Abstract
Artificial neural networks, in interaction with fuzzy logic, genetic algorithms, and fuzzy neural networks, represent an example of a modern interdisciplinary field, especially when it comes to solving certain types of engineering problems that could not be solved using traditional modeling methods and statistical methods. They represent a modern trend in practical developments within the prognostic modeling field and, with acceptable limitations, enjoy a generally recognized perspective for application in construction. Results obtained from numerical analysis, which includes analysis of the behavior of reinforced concrete elements and linear structures exposed to actions of standard fire, were used for the development of a prognostic model with the application of fuzzy neural networks. As fire resistance directly affects the functionality and safety of structures, the significance which new methods and computational tools have on enabling quick, easy, and simple prognosis of the same is quite clear. This paper will consider the application of fuzzy neural networks by creating prognostic models for determining fire resistance of eccentrically loaded reinforced concrete columns.
Highlights
The fire resistance of civil engineering structures can be determined based on the estimated fire resistance of each construction element
Prognostic models based on the connection between popular methods for soft computing, such as fuzzy neural networks, use positive characteristics of neural networks and fuzzy systems
One of the major advantages of the ANFIS method over fuzzy systems is that it eliminates the basic problem of defining the membership function parameters and obtaining a set of fuzzy if- rules
Summary
The fire resistance of civil engineering structures can be determined based on the estimated fire resistance of each construction element (columns, beams, slabs, walls, etc.). Fuzzy neural networks are typical example of a modern interdisciplinary subject that helps solving different engineering problems which cannot be solved by the traditional modeling methods [2,3,4] They are capable of collecting, memorizing, analyzing, and processing large number of data obtained from some experiments or numerical analyses. The trained fuzzy neural network serves as an analytical tool for precise predictions, for any input data which are not included in the training or testing process of the model. Using the concept of the fuzzy neural networks and the results of the performed numerical analyses (as input parameters), the prediction model for defining the fire resistance of eccentrically loaded RC columns incorporated in walls and exposed to standard fire from one side has been made. The numerical results were used as input data in order to create and train the fuzzy neural network so it can provide precise outputs for the fire resistance of eccentrically loaded RC columns for any other input data (RC columns with different dimensions of the cross section, different thickness of the protective concrete layer, different percentage of reinforcement, and for different loads)
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