Seismic vulnerability of above-ground storage tanks with unanchored support conditions for Na-tech risks based on Gaussian process regression

Phan, Hoang Nam; Paolacci, Fabrizio; Filippo, Rocco di; Bursi, Oreste S.

doi:10.1007/s10518-020-00960-7

Cited by 24 publications

(12 citation statements)

References 37 publications

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“…GPR analysis is a type of regression analysis that is adopted when the dependent variable follows the Gaussian process [20,25,26]. Phan et al [27] and Hoang et al [28] used the GPR technique for seismic vulnerability and seismic fragility analysis for storage tanks and reinforced concrete highway bridges. Jung et al [20] also investigated several methods in estimating the maximum displacement and found that the GPR approach showed the best performance.…”

Section: Estimation Model Based On Gaussian Process Regressionmentioning

confidence: 99%

“…The levels for IO, LS, and CP determine the acceptance range of MIDR using the maximum height. The performance levels have threshold values considering the IO (0.7%), LS (2.5%), and CP (5.0%) to provide the MIDR limits [27,28,45,46]. Generally, the structural analysis that produces the fragility curve is conducted based on the PGA of 0.0 g in the fragility function.…”

Section: Six-floor Building Analysis For Development Of the Rapid Prediction Modelmentioning

confidence: 99%

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Gaussian Process Regression-Based Structural Response Model and Its Application to Regional Damage Assessment

Park

Jung

2021

IJGI

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Seismic activities are serious disasters that induce natural hazards resulting in an incalculable amount of damage to properties and millions of deaths. Typically, seismic risk assessment can be performed by means of structural damage information computed based on the maximum displacement of the structure. In this study, machine learning models based on GPR are developed in order to estimate the maximum displacement of the structures from seismic activities and then used to construct fragility curves as an application. During construction of the models, 13 features of seismic waves are considered, and six wave features are selected to establish the seismic models with the correlation analysis normalizing the variables with the peak ground acceleration. Two models for six-floor and 13-floor buildings are developed, and a sensitivity analysis is performed to identify the relationship between prediction accuracy and sampling size. A 10-fold cross-validation method is used to evaluate the model performance, using the R-squared, root mean squared error, Nash criterion, and mean bias. Results of the six-parameter-based model apparently indicate a similar performance to that of the 13-parameter-based model for the two types of buildings. The model for the six-floor building affords a steadily enhanced performance by increasing the sampling size, while the model for the 13-floor building shows a significantly improved performance with a sampling size of over 200. The results indicate that the heighted structure requires a larger sampling size because it has more degrees of freedom that can influence the model performance. Finally, the proposed models are successfully constructed to estimate the maximum displacement, and applied to obtain fragility curves with various performance levels. Then, the regional seismic damage is assessed in Gyeonjgu city of South Korea as an application of the developed models. The damage assessment with the fragility curve provides the structural response from the seismic activities, which can assist in minimizing damage.

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Section: Estimation Model Based On Gaussian Process Regressionmentioning

confidence: 99%

Section: Six-floor Building Analysis For Development Of the Rapid Prediction Modelmentioning

confidence: 99%

Gaussian Process Regression-Based Structural Response Model and Its Application to Regional Damage Assessment

Park

Jung

2021

IJGI

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“…In this paper, unscaled ground motion records are selected, which have been demonstrated as a suitable input for analytical fragility assessments [28]. For a Kriging-based metamodel, a limited number of records can be used [4], hence in this study, a set of 20 records is selected from the PEER ground motion database [29]. The characteristics of the records are summarized in Table 3; these records are mainshock free-field recordings.…”

Section: Ground Motion Selectionmentioning

confidence: 99%

“…In recent decades, owing to the development of the computer's hardware and numerical modeling techniques, different methods of the seismic fragility evaluation for bridge structures in highway networks, especially reinforced concrete (RC) bridges, have been extensively developed also in a parametric form in the case of bridge classes [1]. Innovative numerical models and computational techniques help to simulate and analyze complex and large structures with high accuracy; however, they are computationally expensive [2][3][4]. In addition, one of the challenges in the development of seismic fragility functions is to include various sources of uncertainties, e.g., ground motion and modeling parameters, into the probabilistic seismic demand model.…”

