Sustainable Design of Self-Consolidating Green Concrete with Partial Replacements for Cement through Neural-Network and Fuzzy Technique
- Publisher:
- MDPI
- Publication Type:
- Journal Article
- Citation:
- Sustainability, 15, (6), pp. 4752
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In order to achieve a sustainable mix design this paper evaluates self consolidating green concrete SCGC properties by experimental tests and then examines the design parameters with an artificial intelligence technique In this regard cement was partially replaced in different contents with granulated blast furnace slag GBFS powder volcanic powder fly ash and micro silica Moreover fresh and hardened properties tests were performed on the specimens Finally an adaptive neuro fuzzy inference system ANFIS was developed to identify the influencing parameters on the compressive strength of the specimens For this purpose seven ANFIS models evaluated the input parameters separately and in terms of optimization twenty one models were assigned to different combinations of inputs Experimental results were reported and discussed completely where furnace slag represented the most effect on the hardened properties in binary mixes and volcanic powder played an effective role in slump retention among other cement replacements However the combination of micro silica and volcanic powder as a ternary mix design successfully achieved the most improvement compared to other mix designs Furthermore ANFIS results showed that binder content has the highest governing parameters in terms of the strength of SCGC Finally when compared with other additive powders the combination of micro silica with volcanic powder provided the most strength which has also been verified and reported by the test results
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