Prediction the effects of ZnO2 nanoparticles on splitting tensile strength and water absorption of high strength concrete

Abstract

In the present paper, two models based on artificial neural networks (ANN) and gene expression programming (GEP) for predicting splitting tensile strength and water absorption of concretes containing ZnO 2 nanoparticles at different ages of curing have been developed. To build these models, training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted. The used data in the multilayer feed forward neural networks models and input variables of genetic programming models are arranged in a format of eight input parameters that cover the cement content (C), nanoparticle content (N), aggregate type (AG), water content (W), the amount of superplasticizer (S), the type of curing medium (CM), Age of curing (AC) and number of testing try (NT). According to these input parameters, in the neural networks and genetic programming models, the splitting tensile strength and water absorption values of concretes containing ZnO 2 nanoparticles were predicted. The training and testing results in these two models have shown the strong potential of the models for predicting the splitting tensile strength and water absorption values of concretes containing ZnO 2 nanoparticles. Although neural networks have predicted better results, genetic programming is able to predict reasonable values with a simpler method rather than neural networks.

Multivariate Analysis for Prediction of Splitting Tensile Strength in Concrete Paving Blocks

“…Different regression methods were studied by [20], such as neural networks and gene expression programming, to predict splitting tensile strength and water absorption using predictor variables such as the amount of cement, amount of ZnO 2 nanoparticles, type of aggregate, content of water, amount of superplasticizer, age and cured type, and number of test attempts with various types of concrete including ZnO 2 nanoparticles. The training and validation data are separated to test two models with different design parameters.…”

Scientific applications and prospects of nanomaterials: A multidisciplinary review

“…The development of smart cement materials based on nano-sized materials as additives with desired specific properties that solve or minimize many practical issues in the field has widely been reported in literatures. For example, several types of metal oxide nanoparticles such as nanosilica (Lin et al, 2008;Jo et al, 2007;Qing et al, 2007), TiO 2 Riahi, 2010a, 2011b), Fe 2 O 3 (Li et al, 2004;Khoshakhlagh et al, 2012), Al 2 O 3 Riahi, 2011, 2012), ZrO 3 Riahi, 2010a, 2011b), CuO (Nazari and Riahi, 2011), ZnO 2 (Nazari and Azimzadegan, 2012), and several other types of magnetic nanoparticles (Blyszko et al, 2008) have been used as additives in cement modification. These metal oxide nanoparticles are added mostly to improve Adeola et al 951 several cements and concrete properties such as strength, resistance to water penetration, accelerate hydration reaction, control calcium leaching, to provide self-cleaning properties, and many more.…”

Artificial intelligence in nanotechnology

“…For example, ANN and GA have been used to predict the splitting tensile strength and water absorption values of concretes containing ZnO2 or Cr2O3 nanoparticles. [59,60] The use of these techniques is justified by the large number of parameters that have a strong influence in the properties under study. Some of these parameters are the cement, nanoparticle and water contents, the aggregate type, the amount of superplasticizer, the type of curing medium or the age of curing.…”