Sustainability-focused pavement management under climate variability

This paper explores transformative strategies in urban pavement management by integrating automated technologies and climate-adaptive innovations, specifically under the challenging conditions posed by climate change (CC). The research focuses on implementing advanced asphalt binders coupled with automated construction processes. We employ a dual approach, using machine learning (ML) algorithms and predictive analytics to optimize the environmental impact assessments, particularly targeting CO2 emissions throughout the pavement life cycle. Our findings reveal that incorporating climate-resilient innovations can significantly decrease maintenance frequencies by approximately 20% and extend the pavement lifespan by up to 15 years, aligning closely with climate projections from 2023 to 2060. A detailed life cycle cost analysis (LCCA) suggests potential fiscal savings of up to $500 million in national highway maintenance over the next four decades. Moreover, our automated predictive model boasts accuracy with a coefficient of determination (R2) of 0.85, enhancing the precision and timeliness of maintenance interventions. This paper underscores the imperative of merging automated technologies with climate-adaptive innovations to bolster the sustainability and resilience of urban infrastructures, thereby advancing economically and environmentally sustainable urban development practices. This aligns with the Sustainable Development Goals, emphasizing the urgent need for sustainable cities and communities.