Machine learning-based seismic assessment of framed structures with soil-structure interaction

doi:10.1007/s11709-022-0909-y

Front. Struct. Civ. Eng.

2023, Vol. 17

Issue (2) : 205-223 https://doi.org/10.1007/s11709-022-0909-y

RESEARCH ARTICLE

Machine learning-based seismic assessment of framed structures with soil-structure interaction

Mohamed NOURELDIN, Tabish ALI, Jinkoo KIM(

)

Department of Global Smart City, Sungkyunkwan University, Suwon 16419, Korea

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Abstract

The objective of the current study is to propose an expert system framework based on a supervised machine learning technique (MLT) to predict the seismic performance of low- to mid-rise frame structures considering soil-structure interaction (SSI). The methodology of the framework is based on examining different MLTs to obtain the highest possible accuracy for prediction. Within the MLT, a sensitivity analysis was conducted on the main SSI parameters to select the most effective input parameters. Multiple limit state criteria were used for the seismic evaluation within the process. A new global seismic assessment ratio was introduced that considers both serviceability and strength aspects by utilizing three different engineering demand parameters (EDPs). The proposed framework is novel because it enables the designer to seismically assess the structure, while simultaneously considering different EDPs and multiple limit states. Moreover, the framework provides recommendations for building component design based on the newly introduced global seismic assessment ratio, which considers different levels of seismic hazards. The proposed framework was validated through comparison using non-linear time history (NLTH) analysis. The results show that the proposed framework provides more accurate results than conventional methods. Finally, the generalization potential of the proposed framework was tested by investigating two different types of structural irregularities, namely, stiffness and mass irregularities. The results from the framework were in good agreement with the NLTH analysis results for the selected case studies, and peak ground acceleration (PGA) was found to be the most influential input parameter in the assessment process for the case study models investigated. The proposed framework shows high generalization potential for low- to mid-rise structures.

Keywords seismic hazard artificial neural network soil-structure interaction seismic analysis

Corresponding Author(s): Jinkoo KIM

Just Accepted Date: 25 November 2022 Online First Date: 03 February 2023 Issue Date: 03 April 2023

Cite this article:

Mohamed NOURELDIN,Tabish ALI,Jinkoo KIM. Machine learning-based seismic assessment of framed structures with soil-structure interaction[J]. Front. Struct. Civ. Eng., 2023, 17(2): 205-223.

URL:

https://academic.hep.com.cn/fsce/EN/10.1007/s11709-022-0909-y
https://academic.hep.com.cn/fsce/EN/Y2023/V17/I2/205

Fig.1 Flowchart of the proposed framework.

Tab.1 Section properties and element details of model structures

Fig.2 Configuration of the analysis model frames: (a) plan view; (b) 3-story frame elevation with SSI Model.

Tab.2 Parameters of input earthquake records

Fig.3 Input earthquakes used for the framework: (a) response spectra of 100 input earthquakes; (b) histogram of the PGA.

Tab.3 List of the earthquake records used in the nonlinear dynamic analyses

Fig.4 Response spectra of the 11 earthquakes and the target spectrum for seismic hazards with probability of exceedance in 50 years of: (a) 10%; (b) 2%.

Fig.5 Sensitivity of EDPs to PGA, elastic modulus (E_so), and Poisson’s ratios (n_u): (a) V; (b) D; (c) A.

Fig.6 Normalized V in the 3-story building (1200 samples).

Fig.7 Accuracy of different MLTs for the output EDPs.

Fig.8 Effect of training algorithm on: (a) MSE; (b) R.

Fig.9 Effect of number of hidden layers on: (a) MSE; (b) R.

Fig.10 Effect of number of neurons on: (a) MSE; (b) R.

Fig.11 Effect of activation function on: (a) MSE; (b) R.

Fig.12 Accuracy of the ANN in the example structures: (a) R plot of V in 3-story model; (b) R plot of V in 5-story model; (c) R plot of V in 9-story model; (d) error histogram of D.

Fig.13 Validation of the MLT results using NLTH analysis results: (a) V; (b) A; (c) D.

Fig.14 Comparison of the MLT with the ASCE and NLTH results: (a) V; (b) D.

