Using Optimization Algorithms-Based ANN to Determine the Temperatures in Timber Exposed to Fire for a Long Duration

Abstract

The article investigates the temperature prediction in rectangular timber cross-sections exposed to fire. Timber density, exposure time, and the point coordinates within the cross-section are treated as inputs to determine the temperatures. A total of 54,776 samples of wood cross-sections with a variety of characteristics were considered in this study. Of the sample data, 70% was dedicated to training the networks, while the remaining 30% was used for testing the networks. Feed-forward networks with various topologies were employed to examine the temperatures of timber exposed to fire for more than 1500 s. The weight of the artificial neural network was optimized using bat and genetic algorithms. The results conclude that both algorithms are efficient and accurate tools for determining the temperatures, with the bat algorithm being marginally superior in accuracy than the genetic algorithm.

FBG temperature measurement error compensation method based on LSTM and attention mechanism

“…In [5], for a soil hydrothermal coupling transmission system, a physics-informed neural network (PINN) based network is proposed to realize multi-data compensation in coupled physical processes. In [6], the bat algorithm is used to optimize the feedforward neural network to establish a wood temperature model in fire, and the accuracy effect of the algorithm is better than that of the genetic algorithm. min max min…”