Hybrid particle swarm optimization and support vector machine effectively reduces permeability in ceramsite concrete, indicating improved fire resistance.
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 0.96 x 10 − 10 m/s and R 2 = 0.89, outperforming default parameter SVM and Response Surface Methodology baselines by 13% and 34% error reduction, respectively. Sensitivity and ANOVA analyses identified water-cement ratio as the most influential factor. The optimized formulation reduces predicted post-fire permeability by 35% compared to a conventional mix. Practical mix-design guidelines: w/c of 0.30-0.35, superplasti-cizer dosage of 0.25%–0.35%, and ceramsite content of 55%–65% are proposed to achieve k < 1.0 x 10 − 10 m/s while retaining compressive strength above 32 MPa, streamlining the development of robust, fire-resilient linings for hy-drocarbon containment.
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Liu et al. (2025) studied this question.
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