Analysis on the Ductility of One-Part Geopolymer-Stabilized Soil with PET Fibers: A Deep Learning Neural Network Approach

Guo Hu, Junyi Zhang, Ying Tang, Jun Wu

Geopolymers, as an eco-friendly alternative construction material to ordinary Portland cement (OPC), exhibit superior performance in soil stabilization. However, their inherent brittleness limits engineering applications. To address this, polyethylene terephthalate (PET) fibers can be incorporated into a one-part geopolymer (OPG) binder to enhance ductility while promoting plastic waste recycling. However, the evaluation of ductile behavior of OPG-stabilized soil with PET fiber normally demands extensive laboratory and field experiments. Leveraging artificial intelligence, a predictive model can be developed for this purpose. In this study, data were collected from compressive and tensile tests performed on the OPG-stabilized soil with PET fiber. Four deep learning neural network models, namely ANN, BPNN, CNN, and LSTM, were then used to construct prediction models. The input parameters in the model included the fly ash (FA) dosage, dosage and length of the PET fiber, and the Curing Time. Results revealed that the LSTM model had the best performance in predicting the three ductile properties (i.e., the compressive strength index [UCS], strain energy index [CSE], and tensile strength index [TES]). The SHAP and 2D-PDP methods were further used to verify the rationality of the LSTM model. It is found that the Curing Time was the most important factor for the strength and ductile behavior. The appropriate addition of PET fiber of a certain length had a positive impact on the ductility index. Thus, for the OPG-stabilized soil, the optimal dosage and length of PET fiber were found to be 1.5% and 9 mm, respectively. Additionally, there was a synergistic effect between FA and PET on the ductility metric. This research provides theoretical support for the application of geopolymer and PET fiber in enhancing the ductility of the stabilized soil.