Dali Bondar

Engineering Properties of Alkali-Activated Natural Pozzolan Concrete

The development of alkali-activated binders with superior engineering properties and longer durability has emerged as an alternative to ordinary portland cement (OPC). It is possible to use alkali-activated natural pozzolans to prepare environmentally friendly geopolymer cement leading to the concept of sustainable development. This paper presents a summary of an experimental work that was conducted to determine mechanical strength, modulus of elasticity, ultrasonic pulse velocity, and shrinkage of different concrete mixtures prepared with alkali-activated Iranian natural pozzolans—namely Taftan andesite and Shahindej dacite, both with and without calcining. Test data were used for Taftan pozzolan to identify the effects of water-binder ratios (w/b) and curing conditions on the properties of the geopolymer concrete, whereas the influence of material composition was studied by activating Shahindej pozzolan both in the natural and calcined states. The results show that alkali-activated natural pozzolan (AANP) concretes develop moderate-to-high mechanical strength with a high modulus of elasticity and a shrinkage much lower than with OPC.

Oxygen and Chloride Permeability of Alkali-Activated Natural Pozzolan Concrete

This paper describes how one of the most important factors in the use of portland cement concrete is its durability. Most of the situations where durability is lacking have been identified and strategies to manage durability have been implemented. Geopolymer concrete, made from an alkali-activated natural pozzolan (AANP), provides an important opportunity for the reduction of carbon dioxide (CO2) emissions associated with the manufacture of concrete. However, there is a limited history of durability studies. Until its different properties are well understood there is no desire to adopt this new technology of unknown provenance by the concrete industry. This paper presents an experimental study of oxygen and chloride permeability of AANP concrete prepared by activating Taftan andesite and Shahindej dacite (Iranian natural pozzolans), with and without calcining, and the correlations between these properties and compressive strength. The results presented in the paper show that compared to ordinary portland cement (OPC) concrete, AANP concrete has lower oxygen permeability at later ages; but it shows moderate to high chloride ion penetrability.

Simplified Model for Prediction of Compressive Strength of Alkali-Activated Natural Pozzolans

AbstractThe assessment of pozzolanic activity is essential for estimating the reaction of a material as pozzolan. Natural pozzolans can be activated and condensed with sodium silicate in an alkaline environment to synthesize high performance cementitious construction materials with low environmental impact. In this paper, the pozzolanic activities of five natural pozzolans are studied. The correlation between type and chemical composition of natural pozzolan, which affects the formation of the geopolymer gel phase, both for the calcined and untreated natural pozzolans, have been reviewed. The improvement in pozzolanic properties was studied following heat treatment including calcinations and/or elevated curing temperature by using alkali solubility, and compressive strength tests. A model was developed to allow prediction of the alkali-activated pozzolan strength versus their chemical compositions, alkali solubility, and crystallinity.

Use of a Neural Network to Predict Strength and Optimum Compositions of Natural Alumina-Silica-Based Geopolymers

AbstractIn order to predict compressive strength of geopolymers prepared from alumina-silica natural products, based on the effect of Al2O3/SiO2, Na2O/Al2O3, Na2O/H2O, and Na/[Na+K], more than 50 pieces of data were gathered from the literature. The data was utilized to train and test a multilayer artificial neural network (ANN). Therefore a multilayer feedforward network was designed with chemical compositions of alumina silicate and alkali activators as inputs and compressive strength as output. In this study, a feedforward network with various numbers of hidden layers and neurons were tested to select the optimum network architecture. The developed three-layer neural network simulator model used the feedforward back propagation architecture, demonstrated its ability in training the given input/output patterns. The cross-validation data was used to show the validity and high prediction accuracy of the network. This leads to the optimum chemical composition and the best paste can be made from activated a...