Modeli mehke logike in nevronske mreže za analizo geotehničnih konstrukcij : doktorska disertacija

slovenian

doktorska dela

2013

Univerza v Mariboru, Fakulteta za gradbeništvo, prometno inženirstvo in arhitekturo

In this PhD dissertation, we have developed new models for the analysis of pavement, soil nal structure and underground gas storage. The models were developed by ANFIS method (adaptive network based fuzzy inference system). TheANFIS technique has better prediction capability then conventional analitical methods. The ANFIS models were developed on the basis of geotechnical calculation models and optimization models. The optimization is performed by the non-linear programming (NLP) approach. The accuracy of ANFIS models depends on the nonlinearity of the problem and neural network topologies. Therefore, the ANFIS models with different neural network topologywere developed. To test and validate the ANFIS models, a data sets were chosen, which was not used while training the network, was employed. In the PhD dissertation ANFIS models are developed for the prediction of pavementhorizontal strain at the bottom of the asphalt layer and vertical strain on the top of the sub-grade. The developed ANFIS models for soil nail wall are used to predict safety factor and optimal inclination of soil nail for any design soil nail wall. The ANFIS models for underground gas storage predict the optimal costs per unit of gas and optimal design of underground gas storage.

07.08.2023