Spo2frag V1.0: Software for Pushover-Based Derivation of Seismic Fragility Curves

Iervolino, Iunio; Baltzopoulos, Georgios; Vamvatsikos, Dimitrios; Baraschino, Roberto

doi:10.7712/100016.2233.11553

Cited by 9 publications

(3 citation statements)

References 23 publications

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“…For example, a number of models were created using different floor‐to‐floor connector shear stiffness values ( k s‐f‐f = 10, 3500, 10,000, 35,000, 10 5 , 10 6 , 10 10 kN/m), while other parameters were maintained to reference values. After performing a SPO analysis on each model, fragility curves were generated using the SPO2FRAG method 48,49 …”

Section: Analysis Procedures Of the Proposed Methodsmentioning

confidence: 99%

“…After performing a SPO analysis on each model, fragility curves were generated using the SPO2FRAG method. 48,49 Figure 8 depicts the logical tree diagram that was utilized to determine different structure kinds. For simplicity, just the branches that correspond to the sevenstory structure are depicted in Figure 8.…”

Section: Feature Selection: Input Features Affecting the Structural R...mentioning

confidence: 99%

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Predicting capacity models and seismic fragility estimation for precast parking structures based on machine learning techniques

Li,

Zhou

2024

Structural Concrete

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The development of fragility curves is an important step in seismic risk assessment within the scope of performance‐based earthquake engineering. The goal of this work is to use machine learning methods (regression‐based tools) to forecast the large‐span precast parking structural responses and fragility curves. This study proposes five predicting models based on machine learning to evaluate the seismic performance of the large‐span precast parking structures, including: neural networks, genetic algorithm‐based neural networks, support vector machine, decision tree and random forest. A database that includes 453 numerical synthetic results was used to train and test the machine learning models. The seismic performance of large‐span precast parking structures were predicted using the constructed machine learning models. Finally, the sensitivity analysis of input parameters was conducted. From this paper we can conclude that: (1) the genetic optimization‐based neural networks' predicting model has the most accurate predictive ability for seismic fragility estimation and (2) the structural responses and the fragility curves of parking structures are related to the differences of the stiffness of the connectors and the number of floors, of which the stiffness of the connectors should be given special attention.

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Section: Analysis Procedures Of the Proposed Methodsmentioning

confidence: 99%

Section: Feature Selection: Input Features Affecting the Structural R...mentioning

confidence: 99%

Predicting capacity models and seismic fragility estimation for precast parking structures based on machine learning techniques

Li,

Zhou

2024

Structural Concrete

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“…Compare the Persian-Fasa-curve with the lognormal probability distribution fitted on the seismic fragility data computed with the help of the SPO2FRAG software by (Baltzopoulos et al, 2017). Baltzopoulos et al (2017), made use of the SPO2FRAG software by (Iervolino et al, 2016), to determine the required data for construction of the fragility curve for a five story reinforced concrete building, via more than 500,000 Incremental nonlinear Dynamic Analyses (IDA). Their data are used to select key points and the control parameters in Equation (22).…”

Section: Examplementioning

confidence: 99%

Building Probability Functions by Persian Curve

Ranjbaran¹,

Ranjbaran²,

Ranjbaran³

2020

International Journal of Structural Glass and Advanced Material

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Construction of a probability function for a given random data is the main theme of several branches of human knowledge, where it is an active area of study. In spite of great developments, the solution needs extensive effort and the results contain epistemic uncertainty. In this study, making use of the logical reasoning and concise mathematical logics, a method called the change of state philosophy, which is digested in the Persian curves, is derived. The Persian curves that have the necessary and sufficient condition for a probability function, are explicitly derived, via the ordinates of four specific points on the random data. The proposed Persian curves are free of epistemic uncertainty and their flexibility provide the possibility for the insertion of the expert's will. The work is validated via concise logical formulation and comparison of the results with those of the others.

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SPO2FRAG: software for seismic fragility assessment based on static pushover

Baltzopoulos

Baraschino

Iervolino

et al. 2017

Bull Earthquake Eng

102

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Spo2frag V1.0: Software for Pushover-Based Derivation of Seismic Fragility Curves

Abstract: Abstract. This article presents SPO2FRAG V1.0, the first (beta) version of the Static

Cited by 9 publications

References 23 publications

Predicting capacity models and seismic fragility estimation for precast parking structures based on machine learning techniques

Predicting capacity models and seismic fragility estimation for precast parking structures based on machine learning techniques

Building Probability Functions by Persian Curve

SPO2FRAG: software for seismic fragility assessment based on static pushover

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