Reshaping concrete

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Less Economically Developed Countries (LEDCs) struggle to meet the demand for affordable housing in their growing cities. There are several reasons for this, but a major constraint is the high cost of construction materials. In LEDCs, material costs can constitute 60 to 80 percent of the total cost of residential construction. Nonetheless, their construction mimics the materially inefficient practices of the More Economically Developed Countries (MEDCs), which were developed to reduce labor over material costs. As a result, prismatic beams and flat slabs are often used despite their structural inefficiency. The mounting use of steel-reinforced concrete structures in LEDC cities also raises concern for the environmental costs of construction; construction accounts for 20-30 percent of LEDC carbon emissions. This research addresses these challenges with a flexible and accessible methodology for the design and analysis of materially efficient concrete elements that may reduce the economic and environmental costs of urban construction. Designed for the constraints of LEDCs, structural elements are optimized to reduce the embodied carbon associated with the concrete and reinforcing steel while resisting the required loads of a standard building structure. The optimization method includes a novel approach to 3D-shape parameterization, as well as a decoupled analytical engineering analysis method that accounts for the key failure modes and constraints of reinforced concrete design. This method is then built into an open-source toolkit, combined with machine learning for real-time analysis and visualization, and tested using lab- and full-scale prototypes. The goal of this research is to present several generalizable methods that are applicable and accessible to LEDC building designers. These methods can enable the design of concrete elements for multiple performance criteria such as structural behavior, acoustic transmission, and thermal mass. They can also enable an accessible design practice through machine learning, real-time iterative workflows, and visualization tools that include the end-user in the architectural design process. This paper provides a high-level overview of ongoing research that explores how materially efficient design methods might enable sustainable development through low-cost, low-carbon concrete structural systems for affordable housing in LEDCs.