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A series of forecasting models for seismic evaluation of dams based on ground motion meta-features
Highlights Proposing biometric-like ground motion signals unique signatures. Comparing six forecasting techniques in response prediction of finite element models. Proposing a general framework to reduce computational cost in seismic risk assessment. Performing dynamic simulations with a large set of about 2000 ground motions. Examining the concept of feature selection in the context of seismic response of dams.
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 2000 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.
A series of forecasting models for seismic evaluation of dams based on ground motion meta-features
Highlights Proposing biometric-like ground motion signals unique signatures. Comparing six forecasting techniques in response prediction of finite element models. Proposing a general framework to reduce computational cost in seismic risk assessment. Performing dynamic simulations with a large set of about 2000 ground motions. Examining the concept of feature selection in the context of seismic response of dams.
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 2000 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.
A series of forecasting models for seismic evaluation of dams based on ground motion meta-features
Hariri-Ardebili, Mohammad Amin (author) / Barak, Sasan (author)
Engineering Structures ; 203
2019-09-06
Article (Journal)
Electronic Resource
English
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