Multivariate Classification of Heritage Building Materials for Sustainable Restoration and Retrofit

The conservation of heritage buildings requires non-invasive tools that can predict material performance while maintaining historical integrity and structural safety. This study introduces a multivariate statistical framework that integrates regression analysis, Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA) to classify seven traditional materials adobe, lime mortar, limestone, sandstone, marble, volcanic stone, and wood based on their mechanical, thermal, and moisture-related properties. This study aims to develop a validated multivariate framework for classifying traditional heritage materials based on their mechanical, thermal, and moisture-related properties to support sustainable restoration and retrofit design for classifying traditional materials based on their mechanical, thermal, and moisture-related properties to support sustainable restoration and retrofit design. Unlike prior research limited to single-material assessments, this study standardizes and analyzes data from fourteen peer-reviewed sources using regression models, Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA), complemented by pilot non-destructive validation tests on lime mortar, sandstone, limestone, and marble. The framework compiles and standardizes datasets from fourteen peer-reviewed sources into a unified predictive model. The framework was validated through pilot testing using non-invasive methods (density, ultrasonic pulse velocity, rebound hardness), which confirmed the statistical predictions of robustness versus moisture vulnerability. Advanced cluster solutions identified conservation-relevant subgroups, enabling engineers to distinguish between moisture-sensitive low-density materials and durable lithic stones, with direct implications for sustainable restoration and retrofit practices. The originality of this study lies in transforming fragmented datasets into a validated, decision-support tool that can be embedded into Historic Building Information Modeling (HBIM) platforms for predictive diagnostics, compatibility assessment, and energy-efficient retrofit planning in heritage structures. This study provides the first validated cross-material statistical framework linking traditional conservation materials with predictive digital-modeling tools. This framework further demonstrates that the application of regression, Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA) enables quantitative prediction of material performance through non-destructive parameters. The integration of these techniques provides interpretive value beyond descriptive classification, facilitating preventive diagnostics, compatibility assessments, and energy-oriented retrofit planning within HBIM systems.