A durable and sustainable precast concrete bridge truss system using smart materials

This research aims at developing a new durable and sustainable system for bridge infrastructure. Steel is typically used for bridge truss systems because of its lightweight, high strength in both compression and tension, and simplicity of fabrication. However, the durability and sustainability of steel bridges, especially in highly corrosive environments, has been a major concern. Due to their poor strength in tension, concrete truss systems, on the other hand, have not been utilized extensively in bridge construction projects. In this study, an innovative precast concrete truss system prestressed utilizing a traditional prestressing system and a novel material known as shape memory alloy (SMA) is proposed. SMA is a class of smart material that can remember its original shape after being excessively deformed. The shape memory feature is typically triggered by heating the material. The feasibility of the proposed truss system for transportation infrastructure and its sustainability is assessed by calculating its embodied carbon footprint. In the proposed system, the Howe truss' bottom chord is prestressed utilizing conventional high-strength steel (HSS) reinforcement, while the vertical tension members are thermally prestressed using SMA bars made of FeMnSi alloy. Comparing the SMA bar to the HSS reinforcements in their chemical composition and manufacturing processes allows us to assess the embodied carbon footprint of the proposed system compared to conventional systems. To comply with the service and strength limit states set forth by the American Association of State Highway and Transportation Officials (AASHTO), the proposed concrete truss is designed and numerically validated with respect to a prototype bridge constructed with conventional AASHTO type-II I-girders. Results show that the designed concrete truss meets the AASHTO service and strength limit states while embodying 26% fewer carbon emissions and weighing 37% less than the reference model.