Prediction of Compressive Strength of Calcined Clay Based Cement Mortars Using Support Vector Machine and Artificial Neural Network Techniques

• DOI: 10.29187/jscmt.2020.43
• Publication Date: 2020-04-30T08:35:25Z
• Authors: Joseph M Marangu
• Keywords: cement; Building construction; 0211 other engineering and technologies; 02 engineering and technology; İnşaat Mühendisliği; compressive strength; Civil Engineering; pozzolana; TA401-492; support vector machine; Artificial Neural Network;Blended Calcined Clay;Cement;Compressive Strength;Pozzolana;Support Vector Machine; blended calcined clay; Materials of engineering and construction. Mechanics of materials; artificial neural network; TH1-9745

Prediction of strength in cement based materials is vital construction industry as it forms the basis upon which important tasks can be performed such time for mortar form removal, project scheduling and quality control among others.The paper reports both experimental simulated findings on compressive laboratory prepared blended mortars.The was by blending calcined clays with Ordinary Portland Cement (OPC) at replacement levels ranging from 35% to 50% mass OPC.In approach, mortars measuring 160 mm x 40 were cast using a water (w/c) ratio 0.40, 0.50 0.60 separately cured 2, 7 28 days set-up.Compressive measurements taken each curing ages.Part data obtained used train Support Vector Machine (SVM) Artificial Neural Network (ANN) models while other validation models.The trained predict day strengths w/c 0.35 0.65 also greater than less 35 %.The results showed that training testing, ANN exhibited stronger potential predicting SVM.In addition, further found give more accurate prediction SVM all percentage 0-60 % clays.In conclusion, testing performance 0.88 0.95 respectively.