Eco-efficient Structure-Material Integrated Optimization of Basalt Fiber-Reinforced SMA-13 for Sustainable Pavements Using Adaptive Chaotic PSO and Phenomenological Constitutive Models

Mitigating the life-cycle environmental footprint and resource depletion caused by frequent pavement rehabilitations is a critical challenge for cleaner production in transportation infrastructure. A structure-material integrated optimization model for high-strength stone mastic asphalt (SMA-13) reinforced with basalt fiber (BF) was proposed. Mechanical responses and fatigue evolution of mixtures with varying BF contents (0–0.4%) and PG88/PG100 binders were characterized through dynamic modulus and three-dimensional strength tests. The inherent tension-compression asymmetry of asphalt mixtures was significantly mitigated by BF incorporation. An optimal BF threshold was identified at 0.2%, where the microscopic bridging effect was maximized, promoting a failure mode transition from brittle fracture to quasi-ductile damage. Gaussian decay and exponential saturation phenomenological constitutive models were introduced to characterize the critical threshold features of BF. Furthermore, an adaptive chaotic particle swarm optimization (ACPSO) algorithm coupled with the finite element method (FEM) was developed. The global optimal solution was determined as PG100 binder, 0.23% BF, and 4 cm thickness. A 10.3% cost reduction was achieved by ACPSO compared to conventional particle swarm optimization (PSO). Although the initial construction cost increased by 25.8% over the traditional benchmark, the predicted fatigue life was extended by 220%, reducing the cost-effectiveness ratio (CER) by 60.8%. Ultimately, by significantly minimizing future maintenance frequency and raw material consumption, this value engineering approach provides a practical cleaner production framework for reducing the life-cycle environmental burden of perpetual pavements.