Data-driven compressive strength analysis of nano/micro cellulose-modified cement composites for sustainable design

• Machine learning framework for sustainable cellulose-cement composites is proposed • Strength predictive model for cellulose composites is developed by X-SVR algorithm • Comprehensive database is established for cellulose-modified cement systems • Quantitative analysis reveals key input variable contributions • Cellulose-fly ash synergy enhances compressive strength development Recent advances in concrete technology have identified cellulose as a promising natural fibre for sustainable constructions due to its widespread availability and excellent mechanical properties. However, predicting the mechanical performance of cellulose-modified cement composites remains challenging due to mix design variety and complex interfacial interactions. This research develops a data-driven framework to address the challenges in numerically describing the effect of diverse mixtures and cellulose, to provide robust predictions of the compressive strength of cellulose-modified cement composites. To do so, a comprehensive database is constructed by combining literature data and experimental results. The Extended Support Vector Regression (X-SVR) learning algorithm is employed in correlating strength development with diverse input variants, from which the strength class of cement, water/binder ratio, cellulose dosage, particle size of cellulose, and curing age are identified as the key influential parameters. The study presents three key innovations: construction of the first comprehensive multi-scale database spanning nano to micro cellulose in both pure cement and fly ash-blended systems, first application of X-SVR algorithm to cellulose-cement composites, and introduction of a fly ash contribution factor enabling strength prediction across fly ash blended systems. Sensitivity analysis based on the verified X-SVR aided modelling reveals that water/binder ratio exhibits the highest contribution (32%), with curing age (28%) and cellulose particle size (27%) identified as the next most critical factors governing compressive strength in cellulose-modified pure cement blends. The extended model indicates that cellulose dosage accounts for 15% of the strength contribution by fly ash.

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Publication Details

Journal
Applied Mathematical Modelling
Published
2026-03-03
DOI
https://doi.org/10.1016/j.apm.2026.116881
Primary Topic
Innovative concrete reinforcement materials
Type
article
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article

Data-driven compressive strength analysis of nano/micro cellulose-modified cement composites for sustainable design

Sujeeva Setunge, Yuguo Yu, Chamini Liyanage, Chamila Gunasekara et al.
Applied Mathematical Modelling
Innovative concrete reinforcement materials
article

Data-driven compressive strength analysis of nano/micro cellulose-modified cement composites for sustainable design

Sujeeva Setunge, Yuguo Yu, Chamini Liyanage, Chamila Gunasekara, Charith Jayasekara, David W. Law
article en

Abstract

• Machine learning framework for sustainable cellulose-cement composites is proposed • Strength predictive model for cellulose composites is developed by X-SVR algorithm • Comprehensive database is established for cellulose-modified cement systems • Quantitative analysis reveals key input variable contributions • Cellulose-fly ash synergy enhances compressive strength development Recent advances in concrete technology have identified cellulose as a promising natural fibre for sustainable constructions due to its widespread availability and excellent mechanical properties. However, predicting the mechanical performance of cellulose-modified cement composites remains challenging due to mix design variety and complex interfacial interactions. This research develops a data-driven framework to address the challenges in numerically describing the effect of diverse mixtures and cellulose, to provide robust predictions of the compressive strength of cellulose-modified cement composites. To do so, a comprehensive database is constructed by combining literature data and experimental results. The Extended Support Vector Regression (X-SVR) learning algorithm is employed in correlating strength development with diverse input variants, from which the strength class of cement, water/binder ratio, cellulose dosage, particle size of cellulose, and curing age are identified as the key influential parameters. The study presents three key innovations: construction of the first comprehensive multi-scale database spanning nano to micro cellulose in both pure cement and fly ash-blended systems, first application of X-SVR algorithm to cellulose-cement composites, and introduction of a fly ash contribution factor enabling strength prediction across fly ash blended systems. Sensitivity analysis based on the verified X-SVR aided modelling reveals that water/binder ratio exhibits the highest contribution (32%), with curing age (28%) and cellulose particle size (27%) identified as the next most critical factors governing compressive strength in cellulose-modified pure cement blends. The extended model indicates that cellulose dosage accounts for 15% of the strength contribution by fly ash.

Applied Mathematical ModellingVol. 157
Monash University (AU), RMIT University (AU)
Openalex Percentile: Top 37%
Innovative concrete reinforcement materials
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