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Journals / Gazi University Journal of Science / 2026 / Volume: 38 Issue: 3

Evaluation of Deterioration in Brick Materials in Monumental Buildings Using CNN Models

Murat Şahin(FIRAT UNIVERSITY, FACULTY OF ARCHITECTURE, DEPARTMENT OF ARCHITECTURE)0000-0001-6733-1136
Seda Arslan Tuncer(FIRAT UNIVERSITY, FACULTY OF ENGINEERING, DEPARTMENT OF SOFTWARE ENGINEERING, SOFTWARE ENGINEERING PR.)0000-0001-6472-8306
Çağla Danacı(FIRAT UNIVERSITY, FACULTY OF ENGINEERING, DEPARTMENT OF SOFTWARE ENGINEERING, SOFTWARE ENGINEERING PR.)0000-0003-2414-1310
Gökhan Genç(SAMSUN UNIVERSITY, FACULTY OF ARCHITECTURE AND DESIGN)0000-0002-5753-4885
Pages
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Publication Date
2025-09-01
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This study, which will include planning the basic principles of interventions to be applied to cultural property, will prevent the problems encountered in deterioration and deformation, and objective solutions will be produced.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The materials of historical structures undergo deterioration and deformation over time due to physical, chemical, and biological factors.
📊
Key Findings & Data:However, the detection of such deteriorations involves significant labor, time, and cost, and incorrect diagnoses may lead to irreversible damage and structural issues.
💡
Conclusion & Impact:This study, which will include planning the basic principles of interventions to be applied to cultural property, will prevent the problems encountered in deterioration and deformation, and objective solutions will be produced.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Deep Learning · Explainable Artificial Intelligence · Brick Deterioration

Abstract

The materials of historical structures undergo deterioration and deformation over time due to physical, chemical, and biological factors. Regular inspections by experts are essential for the preservation of these structures. However, the detection of such deteriorations involves significant labor, time, and cost, and incorrect diagnoses may lead to irreversible damage and structural issues. This study aims to minimize human-induced errors in identifying types of deterioration in structures by utilizing deep learning-based convolutional neural network (CNN) models, a subfield of artificial intelligence. With this study, deteriorations in historical brick buildings, which have an important place among historical buildings, will be detected with non-destructive methods, thus contributing to the preservation of historical buildings and the literature. Within the scope of the study, a total of 1709 data consisting of physical deterioration (cracks and fractures, joint discharge, abrasion and piece loss), chemical deterioration and biological deterioration types that are frequently encountered in brick materials in historical buildings were discussed. The classification process was carried out with the inputs given to the ResNet-18, ResNet-50, ResNet-101, VGG16 and VGG19 networks. Model performances were evaluated with precision, recall and F1 score metrics. The best performance values were obtained with ResNet101 (88% Precision, 88% Recall, 87% F1 Score, 88% Accuracy). Then, using Grad-CAM, the points on which the model focused while making predictions were determined. This study, which will include planning the basic principles of interventions to be applied to cultural property, will prevent the problems encountered in deterioration and deformation, and objective solutions will be produced.

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Cite This Article

Şahin, M., Tuncer, S. A., Danacı, Ç., Genç, G., *, M. Ş. (2025). Evaluation of Deterioration in Brick Materials in Monumental Buildings Using CNN Models. Gazi University Journal of Science, Volume: 38 Issue: 3. https://doi.org/10.35378/gujs.1596450

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