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<p>Traditionally, Taguchi methods have been associated with single-response optimization, posing a challenge when dealing with multiple response problems. To address this limitation, this study presents five multi-response optimization methods integrated within the Taguchi design of experiments framework to optimize recycled aggregate concrete (RAC) mixtures. The methods examined include Assignment of Weights (AW), Weighted Grey Relational Analysis (WGRA), Standard Grey Relational Analysis (SGRA), Factor Analysis (FA), and the Multi-Response Normalized Value (MRNV) method. Unlike existing methods such as WGRA and SGRA, the MRNV approach employs a straightforward arithmetic normalization and aggregation procedure without requiring pairwise comparisons, weighting coefficients, or complex matrix operations. This makes MRNV computationally efficient and easy to implement using basic spreadsheet tools, offering greater transparency and reproducibility. A case study involving eleven performance criteria including compressive strength at various ages, workability, and durability indicators is used to evaluate the effectiveness of each method. All five approaches converge on the same optimal factor configuration (A1B1C2D2E2), demonstrating consistency. Results from ANOVA show that MRNV identifies the same statistically significant factors as SGRA, while outperforming WGRA in detecting the influence of Factor E. Pseudo-R<sup>2</sup> values exceeding 90% across all methods further validate the models’ explanatory power. These findings establish MRNV as a computationally accessible and effective alternative for multi-response optimization in sustainable concrete design.</p>

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Application of Taguchi-Based Multi-response Methods for Optimizing the Properties of Recycled Aggregate Concrete

  • 期刊论文

  • 发表日期：

    2025年10月13日

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  • Paterne Cirhuza Badesire,
  • Noëlie Di Cesare,
  • Xuan Hong Vu,
  • Chérif Bishweka

摘要

Traditionally, Taguchi methods have been associated with single-response optimization, posing a challenge when dealing with multiple response problems. To address this limitation, this study presents five multi-response optimization methods integrated within the Taguchi design of experiments framework to optimize recycled aggregate concrete (RAC) mixtures. The methods examined include Assignment of Weights (AW), Weighted Grey Relational Analysis (WGRA), Standard Grey Relational Analysis (SGRA), Factor Analysis (FA), and the Multi-Response Normalized Value (MRNV) method. Unlike existing methods such as WGRA and SGRA, the MRNV approach employs a straightforward arithmetic normalization and aggregation procedure without requiring pairwise comparisons, weighting coefficients, or complex matrix operations. This makes MRNV computationally efficient and easy to implement using basic spreadsheet tools, offering greater transparency and reproducibility. A case study involving eleven performance criteria including compressive strength at various ages, workability, and durability indicators is used to evaluate the effectiveness of each method. All five approaches converge on the same optimal factor configuration (A1B1C2D2E2), demonstrating consistency. Results from ANOVA show that MRNV identifies the same statistically significant factors as SGRA, while outperforming WGRA in detecting the influence of Factor E. Pseudo-R2 values exceeding 90% across all methods further validate the models’ explanatory power. These findings establish MRNV as a computationally accessible and effective alternative for multi-response optimization in sustainable concrete design.