### Abstract

Sensitivity analysis (SA) is able to identify the most influential parameters of a given model. Application of SA is usually critical for reducing the complexity in the subsequent model calibration and use. Unfortunately it is hardly applied, especially when the model is in the form of a computationally expensive black-box computer program. A possible solution concerns applying SA to the metamodel (i.e., an approximation of the computationally expensive model) instead. Among the other options, the use of Gaussian process metamodels (also known as Kriging metamodels) has been recently proposed for the SA of computationally expensive traffic simulation models. However, the main limitation of this approach is its dependence on the model dimensionality. When the model is high-dimensional, the estimation of the Kriging metamodel may still be problematic due to its high computational cost. In order to overcome this problem, in the present paper, the Kriging-based approach has been combined with the quasi-optimized trajectory based elementary effects (quasi-OTEE) approach for the SA of high-dimensional models. The quasi-OTEE SA is used first to screen the influential and non-influential parameters of a high-dimensional model; then the Kriging-based SA is used to calculate the variance-based sensitivity indices, and to rank the most influential parameters in a more accurate way. The application of the proposed sequential SA is illustrated with several numerical experiments. Results show that the method can properly identify the most influential parameters and their ranks, while the number of model evaluations is considerably less than the variance-based SA (e.g., in one of the tests the sequential SA requires over 50 times less model evaluations than the variance-based SA).

Original language | English (US) |
---|---|

Pages (from-to) | 334-344 |

Number of pages | 11 |

Journal | Reliability Engineering and System Safety |

Volume | 134 |

DOIs | |

State | Published - Jan 1 2015 |

### Fingerprint

### Keywords

- High-dimensional and computationally expensive model
- Metamodel
- Screening
- Sensitivity analysis
- Variance-based approach

### ASJC Scopus subject areas

- Safety, Risk, Reliability and Quality
- Industrial and Manufacturing Engineering
- Applied Mathematics

### Cite this

*Reliability Engineering and System Safety*,

*134*, 334-344. https://doi.org/10.1016/j.ress.2014.08.009

**Combining screening and metamodel-based methods : An efficient sequential approach for the sensitivity analysis of model outputs.** / Ge, Qiao; Ciuffo, Biagio; Menendez, Monica.

Research output: Contribution to journal › Article

*Reliability Engineering and System Safety*, vol. 134, pp. 334-344. https://doi.org/10.1016/j.ress.2014.08.009

}

TY - JOUR

T1 - Combining screening and metamodel-based methods

T2 - An efficient sequential approach for the sensitivity analysis of model outputs

AU - Ge, Qiao

AU - Ciuffo, Biagio

AU - Menendez, Monica

PY - 2015/1/1

Y1 - 2015/1/1

N2 - Sensitivity analysis (SA) is able to identify the most influential parameters of a given model. Application of SA is usually critical for reducing the complexity in the subsequent model calibration and use. Unfortunately it is hardly applied, especially when the model is in the form of a computationally expensive black-box computer program. A possible solution concerns applying SA to the metamodel (i.e., an approximation of the computationally expensive model) instead. Among the other options, the use of Gaussian process metamodels (also known as Kriging metamodels) has been recently proposed for the SA of computationally expensive traffic simulation models. However, the main limitation of this approach is its dependence on the model dimensionality. When the model is high-dimensional, the estimation of the Kriging metamodel may still be problematic due to its high computational cost. In order to overcome this problem, in the present paper, the Kriging-based approach has been combined with the quasi-optimized trajectory based elementary effects (quasi-OTEE) approach for the SA of high-dimensional models. The quasi-OTEE SA is used first to screen the influential and non-influential parameters of a high-dimensional model; then the Kriging-based SA is used to calculate the variance-based sensitivity indices, and to rank the most influential parameters in a more accurate way. The application of the proposed sequential SA is illustrated with several numerical experiments. Results show that the method can properly identify the most influential parameters and their ranks, while the number of model evaluations is considerably less than the variance-based SA (e.g., in one of the tests the sequential SA requires over 50 times less model evaluations than the variance-based SA).

AB - Sensitivity analysis (SA) is able to identify the most influential parameters of a given model. Application of SA is usually critical for reducing the complexity in the subsequent model calibration and use. Unfortunately it is hardly applied, especially when the model is in the form of a computationally expensive black-box computer program. A possible solution concerns applying SA to the metamodel (i.e., an approximation of the computationally expensive model) instead. Among the other options, the use of Gaussian process metamodels (also known as Kriging metamodels) has been recently proposed for the SA of computationally expensive traffic simulation models. However, the main limitation of this approach is its dependence on the model dimensionality. When the model is high-dimensional, the estimation of the Kriging metamodel may still be problematic due to its high computational cost. In order to overcome this problem, in the present paper, the Kriging-based approach has been combined with the quasi-optimized trajectory based elementary effects (quasi-OTEE) approach for the SA of high-dimensional models. The quasi-OTEE SA is used first to screen the influential and non-influential parameters of a high-dimensional model; then the Kriging-based SA is used to calculate the variance-based sensitivity indices, and to rank the most influential parameters in a more accurate way. The application of the proposed sequential SA is illustrated with several numerical experiments. Results show that the method can properly identify the most influential parameters and their ranks, while the number of model evaluations is considerably less than the variance-based SA (e.g., in one of the tests the sequential SA requires over 50 times less model evaluations than the variance-based SA).

KW - High-dimensional and computationally expensive model

KW - Metamodel

KW - Screening

KW - Sensitivity analysis

KW - Variance-based approach

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UR - http://www.scopus.com/inward/citedby.url?scp=84913529830&partnerID=8YFLogxK

U2 - 10.1016/j.ress.2014.08.009

DO - 10.1016/j.ress.2014.08.009

M3 - Article

VL - 134

SP - 334

EP - 344

JO - Reliability Engineering and System Safety

JF - Reliability Engineering and System Safety

SN - 0951-8320

ER -