Mathematical modeling of kaolin nanocomposite geosynthetic pavement with intelligent tension and interlocking performance analysis

Pavement design is the application of various building materials and layers to ensure maximum strength. However, the current approaches did not enable flexible pavement to reach its maximum strength and service life under cyclic load circumstances and dynamic temperatures.Hence a novel flexible pavement design named Kaolin Nanocomposite Glass Geosynthetic Pavement design featuring a double-layer composite of geonet and geomembrane, effectively mitigates temperature-induced stiffness, enhancing resilience and longevity. Existing pavement designs suffer from deficiencies in bonding strength, surface texture, absorption efficiency, and filtering effectiveness due to multifaceted dynamic cyclic traffic loading. To address these issues, the solution introduces the Ca(OH)₂ Kaolin nanocomposite geosynthetic clay liner (GCL) layer, combining a triple axial glass fibre geogrid with a GCL nanocomposite incorporating Kaolin clay. By replacing traditional bentonite clay with Kaolin clay, swelling effects are eliminated, resulting in a pavement structure of maximum strength and extended service life. Furthermore, while analyzing the performance of mathematically modeled design, existing mathematical modeling approaches overestimate pavement distresses without investigating pavement design behavior at interface layers. To address this limitation, a novel Non-linear Notch pushing model is introduced. This measures axial tension within the reinforced cushion layer and explores the interlocking geosynthetic effect through static-dynamic frictional forces. By analyzing bond strength under diverse conditions, including temperature, moving loads, and frictional angles, the model offers insights for optimizing pavement design and enhancing performance. Therefore, this model provides the ability to eliminate stiffening and swelling effects and also analyze unique behaviors in the cushion layer with a more normalized value, a lower modulation value, and a fractional coefficient.