1. Home
  2. Vol 8, No 6 (2022)
  3. Al-Rawashdeh

Predicting the Inelastic Response of Base Isolated Structures Utilizing Regression Analysis and Artificial Neural Network

Mohammad Al-Rawashdeh, Isam Yousef, Mohammad Al-Nawaiseh

Abstract


Indeed, utilizing a base isolation system in RC structures can remarkably minimize the possibility of failure, particularly in seismic-prone countries. Despite that, the design of these structures is a long procedure that consists of choosing the appropriate isolator to optimize the nonlinear behavior of the superstructure. Moreover, the numerical simulations require huge computational effort when high accuracy is required. In recent decades, scientists and engineers have applied numerous estimation approaches such as multiple linear regression and artificial neural networks to decrease the required cost and time for daily design problems. Thus, this study's main objective is to solve the difficulty of rapid response prediction by using soft-computing techniques. Additionally, it aims to study the capability of multiple linear regression and artificial neural networks in estimating the seismic performance of base-isolated RC structures under earthquakes. A nonlinear response history analysis of four different lead rubber-bearing isolated RC structures will be performed in order to determine the responses of these structures. Subsequently, the prediction models will be developed using the responses of the structures as inputs for multiple linear regression and artificial neural networks. Lastly, the reliability of both estimation approaches in terms of the response of base-isolated structures will be investigated by comparing the prediction models' capability. In general, the results of the study show that artificial neural networks provide considerably better accuracy in estimating base-isolated structures compared to multiple linear regression, and their performance results in reliable prediction.

 

Doi: 10.28991/CEJ-2022-08-06-07

Full Text: PDF


Keywords


Reinforced Concrete; Lead Rubber Bearing Isolator; Pulse-Like and Non-Pulse-Like Earthquakes; Multiple Linear Regression; Artificial Neural Network.

References


Kitayama, S., & Constantinou, M. C. (2018). Seismic Performance of Buildings with Viscous Damping Systems Designed by the Procedures of ASCE/SEI 7-16. Journal of Structural Engineering, 144(6), 4018050. doi:10.1061/(asce)st.1943-541x.0002048.

Seo, C. Y., Karavasilis, T. L., Ricles, J. M., & Sause, R. (2014). Seismic performance and probabilistic collapse resistance assessment of steel moment resisting frames with fluid viscous dampers. Earthquake Engineering and Structural Dynamics, 43(14), 2135–2154. doi:10.1002/eqe.2440.

Symans, M. D., Cofer, W. F., & Fridley, K. J. (2002). Base isolation and supplemental damping systems for seismic protection of wood structures: Literature review. Earthquake Spectra, 18(3), 549–572. doi:10.1193/1.1503342.

Robinson, W. H. (1982). Lead-rubber hysteretic bearings suitable for protecting structures during earthquakes. Earthquake Engineering & Structural Dynamics, 10(4), 593–604. doi:10.1002/eqe.4290100408.

Sasaki, T., Sato, E., Ryan, K. L., Okazaki, T., Kajiwara, K., & Mahin, S. A. (2012). NEES / E-Defense Base-Isolation Tests: Effectiveness of Friction Pendulum and Lead-Rubber Bearings Systems. 15th World Conference on Earthquake Engineering (15 WCEE), Lisbon, Portugal.

Rong, Q. (2020). Optimum parameters of a five-story building supported by lead-rubber bearings under near-fault ground motions. Journal of Low Frequency Noise Vibration and Active Control, 39(1), 98–113. doi:10.1177/1461348419845829.

Jibson, R. W. (2007). Regression models for estimating coseismic landslide displacement. Engineering Geology, 91(2–4), 209–218. doi:10.1016/j.enggeo.2007.01.013.

Carrillo, J., & Alcocer, S. M. (2012). Backbone model for performance-based seismic design of RC walls for low-rise housing. Earthquake Spectra, 28(3), 943–964. doi:10.1193/1.4000068.

Kaviani, P., Zareian, F., & Taciroglu, E. (2012). Seismic behavior of reinforced concrete bridges with skew-angled seat-type abutments. Engineering Structures, 45, 137–150. doi:10.1016/j.engstruct.2012.06.013.

Adam, B., & Smith, I. F. C. (2008). Active tensegrity: A control framework for an adaptive civil-engineering structure. Computers and Structures, 86(23–24), 2215–2223. doi:10.1016/j.compstruc.2008.05.006.

