The Role of Ai-Supported Models in the Damage Detection Process of Historical Buildings: A Review

Abstract

The sustainable preservation of historical buildings is of great importance for the future preservation of cultural heritage. The advent of artificial intelligence (AI) technologies in recent years has led to significant advancements in damage detection for historical buildings, resulting in enhanced efficiency and speed. Consequently, there has been a notable proliferation of artificial intelligence-based damage detection models in the extant literature. This study aims to examine the role of artificial intelligence-supported models in the damage detection process of historical buildings. A comprehensive review of the extant literature was conducted, encompassing a total of 97 case studies. The analysis revealed that damages to historic buildings can be categorized into three primary classes: disaster damages, structural damages (including structural health monitoring), and surface damages. The study provides a comprehensive analysis of damage detection methods in historical buildings, offering significant insights into the performance of existing artificial intelligence models in each category. The effectiveness of artificial intelligence-supported models in damage detection for historical buildings has been evaluated, and the strengths and shortcomings in the existing literature have been identified. The study further highlights aspects that require improvement in existing approaches and provides recommendations for future research endeavors. This study emphasizes the significance of artificial intelligence-based damage assessment methods for the conservation of historical buildings, laying the groundwork for future research in this field