Analysis the Structural Propagations under Stochastic Variables with Arbitrary Probability Distributions Using Machine Learning

Abstract

Analysing the structural propagations under stochastic variables with arbitrary probability distributions is a complex task due to the inherent uncertainties associated with such variables. Traditional analytical methods often rely on simplifying assumptions, limiting their applicability to scenarios with specific probability distributions. In this study, we propose a novel approach that leverages machine learning techniques to analyze and predict the structural propagations under stochastic variables with arbitrary probability distributions. The main objective of this research is to develop a machine learning-based framework capable of capturing the complex relationships between the stochastic variables and the resulting structural responses. To achieve this, a comprehensive dataset comprising input stochastic variables and corresponding structural responses is collected and pre-processed. Various machine learning algorithms, such as neural networks, random forests, and support vector machines, are trained on the dataset to learn the underlying patterns and correlations.

The trained models are then used to predict the structural propagations for new sets of stochastic variables, allowing for efficient and accurate analysis without the need for exhaustive analytical calculations. The use of machine learning enables the consideration of a wide range of probability distributions, ensuring a more realistic and comprehensive understanding of the structural behaviour under uncertainties. The proposed methodology offers several advantages over traditional analytical approaches. Firstly, it eliminates the need for restrictive assumptions about the probability distributions, enabling a more flexible analysis that can accommodate real-world scenarios. Additionally, the machine learning-based framework allows for efficient analysis by leveraging the computational power of modern algorithms, reducing the time and effort required for complex calculations.

The practical implications of this research are significant in fields such as civil engineering, aerospace, and materials science, where understanding and predicting structural behaviours under uncertainties are crucial. The ability to analyze structural propagations under stochastic variables with arbitrary probability distributions using machine learning opens up new possibilities for design optimization, risk assessment, and decision-making processes.

This study presents a novel approach that utilizes machine learning to analyze and predict the structural propagations under stochastic variables with arbitrary probability distributions. The developed framework offers a flexible and efficient solution for understanding the complex relationships between stochastic variables and structural responses. The findings of this research have significant implications for various industries and pave the way for further advancements in analyzing and managing uncertainties in structural engineering.