Rockfill materials—the quarried rock masses that form the skeletons of embankment dams, road bases, and other heavy civil engineering structures—have long resisted quick, reliable measurement of their most important properties. Engineers need to know how the individual stones are distributed by size, what shapes they take, and how much void space remains between them, because these geometric characteristics govern compaction quality, deformation behavior, and ultimately the safety of rockfill dams. A team of researchers at Xi’an University of Technology in Shaanxi, China, has now introduced an intelligent recognition method that combines the deep learning segmentation architecture Cellpose with the open-source image analysis platform ImageJ to characterize rockfill materials rapidly, non-invasively, and comprehensively, and the approach has passed both laboratory and field tests at a working pumped-storage power station.
The core of the method is a two-stage pipeline. First, photographs of rockfill material—whether spread on a laboratory bench or compacted in layers on a dam site—are fed into Cellpose, a generalist deep learning algorithm originally developed for segmenting individual cells in microscopy images. Cellpose’s neural network learns to delineate the boundaries of touching, overlapping, irregularly shaped objects, which makes it unusually well suited to the visual chaos of a pile of quarried rock. The algorithm produces a high-precision segmentation mask in which every individual particle is separated from its neighbors, solving the classic bottleneck that has limited automated rock image analysis: stones that touch or partially overlap are notoriously difficult for conventional thresholding techniques to separate.
Once Cellpose has produced the segmented image, the researchers turn to the MorphoLibJ plugin of ImageJ, a mature mathematical morphology library, for post-processing and quantitative computation. From each segmented particle, the software extracts geometric features including area, particle size, and aspect ratio. Aggregated over the hundreds or thousands of stones visible in a single image, these features yield the particle size distribution curve—one of the most fundamental descriptors of any granular construction material—without the sieving, weighing, and labor that traditional gradation testing demands. The team reports that the ImageJ-Cellpose method could accurately determine both the particle size distribution curve and the porosity of rockfill materials, results that were validated against conventional screening and porosity experiments conducted in the laboratory.
Beyond reproducing established metrics, the study introduces new shape parameters for rockfill material, including what the authors call the long-short ratio and the area ratio. Shape matters in geotechnical engineering more than casual observers might expect: angular, elongated, and flat particles interlock differently than rounded, equidimensional ones, influencing shear strength, particle breakage under load, and the volumetric behavior of the compacted mass. By statistically analyzing the distribution of these new shape descriptors across particle populations, the researchers offer a quantitative framework for describing rockfill morphology that goes beyond simple size gradation, opening a path toward correlating shape statistics directly with mechanical performance.
The critical question for any laboratory method is whether it survives contact with the real world. To answer it, the team partnered with a pumped-storage power station, where layered rolling experiments were carried out during actual rockfill placement. As the material was compacted by rollers in successive passes, images were collected at each stage and processed through the intelligent recognition pipeline. The results showed that the method could effectively trace how the particle size distribution and porosity of the rockfill evolved with each rolling pass—precisely the kind of real-time feedback that compaction quality control has historically lacked. Encouragingly, the trends identified by the image-based approach aligned well with those derived from the pit measuring method, the traditional but destructive and labor-intensive standard for evaluating compaction in the field.
The significance of this validation lies in what it could change about dam construction practice. Rockfill dams are among the largest man-made structures on Earth, and their long-term safety depends on how well the fill is compacted during construction; poor compaction leads to post-construction settlement, cracking of upstream concrete face slabs, and heightened vulnerability to overtopping failure and seismic loading. Current quality control typically relies on periodic pit sampling, in which crews excavate a hole, weigh the removed material, and measure density—a slow process that samples only a tiny fraction of the dam’s volume. A camera, a laptop running Cellpose and ImageJ, and the proposed analytical workflow could in principle assess compaction indicators continuously and across the entire working surface, converting construction monitoring from intermittent spot checks into comprehensive surveillance.
