Experimental and Machine Learning Strategies with SHAP and PDP Analysis of Self-Healing Characteristics of Bio-concrete Using Bacillus Cereus and Steel Slag
Source abstract
Abstract
Self-healing concrete is a cement-based substance that can mend itself from a hairline crack. This experiment examines the effectiveness of the gram-positive cell bacteria Bacillus cereus on the self-healing characteristics of steel-slag bio-concrete (SSBC). The study's ultimate goal is to discern the best proportions of bacteria to incorporate into bio-concrete by examining the fresh, hardened, and self-healing qualities, as well as machine learning (ML) approaches with SHAP and PDP analysis to forecast crack width. As a result, five distinct mix designs were developed by substituting steel slag for varying percentages of sand, e.g., 0%, 10%, 20%, 30%, and 40%. The behavior of steel-slag concrete (SSC) was evaluated through fresh, mechanical, and non-destructive tests (NDT) to determine the optimal percentage of steel slag. After finding the ideal percentage of steel slag, three different bacterial solutions of 12.5 × 10 5 CFU/ml, 25 × 10 5 CFU/ml, and 31.25 × 10 5 CFU/ml were mixed with calcium lactate as a feeder to make SSBC. The compressive strength of the mix, which consisted of 12.5 × 10 5 CFU/ml, 25 × 10 5 CFU/ml, and 31.25 × 10 5 CFU/ml B. cereus bacteria, along with a constant 30% steel slag, increased by 13.85%, 21.75%, and 31.57% at 28 days. In the NDT test, the assessment of the rebound hammer and ultrasonic pulse velocity (UPV) showed a similar tendency to the compressive strength test. Visual observation and scanning electron microscopy (SEM) were done to examine the crack-healing tendency of the SSBC. SEM images revealed that adding B. cereus improved the physical characteristics of the concrete by forming CaCO 3 crystals, which heal the microcracks. The CatBoost model exhibits greater precision and reliability, even though the regression coefficient (R 2 ) of the random forest (RF) and artificial neural networks (ANN) models is similarly outstanding.
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Research context
OpenAlex topics: Microbial Applications in Construction Materials · Bacterial biofilms and quorum sensing · Odor and Emission Control Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (100+)
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