Prasad, B. K. R., Eskandari, H., & Reddy, B. V. V. (2009). Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN. Construction and Building Materials, 23(1), 117–128. doi:10.1016/j.conbuildmat.2008.01.014.

Maqsoom, A., Aslam, B., Gul, M. E., Ullah, F., Kouzani, A. Z., Parvez Mahmud, M. A., & Nawaz, A. (2021). Using multivariate regression and ann models to predict properties of concrete cured under hot weather: A case of Rawalpindi Pakistan. Sustainability (Switzerland), 13(18), 10164. doi:10.3390/su131810164.

Singh, P., Bhardwaj, S., Dixit, S., Shaw, R.N., Ghosh, A. (2021). Development of Prediction Models to Determine Compressive Strength and Workability of Sustainable Concrete with ANN. Innovations in Electrical and Electronic Engineering. Lecture Notes in Electrical Engineering, 756. doi:10.1007/978-981-16-0749-3_59.

Feng, D. C., Liu, Z. T., Wang, X. D., Chen, Y., Chang, J. Q., Wei, D. F., & Jiang, Z. M. (2020). Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach. Construction and Building Materials, 230, 117000. doi:10.1016/j.conbuildmat.2019.117000.

Lee, T. L., Lin, H. M., & Lu, Y. P. (2009). Assessment of highway slope failure using neural networks. Journal of Zhejiang University: Science A, 10(1), 101–108. doi:10.1631/jzus.A0820265.

Chakraborty, A., & Goswami, D. D. (2017). Slope Stability Prediction using Artificial Neural Network (ANN). International Journal of Engineering and Computer Science, 6(6), 21845–21848. doi:10.18535/ijecs/v6i6.49.

Bakhary, N., Hao, H., & Deeks, A. J. (2007). Damage detection using artificial neural network with consideration of uncertainties. Engineering Structures, 29(11), 2806–2815. doi:10.1016/j.engstruct.2007.01.013.

Chakraverty, S., Gupta, P., & Sharma, S. (2010). Neural network-based simulation for response identification of two-storey shear building subject to earthquake motion. Neural Computing and Applications, 19(3), 367–375. doi:10.1007/s00521-009-0279-6.

Adeli, H., & Panakkat, A. (2009). A probabilistic neural network for earthquake magnitude prediction. Neural Networks, 22(7), 1018–1024. doi:10.1016/j.neunet.2009.05.003.

Maya, M., Yu, W., & Telesca, L. (2022). Multi-Step Forecasting of Earthquake Magnitude Using Meta-Learning Based Neural Networks. Cybernetics and Systems, 53(6), 563–580. doi:10.1080/01969722.2021.1989170.

Kuang, W., Yuan, C., & Zhang, J. (2021). Network-based earthquake magnitude determination via deep learning. Seismological Research Letters, 92(4), 2245–2254. doi:10.1785/0220200317.

ASCE/SEI 7-22. (2022). Minimum Design Loads and Associated Criteria for Buildings and Other Structures. American Society of Civil Engineers (ASCE), Reston, United States. doi:10.1061/9780784415788.

Xu, F., Zhang, X., Xia, X., & Shi, J. (2022). Study on seismic behavior of a self-cantering railway bridge pier with sacrificial components. In Structures 35, 958-967. doi:10.1016/j.istruc.2021.11.061.

Hwang, J. S., & Chiou, J. M. (1996). An equivalent linear model of lead-rubber seismic isolation bearings. Engineering Structures, 18(7), 528–536. doi:10.1016/0141-0296(95)00132-8.

ACI 318-19. (2019). Building Code Requirements for Structural Concrete and Commentary. American Concrete Institute (ACI), Farmington Hills, United States. doi:10.14359/51716937.

NIST GCR 17-917-46v3. (2017). Guidelines for Nonlinear Structural Analysis for Design of Buildings Part IIb – Reinforced Concrete Moment Frasmes. Applied Technology Council, National Institute of Standards and technology (NIST), Gaithersburg, United States. doi: 10.6028/NIST.GCR.17-917-46v3.

Mander, J. B., Priestley, M. J. N., & Park, R. (1988). Theoretical Stress‐Strain Model for Confined Concrete. Journal of Structural Engineering, 114(8), 1804–1826. doi:10.1061/(asce)0733-9445(1988)114:8(1804).

Park, R., & Paulay, T. (1991). Reinforced concrete structures. John Wiley & Sons, Hoboken, United States.