Section: Introductionmentioning

confidence: 99%

“…Considering different sources of uncertainty into a complex numerical model or a class of structures is commonly a hard task due to the time-consuming re-construction and re-evaluation of the numerical model. Therefore, implementing interpolation or regression techniques to accurately predict the seismic response of structural elements that only use a few numbers of dynamic analyses is an alternative in the seismic vulnerability assessment step [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

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Kriging Metamodel-Based Seismic Fragility Analysis of Single-Bent Reinforced Concrete Highway Bridges

et al. 2021

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Uncertainty quantification is an important issue in the seismic fragility analysis of bridge type structures. However, the influence of different sources of uncertainty on the seismic fragility of the system is commonly overlooked due to the costly re-evaluation of numerical model simulations. This paper aims to present a framework for the seismic fragility analysis of reinforced concrete highway bridges, where a data-driven metamodel is developed to approximate the structural response to structural and ground motion uncertainties. The proposed framework to generate fragility curves shows its efficiency while using a few finite element simulations and accounting for various modeling uncertainties influencing the bridge seismic fragility. In this respect, a class of single-bent bridges available in the literature is taken as a case study, whose three-dimensional finite element model is established by the OpenSees software framework. Twenty near-source records from different sources are selected and the Latin hypercube method is applied for generating the random samples of modeling and ground motion parameters. The Kriging metamodel is then driven on the structural response obtained from nonlinear time history analyses. Component fragility curves of the reinforced concrete pier column are derived for different damage states using the Kriging metamodel whose parameters are established considering different modeling parameters generated by Monte Carlo simulations. The results demonstrate the efficiency of the proposed framework in interpolating the structural response and deriving the fragility curve of the case study with any input conditions of the random variables.

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A novel framework for seismic risk assessment of structures

Giannini

Paolacci

Phan

et al. 2022

Earthq Engng Struct Dyn

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Seismic risk assessment of new and existing structures has a long history, which is based on the development of specific methods for seismic hazard and vulnerability analyses. Performance‐based earthquake engineering is the natural evolution and integration of all these aspects that is nowadays implemented in several precodes. However, some critical aspects are not yet completely clarified. As a matter of fact, the vulnerability analysis suffers the so‐called record‐to‐record variability, which is usually controlled through different parameters used as seismic intensity measures (IMs), whose significance is still under discussion. Moreover, this variability represents an additional source of uncertainty, which is added to the dispersion derived from the ground motion prediction equation (GMPE), leading to an overconservatism. For these reasons, the present paper aims to propose a novel framework for the seismic risk assessment of structures, which is found on the idea to control the response variability in evaluating seismic hazard curves without taking into account the randomness (ε) of the GMPE, which instead is transferred to fragility curves. These latter are built by using groups of accelerograms, whose median and 84% fractile spectra fit well, for different return periods, the uniform hazard spectra for ε= 0 and 1, respectively. For this purpose, a new search algorithm for selecting natural records is formulated. The proposed method offers considerable advantages as it is no longer necessary to refer to a specific IM, and allows to select pairs of spectrum‐compatible natural records, which is enable to solve the problem of the seismic assessment of three‐dimensional structures. The procedure is then applied to assess seismic risk of a typical reinforced concrete frame, whose results demonstrate the robustness of the method and the practical independence from the record set used.

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Seismic vulnerability of above-ground storage tanks with unanchored support conditions for Na-tech risks based on Gaussian process regression

Cited by 24 publications

References 37 publications

Gaussian Process Regression-Based Structural Response Model and Its Application to Regional Damage Assessment

Gaussian Process Regression-Based Structural Response Model and Its Application to Regional Damage Assessment

Kriging Metamodel-Based Seismic Fragility Analysis of Single-Bent Reinforced Concrete Highway Bridges

A novel framework for seismic risk assessment of structures

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