Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques

  title={Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques},
  author={P. Awoyera and Mehmet S. Kirgiz and A. Viloria and D. Ovallos-Gazabon},
  journal={Journal of materials research and technology},
Abstract There has been a persistent drive for sustainable development in the concrete industry. While there are series of encouraging experimental research outputs, yet the research field requires a standard framework for the material development. In this study, the strength characteristics of geopolymer self-compacting concrete made by addition of mineral admixtures, have been modelled with both genetic programming (GEP) and the artificial neural networks (ANN) techniques. The study adopts a… Expand
Soft Computing Techniques for the Prediction and Analysis of Compressive Strength of Alkali-Activated Alumino-Silicate Based Strain-Hardening Geopolymer Composites
A futuristic class of concrete that has ductile nature with zeroed cement and eco-friendly materials is popularly known as Engineered Geopolymer Composites (EGC). Research on strain-hardeningExpand
Artificial neural network (ANN) approach to predict unconfined compressive strength (UCS) of oil and gas well cement reinforced with nanoparticles
Abstract The prediction of unconfined compressive strength (UCS) of oil well cement class “H” based on the artificial neural network (ANN) modeling approach is presented in this study. 195 cementExpand
Mathematical prediction of the compressive strength of bacterial concrete using gene expression programming
Abstract The impact of microbial calcium carbonate on concrete strength has been extensively evaluated in the literature. However, there is no predicted equation for the compressive strength ofExpand
Performance assessment of five adsorbents based on fly ash for removal of cadmium ions
Abstract Five adsorbents based on fly ash treated with NaOH for the adsorption of cadmium ion from aqueous solutions were investigated. The materials were characterized using scanning electronExpand
Prediction algorithm for springback of frame-rib parts in rubber forming process by incorporating Sobol within improved grey relation analysis
Abstract A precise springback prediction is of great significance to reduce the manufacturing time and also to improve the forming quality for frame-rib parts. Based on Sobol and improved greyExpand


Models for Predictions of Mechanical Properties of Low-Density Self-compacting Concrete Prepared from Mineral Admixtures and Pumice Stone
This study applies the principle of artificial neural networks for modelling the mechanical characteristics of a lightweight self-compacting concrete containing pumice and mineral admixtures to establish the best model for the tested concrete, based on the minimal error criteria. Expand
Artificial neural networks application to predict the compressive damage of lightweight geopolymer
  • A. Nazari
  • Computer Science, Materials Science
  • Neural Computing and Applications
  • 2012
In this work, compressive strength of lightweight geopolymers produced by fine fly ash and rice husk–bark ash together with palm oil clinker (POC) aggregates has been investigated experimentally andExpand
Model Development for Strength Properties of Laterized Concrete Using Artificial Neural Network Principles
This study develops predictive models for determination of strength parameters of laterized concrete made with ceramic aggregates, based on the principle of Artificial Neural Networks (ANN), where the selected model architecture contains eight-input layer, ten-hidden layer, and two-output layer neurons. Expand
Prediction of compressive strength of geopolymer composites using an artificial neural network
Geopolymers are highly complex materials which involve many variables and make for which modelling the properties is very difficult. There is no systematic approach in mix design for geopolymers.Expand
Predicting the compressive strength and slump of high strength concrete using neural network
High Strength Concrete (HSC) is defined as concrete that meets special combination of performance and uniformity requirements that cannot be achieved routinely using conventional constituents andExpand
Self compacting geopolymer concrete (SCGC) is becoming an innovative sustainable engineered material in the construction industry that doesn’t require both compaction and cement. In this study, SCGCExpand
Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic
The training and testing results in the multilayer feed-forward neural networks and Sugeno-type fuzzy inference models have shown that Neural networks and fuzzy logic systems have strong potential for predicting compressive strength of mortars containing metakaolin. Expand
Application of gene expression programming to predict the compressive damage of lightweight aluminosilicate geopolymer
  • A. Nazari
  • Materials Science, Computer Science
  • Neural Computing and Applications
  • 2012
The training and testing results in the models have shown a strong potential for predicting the compressive strength of the lightweight geopolymer specimens in the considered range and one may predict them with a tiny error. Expand
Predicting compressive strength of different geopolymers by artificial neural networks
In the present study, six different models based on artificial neural networks have been developed to predict the compressive strength of different types of geopolymers. The differences between theExpand
Prediction of compressive strength of recycled aggregate concrete using artificial neural networks
Abstract Recycled aggregates are substantially different in composition and properties compared with natural aggregates, leading it hard to predict the performance of recycled aggregate concrete andExpand