Skip to main content
Have a personal or library account? Click to login
Multi-response optimization of red mud and sand proportions in paving block production via response surface methodology (RSM) Cover

Multi-response optimization of red mud and sand proportions in paving block production via response surface methodology (RSM)

Open Access
|Sep 2026

Abstract

Rising cement and natural aggregate prices have intensified the need for sustainable and optimized paving block mixtures incorporating industrial by-products. This study investigates the multi-response optimization of red mud and stone content in paving block production using response surface methodology (RSM). Red mud, an alkaline by-product of the bauxite refining industry, was utilized as a partial cement replacement to reduce material consumption and environmental impact. The objective was to determine the optimal proportions that satisfy the performance requirements of SNI 03-0691-1996. An RSM-based experimental design evaluated the interactive effects of red mud substitution (0–20% by cement weight) and stone proportion on compressive strength, bulk density, water absorption, and abrasion resistance. Specimens were produced using portland composite cement, sand, stone, water, and pre-treated red mud. Regression models and response surface plots were developed for prediction and optimization. Results indicate that approximately 18.79% red mud replacement with an optimized aggregate proportion yields the best overall performance. The optimized mix achieved a 28-day compressive strength of 22.91 MPa, satisfying SNI class B criteria, while demonstrating reduced water absorption and abrasion loss. This study introduces a statistically robust multi-response RSM approach for sustainable paving block mix design.

DOI: https://doi.org/10.22630/srees.11102 | Journal eISSN: 2543-7496 | Journal ISSN: 1732-9353
Language: English
Page range: 241 - 250
Submitted on: Jan 28, 2026
Accepted on: May 13, 2026
Published on: Sep 30, 2026
Published by: Warsaw University of Life Sciences - SGGW Press
In partnership with: Paradigm Publishing Services

© 2026 Erwin Sutandar, Tavio, Daniel Christianto, published by Warsaw University of Life Sciences - SGGW Press
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.