Multi-Objective Prediction of the Mechanical Properties and Environmental Impact Appraisals of Self-Healing Concrete for Sustainable Structures
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Date
2022
Authors
Onyelowe, Kennedy C.
Ebid, Ahmed M.
Riofrio, Ariel
Baykara, Haci
Soleymani, Atefeh
Mahdi, Hisham A.
Jahangir, Hashem
Ibe, Kizito
Journal Title
Journal ISSN
Volume Title
Publisher
Sustainability
Abstract
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.
Description
Keywords
Self-healing concrete (SHC), Bacillus subtilis, Life cycle assessment (LCA), Environmental impact, Sustainable environment, Sustainable construction, Concrete strength and workability
Citation
Onyelowe, K.C.; Ebid, A.M.; Riofrio, A.; Baykara, H.; Soleymani, A.; Mahdi, H.A.; Jahangir, H.; Ibe, K. Multi-Objective Prediction of the Mechanical Properties and Environmental Impact Appraisals of Self-Healing Concrete for Sustainable Structures. Sustainability 2022, 14, 9573. https://doi.org/10.3390/su14159573