MLCULT · Machine Learning for Structural Health Monitoring of Cultural Heritage

Machine Learning for Structural Health Monitoring of Cultural Heritage

Cultural heritage (CH) structures are generally inspected by manual visual inspections done simply by using the naked eye. In simple words, we see the damage in the building with our eyes and then decide what to do with it. With progress in computer science, researchers/inspection professionals are augmenting their capacity to inspect CH structures via analysis of digital images taken by cameras, drones, etc., using deep learning (DL) techniques. These DL techniques can even identify defects missed by human eyes and can analyse large amounts of building damage pictures once the model is trained, without much human intervention. The proposed research project aimed to develop DL-models for surface damage diagnosis by using machine learning (ML) techniques, particularly DL techniques, for reducing uncertainties in structural health diagnosis, which would enable more efficient structural interventions and repair. The objective of the MLCULT project was to use computer vision (CV) techniques to assess the surface damage condition of CH constructions for reducing condition assessment costs and assessing building safety with minimum building intrusion. CV-based models are available in concrete buildings, but in CH structures their applications are scarce. Additionally, the objective of the project was also to provide open access databases of damaged CH components to the public, as CH damage typologies are more complex and varied than modern buildings. The MLCULT project demonstrated how DL models can be trained efficiently by using proper CH damage data and then deployed in situ to automate damage identification of CH structure. Finally, the end objective was a pilot demonstration of the AI-inspect system in real case studies, basically data from the real field. The models developed provide a helping hand to CH inspectors, who can use this tool at their own advantage. For example, inspectors do not need to go to risky locations that may compromise their safety and may use drones to take pictures, which can be later fed into the DL-model to identify the damage typologies. Then, it is up to them to decide the intervention solution. The AI does not take the authority to decide the intervention and only says "these are the damages I have found" and it is for the engineer to decide the next step. Recommendations for heritage professionals using computer-vision (CV)-based models and literature reviews performed before the proposal were extended to damage assessment techniques using image processing applied to CH. The project contributes to the way we will perform visual inspections and makes our CH structures safer as CV-techniques will not miss any defects that can jeopardise the safety of structure in the long run.

Data: CORDIS, © European Union