Journal of Rock Mechanics and Geotechnical Engineering (Apr 2023)
Stochastic analysis of load-transfer mechanism of energy piles by random finite difference model
Abstract
The surge in demand for renewable energy to combat the ever-escalating climate crisis promotes development of the energy-saving, carbon saving and reduction technologies. Shallow ground-source heat pump (GSHP) system is a promising carbon reduction technology that can stably and effectively exploit subsurface geothermal energy by taking advantage of load-bearing structural elements as heat transfer medium. However, the transformation of conventional geo-structures (e.g. piles) into heat exchangers between the ground and superstructures can potentially induce variable thermal axial stresses and displacements in piles. Traditional energy pile analysis methods often rely on deterministic and homogeneous soil parameter profiles for investigating thermo-mechanical soil-structure interaction, without consideration of soil spatial variability, model uncertainty or statistical uncertainty associated with interpolation of soil parameter profiles from limited site-specific measurements. In this study, a random finite difference model (FDM) is proposed to investigate the thermo-mechanical load-transfer mechanism of energy piles in granular soils. Spatially varying soil parameter profile is interpreted from limited site-specific measurements using Bayesian compressive sensing (BCS) with proper considering of soil spatial variability and other uncertainties in the framework of Monte Carlo simulation (MCS). Performance of the proposed method is demonstrated using an illustrative example. Results indicate that the proposed method enables an accurate evaluation of thermally induced axial stress/displacement and variation in null point (NP) location with quantified uncertainty. A series of sensitivity analyses are also carried out to assess effects of the pile-superstructure stiffness and measurement data number on the performance of the proposed method, leading to useful insights.