Technically, the study is notable for transplanting tools from an unexpected domain. Cellpose was created by biologists to solve the problem of segmenting cells in diverse image types without retraining a network for each new dataset, and its generalist design proved transferable to geological imagery. ImageJ, likewise, grew out of biomedical imaging but ships with general-purpose morphology operators that apply equally well to mineral particles. By connecting a state-of-the-art segmentation network to a battle-tested analysis library, the Xi’an team sidestepped the need to build bespoke deep learning infrastructure or large annotated rockfill datasets from scratch, lowering the barrier to adoption for engineering firms and construction supervisors who cannot maintain their own machine learning teams.
The method also complements a growing body of work on computer vision in geotechnical monitoring. Prior studies have used video image recognition to detect rockfill gradation, deep learning segmentation to calculate gradation from photographs, and instance segmentation to predict gradation for engineering projects. What distinguishes the new approach is its end-to-end coverage: rather than estimating only particle sizes, it simultaneously delivers size distribution, individual particle shape metrics through newly proposed parameters, and porosity, all from ordinary images, and it does so in a form validated against both sieving tests in the laboratory and pit measurements on an active construction site. The authors argue that this makes the technique suitable for real-world engineering applications and a robust means of ascertaining particle size information during the construction of rockfill dams.
Looking forward, the research points toward a swift and precise evaluation method for the compaction quality of rockfill material—a capability the authors describe as paramount for the comprehensive understanding and analysis of the safety conditions of rockfill dams and similar projects. If deployed at scale, the ImageJ-Cellpose workflow could give dam engineers a continuous, quantitative picture of how each roller pass is reshaping the granular skeleton of an embankment, catch gradation or compaction anomalies before they are buried under the next lift of rock, and feed high-quality geometric data into the numerical models used to predict settlement, seepage, and seismic response. In an era when aging dam infrastructure faces intensifying climate and seismic stresses, the idea that a deep learning algorithm designed for counting cells can help safeguard structures holding back billions of liters of water is a striking demonstration of how tools migrate across scientific boundaries—and of how much of modern construction safety may soon rest on image recognition pipelines that work quietly, frame by frame, on the rocks beneath the rollers.
Subject of Research: Intelligent image-based geometric characterization of rockfill materials using deep learning segmentation for dam compaction analysis
Article Title: Intelligent geometric characterization of rockfill materials using ImageJ-Cellpose: a novel approach for accurate morphological analysis
Article References: Ma, C., Zhou, C., Hou, Y., & Cheng, L. (2026). Intelligent geometric characterization of rockfill materials using ImageJ-Cellpose: a novel approach for accurate morphological analysis. Cluster Computing, 29(13), Article 779. https://doi.org/10.1007/s10586-026-06480-4
Image Credits: AI Generated
DOI: 10.1007/s10586-026-06480-4
Keywords: rockfill materials, deep learning, Cellpose, ImageJ, image segmentation, particle size distribution, porosity, particle shape, compaction quality, rockfill dams, image recognition, geotechnical engineering
Cite Scienmag News
APA MLA Chicago
Violet Maxwell. (September 23, 2026). AI-Powered Image Analysis Delivers Precise Rockfill Characterization for Dam Safety. Scienmag. https://scienmag.com/ai-powered-image-analysis-delivers-precise-rockfill-characterization-for-dam-safety/
Violet Maxwell. “AI-Powered Image Analysis Delivers Precise Rockfill Characterization for Dam Safety.” Scienmag, 23 September 2026, https://scienmag.com/ai-powered-image-analysis-delivers-precise-rockfill-characterization-for-dam-safety/. Accessed 23 September 2026.
Violet Maxwell. “AI-Powered Image Analysis Delivers Precise Rockfill Characterization for Dam Safety.” Scienmag. September 23, 2026. https://scienmag.com/ai-powered-image-analysis-delivers-precise-rockfill-characterization-for-dam-safety/
Copy citation Download RIS
Tags: AI-based image analysis for dam safetyautomated rock size and shape measurementCellposeCellpose deep learning architecturecompaction qualitydeep learningdeep learning segmentation in civil engineeringgeotechnical engineeringheavy civil engineering material analysisimage recognitionimage segmentationImageJImageJ image analysis platformintelligent recognition of rockpile structureslaboratory and field testing of image analysis methodsnon-invasive rockfill characterizationparticle shapeparticle size distributionporosityremote sensing for dam safety monitoringrockfillrockfill damsrockfill material property assessmentrockfill materials
