With the increasing scarcity and cost of virgin materials for asphalt mixtures, the exploration of alternative components has intensified. Reclaimed asphalt pavement (RAP), crumb rubber (CR), steel slag (SS), and waste engine oil (WEO) have emerged as promising alternatives. Individually, RAP enhances rutting resistance but may compromise cracking tolerance; CR boosts cracking resistance; WEO affects cracking and rutting differently; and SS can influence moisture sensitivity. However, their combined impacts on asphalt performance, specifically on moisture damage, rutting, and cracking resistance, remain underexplored. In this study, 44 mixtures were assessed with varying RAP (0–75%), WEO (0–15%), and CR (0–15%) contents, alongside a constant SS aggregate (0% or 20%). The results indicate that specific combinations of these alternative materials can satisfy all performance thresholds for rutting, cracking, and moisture damage. To pinpoint ranges of optimal material contents for different high-traffic scenarios, prediction models were crafted using techniques like feed-forward neural network (FNN), generalized linear model (GLM), support vector regression (SVM), and Gaussian process regression (GPR). Among these, GPR demonstrated superior efficacy, effectively identifying regions of satisfactory performance.

Power systems are transitioning toward renewable sources and electrification, introducing significant uncertainties in generation and demand that optimal power flow (OPF) methods must manage. Traditional deterministic methods struggle with these variabilities. Additionally, addressing uncertainty in OPF calculations incurs computational burdens due to the need for multiple evaluations across various scenarios. This necessitates the use of advanced surrogate models. However, these models require significant data for training, and surrogate-based optimization can yield unreliable results due to inaccuracies in constraint handling. To overcome these issues, this paper proposes a novel surrogate-based hybrid chance-constrained optimal power flow (HCC-OPF) methodology employing enhanced multi-fidelity graph neural networks (EMF-GNN) as power flow solver surrogates. This model integrates low-fidelity and high-fidelity simulations to significantly reduce training cost while maintaining high accuracy. We further enhance the robustness and accuracy of OPF solutions through a hybrid methodology that selectively uses exact power flow solver to correct surrogate inaccuracies near critical thresholds. Extensive testing on multiple IEEE systems under high-dimensional correlated uncertainty of load and generation shows the EMF-GNN model outperforms existing single-fidelity and multi-fidelity models. Furthermore, the proposed HCC-OPF methodology accurately solves OPF problems across various system sizes and conditions, exhibiting scalability and efficiency. Additionally, it effectively manages N-1 security constraints to further exhibit its robustness under operational challenges.