Expansive soils present critical geotechnical challenges in pavement subgrade construction due to excessive swell-shrink potential and inadequate bearing capacity under repetitive traffic loading. This research develops alkali-activated binary blends of GGBS, GP, and MSWI — activated by Red Mud — to stabilize artificially expansive soil for inverted pavement subgrade applications.

A comprehensive laboratory program including UCS, CBR, free swell, cyclic repetitive loading, cyclic triaxial, permeability, leachate assessment, and SEM is conducted to evaluate the mechanical, hydraulic, environmental, and microstructural performance of each binary blend. Key pavement design parameters — including resilient modulus, permanent deformation, and shear strength — are investigated to provide a comprehensive performance framework for the stabilized subgrade geomaterial.

Sedimentation of dredged soils plays a key role in subsurface layer formation and governs the geotechnical behavior of deposited materials in coastal and reclaimed areas. Variations in flow conditions, water content, and soil properties lead to heterogeneous particle segregation, influencing consolidation, strength, and the long-term stability of soil deposits.

Experimental and numerical approaches are employed to investigate sediment deposition and layer formation. Controlled 1D and 2D sedimentation experiments quantify the effects of flow rate, particle size distribution, and water content, while coupled SPH–DEM simulations provide higher-resolution insight into flow–sediment interactions. The model is calibrated and validated against experimental data to ensure reliable representation of sedimentation behavior.

Physics-informed machine learning for ground settlement prediction in spatially heterogeneous ground conditions, integrating physical governing equations, inverse parameter estimation, and field monitoring data.

Three-dimensional site characterization and spatial transfer learning for geotechnical prediction across sites with varying ground improvement conditions and limited subsurface investigation.

Detecting voids beneath the surface is critical for maintaining the safety, integrity, and performance of underground structures such as tunnels, pipelines, foundations, and roadways. These hidden anomalies—often caused by soil erosion, poor compaction, or groundwater movement—can lead to structural failures if left undetected. 

Development of advanced techniques such as application of stereo microphones to identify and characterize underground voids using impact-echo method. Using signal processing methods (eg. FFT and wavelet) in conjunction with machine learning, voids are clearly detected.

Heat transfer in soils is governed by thermal properties, especially the thermal conductivity of the soil, which is also one of the essential parameters related to heat exchange, and a factor in analysing the performance of energy geo-structure.

Geo-COUS (FEM software) which employs thermal-hydraulic phenomenon, is used to simulate heat transfer of soil with a geo-energy wall.  The numerical result is validated to compare the measured surface temperature obtained and the calculated surface temperature. Seasonal variation of soil temperature effect on the subway system was also estimated.

Dynamic geo-centrifuge facility is used to model soil-structure interaction (SSI) during an earthquake with bender element array to measure shear-wave velocity profile, which should be matched with dynamic soil properties observed by Oedometer tests and resonant column (RC) test.

Flac3D simulation with nonlinear soil properties (i.e., constitutive soil model and hysteretic damping) obtained from the results of RC and Oedometer tests, and implement of free-field and interface elements. Properties of structures could be estimated by performing impact hammer testing on small-scale structural models with considering scaling law.