Search

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. [ABSTRACT FROM AUTHOR]

[Display omitted] • Explainable machine learning is used to obtain important design insight of hexachiral structure. • The data were generated via finite element method to build the predictive model based on Gaussian Process Regression. • Quasi-static compression tests were performed to validate the finite element results. • The results show that the strut thickness is the most deciding parameter for Poisson's ratio. • The lowest Poisson's ratio value is obtained by the design with minimum strut thickness, minimum node radius, and maximum strut length. A design exploration of hexachiral structures using explainable machine learning (ML) is performed in this work. The hexachiral structures are fabricated using resin via vat photopolymerization (VPP). The ML model is used to build the function that explains the association between Poisson's ratio and the hexachiral design parameters. The data set for ML model construction is first collected by using the Halton sequence and simulated using the finite element method (FEM). To validate the data set, the results obtained from the FEM simulation are compared with those obtained from the compression test. The Gaussian Process Regression (GPR) models for Poisson's ratio and porosity are constructed to extract important design insight. A Global Sensitivity Analysis (GSA) and Shapley Additive Explanations (SHAP) are used to analyze the sensitivity of the porosity and Poisson's ratio to the hexachiral design parameters. GSA result shows that the strut's thickness is the most decisive parameter that affects the Poisson's ratio. The application of SHAP also reveals that the relationship between the strut thickness and Poisson's ratio is nonlinear. Finally, the minimum Poisson's ratio value is achieved by design with minimum strut thickness, minimum node radius, and maximum strut length. [ABSTRACT FROM AUTHOR]

This paper discusses the experimental optimization of an artificial caudal fin to maximize thrust production. A combination of direct experimental measurement and Gaussian Process modeling was applied to optimize the time-averaged net thrust force produced by the fin panel. Specifically, this paper investigates the effect of the fin panel's stiffness and flapping frequency on the thrust force. The Gaussian Process regression is primarily used to model the relationship between the flapping frequency and thrust force, with variations in the fin panel's stiffness. Results show that the optimum flapping frequency that yields maximum force tends to decrease as the stiffness decreases. Furthermore, increasing the lateral amplitude displacement of the fin base leads to a higher time-averaged net thrust force. Further investigation shows that the phase difference between the trailing and leading edge of the oscillating fin should be about 90°to yield optimum time-averaged net thrust force. • Optimization of a flexible caudal fin for maximizing thrust is conducted. • The design variables are the stiffness of the fin and the flapping frequency. • The observed relationship is nonlinear, implying the importance of optimization. • The frequency that yields maximum force tends to decrease as the stiffness decreases. • The phase difference should be about 90°to maximize the net-thrust force. [ABSTRACT FROM AUTHOR]