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Prediction and measurement of damage to architectural heritages facades using convolutional neural networks

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Abstract

This paper set out an automatic multicategory damage detection technique using convolutional neural networks (CNN) models based on image classification and features’ extraction, to detect damages of historic structures such as: erosion, material loss, color change of the stone, and sabotage issues. The city of “Al-Salt” in Jordan was selected for the case study in this research. The best model showed an average damage detection accuracy of 95%. It was demonstrated that the proposed CNN model was significantly powerful, effective and reliable for damage detection of historic masonry buildings using features’ extraction based on imaging, and it contributed to the management and safety of historic heritage and preservation.

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Authors and Affiliations

  1. Industrial Engineering Department, German Jordanian University (GJU), Madaba, Jordan

    Murad Samhouri & Lujain Al-Arabiat

  2. Architecture & Interior Architecture Department, German Jordanian University (GJU), Madaba, Jordan

    Farah Al-Atrash

Authors
  1. Murad Samhouri
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  2. Lujain Al-Arabiat
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  3. Farah Al-Atrash
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Correspondence to Murad Samhouri.

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Samhouri, M., Al-Arabiat, L. & Al-Atrash, F. Prediction and measurement of damage to architectural heritages facades using convolutional neural networks. Neural Comput & Applic 34, 18125–18141 (2022). https://doi.org/10.1007/s00521-022-07461-5

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