An improved framework for multi-objective optimization of cementitious composites using Taguchi-TOPSIS approach

Rawat, S; Cui, H; Xie, Y; Guo, Y; Lee, CK; Zhang, Y

An improved framework for multi-objective optimization of cementitious composites using Taguchi-TOPSIS approach

Rawat, S

Cui, H Xie, Y Guo, Y Lee, CK Zhang, Y

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Expert Systems with Applications, 2025, 272
Issue Date:: 2025-05-05

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Field	Value	Language
dc.contributor.author	Rawat, S https://orcid.org/0000-0002-1985-7579
dc.contributor.author	Cui, H
dc.contributor.author	Xie, Y
dc.contributor.author	Guo, Y
dc.contributor.author	Lee, CK
dc.contributor.author	Zhang, Y
dc.date.accessioned	2025-07-16T23:18:42Z
dc.date.available	2025-07-16T23:18:42Z
dc.date.issued	2025-05-05
dc.identifier.citation	Expert Systems with Applications, 2025, 272
dc.identifier.issn	0957-4174
dc.identifier.issn	1873-6793
dc.identifier.uri	http://hdl.handle.net/10453/188497
dc.description.abstract	The traditional Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodology is commonly used for the multi-objective optimization of cementitious composites, allowing the simultaneous optimization of various mechanical and physical properties. Due to the significant scale differences among these properties, such as target strength (ranging from tens to hundreds) and strain (typically 0–1%), normalization is essential for accurate comparison. However, current civil engineering practices often employ fixed normalization methods, which may not always lead to optimal performance. This study addresses this limitation by proposing a novel framework for evaluating normalization methods within the TOPSIS process. The framework integrates metrics such as the Ranking Consistency Index (RCI), Spearman Correlation (SC), Rank Variance (RV), plurality voting, and Pareto dominance sorting to identify and exclude unsuitable normalization techniques. It was validated using three experimental datasets: hybrid fibre engineered cementitious composites, recycled aggregate concrete, and geopolymer concrete. The results showed considerable variation in optimization outcomes depending on the normalization method. For the tested datasets, the framework identified the Linear max–min and Lai and Hwang methods as superior due to their higher RCI, SC and lower RV, and these methods also resulted in optimal properties, thereby confirming the effectiveness of the framework. Overall, the study highlights the critical role of selecting suitable normalization methods in multi-response optimization and demonstrates how the proposed framework improves optimization accuracy.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Expert Systems with Applications
dc.relation.isbasedon	10.1016/j.eswa.2025.126732
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	An improved framework for multi-objective optimization of cementitious composites using Taguchi-TOPSIS approach
dc.type	Journal Article
utslib.citation.volume	272
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Advanced Manufacturing (CAM)
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-07-16T23:18:40Z
pubs.publication-status	Published
pubs.volume	272

Abstract:

The traditional Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodology is commonly used for the multi-objective optimization of cementitious composites, allowing the simultaneous optimization of various mechanical and physical properties. Due to the significant scale differences among these properties, such as target strength (ranging from tens to hundreds) and strain (typically 0–1%), normalization is essential for accurate comparison. However, current civil engineering practices often employ fixed normalization methods, which may not always lead to optimal performance. This study addresses this limitation by proposing a novel framework for evaluating normalization methods within the TOPSIS process. The framework integrates metrics such as the Ranking Consistency Index (RCI), Spearman Correlation (SC), Rank Variance (RV), plurality voting, and Pareto dominance sorting to identify and exclude unsuitable normalization techniques. It was validated using three experimental datasets: hybrid fibre engineered cementitious composites, recycled aggregate concrete, and geopolymer concrete. The results showed considerable variation in optimization outcomes depending on the normalization method. For the tested datasets, the framework identified the Linear max–min and Lai and Hwang methods as superior due to their higher RCI, SC and lower RV, and these methods also resulted in optimal properties, thereby confirming the effectiveness of the framework. Overall, the study highlights the critical role of selecting suitable normalization methods in multi-response optimization and demonstrates how the proposed framework improves optimization accuracy.

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http://hdl.handle.net/10453/188497