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Keywords: features selection
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Proceedings Papers

A Novel Machine Learning Based Framework for the Seismic Risk Assessment of Industrial Plant

Gianluca Quinci, Fabrizio Paolacci
Proc. ASME. PVP2023, Volume 7: Seismic Engineering; ASME Nondestructive Evaluation, Diagnosis and Prognosis (NDPD) Division, V007T08A010, July 16–21, 2023
Publisher: American Society of Mechanical Engineers
Paper No: PVP2023-106452
https://doi.org/10.1115/PVP2023-106452
... in the same framework. In this respect, a comprehensive sensitivity analysis of the ANN input parameters is performed (features selection), identifying the type and number of Intensity Measures (IMs) that represent the seismic input best related to the structural output, using Principal Component Analysis...
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Topics: Artificial neural networks, Cities, Industrial plants, Machine learning, Principal component analysis, Seismic hazard assessment, Risk assessment, Finite element model, Hazards, Algorithms
Proceedings Papers

On the Use of Artificial Neural Network Technique for Seismic Fragility Analysis of a Three-Dimensional Industrial Frame

Gianluca Quinci, Nam Hoang Phan, Fabrizio Paolacci
Proc. ASME. PVP2022, Volume 5: Operations, Applications, and Components; Seismic Engineering; ASME Nondestructive Evaluation, Diagnosis and Prognosis (NDPD) Division, V005T08A013, July 17–22, 2022
Publisher: American Society of Mechanical Engineers
Paper No: PVP2022-83874
https://doi.org/10.1115/PVP2022-83874
... the probability function. This methodology is then applied to estimate the probability of failure of a non-structural component (NSC), i.e., vertical tank, located on a typical three-dimensional industrial frame. First, an extensive sensitivity analysis on the ANN input parameters is performed (feature selection...
View Paper titled, On the Use of Artificial Neural Network Technique for Seismic Fragility Analysis of a Three-Dimensional Industrial Frame
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Topics: Artificial neural networks, Simulation