A Multiscale Mathematical Model for Predicting the Long-Term Environmental Impact of Recycled Construction Materials

Recycled construction materials are becoming increasingly important in ensuring sustainable development and reducing the environmental impact of the construction industry. However, predicting their long-term environmental effects remains challenging because of complex degradation mechanisms, material heterogeneity, and variable exposure conditions. This paper develops a multiscale mathematical model to evaluate the long-term environmental consequences of recycled construction materials by explicitly coupling microstructural degradation, mesoscale mechanical response, and macroscale lifecycle emissions. At the microscale, physicochemical processes such as hydration, carbonation, and leaching are described by ordinary differential equations of the form ^| =— kCn, calibrated using long-term leaching and carbonation data for recycled concrete aggregates. At the mesoscale, a finite element damage model represents the heterogeneous recycled composite (recycled aggregate, new mortar, old mortar, and interfacial transition zones) and predicts stiffness and strength loss under realistic loading and exposure histories. At the macroscale, a time-resolved environmental impact function integrates leachate toxicity, greenhouse gas emissions, and structural degradation into an Environmental Impact Index (EII) consistent with LCA practice. Compared with conventional non-recycled concrete, simulations over a 50-year service life indicate reductions of 30-60% in cumulative CO? emissions and 25-55% in EII when using recycled concrete aggregate, reclaimed asphalt pavement, or fly ash cement blends, while maintaining acceptable structural performance. Model predictions of leachate concentration and strength retention show good agreement with published long-term field and accelerated ageing data, with typical errors below 10-15%. The proposed framework thus provides a predictive tool for engineers and regulators to quantify environmental risks, optimize mix design and recycling strategies, and support circular-economy-oriented material selection.