Ahmet Benli

Application of adaptive neuro-fuzzy technique and regression models to predict the compressive strength of geopolymer composites

This article introduces an adaptive network-based fuzzy inference system (ANFIS) model and two linear and nonlinear regression models to predict the compressive strength of geopolymer composites. Geopolymers are highly complex materials which involve many variables which make modeling its properties very difficult. There is no systematic approach in the mix design for geopolymers. The amounts of silica modulus, Na2O content, w/b ratios, and curing time have a great influence on the compressive strength. In this study, by developing and comparing parametric linear and nonlinear regressions and ANFIS models, we dealt with predicting the compressive strength of geopolymer composites for possible use in mix-design framework considering the mentioned complexities. ANFIS model developed by generalized bell-shaped membership function was recognized the best approach, and the prediction results of linear and nonlinear regression models as empirical methods showed the weakness of these models comparing ANFIS model.

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. Since the amounts of silica modulus, Na2O content, w/b ratios and curing time have a great influence on the compressive strength, an ANN (artificial neural network) method has been established for predicting compressive strength of ground pumice based Geopolymers and the possibilities of adapting ANN and artificial intelligence system for predicting the compressive strength have been studied. Consequently, a multilayer ANN by using back propagation architecture can be developed for geopolymer compressive strength prediction. In this study, the coefficient of determination (R2) has been used for investigating the proposed model accuracy. As a result, proposed ANN model can predict the compressive strength of geopolymer with R2 = 0.958.

Masonry Infill Walls Effect In Short Column Formation In Rc Buildings: A Case Study

The analyses of infill frame structures are generally done ignoring the presence of brick masonry in the analytical models but it is a prevalent mistake. Behaviors of such buildings vary significantly during the earthquake events. The lateral resisting capacity of infill wall actually restricts the column only up to the wall height but above the wall height, the free column deforms easily. In this paper, the effect of infill wall in formation of short column at military aid watchtower in Turkey has been analyzed and the analysis result compared with effect of earthquake that have been seen after earthquake