Fig.15 Irregular framed structures: (a) 3-story with mass irregularity; (b) 4-story with stiffness irregularity.

Fig.16 Results of the mass irregular 3-story model obtained from NLTH analysis and the proposed MLT for the DBE and MCE level earthquakes: (a) V; (b) A; (c) D.

Fig.17 Results of the stiffness irregular 4-story model obtained from NLTH analysis and the proposed MLT using 5 different earthquake records: (a) V; (b) A; (a) D.

model	limit state/EQ level	$(F V) V a v g V l i m$	$(F A) A a v g A l i m$	$(F D) D a v g D l i m$	global seismic assessment ratios (G)	seismic performance classifications
regular 3-story	life safety (DBE)	(1/3) 0.43	(1/3) 0.40	(1/3) 0.65	0.51	moderate
regular 3-story	collapse prevention (MCE)	(1/3) 0.6	(1/3) 0.65	(1/3) 0.52	0.41	poor
mass-irregular 3-story	life safety (DBE)	(0.1) 0.38	(0.8) 0.67	(0.1) 0.60	0.36	poor
mass-irregular 3-story	collapse prevention (MCE)	(0.1) 0.67	(0.8) 0.96	(0.1) 0.43	0.12	not acceptable
stiffness-irregular 4-story	selected	(0.1) 0.59	(0.1) 0.58	(0.8) 0.55	0.44	poor

Tab.4 Global seismic assessment ratio (G)

