Structural engineering from an inverse problems perspective

TL;DR

This paper explores how inverse problems are fundamental to various structural engineering sub-fields and discusses their potential to advance through methodologies from the inverse problems community, impacting education and machine learning.

Contribution

It highlights the significance of inverse problems in structural engineering and advocates for integrating inverse problem methodologies into education and machine learning advancements.

Findings

Many structural engineering tasks can be viewed as inverse problems.

Inverse problem techniques can enhance structural health monitoring.

Future developments depend on education and machine learning integration.

Abstract

The field of structural engineering is vast, spanning areas from the design of new infrastructure to the assessment of existing infrastructure. From the onset, traditional entry-level university courses teach students to analyse structural response given data including external forces, geometry, member sizes, restraint, etc. -- characterising a forward problem (structural causalities $\to$ structural response). Shortly thereafter, junior engineers are introduced to structural design where they aim to, for example, select an appropriate structural form for members based on design criteria, which is the inverse of what they previously learned. Similar inverse realisations also hold true in structural health monitoring and a number of structural engineering sub-fields (response $\to$ structural causalities). In this light, we aim to demonstrate that many structural engineering sub-fields may be fundamentally or partially viewed as inverse problems and thus benefit via the rich and established methodologies from the inverse problems community. To this end, we conclude that the future of inverse problems in structural engineering is inexorably linked to engineering education and machine learning developments.