Multi-Objective Prediction of the Mechanical Properties and Environmental Impact Appraisals of Self-Healing Concrete for Sustainable Structures - Onyelowe, Kennedy C.; Ebid, Ahmed M.; Riofrio, Ariel; Baykara, Haci; Soleymani, Atefeh; Mahdi, Hisham A.; Jahangir, Hashem; Ibe, Kizito (Sustainability, 2022)
As the most commonly used construction material, concrete produces extreme amounts of carbon dioxide (CO2) yearly. For this resulting environmental impact on our planet, supplementary materials are being studied daily for their potentials to replace concrete constituents responsible for the environmental damage caused by the use of concrete. Therefore, the production of bioconcrete has been studied by utilizing the environmental and structural benefit of the bacteria, Bacillus subtilis, in concrete. This bio-concrete is known as self-healing concrete (SHC) due to its potential to trigger biochemical processes which heal cracks, reduce porosity, and improve strength of concrete throughout its life span. In this research paper, the life cycle assessment (LCA) based on the environmental impact indices of global warming potential, terrestrial acidification, terrestrial eco-toxicity, freshwater eco-toxicity, marine eco-toxicity, human carcinogenic toxicity, and human noncarcinogenic toxicity of SHC produced with Bacillus subtilis has been evaluated. Secondly, predictive models for the mechanical properties of the concrete, which included compressive (Fc), splitting tensile (Ft), and flexural (Ff) strengths and slump (S), have been studied by using artificial intelligence techniques. The results of the LCA conducted on the multiple data of Bacillus subtilis-based SHC mixes show that the global warming potential of SHC-350 mix (350 kg cement mix) is 18% less pollutant than self-healing geopolymer concrete referred to in the literature study. The more impactful mix in the present study has about 6% more CO2 emissions. In the terrestrial acidification index, the present study shows a 69–75% reduction compared to the literature. The results of the predictive models show that ANN outclassed GEP and EPR in the prediction of Fc, Ft, Ff, and S with minimal error and overall performance.
Keywords: Self-healing concrete (SHC); Bacillus subtilis; Life cycle assessment (LCA); Environmental impact; Sustainable environment; Sustainable construction; Concrete strength and workability