This paper investigates the application of surrogate modelling in the design and thermal response
assessment of Pavement Solar Collectors (PSCs). The PSC system is a sustainable infrastructure solution
that utilises both solar and shallow geothermal energy. PSCs incorporate a network of pipes embedded
in the asphalt layer to create a heat exchange layer. During warm months, water circulating through
this layer captures solar heat, which can then be used for snow melting in winter, enhancing road
safety, or for domestic and industrial heating applications.
Finite Element (FE) analysis is a widely used method for evaluating the thermal response of PSCs to
optimize their design. However, the substantial computational requirements of numerical modelling,
especially for long-term time-dependent analyses, pose significant challenges in assessing the long term thermal behaviour of PSCs. Surrogate models, approximating complex physics-based simulations,
drastically reduce computational demands, enabling rapid and accurate evaluations of various design
parameters and scenarios. In this study, a validated FE simulation framework was employed to
generate data, which was then used to develop a data-driven surrogate model for PSCs.
In order to refine the surrogate model's performance to its optimal level, hyperparameter optimisation
was carried out. The comparison of outlet water temperature results between finite element and
surrogate models showed a high correlation, with a coefficient of determination of 0.97 observed for
both training and test data sets. Subsequently, the surrogate model was integrated as an objective
function in a Particle Swarm Optimization (PSO) algorithm to automate the Heat Harvesting Capacity
(HHC) optimisation of PSCs. The PSO algorithm demonstrates robust performance in identifying
optimal solutions while also offering a substantial reduction in computational costs compared to FE
simulations.