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Evaluating Long-Term Durability of Decorative Paints Through Wet Scrub Resistance

Author

Listed:
  • Vaida Dobilaitė

    (Institute of Architecture and Construction, Kaunas University of Technology, 44405 Kaunas, Lithuania)

  • Milda Jucienė

    (Institute of Architecture and Construction, Kaunas University of Technology, 44405 Kaunas, Lithuania)

  • Kęstutis Miškinis

    (Institute of Architecture and Construction, Kaunas University of Technology, 44405 Kaunas, Lithuania)

  • Valdas Paukštys

    (Institute of Architecture and Construction, Kaunas University of Technology, 44405 Kaunas, Lithuania)

Abstract

The durability of interior coatings is an important factor in the environmental performance of buildings, as the service life of the coatings directly determines the frequency of maintenance, material costs, and the overall life cycle impact. This study proposes the use of wet scrub resistance as a functional indicator of durability, providing an open dataset of commercial paints, analyzing their performance trends, and developing an integrated assessment framework. Data were collected through long-term tests according to EN ISO 11998 and EN 13300 standards from 2004 to 2025, ensuring the reliability and comparability of the results. The analysis shows that 56.8% of the tested paints met resistance class 1 and 31.5% met resistance class 2, meaning that these two classes account for almost 90% of all samples. Only around 10% of the paints were classified as class 3, while the share of the worst paints (classes 4–5) was only 1.6%. Long-term data show that class 1 has remained dominant for many years, exceeding 80% in some periods, but an increase in class 2 paints has been observed in recent years. The results of the study provide a quantitative basis for assessing the durability of coatings, allow for the prediction of maintenance intervals and analysis of technological advances, and facilitate data-driven decision-making, including the selection of sustainable building materials. The structured and standardized nature of the dataset also allows for its application in data-driven materials science, including the future development of machine learning models for predicting the durability of coatings and optimizing paint formulations based on sustainability criteria.

Suggested Citation

  • Vaida Dobilaitė & Milda Jucienė & Kęstutis Miškinis & Valdas Paukštys, 2026. "Evaluating Long-Term Durability of Decorative Paints Through Wet Scrub Resistance," Sustainability, MDPI, vol. 18(8), pages 1-15, April.
  • Handle: RePEc:gam:jsusta:v:18:y:2026:i:8:p:3794-:d:1918138
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