Withee, J. (2016). FHWA Highway Materials Engineering Course, Federal Highway Administration, Washington, DC.
 Mora, C. F., Kwan, A. K. H., Chan, H. C. (1998). Particle size distribution analysis of coarse aggregate using digital image processing. Cement and Concrete Research, 28(6), 921-932.
 Han, J., Wang, K., Wang, X., Monteiro, P. J. (2016). 2D image analysis method for evaluating coarse aggregate characteristic and distribution in concrete. Construction and Building Materials, 127, 30-42.
 Mouret, M., Ringot, E., & Bascoul, A. (2001). Image analysis: a tool for the characterisation of hydration of cement in concrete–metrological aspects of magnification on measurement. Cement and Concrete Composites, 23(2-3), 201-206.
 Baddeley, A., Jensen, E. B. V. (2004). Stereology for statisticians. CRC Press.
 Ozen, M., Guler, M. (2014). Assessment of optimum threshold and particle shape parameter for the image analysis of aggregate size distribution of concrete sections. Optics and Lasers in Engineering, 53, 122-132.
 Yang, J., Yu, W., Fang, H. Y., Huang, X. Y., & Chen, S. J. (2018). Detection of size of manufactured sand particles based on digital image processing. PloS one, 13(12).
 Barman, U., & Choudhury, R. D. (2019). Soil texture classification using multi class support vector machine. Information Processing in Agriculture.
 Gui, X., Zheng, X. Y., Song, J. W., & Peng, X. (2011). Automation bridge design and structural optimization. In Applied Mechanics and Materials (Vol. 63, pp. 457-460). Trans Tech Publications Ltd.
 Alqedra, M., Arafa, M., & Ismail, M. (2011). Optimum cost of prestressed and reinforced concrete beams using genetic algorithms. Journal of artificial intelligence, 4(1).
 Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
 Meziane, S., Bahi, L., & Ouadif, L. (2018, November). Automatic Recognition of Pavement Degradation: Case of Rif Chain. In International Congress and Exhibition" Sustainable Civil Infrastructures: Innovative Infrastructure Geotechnology" (pp. 135-144). Springer, Cham.
 de Oliveira Morais, P. A., de Souza, D. M., de Melo Carvalho, M. T., Madari, B. E., & de Oliveira, A. E. (2019). Predicting soil texture using image analysis. Microchemical Journal, 146, 455-463.
 Cortina-Januchs, M. G., Quintanilla-Dominguez, J., Vega-Corona, A., Tarquis, A. M., & Andina, D. (2011). Detection of pore space in CT soil images using artificial neural networks. Biogeosciences, 8(2), 279-288.
 Azizi, A., Gilandeh, Y. A., Mesri-Gundoshmian, T., Saleh-Bigdeli, A. A., & Moghaddam, H. A. (2020). Classification of soil aggregates: A novel approach based on deep learning. Soil and Tillage Research, 199, 104586.
 ASTM D6913-04, “standard test methods for particle size distribution of soils,” American Society for Testing of Materials, Pennsylvania, PA, USA
 Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9).
 Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
 Shu, M. (2019). Deep learning for image classification on very small datasets using transfer learning.
 Alqahtani, A., & Whyte, A. (2016). Estimation of life-cycle costs of buildings: regression vs artificial neural network. Built Environment Project and Asset Management.
 Newman, M. E. (2005). Power laws, Pareto distributions and Zipf's law. Contemporary physics, 46(5), 323-351.
 Sitton, J. D., Zeinali, Y., & Story, B. A. (2017). Rapid soil classification using artificial neural networks for use in constructing compressed earth blocks. Construction and Building Materials, 138, 214-221.
 Srivastava, P., Shukla, A., & Bansal, A. (2021). A comprehensive review on soil classification using deep learning and computer vision techniques. Multimedia Tools and Applications, 1-28.