Comparison of Machine Learning Approaches with Traditional Methods for Predicting the Compressive Strength of Rice Husk Ash Concrete

• Published in: Crystals (Basel) Vol. 11; no. 7; p. 779
• Main Authors: Amin, Muhammad Nasir, Iqtidar, Ammar, Khan, Kaffayatullah, Javed, Muhammad Faisal, Shalabi, Faisal I., Qadir, Muhammad Ghulam
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-07-2021
• Subjects: ANFIS modeling; ANN modeling; Artificial intelligence; Artificial neural networks; Ashes; Back propagation; Cement; Compressive strength; concrete; Concrete mixing; Construction industry; Fuzzy logic; Gene expression; Machine learning; Mathematical models; Neural networks; Regression analysis; rice husk ash; Superplasticizers; Waste materials
• ISSN: 2073-4352; 2073-4352
• DOI: 10.3390/cryst11070779

Efforts are being devoted to reducing the harmful effect of the construction industry around the globe, including the use of rice husk ash as a partial replacement of cement. However, no method is available to date to predict the compressive strength (CS) of rice husk ash blended concrete (RHAC). In this study, advanced machine learning techniques (artificial neural network, artificial neuro-fuzzy inference system) were used to predict the CS of RHAC. Based on the published literature, six inputs, i.e., age of specimen, percentage of rice husk ash, percentage of superplasticizer, aggregates, water, and amount of cement, were selected. Results obtained from machine learning methods were compared with traditional methods such as linear and non-linear regressions. It was observed that the performance of machine learning methods was superior to traditional methods for determining the CS of RHAC. This study will prove beneficial in minimizing the cost and time of executing laboratory experiments for designing the optimum content portions of RHAC.