MULTI-OBJECTIVE OPTIMIZATION OF COST, SCHEDULE, AND CARBON FOOTPRINT FOR STRUCTURAL CONCRETE FOUNDATIONS IN CHALLENGING GEOTECHNICAL TERRAINS

Abstract

As the world's construction industry is undergoing a critical conversion, the industry is facing a pressing need to reduce the impact of climate change, speed up project delivery, and ensure economic viability. Design and construction of structural concrete foundations, especially in difficult geotechnical environments, is one of the areas where these needs converge. This article thoroughly investigates the multi-objective optimization (MOO) of the cost, schedule and carbon footprint of concrete foundations, in complex soil conditions. Geologic conditions, such as expansive soils, low elevations, soft clays and karst topography, create uncertainty that can greatly increase the complexity of foundation design. The traditional single objective or sequential design approaches are not able to account for the various tradeoffs that exist between minimizing financial cost, construction time and reduction in embodied carbon emissions. This paper is a synthesis of advanced geotechnical engineering and structural mechanics, life cycle assessment (LCA), and evolutionary computational algorithms that provide a solid framework for MOO in foundation engineering. We explain the mathematical modeling of the three constraints, discuss the performance of the algorithms like NSGA-II and NSGA-III, and discuss some innovations in materials for low carbon concrete. In addition, because the article uses detailed case studies and an analysis of digital twin integrations, it provides a clear illustration of how multi-objective optimization can take a risk-laden, carbon-heavy foundation engineering practice and make it one of the most efficient, sustainable and predictable disciplines in existence.