Predicting clay compressibility for foundation design with high reliability and safety: A geotechnical engineering perspective using artificial neural network and five metaheuristic algorithms

Abstract

This study aims at predicting clay compressibility (clay compressibility) for foundation design with unprecedented reliability and safety. To achieve this goal, we set forth several scientific objectives to enhance the accuracy of clay compressibility predictions and ensure the robustness of our models. Firstly, we explored an updated dataset comprising diverse clay types, focusing on the intricate relationship between clay compressibility and essential soil properties. By incorporating particle size distribution, the ratio between liquid limit (wLL) and plasticity index (PI), and the ratio between void ratio (eLL) and wLL, our objective was to gain a comprehensive understanding of clay behavior, facilitating more accurate predictions. Secondly, we employed Artificial Neural Networks (ANNs) as our primary modeling tool. ANNs, with their capacity to discern complex patterns within data, formed the foundation of our predictive model. Our scientific objective was to harness the power of ANNs to capture subtle relationships among input variables, thereby ensuring a robust foundation for clay compressibility predictions. To further refine our predictions, our third objective was to optimize the ANN model. We utilized five distinct metaheuristic algorithms—Bird Swarm Algorithm (BSA), Dragonfly Optimization (DO), Elephant Herding Optimization (EHO), Jaya Algorithm (JA), and Whale Optimization Algorithm (WOA). Our aim was to fine-tune the ANN model's parameters using these algorithms, capitalizing on their strengths to achieve superior predictive performance. Additionally, our study aimed to pioneer the development and evaluation of hybrid models for forecasting clay compressibility. By integrating the ANN model with the metaheuristic algorithms (BSA-ANN, DO-ANN, EHO-ANN, JA-ANN, and WOA-ANN), our objective was to create hybrid frameworks that synergistically combined the strengths of both approaches. We rigorously evaluated these hybrid models, ensuring their efficacy and reliability in practical engineering applications. Crucially, we validated our models through practical engineering scenarios. For this purpose, we prepared 30 new soil samples mirroring real-world conditions. Our objective was to apply the developed models to these samples, affirming their effectiveness and reliability in actual foundation design applications. In pursuit of identifying the most accurate and reliable predictive model, our final objective focused on the BSA-ANN model. Remarkably, this model demonstrated an exceptional accuracy of 99.8%. Our objective was to establish the BSA-ANN model as the preferred choice for practical engineering applications, ensuring high reliability and safety in foundation design.