A Look into How Machine Learning is Reshaping Engineering Models: the Rise of Analysis Paralysis, Optimal yet Infeasible Solutions, and the Inevitable Rashomon Paradox

TL;DR

This paper explores how machine learning is transforming engineering models, highlighting philosophical tensions, paradoxes like analysis paralysis and the Rashomon effect, and advocating for epistemological shifts in engineering education and practice.

Contribution

It introduces a framework for integrating ML into engineering through deduction, induction, and abduction, and identifies key paradoxes arising from ML adoption in engineering.

Findings

ML improves predictive accuracy and design optimization in engineering.

Adoption of ML leads to analysis paralysis and challenges in interpretability.

The paper advocates rethinking engineering epistemology and education to integrate ML effectively.

Abstract

The widespread acceptance of empirically derived codal provisions and equations in civil engineering stands in stark contrast to the skepticism facing machine learning (ML) models, despite their shared statistical foundations. This paper examines this philosophical tension through the lens of structural engineering and explores how integrating ML challenges traditional engineering philosophies and professional identities. Recent efforts have documented how ML enhances predictive accuracy, optimizes designs, and analyzes complex behaviors. However, one might also raise concerns about the diminishing role of human intuition and the interpretability of algorithms. To showcase this rarely explored front, this paper presents how ML can be successfully integrated into various engineering problems by means of formulation via deduction, induction, and abduction. Then, this paper identifies three principal paradoxes that could arise when adopting ML: analysis paralysis (increased prediction accuracy leading to a reduced understanding of physical mechanisms), infeasible solutions (optimization resulting in unconventional designs that challenge engineering intuition), and the Rashomon effect (where contradictions in explainability methods and physics arise). This paper concludes by addressing these paradoxes and arguing the need to rethink epistemological shifts in engineering and engineering education and methodologies to harmonize traditional principles with ML.