1	J M Mayoral, D Asimaki, S Tepalcapa, C Wood, A Roman-de la Sancha, T Hutchinson, K Franke, G Montalva. Site effects in Mexico City basin: Past and present. Soil Dynamics and Earthquake Engineering, 2019, 121: 369–382 https://doi.org/10.1016/j.soildyn.2019.02.028
2	P-58-1. 2nd ed FEMA. Seismic Performance Assessment of Buildings—Volume 1—Methodology FEMA P-58-1. Washington, D.C.: Federal Emergency Management Agency, 2018
3	R D Bertero, V V Bertero. Performance-based seismic engineering: the need for a reliable conceptual comprehensive approach. Earthquake Engineering & Structural Dynamics, 2002, 31(3): 627–652 https://doi.org/10.1002/eqe.146
4	S Lee, J Ha, M Zokhirova, H Moon, J Lee. Background information of deep learning for structural engineering. Archives of Computational Methods in Engineering, 2018, 25(1): 121–129 https://doi.org/10.1007/s11831-017-9237-0
5	H Salehi, R Burgueno. Emerging artificial intelligence methods in structural engineering. Engineering Structures, 2018, 171: 170–189 https://doi.org/10.1016/j.engstruct.2018.05.084
6	R Falcone, C Lima, E Martinelli. Soft computing techniques in structural and earthquake engineering: A literature review. Engineering Structures, 2020, 207: 110269 https://doi.org/10.1016/j.engstruct.2020.110269
7	N K Psyrras, A G Sextos. Build-X: Expert system for seismic analysis and assessment of 3D buildings using OpenSees. Advances in Engineering Software, 2018, 116: 23–35 https://doi.org/10.1016/j.advengsoft.2017.11.007
8	A Berrais, A S Watson. Expert systems for seismic engineering: The state-of-the-art. Engineering Structures, 1993, 15(3): 146–154 https://doi.org/10.1016/0141-0296(93)90049-A
9	A Berrais. A knowledge-based expert system for earthquake-resistant design of reinforced concrete buildings. Expert Systems with Applications, 2005, 28(3): 519–530 https://doi.org/10.1016/j.eswa.2004.12.013
10	N Sharma, K Dasgupta, A Dey. Natural period of reinforced concrete building frames on pile foundation considering seismic soil-structure interaction effects. Structures, 2020, 27: 1594–1612 https://doi.org/10.1016/j.istruc.2020.07.010
11	D van Nguyen, D Kim, D Duy Nguyen. Nonlinear seismic soil-structure interaction analysis of nuclear reactor building considering the effect of earthquake frequency content. Structures, 2020, 26: 901–914 https://doi.org/10.1016/j.istruc.2020.05.013
12	N D Dao, K L Ryan. Soil-structure interaction and vertical-horizontal coupling effects in buildings isolated by friction bearings. Journal of Earthquake Engineering, 2020, 26: 2124–2147
13	B Fatahi, H R Tabatabaiefar, B Samali. Performance based assessment of dynamic soil-structure interaction effects on seismic response of building frames. In: Proceedings of Geo-Risk 2011: Risk Assessment and Management. Reston: ASCE, 2011, 344–351
14	Y Tang, J Zhang. Probabilistic seismic demand analysis of a slender RC shear wall considering soil-structure interaction effects. Engineering Structures, 2011, 33(1): 218–229 https://doi.org/10.1016/j.engstruct.2010.10.011
15	S H Reza Tabatabaiefar, B Fatahi, B Samali. Seismic behavior of building frames considering dynamic soil-structure interaction. International Journal of Geomechanics, 2013, 13(4): 409–420 https://doi.org/10.1061/(ASCE)GM.1943-5622.0000231
16	J Yang, Z Lu, P Li. Large-scale shaking table test on tall buildings with viscous dampers considering pile-soil-structure interaction. Engineering Structures, 2020, 220: 110960 https://doi.org/10.1016/j.engstruct.2020.110960
17	S Liu, P Li, W Zhang, Z Lu. Experimental study and numerical simulation on dynamic soil-structure interaction under earthquake excitations. Soil Dynamics and Earthquake Engineering, 2020, 138: 106333
18	S Vaseghiamiri, M Mahsuli, M A Ghannad, F Zareian. Probabilistic approach to account for soil structure interaction in seismic design of building structures. Journal of Structural Engineering, 2020, 146(9): 04020184 https://doi.org/10.1061/(ASCE)ST.1943-541X.0002741
19	M Khatibinia, M Javad Fadaee, J Salajegheh, E Salajegheh. Seismic reliability assessment of RC structures including soil-structure interaction using wavelet weighted least squares support vector machine. Reliability Engineering & System Safety, 2013, 110: 22–33 https://doi.org/10.1016/j.ress.2012.09.006
20	H A Farfani, F Behnamfar, A Fathollahi. Dynamic analysis of soil-structure interaction using the neural networks and the support vector machines. Expert Systems with Applications, 2015, 42(22): 8971–8981 https://doi.org/10.1016/j.eswa.2015.07.053
