Evolutionary metaheuristic intelligence to simulate tensile loads in reinforcement for geosynthetic-reinforced soil structures
The accurate estimation of reinforcement tensile loads is crucial for the evaluation of the internal stabilities of geosynthetic-reinforced soil (GRS) structures. This study developed an evolutionary metaheuristic intelligence model for efficiently and accurately estimating reinforcement loads. The proposed model improves the prediction capability of the firefly algorithm (FA) by integrating intelligent components, namely, a chaotic map, an adaptive inertia weight, and a Lvy flight. The enhanced FA is then used to optimise the hyperparameters for a least squares support vector regression model. The proposed model was validated using a database of 15 wall case studies (94 data points in total) via a cross-validation algorithm. The method was then compared with conventional prediction methods in terms of the accuracy for predicting the reinforcement tensile loads of GRS structures. The cross-validation results demonstrated that the proposed model has a superior accuracy and mean absolute percentage errors lower than 10%. Moreover, a comparison with the baseline models and empirical methods indicate that the evolutionary metaheuristic intelligence model provides a significant improvement in terms of the root mean square errors (by 63.6192.30%). This study validates the effectiveness of the proposed model for predicting reinforcement tensile loads and its feasibility for facilitating early designs of GRS structures.
evolutionary metaheuristic intelligence geosynthetic-reinforced soil structures reinforcement loads optimisation machine learning