Using artificial neural networks for calculation of temperatures in timber under fire loading

Artificial neural networks have been used in recent years as a tool to model properties and behavior of materials in many areas of civil engineering applications. Because of their ability to learn and adapt they can be used to find complex relations between different properties. In the present paper artificial neural networks are used for predicting the temperatures in timber under fire loading. The artificial neural network model has been trained and tested using available numerical results obtained using design methods of Eurocode 5 for the calculation of temperatures in timber under fire loading. A multilayer feed forward network has been used with input data arranged in a format of three input parameters that cover the density of timber, the time of fire exposure and the distance from exposed side and the output parameter being the temperature in timber. The training and testing results in the neural network model have shown that neural networks can accurately calculate the temperature in timber members subjected to fire.

► This paper describes the applicability of artificial neural networks (ANN) for the prediction of temperatures in timber under fire loading.
► This paper shows that ANN can accurately model the thermal field within a timber member by describing data used for the learning process and the ANN performance.
► The use of ANN can considerably simplify the use of advanced models because ANN can be implemented in any software to give the temperature profile within timber.