21	R T Mirhosseini. Seismic response of soil-structure interaction using the support vector regression. Structural Engineering and Mechanics, 2017, 63(1): 115–124
22	A Siam, M Ezzeldin, W El-Dakhakhni. Machine learning algorithms for structural performance classifications and predictions: Application to reinforced masonry shear walls. Structures, 2019, 22: 252–265 https://doi.org/10.1016/j.istruc.2019.06.017
23	J M C Estêvão. Feasibility of using neural networks to obtain simplified capacity curves for seismic assessment. Buildings, 2018, 8(11): 151–161 https://doi.org/10.3390/buildings8110151
24	B K OhB GlisicS W ParkH S Park. Neural network-based seismic response prediction model for building structures using artificial earthquakes. Journal of Sound and Vibration, 2020, 468: 115109
25	Y Zhang, Z Gao, X Wang, Q Liu. Predicting the pore-pressure and temperature of fire-loaded concrete by a hybrid neural network. International Journal of Computational Methods, 2022, 468(3): 2142011 https://doi.org/10.1142/S0219876221420111
26	Y ZhangZ GaoX WangQ Liu. Image representations of numerical simulations for training neural networks. Computer Modeling in Engineering and Sciences, 134(2): 1−13
27	M Noureldin, A Ali, M S Nasab, J Kim. Optimum distribution of seismic energy dissipation devices using neural network and fuzzy inference system. Computer-Aided Civil and Infrastructure Engineering, 2021, 36(10): 1306–1321 https://doi.org/10.1111/mice.12673
28	B K OhY ParkH S Park. Seismic response prediction method for building structures using convolutional neural network. Structural Control and Health Monitoring, 2020, 27(5): 1−10
29	7-16 ASCE. Minimum Design Loads for Buildings and Other Structures. Chicago: American Society of Civil Engineers, 2016
30	F KhosravikiaM MahsuliM A Ghannad. Comparative assessment of soil-structure interaction regulations of ASCE 7-16 and ASCE 7-10. In: Proceedings of Structures Congress 2018. Reston: ASCE, 2018
31	F KhosravikiaM MahsuliM A Ghannad. Soil-structure interaction in seismic design code: Risk-based evaluation. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems. Part A, Civil Engineering, 2018, 4(4): 04018033
32	J Won, J Shin. Machine learning-based approach for seismic damage prediction method of building structures considering soil-structure interaction. Sustainability (Basel), 2021, 13(8): 1–14 https://doi.org/10.3390/su13084334
33	J Xu, B F Jr Spencer, X Lu, X Chen, L Lu. Optimization of structures subject to stochastic dynamic loading. Computer-Aided Civil and Infrastructure Engineering, 2017, 32(8): 657–673 https://doi.org/10.1111/mice.12274
34	G P CimellaroA M ReinhornM Bruneau. Seismic resilience of a hospital system. Structure and Infrastructure Engineering, 2010, 6(1−2): 127−144
35	H S Kim, P N Roschke. Fuzzy control of base-isolation system using multi-objective genetic algorithm. Computer-Aided Civil and Infrastructure Engineering, 2006, 21(6): 436–449 https://doi.org/10.1111/j.1467-8667.2006.00448.x
36	W Y Kam. Selective weakening and post-tensioning for the seismic retrofit of non-ductile RC frames. Dissertation for the Doctoral Degree. Christchurch: University of Canterbury, 2010, 532
37	M Nour Eldin, A Naeem, J Kim. Seismic retrofit of a structure using self-centering precast concrete frames with enlarged beam ends. Magazine of Concrete Research, 2020, 72(22): 1155–1170 https://doi.org/10.1680/jmacr.19.00012
38	M Noureldin, S A Memon, M Gharagoz, J Kim. Performance-based seismic retrofit of RC structures using concentric braced frames equipped with friction dampers and disc springs. Engineering Structures, 2021, 243: 112555 https://doi.org/10.1016/j.engstruct.2021.112555
39	M N Eldin, A J Dereje, J Kim. Seismic retrofit of framed buildings using self-centering PC frames. Journal of Structural Engineering, 2020, 146(10): 04020208 https://doi.org/10.1061/(ASCE)ST.1943-541X.0002786
40	41 ASCE. Seismic Evaluation and Retroﬁt of Existing Buildings. Chicago: American Society of Civil Engineers, 2017
41	D H Nguyen-Le, Q B Tao, V H Nguyen, M Abdel-Wahab, H Nguyen-Xuan. A data-driven approach based on long short-term memory and hidden Markov model for crack propagation prediction. Engineering Fracture Mechanics, 2020, 235: 107085 https://doi.org/10.1016/j.engfracmech.2020.107085
42	S Khatir, S Tiachacht, C Le Thanh, E Ghandourah, S Mirjalili, M Abdel Wahab. An improved Artificial Neural Network using Arithmetic Optimization Algorithm for damage assessment in FGM composite plates. Composite Structures, 2021, 273: 114287 https://doi.org/10.1016/j.compstruct.2021.114287
