Pavement condition prediction under small-sample conditions using a particle swarm optimization-based support vector machine

Precise forecasting of the Pavement Condition Index (PCI) is essential for efficient pavement maintenance planning within budget constraints. This research formulates and evaluates three models: Support Vector Machine (SVM), Back Propagation Neural Network (BPNN), and Particle Swarm Optimization-optimized SVM (PSO-SVM), using field data from two roads in China: one ordinary and one expressway. Five influential factors-road age, annual average daily traffic per lane, average annual temperature, annual precipitation, and relative humidity-function as inputs, with PCI as the resultant output. PSO is used to tune the hyperparameters of SVMs, specifically c and γ. The results indicate that PSO-SVM, as an optimization-enhanced model, achieves higher prediction accuracy and more stable comparative performance than the baseline SVM and BPNN under the present small-sample conditions. Random Forest (RF) study indicates that road age, traffic volume, and temperature are the primary determinants influencing PCI. This method provides practical guidance for pavement repair decision-making in frigid regions. The model's performance was assessed by a 70/30 hold-out split and 5-fold cross-validation to mitigate partition bias in a small-sample context.