Fire safety assessment of high-rise buildings based on fuzzy theory and radial basis function neural network

Featuring complex functions, dense population, and large span, high-rise buildings are an iconic product of economic and technical growths in modern society. Fire poses an imminent threat to high-rise buildings. Once a high-rise building catches fire, the loss of life and property will be incalculable. However, the traditional assessment methods for fire safety of high-rise buildings are incapable of handling the complex influencing factors. To solve the problem, this paper combines the fuzzy logic inference system and radial basis function neural network (RBFNN) into an intelligent assessment method mimicking the nonlinear inference process of fire safety experts. Firstly, the factors affecting the fire safety of high-rise buildings were quantified, and the relevant rating standard was established. Next, the classic statistics were transformed into fuzzy indices, using the fuzzy logic system. To avoid the local minimum trap, the RBFNN was adopted to replace the traditional backpropagation neural network (BPNN), and integrated with the fuzzy logic system. creating an adaptive fuzzy-RBFNN to assess the fire safety of high-rise buildings. The proposed network was trained by numerous expert evaluation samples, and verified by examples. The simulation results show that the proposed network could mimic the nonlinear' inference process of fire safety experts, and evaluate the fire safety of high-rise buildings in real time with little error. The research results provide new insights on the application of artificial intelligence (AI) in fire safety assessment. © 2020 International Information and Engineering Technology Association. All rights reserved.