# Establishing a common data base of ice experiments and using machine learning to understand and predict ice behavior

## TL;DR

This paper creates a shared ice experiment database and applies machine learning to analyze how parameters like temperature affect ice behavior and peak stress.

## Contribution

It introduces a standardized small-scale ice experiment database and demonstrates machine learning's use in understanding ice behavior.

## Key findings

- Parameters like temperature significantly influence peak stress.
- Machine learning models can predict ice behavior based on experimental data.
- A common database facilitates future research in ice mechanics.

## Abstract

Machine learning and statistical tools are applied to identify how parameters, such as temperature, influence peak stress and ice behavior. To enable the analysis, a common and small scale experimental data base is established.

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Source: https://tomesphere.com/paper/1812.03994