Abstract
Maintaining the integrity of oil-storage pits lined with ceramsite-aggregate concrete pebble substrates is crucial for preventing environmental contamination, especially during fire events when a sudden increase in permeability can result in hydrocarbon leakage. Traditional trial-and-error mix designs struggle to navigate the high-dimensional parameter space needed to maintain ultra-low permeability after exposure to temperatures up to 50°C. Data-driven optimization offers a systematic alternative. This study introduces an enhanced hybrid framework combining Particle Swarm Optimization (PSO) with a Support Vector Machine (SVM) regression model to predict and minimize the permeability coefficient k of fireproof mortar mixes. An adaptive PSO algorithm incorporates dynamic inertia weighting and Gaussian mutation to efficiently tune the SVM’s RBF-kernel hyperparameters (C,γ). A dataset of 240 mortar specimens varying water-cement ratio and superplasticizer dosage was evaluated under both ambient and post-fire conditions. High-definition microstructural analyses (fluorescence microscopy, SEM, MIP, XRD, TGA) elucidated the mechanistic links between mix composition, pore-network evolution, and thermal damage. On a hold-out test set, the optimized PSO-SVM achieved an RMSE of
Keywords
Get full access to this article
View all access options for this article.
