., 12, 2825-2830.
  48. Raschka, S. (2018), "Model evaluation, model selection and algorithm selection in machine learning", arXiv preprint arXiv:1811.12808. https://doi.org/10.48550/arXiv.1811.12808.
  49. Shariati, M., Mafipour, M. S., Mehrabi, P., Zandi, Y., Dehghani, D., Bahadori, A., Shariati, A., Trung, N. T., Salih, M. N. A. and Poi-Ngian, S. (2019), "Application of Extreme Learning Machine (ELM) and Genetic Programming (GP) to design steelconcrete composite floor systems at elevated temperatures", Steel Compos. Struct., 33(3), 319-332. https://doi.org/10.12989/scs.2019.33.3.319.
  50. Shariati, M., Trung, N.T., Wakil, K., Mehrabi, P., Safa, M. and Khorami, M. (2019), "Moment-rotation estimation of steel rack connection using extreme learning machine", Steel Compos. Struct., 35(5), 427-435. https://doi.org/10.12989/scs.2019.31.5.427.
  51. Sohil, F., Sohali, M.U. and Shabbir, J. (2021), "An introduction to statistical learning with applications in R", Statistical Theory Related Fields, 6(1), 87. https://doi.org/10.1080/24754269.2021.1980261
  52. Tran, V.L., Jang, Y. and Kim, S.E. (2021), "Improving the axial compression capacity prediction of elliptical CFST columns using a hybrid ANN-IP model", Steel Compos. Struct., 39(3), 319-335. https://doi.org/10.12989/scs.2021.39.3.319
  53. Truong, V.H., Vu, Q.V., Thai, H.T. and Ha, M.H. (2020), "A robust method for safety evaluation of steel trusses using Gradient Tree Boosting algorithm", Adv. Eng. Software, 147, 102825. https://doi.org/10.1016/j.advengsoft.2020.102825.
  54. Vu, Q.V., Truong, V.H. and Thai, H.T. (2021), "Machine learningbased prediction of CFST columns using gradient tree boosting algorithm", Compos. Struct., 259, 113505. https://doi.org/10.1016/j.compstruct.2020.113505.
  55. Xie, Y., Ebad Sichani, M., Padgett, J.E. and DesRoches, R. (2020), "The promise of implementing machine learning in earthquake engineering: A state-of-the-art review", Earthq. Spectra, 36(4), 1769-1801. https://doi.org/10.1177/8755293020919419.
  56. Zhou, Z.H. (2015), "Ensemble learning", Encyclopedia of Biometrics, 411-416. Springer, New York, USA. https://doi.org/10.1007/978-1-4899-7488-4_293.