ANN, M5P-tree and nonlinear regression approaches with statistical evaluations to predict the compressive strength of cement-based mortar modified with fly ash

Journal of Materials Research and Technology

Warzer Sarwar¹  Serwan Rafiq²  Kawan Ghafor³  Ahmed Mohammed⁴  Parveen Sihag⁴  Rawaz Kurda⁵  Wael Mahmood⁶ 

[1] CERIS, Civil Engineering, Architecture and Georesources Department, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal;Corresponding author.;Scientific Research and Development Center, Nawroz University, Duhok, Kurdistan-Region, Iraq;Civil Engineering Department, College of Engineering, University of Sulaimani, Kurdistan, Iraq;Civil Engineering Department, Shoolini University, Solan, Himachal Pradesh, India;Department of Civil Engineering, Technical Engineering College, Erbil Polytechnic University, Kurdistan Region, Erbil, Iraq;

关键词: Water-to-binder ratio;    Fly ash content;    Compressive strength;    Statistical analysis;    Modelling.;   

DOI  :  

来源: DOAJ

【 摘 要 】

This study aims to establish systematic multiscale models to predict the compressive strength of cement mortar containing a high volume of fly ash (FA) and to be used by the construction industry with no theoretical restrictions. For that purpose, a wide experimental data (a total of 450 tested cement mortar modified with FA) from different academic research studies have been statically analyzed and modeled. For that purpose, Linear and Nonlinear regression, M5P-tree, and Artificial Neural Network (ANN) technical approaches were used for the qualifications. In the modeling process, most relevant parameters affecting the strength of cement mortar, i.e. fly ash (class C and F) incorporation ratio (0−70% of cement's mass), water-to-binder ratio (0.235–1.2), and curing ages (1–365 days). According to the correlation coefficient (R), mean absolute error and the root mean a square error, the compressive strength of cement mortar can be well predicted in terms of w/b, fly ash, and curing time using various simulation techniques. The results of this study suggest that the Non-linear regression-based model (NLR) and ANN are performing better than other applied models using training and testing datasets. The sensitivity investigation concludes that the curing time is the most dominating parameter for the prediction of the compressive strength of cement mortar with this data set.

【 授权许可】

Unknown