Rapid design for large-scale parallel CFRP cable with multi-source experimental data

With advancements in material properties and reduced costs, carbon fibre reinforced polymer (CFRP) cables are gaining popularity in engineering applications due to their superior strength-to-mass ratio and durability. However, the tensile strength of large-scale parallel CFRP cables remains a critical issue, warranting further research and engineering expertise. To address this issue, this paper proposes a method to rapidly predict and adaptively correct the tensile strength of large-scale parallel CFRP cables using multi-source experimental data and an integrated approach that incorporates Monte Carlo simulations, Neural Network algorithms, and Genetic algorithms. This comprehensive method takes into account the influence of various factors including small-scale material strength and its coefficient of variation, cable length, the number of parallel wires, installation errors, and anchorage errors. Validated by reported experimental data, the method demonstrates its effectiveness in accurately predicting the tensile strength of parallel CFRP cables. Moreover, a design method for large-scale parallel CFRP cables is proposed based on the reliability theory. Lastly, the efficiency and effectiveness of the proposed method are validated through the design and optimization of a cable-stayed bridge featuring a main span of 1984 m, utilising both parallel steel and CFRP cables.