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A series of forecasting models for seismic evaluation of dams based on ground motion meta-features

A series of forecasting models for seismic evaluation of dams based on ground motion meta-features
A series of forecasting models for seismic evaluation of dams based on ground motion meta-features
Uncertainty quantification (UQ) due to seismic ground motions variability is an important task in risk-informed condition assessment of infrastructures. Since performing multiple dynamic analyses is computationally expensive, it is valuable to develop a series of forecasting models based on the unique ground motion characteristics.

This paper discusses the application of six different machine learning techniques on forecasting the structural behavior of gravity dams. Various time-, frequency-, and intensity-dependent characteristics are extracted from ground motion signals and used in machine learning. A large set of about 2,000 real ground motions are used, each includes about 35 meta-features. The major outcome of this study is to show the applicability of meta-modeling-based UQ in seismic safety evaluation of dams. As an intermediary result, the advantages of different machine learning algorithms, as well as meta-feature selection possibility is discussed for the current dataset. This paper proposes a feasibility study to reduce the computational costs in UQ of large-scale infra-structural systems.
0141-0296
Hariri-Ardebili, Mohammad Amin
f0ed500f-f805-4060-ad4b-625efd3517ed
Barak, Sasan
f82186de-f5b7-4224-9621-a00e7501f2c3
Hariri-Ardebili, Mohammad Amin
f0ed500f-f805-4060-ad4b-625efd3517ed
Barak, Sasan
f82186de-f5b7-4224-9621-a00e7501f2c3

Hariri-Ardebili, Mohammad Amin and Barak, Sasan (2019) A series of forecasting models for seismic evaluation of dams based on ground motion meta-features. Engineering Structures. (doi:10.1016/j.engstruct.2019.109657).

Record type: Article

Abstract

Uncertainty quantification (UQ) due to seismic ground motions variability is an important task in risk-informed condition assessment of infrastructures. Since performing multiple dynamic analyses is computationally expensive, it is valuable to develop a series of forecasting models based on the unique ground motion characteristics.

This paper discusses the application of six different machine learning techniques on forecasting the structural behavior of gravity dams. Various time-, frequency-, and intensity-dependent characteristics are extracted from ground motion signals and used in machine learning. A large set of about 2,000 real ground motions are used, each includes about 35 meta-features. The major outcome of this study is to show the applicability of meta-modeling-based UQ in seismic safety evaluation of dams. As an intermediary result, the advantages of different machine learning algorithms, as well as meta-feature selection possibility is discussed for the current dataset. This paper proposes a feasibility study to reduce the computational costs in UQ of large-scale infra-structural systems.

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Accepted/In Press date: 6 September 2019
e-pub ahead of print date: 25 October 2019

Identifiers

Local EPrints ID: 434746
URI: https://eprints.soton.ac.uk/id/eprint/434746
ISSN: 0141-0296
PURE UUID: d58cfeba-c85c-4998-8a9d-c8b03c96d1c8
ORCID for Sasan Barak: ORCID iD orcid.org/0000-0001-7715-9958

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Date deposited: 08 Oct 2019 16:30
Last modified: 09 Nov 2019 01:20

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Contributors

Author: Mohammad Amin Hariri-Ardebili
Author: Sasan Barak ORCID iD

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