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Applying Deep Learning to Finite Element Model Calibration Research Intern: Subrata SahaAdvisor: Zheng Yi Wu

2015 Bentley Systems, Incorporated

# | WWW.BENTLEY.COM | 2015 Bentley Systems, Incorporated Needs for Model CalibrationAdequately represent conditions of in-service infrastructuresAbove- and underground infrastructure systemsAssess infrastructure performance Functionality, capacity, serviceability, safety and deficiency etc.Support decision-making for proactive maintenance Be preventive instead of reactive

# | WWW.BENTLEY.COM | 2015 Bentley Systems, Incorporated What is it?Start with initial model, e.g. design modelAdjust model parameters to minimize goodness-of-fit scoreChallenges: intensive computationsFE SolverAdjust parametersGoodness-of-fit scoreStop?Initial modelMeasured responsesCalibrated model

# | WWW.BENTLEY.COM | 2015 Bentley Systems, Incorporated Apply Surrogate ModelConstruct a meta-model (approximation), e.g. deep learning, or CMS Replace FE full analysis to Improve iterative calibrationFE SolverAdjust parametersGoodness-of-fit scoreStop?Initial modelMeasured responsesCalibrated modelSurrogate solver

# | WWW.BENTLEY.COM | 2015 Bentley Systems, Incorporated Apply Deep Learning SolutionInput parametersFE Model Solver

Goodness-of-fitInput parametersPrediction (DBN DLL)

Goodness-of-fit

Training DatasetDBN Training

Trained Model

# | WWW.BENTLEY.COM | 2015 Bentley Systems, Incorporated (1) UCLA factor building model (2) 60 decision variables - 30 for elasticity - 30 for stiffness (3) 1357 beams (4) 30 groups

Dataset

# | WWW.BENTLEY.COM | 2015 Bentley Systems, Incorporated

CommandCombined OutputInstance Generation RequestInstance GeneratedTraining Set GenerationInput variables:60Output:Goodness-of-fit scoreTraining dataset6,00012 hours

1. Generate random number using uniform distribution within a certain range for each of the decision variables2. Compute score using FE solver

# | WWW.BENTLEY.COM | 2015 Bentley Systems, Incorporated DBN Prediction Accuracy

# | WWW.BENTLEY.COM | 2015 Bentley Systems, Incorporated DBN Prediction Accuracy

# | WWW.BENTLEY.COM | 2015 Bentley Systems, Incorporated DBN Prediction Accuracy

# | WWW.BENTLEY.COM | 2015 Bentley Systems, Incorporated Comparison between FEM and DBN Calibrator RMS error: 0.304611778

DBN Calibration

# | WWW.BENTLEY.COM | 2015 Bentley Systems, Incorporated Scores from FEM and DBN Calibrator given top solutionDBN Calibration

# | WWW.BENTLEY.COM | 2015 Bentley Systems, Incorporated DBN Tune CalibrationRMS error: 0.306776256

Comparison between FEM and DBN Tune Calibrator

# | WWW.BENTLEY.COM | 2015 Bentley Systems, Incorporated Scores from FEM and DBN Tune given top solutionDBN Tune Calibration

# | WWW.BENTLEY.COM | 2015 Bentley Systems, Incorporated Calibration Time

# | WWW.BENTLEY.COM | 2015 Bentley Systems, Incorporated Trial vs Fitness Score

# | WWW.BENTLEY.COM | 2015 Bentley Systems, Incorporated Trial vs Time

# | WWW.BENTLEY.COM | 2015 Bentley Systems, Incorporated Software

# | WWW.BENTLEY.COM | 2015 Bentley Systems, Incorporated Thank You!

# | WWW.BENTLEY.COM | 2015 Bentley Systems, Incorporated