Inversion of image-only intrinsic parameters for steel fibre concrete under combined rate-temperature conditions: An adaptively enhanced machine learning approach

Concrete not only bears quasi-static loads during the service of engineering structures, but also bears impact or explosion due to accidental accidents, so more and more attention has been paid to the study of concrete deformation characteristics and stress distribution. Obtaining the correct constitutive parameters is crucial for the study of the mechanical behavior of concrete, and the determination of constitutive parameters is essentially an inverse process, which is very challenging. In this paper, third-order Bessel curves are used to construct dynamic constitutive equations for steel-fibre concrete under rate-temperature union conditions, and to establish a database of the constitutive parameters corresponding to the factors influencing the mechanical behaviour of concrete. In order to select highly accurate and adaptive intelligent inversion models, Firstly, the black-winged kite optimisation algorithm (BKA) has been improved by improving the black-winged kite leader condition and integrating the optimal perturbation strategy of Morlet wavelet factor, which proves the superiority of the MBKA algorithm by comparing it with the BKA, Dung Beetle Optimisation Algorithm (DBO), Grey Wolf Optimisation Algorithm (GWO), and Harris Hawk Algorithm (HHO) in searching for the optimal results. Secondly, based on the database, the MBKA-LSSVR model, MBKA-LSSVR-Adaboost model, CNN-GRU model and CNN-LSTM model were built sequentially, respectively. The final results show that the MBKA-LSSVR-Adaboost model has the highest accuracy and the best performance for the parameter Pi (i = 0,1,2,3), and the inverted stress-strain curve proves that the proposed method is effective and accurate in determining the proposed constitutive parameters.