Kalantari, A., & Roohbakhsh, H. (2020). Expected seismic fragility of code-conforming RC moment resisting frames under twin seismic events. Journal of Building Engineering, 28, 101098. doi:10.1016/j.jobe.2019.101098.

Kangda, M. Z., & Bakre, S. (2018). The Effect of LRB Parameters on Structural Responses for Blast and Seismic Loads. Arabian Journal for Science and Engineering, 43(4), 1761–1776. doi:10.1007/s13369-017-2732-7.

FEMA P695. (2009). Quantification of building seismic performance factors. Federal Emergency Management Agency, Washington, United States.

ASCE/SEI 7-10. (2013). Minimum design loads for buildings and other structures.. American Society of Civil Engineers, Reston, United States. doi:10.1061/9780784412916.

Michaud, D., & Léger, P. (2014). Ground motions selection and scaling for nonlinear dynamic analysis of structures located in Eastern North America. Canadian Journal of Civil Engineering, 41(3), 232–244. doi:10.1139/cjce-2012-0339.

Sinharay, S. (2010). An overview of statistics in education. International Encyclopedia of Education (3rd Ed.), 1–11, Elsevier, Amsterdam, Netherlands. doi:10.1016/B978-0-08-044894-7.01719-X.

Achen, C. H. (1982). Interpreting and using regression (Vol. 29). Sage Publications, Newbury Park, United States. doi:10.4135/9781412984560.

Kompoliti, K., & Metman, L. V. (2010). Encyclopedia of movement disorders. In Encyclopedia of Movement Disorders. Academic Press, Massachusetts, United States. doi:10.1016/c2009-1-03732-0.

Pandelea, A., Budescu, M. & Covatariu, G. (2014). Applications of Artificial Neural Networks in Civil Engineering. 2nd International Conference for Ph.D. students in Civil Engineering and Architecture, 10-13 December, 2014, Cluj-Napoca, Romania.

Topçu, I. B., & Saridemir, M. (2007). Prediction of properties of waste AAC aggregate concrete using artificial neural network. Computational Materials Science, 41(1), 117–125. doi:10.1016/j.commatsci.2007.03.010.

Alshihri, M. M., Azmy, A. M., & El-Bisy, M. S. (2009). Neural networks for predicting compressive strength of structural light weight concrete. Construction and Building Materials, 23(6), 2214–2219. doi:10.1016/j.conbuildmat.2008.12.003.

Hagan, M. T., & Menhaj, M. B. (1994). Training Feedforward Networks with the Marquardt Algorithm. IEEE Transactions on Neural Networks, 5(6), 989–993. doi:10.1109/72.329697.

Marquardt, D. W. (1963). An Algorithm for Least-Squares Estimation of Nonlinear Parameters. Journal of the Society for Industrial and Applied Mathematics, 11(2), 431–441. doi:10.1137/0111030.

FEMA 451B. (2007). NEHRP Recommended Provisions for New Buildings and Other Structures: Training and Instructional Materials. Federal Emergency Management Agency, Washington, United States.

Rahimi, F., Aghayari, R., & Samali, B. (2020). Application of tuned mass dampers for structural vibration control: a state-of-the-art review. Civil Engineering Journal, 6(8), 1622-1651. doi:10.28991/cej-2020-03091571.

Asuero, A. G., Sayago, A., & González, A. G. (2006). The correlation coefficient: An overview. Critical Reviews in Analytical Chemistry, 36(1), 41–59. doi:10.1080/10408340500526766.

Vatcheva, K. P., & Lee, M. (2016). Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies. Epidemiology: Open Access, 06(02). doi:10.4172/2161-1165.1000227.

Khademi, F., Jamal, S. M., Deshpande, N., & Londhe, S. (2016). Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System and Multiple Linear Regression. International Journal of Sustainable Built Environment, 5(2), 355–369. doi:10.1016/j.ijsbe.2016.09.003.

Patil, S. V., Balakrishna Rao, K., & Nayak, G. (2021). Prediction of recycled coarse aggregate concrete mechanical properties using multiple linear regression and artificial neural network. Journal of Engineering, Design and Technology. doi:10.1108/JEDT-07-2021-0373.


Full Text: PDF

DOI: 10.28991/CEJ-2022-08-06-07

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Copyright (c) 2022 Mohammad Al-Rawashdeh, Isam Yousef, Mohammad Al-Nawaiseh

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
x
Message