Predicting the Pore-Pressure and Temperature of Fire-Loaded Concrete by a Hybrid Neural Network

Fire-loaded concrete structures may experience explosive spalling, i.e., violent splitting of concrete pieces from the heated surfaces, greatly jeopardizing the load carrying capacity and durability. Spalling is closely correlated with the evolution and distribution of pore-pressure p(g) and temperature T in heated concrete. Conventionally complicated thermo-hydro-chemical (THC) models are necessary for capturing this information. In this work, we proposed a hybrid neural network for quickly obtaining p(g), T of heated concrete. The neural network includes two parts: (i) a well-established autoencoder (AE) and (ii) a fully connected neural network (FNN). A strongly coupled THC model was first used to provide large amounts of results represented by thousands RGB images. The AE was used to condense the images into characteristic vectors, which were used for training the FNN. After training, the FNN can be used for predicting the corresponding characteristic vectors considering different concrete properties, moisture and fire loadings. Then the decoder of the AE is used to translate the characteristic vectors into RGB images, storing the information of p(g) and T. Numerical tests indicate the effectiveness and reliability of the proposed model.