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Research Papers

Robust Resource Allocation for Calibration and Validation Tests

[+] Author and Article Information
Chenzhao Li, Sankaran Mahadevan

Department of Civil and
Environmental Engineering,
Vanderbilt University,
Nashville, TN 37235

Manuscript received July 25, 2016; final manuscript received July 15, 2017; published online August 1, 2017. Editor: Ashley F. Emery.

J. Verif. Valid. Uncert 2(2), 021004 (Aug 01, 2017) (15 pages) Paper No: VVUQ-16-1021; doi: 10.1115/1.4037313 History: Received July 25, 2016; Revised July 15, 2017

Model calibration and validation are two activities in system model development, and both of them make use of test data. Limited testing budget creates the challenge of test resource allocation, i.e., how to optimize the number of calibration and validation tests to be conducted. Test resource allocation is conducted before any actual test is performed, and therefore needs to use synthetic data. This paper develops a test resource allocation methodology to make the system response prediction “robust” to test outcome, i.e., insensitive to the variability in test outcome; therefore, consistent system response predictions can be achieved under different test outcomes. This paper analyzes the uncertainty sources in the generation of synthetic data regarding different test conditions, and concludes that the robustness objective can be achieved if the contribution of model parameter uncertainty in the synthetic data can be maximized. Global sensitivity analysis (Sobol’ index) is used to assess this contribution, and to formulate an optimization problem to achieve the desired consistent system response prediction. A simulated annealing algorithm is applied to solve this optimization problem. The proposed method is suitable either when only model calibration tests are considered or when both calibration and validation tests are considered. Two numerical examples are provided to demonstrate the proposed approach.

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References

Figures

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Fig. 1

System response prediction: nonrobust to robust

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Fig. 2

Synthetic data: single type of test and single specimen

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Fig. 3

Synthetic data: q types of tests and single specimen for each type

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Fig. 4

Simulated annealing algorithm

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Fig. 5

System response prediction after model calibration with test data

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Fig. 6

Optimization of the mathematical example based on Eq. (16): (a) history of accepted random walks and (b) history of the Sobol’ indices sum SθE(Y)+SθV(Y)

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Fig. 7

Family of system response prediction PDFs at the solution of Eq. (16) of (N1,N2)=(2,8): (a) θ1=4.9,θ2=9.5, (b) θ1=5.4,θ2=9.8, and (c) θ1=5.0,θ2=10.5

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Fig. 8

Family of system response prediction PDFs at the suboptimal solution of (N1,N2)=(1,12): (a)θ1=4.9,θ2=9.5, (b) θ1=5.4,θ2=9.8, and (c) θ1=5.0,θ2=10.5

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Fig. 9

Optimization of the mathematical example based on Eq. (17): (a) history of accepted random walks and (b) history of cost 4N1+N2

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Fig. 10

Family of system response prediction PDFs at the solution of Eq. (17) of (N1,N2)=(3,7): (a) θ1=5.7,θ2=10.5, (b) θ1=5.2,θ2=9.1, and (c) θ1=4.6,θ2=10.8

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Fig. 11

Optimization of the mathematical example based on Eq. (18): (a) history of accepted random walks and (b) history of the Sobol' indices sum SθE(Y)+SθV(Y)

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Fig. 12

Family of system response prediction PDFs at the solution of Eq. (18) of (N1,N2)=(5,13): (a) Pθ={4.2,0.9,8.3,1.1}, (b) Pθ={5.8,0.4,9.1,0.9}, and (c) Pθ={4.7,0.6,9.6,1.2}

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Fig. 13

Family of system response prediction PDFs at the suboptimal solution of (N1,N2)=(4,17): (a) Pθ={4.2,0.9,8.3,1.1}, (b) Pθ={5.8,0.4,9.1,0.9}, and (c) Pθ={4.7,0.6,9.6,1.2}

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Fig. 14

Optimization of the mathematical example based on Eq. (19): (a) history of accepted random walks and (b) history of cost 4N1+N2

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Fig. 15

Family of system response prediction PDFs at the solution of Eq. (19) of (N1,N2)=(5,10): (a)Pθ={4.7,0.3,8.1,1.0}, (b) Pθ={5.7,0.9,9.0,0.4}, and (c) Pθ={4.9,0.5,9.4,1.2}

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Fig. 16

Structural dynamics challenge problem [30]: (a) level 1 (for testing), (b) level 2 (for testing), and (c) level 3 (for system prediction)

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Fig. 17

Optimization of the multilevel problem based on Eq. (21)

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Fig. 18

Family of system response prediction PDFs at (N1,N2,N3,N4)=(11,9,6,2): (a) k={5600,10433,8638}, (b) k={4483,9112,9987}, and (c) k={5776,9812,9393}

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Fig. 19

Optimization of the multilevel problem based on Eq. (22)

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Fig. 20

Family of system response prediction PDFs at (N1,N2,N3,N4)=(11,10,6,3): (a) k={4492,11183,9116}, (b) k={5074,8760,7812}, and (c) k={5276,9883,9518}

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