Evaluation and Intelligent Modelling for Predicting the Amplitude of Footing Resting on Geocell-Based Weak Sand Bed Under Vibratory Load

Abstract

The main consideration while designing geo-structures to sustain vibration loads is the accuracy of the displacement amplitude estimation. The displacement amplitude of a footing on a geocell-reinforced bed exposed to vibration stress can be computed using sophisticated data. AI modelling has replaced many traditional methodologies. Thus, the current work introduces a hybrid paradigm called NFC-TSA, which stands for neuro-fuzzy controller and tunicate swarm algorithm. Comprehensive field vibration experiments provided the reliable database utilised to train and evaluate the model. To develop the model's precise prediction objective, displacement amplitude was used as an output index. Several parameters impacting the foundation bed, geocell reinforcement, and dynamic excitation were considered as input variables. Existing methods, ANN-EHO, JSA, MOA, RNN, and ANN-MGSA, were compared to the NFC-anticipated TSA's accuracy.