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Applied Sciences
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Building Materials for Sustainable Restoration
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Building Materials for Sustainable Restoration

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Materials Science and Engineering".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 307

Special Issue Editor

Dr. Stefania Liuzzi

E-Mail Website
Guest Editor
Dipartimento di Architettura Costruzione e Design, Politecnico di Bari, Via Orabona n. 4, 70125 Bari, Italy
Interests: building; building materials; construction; construction engineering; construction materials; sustainable construction; sustainable architecture; built environment; architecture; sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The restoration of existing buildings—particularly historical and culturally significant structures—plays a crucial role in the global transition toward sustainability. This Special Issue focuses on the development, characterization, and application of materials that support environmentally responsible restoration practices. It brings together research on traditional and innovative materials, including natural, recycled, bio-based, and low-impact products, assessing their performance in terms of durability, compatibility, and environmental footprint. Special attention is given to solutions that balance technical performance with respect for historical authenticity and architectural value. Topics include the life cycle assessment of materials, the improvement in energy efficiency in retrofitting, and the integration of new technologies in heritage conservation. By bridging material science, conservation theory, and sustainable design, this Special Issue aims to highlight strategies that enable long-lasting, resource-efficient, and culturally sensitive restoration. The goal is to foster interdisciplinary dialogue and provide a scientific foundation for future policies and practices in sustainable restoration.

Dr. Stefania Liuzzi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sustainable restoration
  • building materials
  • cultural heritage conservation
  • eco-friendly materials
  • recycled construction materials
  • bio-based materials
  • energy efficiency

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Published Papers (1 paper)

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Research

17 pages, 1277 KB  
Article
Multivariate Classification of Heritage Building Materials for Sustainable Restoration and Retrofit
by Mohammed A. Albadrani
Appl. Sci. 2025, 15(22), 12169; https://doi.org/10.3390/app152212169 - 17 Nov 2025
Viewed by 232
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Building Materials for Sustainable Restoration)
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