Structural Performance of Self-Healing Bacteria Treated Recycled Aggregate Concretes

Sustainability in concrete production could be fully exploited when sustainability of its components (coarse aggregate, cement, sand, and water) is given due individual attent...tion. For example, recycled concrete aggregate (RCA) generated from construction and demolishing waste (CDW) has been established as one of the popular environmental-friendly replacements for natural coarse aggregates (NCA) in new concrete construction. Unfortunately, recycled aggregate concrete (RAC) possesses two interfacial transition zones (ITZ) compared to just one ITZ in natural aggregate concrete (NAC). This leads to more water absorption and weaker concrete structurally. Fortunately, treating RCA by removing the attached mortar or sealing it significantly improves its properties. Hence, the first phase of the proposed research aims to conduct an assessment of the performance of treating RCA using self-healing bacteria. In addition to addressing the concern of unsustainable use of NCA, the environmental drawbacks of Portland cement (generation of considerable amount of carbon dioxide and high energy consumption in its production) cannot be overemphasized. Hence, use of alkali-activated slag, as an alternative binder, to increase the degree of sustainability in the concrete will also be explored in the proposed study. Both fresh and hardened concrete properties will be evaluated for different mixes containing Portland cement, alkali-activated slag, natural aggregate, untreated recycled aggregate, and treated recycled aggregate. The second phase of the work will select an optimum mix of the treated RCA and deploy it to fabricate large scale structural elements, namely, beam-column joints with natural aggregate, on one hand, and treated and untreated recycled aggregates on the other hand to establish their performance beyond the small-scale evaluation. Finally, numerical models of the structural tests will be developed and used to verify the experimental results for more effective predictive purposes.