The implementation of a multi-layer perceptron model using meta-heuristic algorithms for predicting undrained shear strength

In structural engineering applications, such as the construction of foundations, earth dams, rock fill, slope stability assessment, and roads and railroads, the undrained shear strength (USS) is an essential metric. In recent times, certain empirical and theoretical methodologies have emerged in the estimation of the USS by utilizing soil characteristics and conducting field tests. The majority of these methodologies entail underlying correlation-based presumptions, thus yielding imprecise outcomes. Furthermore, traditional methods often exhibit minimal efficiency in terms of time and cost. Since these types of examinations are deemed not economical, employing distinctive approaches in projecting their outcomes appears essential. The advancement of artificial intelligence (AI) techniques leads to the development of novel models and algorithms. Utilizing these techniques allows researchers to select a predictive approach as an alternative to experimental methodologies. The present study implemented the AI methodology to assess the USS of soils with high sensitivity. The multi-layer perceptron (MLP) was employed as a means to address a problem in the development of a machine learning methodology. This approach employs empirical samples to address a specific problem. Four predictor variables, overburden weight (OBW), liquid limit (LL), sleeve friction (SF), and plastic limit (PL), were utilized for training the models. To enhance the resultant output, a set of three optimizers, namely the dynamic control cuckoo search (DCCS), smell agent optimization (SAO), and bonobo optimizer (BO), were employed. This research significantly advances USS evaluation for sensitive soils by employing the MLP and three optimizers. It introduces a sophisticated AI approach, promising improved accuracy and efficiency compared to traditional methods in geotechnical engineering.