# Image-Based Structural Analysis Using Computer Vision and LLMs: PhotoBeamSolver

arXiv: https://arxiv.org/abs/2603.21432 · HTML: https://mlsanity.com/paper/2603.21432/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 22 Mar 2026.

## Builds on (most cited first, 4 of 4)

- [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https://mlsanity.com/paper/1910.09457/) (2019)
- [A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://mlsanity.com/paper/2107.07511/) (2021)
- [Learning to simulate and design for structural engineering](https://mlsanity.com/paper/2003.09103/) (2020)
- [Automated Bridge Component Recognition using Video Data](https://mlsanity.com/paper/1806.06820/) (2018)

## Built on by (most cited first, 1 of 1)

- [BlueprintAgent: Constraint-Triggered Targeted Revisits for Simulation-Ready Generation from Scanned Structural Blueprints](https://mlsanity.com/paper/2609.07362/) (2026)

## Abstract (arXiv)

This paper presents the development of a documented program capable of solving idealized beam models, such as those commonly used in textbooks and academic exercises, from drawings made by a person. The system is based on computer vision and statistical learning techniques for the detection and visual interpretation of structural elements. Likewise, the main challenges and limitations associated with the integration of computer vision into structural analysis are analyzed, as well as the requirements necessary for its reliable application in the field of civil engineering. In this context, the implementation of the PhotoBeamSolver program is explored, and the current state of computer vision in civil engineering is discussed, particularly in relation to structural analysis, infrastructure inspection, and engineering decision-support systems.