In this section
@inproceedings{Ghalandari:2026:10.53243/ICSMGE2026-1074,
author = {Ghalandari, T and Vuye, C and Taborda, D},
doi = {10.53243/ICSMGE2026-1074},
pages = {2077--2080},
publisher = {ÖGG, Austrian Society for Geomechanics},
title = {Data-driven surrogate modeling for thermo-active road design},
url = {http://dx.doi.org/10.53243/ICSMGE2026-1074},
year = {2026}
}
TY - CPAPER
AB - The traditional iterative design process for engineering problems involves evaluating the performance of initial solutions based on prior experience and expertise, comparing them against specified project criteria, and refining design variables to achieve the desired system performance. This cycle continues until a satisfactory solution is reached. However, for more complex problems, the computational demands of simulation modelling can become prohibitively high, especially in design optimization tasks. Surrogate models are machine learning-based representations of complex physics-driven simulations, enabling significantly faster computations. They provide a rapid and accurate means to evaluate responses across multiple design scenarios, making them invaluable for efficient design exploration and optimization. This paper investigates the application of surrogate modelling for the design and thermal performance assessment of thermo-active roads, with a particular focus on systems that integrate a heat exchange layer with embedded pipes in the asphalt pavement, known as Pavement Solar Collectors (PSCs). The surrogate model developed in this study is based on an artificial neural network (ANN) trained on a comprehensive database generated from finite element simulations. These simulations account for variations in geometric configurations and thermophysical properties of materials. The ANN model is designed to predict the outlet water temperature of PSC systems, which is then used to calculate their heat harvesting capacity. Following hyperparameter optimization to enhance the performance of the surrogate model, the proposed framework demonstrated its effectiveness in optimizing the design of a PSC system in a case study. This highlights the model's potential to simplify and improve the efficiency of the design process for thermo-active road systems.
AU - Ghalandari,T
AU - Vuye,C
AU - Taborda,D
DO - 10.53243/ICSMGE2026-1074
EP - 2080
PB - ÖGG, Austrian Society for Geomechanics
PY - 2026///
SP - 2077
TI - Data-driven surrogate modeling for thermo-active road design
UR - http://dx.doi.org/10.53243/ICSMGE2026-1074
ER -
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