Determination of Elongated Aggregates through Computer Vision-Based Technique
Publication: Geo-Congress 2024
ABSTRACT
The traditional measurement technique for finding elongated aggregates involves sieve analysis followed by passing each particle through an elongation gauge. In this study, machine learning has been used as a computer vision-based methodology to determine the elongation index of coarse aggregate particles. Images of aggregates were captured, and an algorithm was developed for contour detection of each particle. The particle size and the number of elongated particles were then computed in an automated form. The laboratory test results were compared with the results obtained from a machine learning-based algorithm. The results so obtained on the segregation of particles in various sizes give significant accuracy with a deviation of ±1 number of particles. The method so developed can be used by researchers and field engineers for the determination of the physical parameters of aggregates.
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Information & Authors
Information
Published In
Geo-Congress 2024
Pages: 298 - 306
History
Published online: Feb 22, 2024
ASCE Technical Topics:
- Aggregates
- Algorithms
- Artificial intelligence and machine learning
- Computer programming
- Computer vision and image processing
- Computing in civil engineering
- Earth materials
- Engineering fundamentals
- Engineering materials (by type)
- Geomaterials
- Geotechnical engineering
- Infrastructure
- Laboratory tests
- Materials engineering
- Mathematics
- Methodology (by type)
- Particle size distribution
- Particles
- Pavements
- Tests (by type)
- Transportation engineering
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