Utilizing the GOA-RF hybrid model, predicting the CPT-based pile set-up parameters

The undrained shear strength of soil is considered one of the engineering parameters of utmost significance in geotechnical design methods. In-situ experiments like cone penetration tests (𝐶𝑃𝑇) have been used in the last several years to estimate the undrained shear strength depending on the characteristics of the soil. Nevertheless, the majority of these techniques rely on correlation presumptions, which may lead to uneven accuracy. This research' general aim is to extend a new united soft computing model, which is a combination of random forest (𝑅𝐹) with grasshopper optimization algorithm (𝐺𝑂𝐴) to the pile set-up parameters better approximation from 𝐶𝑃𝑇, based on two different types of data as inputs. Data type 1 contains pile parameters, and data type 2 consists of soil properties. The contribution of this article is that hybrid 𝐺𝑂𝐴−𝑅𝐹 for the first time, was suggested to forecast the pile set-up parameter from 𝐶𝑃𝑇. In order to do this, CPT data and related bore log data were gathered from 70 various locations across Louisiana. With an 𝑅2 greater than 0.9098, which denotes the permissible relationship between measured and anticipated values, the results demonstrated that both models perform well in forecasting the set-up parameter. It is comprehensible that, in the training and testing step, the model with data type 2 has finer capability than the model using data type 1, with 𝑅2 and 𝑅𝑀𝑆𝐸 are 0.9272 and 0.0305 for the training step and 0.9182 and 0.0415 for the testing step. All in all, the models results depict that the A parameter could be forecasted with adequate precision from the 𝐶𝑃𝑇 data with the usage of hybrid 𝐺𝑂𝐴−𝑅𝐹 models. However, the 𝑅𝐹 model with soil features as input parameters results in a finer commentary of pile set-up parameters.