Prediction of Geometric Dimensions in 3D Printed Concrete: BP Neural Network Optimised by Enhanced Crayfish Algorithm

Abstract

3D printed concrete technology has revolutionised construction methods by enabling the fabrication of complex structures. However, complex nonlinear coupling relationships exist among its process parameters, such as extrusion velocity, printing speed and printing height, which critically influence forming accuracy. Therefore, achieving precise prediction of geometric dimensions is essential for high-precision construction. This paper first establishes a nonlinear mapping model between process parameters and geometric dimensions through experiments, effectively addressing the challenge of multi-parameter coupling effects. To overcome the limitations of traditional optimisation algorithms—namely slow convergence in high-dimensional parameter spaces and susceptibility to local optima—this study proposes an (ECOA). This algorithm integrates two key innovations: the exploration phase of the Gorilla Troop Optimiser and the vertical crossover strategy. The ECOA-BPNN model for geometric dimension prediction is constructed using the BP neural network (BPNN) optimised by ECOA. Experimental results demonstrate that among 23 standard test functions and the CEC2019 test function, ECOA achieved global optimal solutions for 18 functions, whereas the fitness values for the remaining functions also outperformed other comparison algorithms, validating the optimisation capability of ECOA. The ECOA-BPNN model significantly outperformed traditional BPNN in prediction accuracy, reducing MAPE and RMSE by 1.2862% and 0.6828%, respectively, and demonstrating its effectiveness in predicting 3D printed concrete geometric dimensions. Based on this model, optimised process parameter combinations were further derived, enabling precise control and high-quality forming.