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Optimizing Shear Strength Performance of Incinerator Bottom Ash Composites Using Neural Networks

  • Geotechnical Engineering
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KSCE Journal of Civil Engineering Aims and scope

Abstract

This study investigates the behavior of stabilized Incinerated Bottom Ash (BA) reinforced with fiber when tested under Triaxial conditions. A number of lab-based Unconsolidated Undrained (UU) tests were performed on BA specimens with changing cement content (0–10%) and fiber length (6–18 mm). Cement stabilization enhances shear strength, while fiber inclusion shifts behavior from brittle to ductile. Initially, unconsolidated-undrained strength increases with fiber addition, then decreases. Optimal results were achieved with 0.5% 12 mm fibers and 7% cement, showing a 71.43% strength improvement after 28 days. Prediction of shear strength using a Back Propagation neural network and Adaptive Neuro-fuzzy system yields stronger correlations than regression analysis. The properties of stabilizer and reinforcement on compaction, shear strength, and secant modulus of elasticity (E50) were investigated. PP fiber inclusion enhances shear strength and ductility, with cement content and curing period as dominant factors. Scanning electron microscopy shows stabilizer addition leads to cementitious compounds binding bottom particles, increasing interaction between ash and fiber, thus affecting overall shear strength.

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Correspondence to Sanjeev Kumar.

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Kumar, S., Singh, D. Optimizing Shear Strength Performance of Incinerator Bottom Ash Composites Using Neural Networks. KSCE J Civ Eng (2024). https://doi.org/10.1007/s12205-024-1302-2

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  • DOI: https://doi.org/10.1007/s12205-024-1302-2

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