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

Altamirano-Muñiz Emilio Fernando

cs.CVMar 22, 2026

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

Authors: Altamirano-Muñiz Emilio Fernando

Abstract

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.

Figures & tables

Panel of Figure 1 — Figure 1: On the left, structural component recognition is applied on a bridge [ 4 ] . In the center, the detection of an excavator arm is performed [ 8 ] . On the right, detection of workers and machinery in situ. [ 7 ]

Panel of Figure 2 — Figure 2: Object detection applied to supports of a theoretical beam (left) and an example of PhotoBeamSolver using YOLOv5.(right)

Panel of Figure 3 — Figure 3: Examples of training datasets

Figure 5: Conceptualization of a computer vision based structural analysis.

Epoch	Precision	mAP	Error
			pload	dload	fixed	roller	simple	moment
1	0.012942	0.0089532	0.073366	0.0083674	0.036995	0.00920414	0.03551520	0.036138
2	0.66248	0.022613	0.068911	0.0088902	0.039631	0.00977922	0.03804576	0.038322
3	0.56978	0.017702	0.063276	0.0078639	0.033436	0.00865029	0.03209856	0.032504
4	0.67439	0.059428	0.057429	0.0081983	0.029804	0.00901813	0.02861184	0.028587
5	0.63468	0.12734	0.056769	0.0082484	0.024758	0.00907324	0.02376768	0.024040

Table 1: Training results of the neural network using a dataset of 532 images

Panel of Figure 7 — Figure 7: Bending moment diagram displayed in PBS. Units [kg·cm] (left), shear force diagram displayed in PBS. Units [kg] (center) and deflection diagram displayed in PBS. Units [mm] (right)

Structural Engineering

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Aug 18, 2026J/K move · Enter open · S save

Evaluation of AI-based Visual Crack Detection in Steel Bridges Using Probability of Detection

Andrii Kompanets, Finn Michael Sherry, Remco Duits, Davide Leonetti+1

Eindhoven University of Technology, Department of the Built Environment, Eindhoven, The Netherlands · Eindhoven Artificial Intelligence Systems Institute, Eindhoven University of Technology, Eindhoven, The Netherlands · Eindhoven University of Technology, Department of Mathematics and Computer Science, Eindhoven, The Netherlands

Fault Detection Structural Health Monitoring Automated Evaluation

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Bridge structures are regularly inspected for structural damage such as cracks and corrosion in order to ensure public safety and reduce maintenance costs. Much research has been done on automating this process using computer vision methods, which are often evaluated and compared using metrics such as intersection over union, mean average precision, etc. However, predicting the actual effectiveness of an inspection method within the field of structural engineering from these metrics remains challenging. To enable the systematic use of these increasingly popular methods in engineering practice, evaluating the performance of these methods in a way that is compatible with standard engineering approaches is therefore an urgent necessity. We present a new statistical evaluation framework to allow the comparison of computer vision methods with conventional visual inspection for crack detection in steel bridges. The framework is based on probability of detection curves and can account for the influence of image resolution. We apply this evaluation method to the real-world ``Cracks in Steel Bridges'' dataset, which contains annotated images of cracks in bridge structures. The quantification of the probability of detection and its uncertainty enables a practical assessment of the effect of automated methods for damage detection in structural reliability analyses. In turn, this enables the wide-spread use of automated (AI-based) damage detection in safety critical applications. This evaluation method provides evidence that the proposed computer vision approach approach is robust for the crack detection task and can have a high added value as an addition to conventional visual inspection methods.

Abstract

Structural Engineering

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