43	S Wang, H Wang, Y Zhou, J Liu, P Dai, X Du, M Abdel Wahab. Automatic laser profile recognition and fast tracking for structured light measurement using deep learning and template matching. Measurement, 2021, 169: 108362 https://doi.org/10.1016/j.measurement.2020.108362
44	L V Ho, T T Trinh, G De Roeck, T Bui-Tien, L Nguyen-Ngoc, M Abdel Wahab. An efficient stochastic-based coupled model for damage identification in plate structures. Engineering Failure Analysis, 2022, 131: 105866 https://doi.org/10.1016/j.engfailanal.2021.105866
45	L V Ho, T T Trinh, G De Roeck, T Bui-Tien, L Nguyen-Ngoc, M Abdel Wahab. A hybrid computational intelligence approach for structural damage detection using marine predator algorithm and feedforward neural networks. Computers & Structures, 2021, 252: 106568 https://doi.org/10.1016/j.compstruc.2021.106568
46	Open System for Earthquake Engineering Simulation OpenSees.. Berkeley: Pacific Earthquake Engineering Research Center, 2011
47	MATLAB. Version R2020b. Reference Manual. 2020
48	P695 FEMA. Quantification of Building Seismic Performance Factors. Washington, D.C.: MathWorks, 2009
49	Y Zhang, X Zhuang. Cracking elements: A self-propagating strong discontinuity embedded approach for quasi-brittle fracture. Finite Elements in Analysis and Design, 2018, 144: 84–100 https://doi.org/10.1016/j.finel.2017.10.007
50	Y Zhang, H A Mang. Global cracking elements: A novel tool for Galerkin-based approaches simulating quasi-brittle fracture. International Journal for Numerical Methods in Engineering, 2020, 121(11): 2462–2480 https://doi.org/10.1002/nme.6315
51	Y Zhang, J Huang, Y Yuan, H A Mang. Cracking elements method with a dissipation-based arc-length approach. Finite Elements in Analysis and Design, 2021, 195: 103573 https://doi.org/10.1016/j.finel.2021.103573
52	Y Zhang, Z Gao, Y Li, X Zhuang. On the crack opening and energy dissipation in a continuum based disconnected crack model. Finite Elements in Analysis and Design, 2020, 170: 103333 https://doi.org/10.1016/j.finel.2019.103333
53	T Rabczuk, T Belytschko. Cracking particles: A simplified meshfree method for arbitrary evolving cracks. International Journal for Numerical Methods in Engineering, 2004, 61(13): 2316–2343 https://doi.org/10.1002/nme.1151
54	T Rabczuk, G Zi, S Bordas, H Nguyen-Xuan. A simple and robust three-dimensional cracking-particle method without enrichment. Computer Methods in Applied Mechanics and Engineering, 2010, 199(37−40): 2437–2455 https://doi.org/10.1016/j.cma.2010.03.031
55	Y Zhang, R Lackner, M Zeiml, H A Mang. Strong discontinuity embedded approach with standard SOS formulation: Element formulation, energy-based crack-tracking strategy, and validations. Computer Methods in Applied Mechanics and Engineering, 2015, 287: 335–366 https://doi.org/10.1016/j.cma.2015.02.001
56	D G Lignos, H Krawinkler. Deterioration modeling of steel components in support of collapse prediction of steel moment frames under earthquake loading. Journal of Structural Engineering, 2011, 137(11): 1291–1302 https://doi.org/10.1061/(ASCE)ST.1943-541X.0000376
57	E Esmaeilzadeh Seylabi, C Jeong, E Taciroglu. On numerical computation of impedance functions for rigid soil-structure interfaces embedded in heterogeneous half-spaces. Computers and Geotechnics, 2016, 72: 15–27 https://doi.org/10.1016/j.compgeo.2015.11.001
58	S J Feng, X K Zhang, Q T Zheng, L Wang. Simulation and mitigation analysis of ground vibrations induced by high-speed train with three dimensional FEM. Soil Dynamics and Earthquake Engineering, 2017, 94: 204–214 https://doi.org/10.1016/j.soildyn.2017.01.022
59	J E Bowles. Foundation Analysis and Design. 5th ed. New York: McGraw-Hill, 1996
60	W WenC ZhangC Zhai. Rapid seismic response prediction of RC frames based on deep learning and limited building information. Engineering Structures, 2022, 267(15): 114638
61	T Kim, O S Kwon, J Song. Response prediction of nonlinear hysteretic systems by deep neural networks. Neural Networks, 2019, 111: 1–10 https://doi.org/10.1016/j.neunet.2018.12.005
62	PEER. Pacific Earthquake Engineering Research Center: NGA Database. Berkeley: University of California, 2019
63	GCR 12-917-21 NIST. Soil-structure Interaction for Building Structures. Berkeley: University of California, 2012
64	H LiC L P ChenH P Huang. Fuzzy Neural Intelligent Systems: Mathematical Foundation and the Applications in Engineering. Baca Raton: CRC Press, 2000
65	M Noureldin, J Kim. Parameterized seismic life-cycle cost evaluation method for building structures. Structure and Infrastructure Engineering, 2021, 17(3): 425–439 https://doi.org/10.1080/15732479.2020.1759656

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