IWOA-LSTM based intrinsic structural identification of steel fiber concrete

Fracture damage in steel fiber concrete (SFRC) is a developmental process in which deformation and damage are coupled with each other. In order to accurately identify the high-temperature constitutive model taking into account the damage evolution, a high-temperature constitutive identification model using the Improved Whale Algorithm (IWOA) optimised Long Short-Term Memory (LSTM) neural network is presented. Firstly, the Laplace crossover operator strategy, the optimal neighbourhood perturbation strategy, the adaptive weighting strategy and the updating strategy of the variables helix position are introduced to solve the problems of the Whale Optimisation Algorithm (WOA) in relation to its slow convergence rate and its tendency to fall into the locally optimal solution. The supremacy of the IWOA has been demonstrated by comparing IWOA with WOA, Crown Porcupine Optimisation Algorithm (CPO), Butterfly Optimisation Algorithm (BOA) and Grey Wolf Optimisation Algorithm (GWO) in terms of optimisation search. Secondly, based on the experimental data, LSTM model, WOA-LSTM model and IWOA-LSTM model were established, where the MSE of IWOA-LSTM model was improved by 47.66% and 65.60% compared to WOA-LSTM model as well as LSTM model. Finally, the constitutive identification model of SFRC using the IWOA-LSTM model was applied to decouple the damage and plastic strain by the comparative analysis of the measured curves and the prediction curves without the damage, so that the damage and its evolution law of steel fiber concrete at different temperatures (T = 200 °C, T = 400 °C and T = 520 °C) were obtained. The degree of approximation between the IWOA-LSTM model's prediction and experimental data shows that the trained model has a high learning accuracy and good generalization capability, making it appropriate for use in structural engineering applications.