2022 | Computer Software | Early stage | Jordan | SDG11 | SDG8 | SDG9
An AI based Classification System for Aggregate using Image Processing And Artificial Neural Network

Company or Institution

Jadara University


Computer Software





Sustainable Development Goals (SDGs)

SDG 8: Decent Work and Economic Growth

SDG 9: Industry, Innovation and Infrastructure

SDG 11: Sustainable Cities and Communities

General description of the AI solution

Aggregate’s shape and surface texture immensely influence the strength and structure of the resulting concrete. Traditionally, mechanical sieving and manual gauging are used to determine both the size and shape of the aggregates. These methods, which are often performed manually, tend to be slow, highly subjective and laborious. Therefore, in this research, an intelligent classification system consisting of the Automatic Features Extraction algorithm and the intelligent Neural Network classification for aggregate recognition will be designed and developed. The system will be capable of automatically capturing the image, extracting the features and classifying the aggregate in less than one second. the system will classify the aggregates into six shapes with an high accuracy.

Github, open data repository, prototype or working demo



1. Isa NAM, Sani ZM, Al-Batah MS. Automated Intelligent real-time system for aggregate classification. International Journal of Mineral Processing. Elsevier BV; 2011 Jul;100(1-2):41–50. Available from: http://dx.doi.org/10.1016/j.minpro.2011.04.009
Journal Website: http://www.journals.elsevier.com/international-journal-of-mineral-processing/

2. Mohammad Subhi Al-Batah, Zabian A., Abdel-wahed M. (2011) Suitable Features Selection for the HMLP Network using Circle Segments Method, European Journal of Scientific Research, ISSN 1450-216X, Vol.67 No.1 (2011), pp. 52-65, EUROJOURNALS, LONDON, ENGLAND.
Journal Website: http://www.europeanjournalofscientificresearch.com

3. Al-Batah MS, Mat Isa NA, Zamli KZ, Sani ZM, Azizli KA. A novel aggregate classification technique using moment invariants and cascaded multilayered perceptron network. International Journal of Mineral Processing. Elsevier BV; 2009 Jul;92(1-2):92–102. Available from: http://dx.doi.org/10.1016/j.minpro.2009.03.004
Journal Website: http://www.journals.elsevier.com/international-journal-of-mineral-processing/ .

4. Isa NAM, Subhi Al-Batah M, Zamli KZ, Azizli KA, Joret A, Noor NRM. Suitable features selection for the HMLP and MLP networks to identify the shape of aggregate. Construction and Building Materials. Elsevier BV; 2008 Mar;22(3):402–10. Available from: http://dx.doi.org/10.1016/j.conbuildmat.2006.08.005
Journal Website: http://www.journals.elsevier.com/construction-and-building-materials/



Mentorship Program


International Research Centre
on Artificial Intelligence (IRCAI)
under the auspices of UNESCO 

Jožef Stefan Institute
Jamova cesta 39
SI-1000 Ljubljana



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