Shapley values and machine learning to characterize metamaterials for seismic applications

TL;DR

This paper introduces a machine learning approach combined with Shapley values to efficiently analyze and predict the seismic wave attenuation properties of metamaterials-based barriers, aiding seismic isolation design.

Contribution

It develops a novel method using Shapley values and machine learning to quantify parameter influence on metamaterial band-gaps, reducing reliance on costly traditional analysis.

Findings

Shapley values effectively identify key parameters affecting band-gaps.

Machine learning models accurately predict seismic wave attenuation characteristics.

The approach offers a computationally efficient alternative to traditional methods.

Abstract

Given the damages from earthquakes, seismic isolation of critical infrastructure is vital to mitigate losses due to seismic events. A promising approach for seismic isolation systems is metamaterials-based wave barriers. Metamaterials -- engineered composites -- manipulate the propagation and attenuation of seismic waves. Borrowing ideas from phononic and sonic crystals, the central goal of a metamaterials-based wave barrier is to create band gaps that cover the frequencies of seismic waves. The two quantities of interest (QoIs) that characterize band-gaps are the first-frequency cutoff and the band-gap's width. Researchers often use analytical (band-gap analysis), experimental (shake table tests), and statistical (global variance) approaches to tailor the QoIs. However, these approaches are expensive and compute-intensive. So, a pressing need exists for alternative easy-to-use methods to quantify the correlation between input (design) parameters and QoIs. To quantify such a correlation, in this paper, we will use Shapley values, a technique from the cooperative game theory. In addition, we will develop machine learning models that can predict the QoIs for a given set of input (material and geometrical) parameters.