IJIREM

Abstract

In the present time, machine learning algorithms are used for design concreate strength prediction models due to numerous advantages over the manual methods such as cost and time effective and more effectively predict the strength based on the historical information. Therefore, the main aim of this paper, is to study and analyse the various machine learning algorithms are used to design concrete strength prediction model. In the literature, last five years paper is taken under consideration. Based on literature survey, open research gaps are defined which helps the other authors to contribute their research in this area.

Keywords

Concrete Strength Prediction, Machine Learning Algorithms.

Reference

Muliauwan, H.N., Prayogo, D., Gaby, G. and Harsono, K., 2020, September. Prediction of concrete compressive strength using artificial intelligence methods. In Journal of Physics: Conference Series (Vol. 1625, No. 1, p. 012018). IOP Publishing.
Qi, C., Huang, B., Wu, M., Wang, K., Yang, S. and Li, G., 2022. Concrete Strength Prediction Using Different Machine Learning Processes: Effect of Slag, Fly Ash and Superplasticizer. Materials, 15(15), p.5369.
BKA, M.A.R., Ngamkhanong, C., Wu, Y. and Kaewunruen, S., 2021. Recycled aggregates concrete compressive strength prediction using artificial neural networks (ANNs). Infrastructures, 6(2), p.17.
Naderpour, H., Rafiean, A.H. and Fakharian, P., 2018. Compressive strength prediction of environmentally friendly concrete using artificial neural networks. Journal of Building Engineering, 16, pp.213-219.
Chen, S.Z., Zhang, S.Y., Han, W.S. and Wu, G., 2021. Ensemble learning based approach for FRP-concrete bond strength prediction. Construction and Building Materials, 302, p.124230.
Salami, B.A., Olayiwola, T., Oyehan, T.A. and Raji, I.A., 2021. Data-driven model for ternary-blend concrete compressive strength prediction using machine learning approach. Construction and Building Materials, 301, p.124152.
Ahmad, A., Ahmad, W., Aslam, F. and Joyklad, P., 2022. Compressive strength prediction of fly ash-based geopolymer concrete via advanced machine learning techniques. Case Studies in Construction Materials, 16, p.e00840.
Barkhordari, M.S., Armaghani, D.J., Mohammed, A.S. and Ulrikh, D.V., 2022. Data-Driven Compressive Strength Prediction of Fly Ash Concrete Using Ensemble Learner Algorithms. Buildings, 12(2), p.132.
Ahmad, M., Hu, J.L., Ahmad, F., Tang, X.W., Amjad, M., Iqbal, M.J., Asim, M. and Farooq, A., 2021. Supervised learning methods for modeling concrete compressive strength prediction at high temperature. Materials, 14(8), p.1983.
Latif, S.D., 2021. Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment. Environmental Science and Pollution Research, 28(23), pp.30294-30302.
Sharafati, A., Naderpour, H., Salih, S.Q., Onyari, E. and Yaseen, Z.M., 2021. Simulation of foamed concrete compressive strength prediction using adaptive neuro-fuzzy inference system optimized by nature-inspired algorithms. Frontiers of Structural and Civil Engineering, 15(1), pp.61-79.
Rajakarunakaran, S.A., Lourdu, A.R., Muthusamy, S., Panchal, H., Alrubaie, A.J., Jaber, M.M., Ali, M.H., Tlili, I., Maseleno, A., Majdi, A. and Ali, S.H.M., 2022. Prediction of strength and analysis in self-compacting concrete using machine learning based regression techniques. Advances in Engineering Software, 173, p.103267.

Cites this article as

[Er. Tushant Seth, Dr. Sandeep Singla (2023), A Review on Concrete Strength Prediction Models Based on Machine Learning Algorithms, International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-10, Issue-1, Page No-55-58], (ISSN 2347 - 5552). www.ijirem.org