Inverse Model for Fire Heat Release Rate Using Deep Neural Networks

Abstract The estimation of heat release rate (HRR) in a building fire is a meaningful yet challenging task to improve first emergency response. Inspired by the capability of deep learning method that mines patterns from raw data, the inverse model based on Gated Recurrent Unit (GRU) is presented for the inversion of HRR. First, a series of fire scenarios is simulated to form the dataset by forward fire modeling. Second, GRU is applied to learn representations from windows of time-series sensor data. Output layer is used to map the learned representations to targets. Third, the HRR is estimated by the trained GRU network with observed data from the fire. Finally, the accuracy and efficiency of the inverse model are evaluated in a multi-compartment configuration. Preliminary results indicate that GRU network can be applied to the inversion of fire HRR with higher accuracy and efficiency.