Ahmet Tortum

Prediction of concrete elastic modulus using adaptive neuro-fuzzy inference system

The prediction of elastic modulus is one of the fundamental facts of structural engineering studies. The performance of adaptive neuro-fuzzy inference system (ANFIS) for predicting the elastic modulus of normal- and high-strength concrete was investigated. Results indicate that the proposed ANFIS modeling approach outperforms the other given models in terms of prediction capability. According to the results, the ANFIS approach is a viable tool for modeling the elastic modulus, as it results in more accurate predictions.

Determination of the apparent rate constants of the degradation of humic substances by ozonation and modeling of the removal of humic substances from the aqueous solutions with neural network.

In this study, the degradation rate constants of humic substances by ozonation under the different empirical conditions such as ozone-air flow rate, ozone generation potential, pH, temperature, powdered activated carbon (PAC) dosage and HCO(3)(-) ions concentration were determined. The ozonation of humic substances in the semi-batch reactor was found to fit pseudo-first-order reaction. The values of apparent rate constant of humic substances degradation increased with the increase of initial ozone-air flow rates, ozone generation potential, pH, temperatures and PAC dosage, but decreased with the increase of HCO(3)(-) concentration of the solution. Using Arrhenius equation, the activation energy (E(a)) of the reaction was found as 1.96 kJ mol(-1). The reaction of ozonation of humic substances under the different temperatures was defined as diffusion control according to E(a). The model based on artificial neural network (ANN) could predict the concentrations of humic substances removal from aqueous solution during ozonation. A relationship between the predicted results of the designed ANN model and experimental data was also conducted. The ANN model yielded determination coefficient of (R(2)=0.995), standard deviation ratio (0.065), mean absolute error (4.057) and root mean square error (5.4967).

The modeling of mode choices of intercity freight transportation with the artificial neural networks and adaptive neuro-fuzzy inference system

Mode choice modeling is probably the most important element of transportation planning. It affects the general efficiency of travel and the allocation of resources. The development of mode choice models has recently witnessed significant advances in many fields, such as passenger and freight transport. A large number of mathematical models have been used to model the travelers choice of mode and destination and the shippers choice of mode, shipment size and supply market, among others. Such models are not only becoming almost intractable but also data intensive, difficult to calibrate and update, and intransferable. These models cover a wide range of mathematical complexity and accuracy. This paper describes a new approach to mode choice of intercity freight transport modeling using artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) models. The new approach combines the learning ability of artificial neural networks and the transparent nature of fuzzy logic. The approach is found to be highly adaptive and efficient in investigating non-linear relationships among different variables. The adaptive neuro-fuzzy inference system model is tested on the freight transport market in Turkey, Germany, France and Austria by using information on the freight flows and their attributes. The ANNs and ANFIS models are more successful in the representation of the non-linear behavior of mode choice of intercity freight transport compared to the classical models.