The rigid inclusion (RI) reinforcement technique has been increasingly used to reduce settlements for engineering constructions over soft soils. However, the load transfer mechanisms within the granular mattress are not well understood, especially for the RI-reinforced systems with the presence of structural elements such as footings. To evaluate the performance of a single footing over RI-reinforced soft soils, a novel intelligence approach combining the white shark optimizer (WSO) and the Logistic chaotic mapping (LCM) was proposed to optimize the random forest (RF) model for predicting the load transfer efficiency (LTE). Seven variables were considered to systematically investigate the LTE, including the loading magnitude (L), the load eccentricity (LE), the geometrical and strength parameters of the load transfer platform thickness (LT), Young's modulus (E), cohesion (C) and friction angle (F)) and the compression ratio (CR). Based on the feature selection method, nine different combination models were developed to estimate the LTE. Their performances were compared using the determination coefficient (R2), the root mean squared error (RMSE), the variance accounted for (VAF) and the Willmott's index (WI). The evaluation results indicated that the LWSO-RF model containing seven variables (L, LT, LE, E, CF, FA, CR) is the best one as it gives the highest prediction accuracy in both the training and testing phases. The sensitivity results demonstrated that LT is the most important variable for the LTE prediction. This work utilizes an intelligence approach to extend the traditional numerical simulation analysis for predicting the LTE, which provides additional guidance to further assess the performance of foundations over RI-reinforced soft soil.
