AI Summary & Key Takeaways
AI InsightThese models revealed that genetic algorithm based modeling has a reliable potential for estimating the strength of LWGMs.
📌 3 Key Takeaways
Abstract
This article presents a comprehensive study aimed at developing suitable mathematical models for the prediction of compressive strength of lightweight geopolymer mortar LWGM with different types and amounts binders with different curing regimes. Lightweight pumice aggregate, alkali activated powder materials are the main components of geopolymer binder. From the experimental study 306 data samples were obtained and these were used to derive explicit formulas for estimation of the compressive strength of LWGMs. Two methods are used to produce the models. The first is the simplified linear step-wise regression, while the second method is the genetic expression programming. Step-wise regression is a statistical tool that uses the impact of each factor to evaluate its effect on the equation. This impact is calculated based on the probability effect based on the F-distribution and the null-hypothesis. The default value of probability that refers to the significance of each factor is 0.05. Thus, the software calculates the probability of each of the independent variables and includes only those with probability values less than 0.05. Based on the included independent variables, simplified linear regression equation is introduced. The genetic programming on the other hand, is much more sophisticated method that uses the principles of gene evolution. The modeling is separated for each type of binder. Thus, two sets of formulas are obtained from each modeling, one for the granulated blast furnace slag -based LWGM, while the second is for the fly ash-based LWGM. These models revealed that genetic algorithm based modeling has a reliable potential for estimating the strength of LWGMs.