Predicting and evaluating the engineering properties of civic garbage torched bottom ash and sisal fibre-reinforced earth blocks

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Abstract

This study examines the impact of civic garbage torched bottom ash (CGTBA), sisal fibre, and cement content on the compressed stabilized earth blocks (CSEB) with respect to their compressive strength and flexural strength. The properties are predicted using artificial neural network (ANN) analysis and response surface methodology (RSM). The study contributes to sustainable construction by emphasizing innovative solutions to reduce waste and improve building materials. The experiment includes four different cement concentrations (6 %, 8 %, 10 %, and 12 %), CGTBA contents (10 %, 20 %, 30 %, and 40 %), and sisal fibre contents (0,25 %, 0,50 %, 0,75 %, and 1,00 %). ANN models predict compressive and flexural strengths with high accuracy (R² values: 0,98189 and 0,94951, respectively). Optimization yields a desirability index of 0,724. A detailed comparison between actual and predicted values demonstrates close alignment, validating the ANN-RSM technique's efficacy in estimating responses and identifying influential parameters. Additionally, the ANN-RSM approach optimizes CSEB performance, providing valuable insights into parameter optimization. The use of CSEB stabilized with cement, CGTBA, and sisal fibre has the potential to transform into a sustainable approach to construction materials.