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Against the grain: Leveraging machine learning to analyze mudbrick structures
Sofia Kouki1, Marta Lorenzon2,3, Benjamín Cutillas-Victoria4
1Interdisciplinary Center for Archaeology and the Evolution of Human Behavior, University of Algarve, Faro, Portugal.
Plos One
|May 21, 2026
Summary
Automated image analysis quantifies mudbrick fabric, revealing significant differences in grain size and shape between archaeological sites. This computational approach offers a reproducible method for characterizing ancient building materials.
Area of Science:
- Archaeological science
- Materials science
- Computational archaeology
Background:
- Mudbricks are ancient building materials with complex compositional variability.
- Traditional petrographic point counting for mudbrick fabric characterization is time-consuming and subjective.
- Quantitative grain morphometry offers insights into raw material selection and construction techniques.
Purpose of the Study:
- To evaluate automated image analysis for quantitative grain morphometry in archaeological mudbricks.
- To develop a computational workflow for fabric characterization.
- To compare morphometric differences between two distinct archaeological sites.
Main Methods:
- Developed a K-means clustering workflow for automated grain size, sorting, and shape analysis.
- Analyzed 45 cross-polarized light images from Artaxata (Armenia) and Los Villares de la Encarnación (Spain).
- Employed non-parametric statistics, multivariate ordination, and supervised classification for comparative analysis.
Main Results:
- Statistically significant differences in grain size, sorting, and shape were identified between the two sites.
- Multivariate analysis showed clear separation of samples based on morphometric data.
- Automated morphometry achieved high classification accuracy (>84%) and corresponded well with existing petrographic fabrics.
Conclusions:
- Automated grain morphometry effectively captures assemblage-level technological variability in mudbricks.
- The proposed computational workflow provides a foundation for quantitative and comparative studies in earthen architecture.
- This method supports the integration of computational tools into archaeological thin-section petrography for enhanced reproducibility.
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