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Volume 13
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10.3390/buildings13010059
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Article

Gaussian Process Regression for Seismic Fragility Assessment: Application to Non-Engineered Residential Buildings in Indonesia

by
Prasanti Widyasih Sarli
1,*,
Pramudita Satria Palar
2,
Yuni Azhari
1,
Andri Setiawan
3,
Yongky Sanjaya
1,
Sophia C. Sharon
1 and
Iswandi Imran
1
1
Faculty of Civil and Environmental Engineering, Institut Teknologi Bandung, Bandung 40132, Indonesia
2
Faculty of Mechanical and Aerospace Engineering, Institut Teknologi Bandung, Bandung 40132, Indonesia
3
Structural Concrete Laboratory (iBETON), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
*
Author to whom correspondence should be addressed.
Buildings 2023, 13(1), 59; https://doi.org/10.3390/buildings13010059
Submission received: 4 November 2022 / Revised: 14 December 2022 / Accepted: 21 December 2022 / Published: 27 December 2022
(This article belongs to the Section Building Structures)
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Abstract

Indonesia is located in a high-seismic-risk region with a significant number of non-engineered houses, which typically have a higher risk during earthquakes. Due to the wide variety of differences even among parameters within one building typology, it is difficult to capture the total risk of the population, as the typical structural engineering approach to understanding fragility involves tedious numerical modeling of individual buildings—which is computationally costly for a large population of buildings. This study uses a statistical learning technique based on Gaussian Process Regression (GPR) to build the family of fragility curves. The current research takes the column height and side length as the input variables, in which a linear analysis is used to calculate the failure probability. The GPR is then utilized to predict the fragility curve and the probability of collapse, given the data evaluated at the finite set of experimental design. The result shows that GPR can predict the fragility curve and the probability of collapse well, efficiently allowing rapid estimation of the population fragility curve and an individual prediction for a single building configuration. Most importantly, GPR also provides the uncertainty band associated with the prediction of the fragility curve, which is crucial information for real-world analysis.
Keywords: fragility curve; non-engineered buildings; Gaussian Process Regression fragility curve; non-engineered buildings; Gaussian Process Regression

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MDPI and ACS Style

Sarli, P.W.; Palar, P.S.; Azhari, Y.; Setiawan, A.; Sanjaya, Y.; Sharon, S.C.; Imran, I. Gaussian Process Regression for Seismic Fragility Assessment: Application to Non-Engineered Residential Buildings in Indonesia. Buildings 2023, 13, 59. https://doi.org/10.3390/buildings13010059

AMA Style

Sarli PW, Palar PS, Azhari Y, Setiawan A, Sanjaya Y, Sharon SC, Imran I. Gaussian Process Regression for Seismic Fragility Assessment: Application to Non-Engineered Residential Buildings in Indonesia. Buildings. 2023; 13(1):59. https://doi.org/10.3390/buildings13010059

Chicago/Turabian Style

Sarli, Prasanti Widyasih, Pramudita Satria Palar, Yuni Azhari, Andri Setiawan, Yongky Sanjaya, Sophia C. Sharon, and Iswandi Imran. 2023. "Gaussian Process Regression for Seismic Fragility Assessment: Application to Non-Engineered Residential Buildings in Indonesia" Buildings 13, no. 1: 59. https://doi.org/10.3390/buildings13010059

APA Style

Sarli, P. W., Palar, P. S., Azhari, Y., Setiawan, A., Sanjaya, Y., Sharon, S. C., & Imran, I. (2023). Gaussian Process Regression for Seismic Fragility Assessment: Application to Non-Engineered Residential Buildings in Indonesia. Buildings, 13(1), 59. https://doi.org/10.3390/buildings13010059

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