Supervised probabilistic failure prediction of tuned mass damper-equipped high steel frames using machine learning methods

Feature	Value
Height of structure (m)	60
Period of structure (s)	1.7247
Coefficient of reflection	0.9023
Coefficient of importance	1
Coefficient of behavior	7.5
Acceleration scheme	0.35
Coefficient of earthquake	0.0421

Table 3

Studied earthquake records and their characteristics.

Event	Year	Magnitude	Radius (km)	Station
Cape Mendocino	1992	7.1	23.6	Fortuna – Fortuna Blvd
Cape Mendocino	1992	7.1	18.5	Rio Dell Overpass – FF
Duzce	1999	7.1	15.6	Lamont
Northridge	1994	6.7	23.9	N Faring Rd
Northridge	1994	6.7	29.5	N Fletcher Dr
Loma Prieta	1989	6.9	19.9	Gilory Array #6
Loma Prieta	1989	6.9	21.4	Anderson Dam (Downst)
Loma Prieta	1989	6.9	21.4	Anderson Dam (Abut)
Loma Prieta	1989	6.9	22.3	Coyote Lake Dam
Northridge	1994	6.7	22.6	Castaic – Old Ridge Route
Northridge	1994	6.7	31.3	LA – Baldwin Hills
Northridge	1994	6.7	20.8	Beverly Hills – 12520 Mulhol
Northridge	1994	6.7	24.0	Big Tujunga, Angeles Nat F
Northridge	1994	6.7	25.7	LA – Century City C C North
Northridge	1994	6.7	23.7	LA – Chalon Rd
Northridge	1994	6.7	17.7	Sunland – Mt Gleason Ave
Northridge	1994	6.7	20.0	Burbank – Howard Rd
Northridge	1994	6.7	25.7	Hollywood – Willoughby A
Northridge	1994	6.7	24.5	Vasquez Rocks Park
San Fernando	1971	6.6	24.9	Castaic – Old Ridge Route

IDA analysis of the (a,b) first, (c,d) fifth, (e,f) tenth, (g,h) fifteenth, and (i,j) twentieth story of the 20-story structure (a,c,e,g,i) not equipped with TMD, and (b,d,f,h,j) equipped with TMD

Table 4

Performance evaluation of different models in prediction of the story drifts (parameters of each model are demonstrated in the text).

Regression model	Parameters	Normalized MSE		R²

		Train	Test	Train	Test
k-NN	k = 1	0.036	0.049	0.932	0.855
	k = 2	0.036	0.050	0.932	0.849
	k = 3	0.040	0.053	0.912	0.830
	k = 4	0.049	0.068	0.863	0.725
	k = 5	0.045	0.066	0.886	0.740
DT	type = ID3	0.022	0.038	0.988	0.918
	type = C4.5	0.020	0.033	0.994	0.942
	type = CART	0.025	0.042	0.978	0.896
	type = CHAID	0.043	0.060	0.897	0.783
	type = MARS	0.049	0.056	0.863	0.810
RF	-	0.033	0.048	0.946	0.862
ANN	hidden layer size = 2	0.050	0.066	0.857	0.740
	hidden layer size = 4	0.040	0.059	0.912	0.790
	hidden layer size = 6	0.039	0.054	0.917	0.824
	hidden layer size = 8	0.046	0.070	0.880	0.710
	hidden layer size = 10	0.039	0.052	0.917	0.837
	hidden layer size = 12	0.030	0.049	0.959	0.855
	hidden layer size = 14	0.037	0.058	0.927	0.797
	hidden layer size = 16	0.042	0.061	0.902	0.776
	hidden layer size = 18	0.051	0.067	0.851	0.732
	hidden layer size = 20	0.050	0.058	0.857	0.797
SVM	kernel type = linear, C = 10	0.019	0.029	0.998	0.960
	kernel type = linear, C = 20	0.022	0.031	0.988	0.951
	kernel type = linear, C = 30	0.021	0.035	0.991	0.933
	kernel type = Gaussian, C = 10	0.018	0.026	0.995	0.972
	kernel type = Gaussian, C = 20	0.017	0.025	0.995	0.976
	kernel type = Gaussian, C = 30	0.008	0.010	0.999	0.993
	kernel type = RBF, C = 10	0.030	0.038	0.959	0.918
	kernel type = RBF, C = 20	0.028	0.046	0.966	0.873
	kernel type = RBF, C = 30	0.029	0.046	0.962	0.873
NB	-	0.041	0.063	0.907	0.762

Table 5

Results of feature selection for sensitivity analysis of seismic and structural uncertainties.

Story	Seismic uncertainty				Structural uncertainty

	Relieff	t-test	SFS	CSC	Relieff	t-test	SFS	CSC
1	0.44	0.51	0.50	0.68	0.23	0.30	0.29	0.11
5	0.53	0.60	0.52	0.69	0.44	0.35	0.45	0.32
10	0.85	1.00	0.76	0.82	0.70	0.59	0.71	0.77
15	1.00	0.95	1.00	1.00	0.73	0.86	0.80	0.83
20	0.84	0.81	0.79	0.78	0.70	0.79	0.74	0.79

References

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Articles in this issue

DOI: https://doi.org/10.2478/sgem-2019-0043 | Journal eISSN: 2083-831X (formerly 0137-124X) | Journal ISSN: 0137-6365

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Language: English

Page range: 179 - 190

Submitted on: May 28, 2019

Accepted on: Jan 21, 2020

Published on: Sep 30, 2020

Published by: Wroclaw University of Science and Technology

In partnership with: Paradigm Publishing Services

Keywords:

Failure analysis,

supervised machine learning,

Related subjects:

Physics,

Mechanics and fluid dynamics

© 2020 Farshid Farrokhi, Sepideh Rahimi, published by Wroclaw University of Science and Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 42 (2020): Issue 3 (September 2020)

Supervised probabilistic failure prediction of tuned mass damper-equipped high steel frames using machine learning methods

Figures & Tables

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Table 1

Table 2

Figure 2

Table 3

Figure 3

Table 4

Table 5

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