  In deep geological repositories for high level nuclear waste with close
canister spacings, bentonite buffers can experience temperatures higher than
100 {\deg}C. In this range of extreme temperatures, phenomenological
constitutive laws face limitations in capturing the thermo-hydro-mechanical
(THM) behavior of the bentonite, since the pre-defined functional constitutive
laws often lack generality and flexibility to capture a wide range of complex
coupling phenomena as well as the effects of stress state and path dependency.
In this work, a deep neural network (DNN)-based soil-water retention curve
(SWRC) of bentonite is introduced and integrated into a Reproducing Kernel
Particle Method (RKPM) for conducting THM simulations of the bentonite buffer.
The DNN-SWRC model incorporates temperature as an additional input variable,
allowing it to learn the relationship between suction and degree of saturation
under the general non-isothermal condition, which is difficult to represent
using a phenomenological SWRC. For effective modeling of the tank-scale test,
new axisymmetric Reproducing Kernel basis functions enriched with singular
Dirichlet enforcement representing heater placement and an effective convective
heat transfer coefficient representing thin-layer composite tank construction
are developed. The proposed method is demonstrated through the modeling of a
tank-scale experiment involving a cylindrical layer of MX-80 bentonite exposed
to central heating.
