Annals of Computer Science and Information Systems, Volume 23

DOI: http://dx.doi.org/10.15439/2020F170

Citation: Communication Papers of the 2020 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 23, pages 7–12 (2020)

Full text

Abstract. In this paper, we propose an iterative descent method to predict compressive cement strength estimated parameters for lime and cement as coating substances. We first propose a formal description of the problem by using a mathematical model that is based upon a series of equations. The aforementioned equations are related to both the ratio of quantity of water to quantity of coating substance and the ratio of quantity of straw to quantity of coating substance. Second, we propose to solve the model by applying a gradient descent method. It is applied for reaching results closest to the data gathered from a real experimental studies conducted on the coating of flax straw before incorporating them into a cement matrix. The experimental part shows that the proposed model is capable to predict The the estimation of the parameters necessary for such study.

References

1. Snell, L. M., Van Roekel, J.,& Wallace, N. D. (1989). Predicting early concrete strength. Concrete International, 11(12), 43-47.
2. de Siqueira Tango, C. E. (1998). An extrapolation method for compressive strength prediction of hydraulic cement products. Cement and concrete research, 28(7), 969-983.
3. Baykasoğlu, A., Dereli, T., & Tanış, S. (2004). Prediction of cement strength using soft computing techniques. Cement and concrete research, 34(11), 2083-2090.
4. Baykasoglu, A., Oztas, A., & Ozbay, E. (2009). Prediction and multi-objective optimization of high-strength concrete parameters via soft computing approaches. Expert Systems with Applications, 36(3), 6145-6155.
5. Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv preprint https://arxiv.org/abs/1609.04747.
6. Bengio, Y., Boulanger-Lewandowski, N., & Pascanu, R. (2013, May). Advances in optimizing recurrent networks. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 8624-8628). IEEE.
7. Ng, A. (2018). Machine Learning. Stanford University. línea. Available: https://www.coursera.org/learn/machine-learning. [Último acceso: 14 apr 2017].