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These earthquakes demonstrated the urgent need for advanced automated damage detection models to help people.\u00a0This study introduces a novel solution to address this challenge through the AttentionPoolMobileNeXt model, derived from a modified MobileNetV2 architecture. To rigorously evaluate the effectiveness of the model, we meticulously curated a dataset comprising instances of construction damage classified into <jats:italic>five<\/jats:italic> distinct classes. Upon applying this dataset to the AttentionPoolMobileNeXt model, we obtained an accuracy of 97%.\u00a0In this work, we have created a dataset consisting of five distinct damage classes, and achieved 97% test accuracy using our proposed AttentionPoolMobileNeXt model. Additionally, the study extends its impact by introducing the AttentionPoolMobileNeXt-based Deep Feature Engineering (DFE) model, further enhancing the classification performance and interpretability of the system. The presented DFE significantly increased the test classification accuracy from 90.17% to 97%, yielding improvement over the baseline model. AttentionPoolMobileNeXt and its DFE counterpart collectively contribute to advancing the state-of-the-art in automated damage detection, offering valuable insights for disaster response and recovery efforts.<\/jats:p>","DOI":"10.1007\/s11042-024-19163-2","type":"journal-article","created":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T06:02:23Z","timestamp":1713333743000},"page":"1821-1843","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AttentionPoolMobileNeXt: An automated construction damage detection model based on a new convolutional neural network and deep feature engineering models"],"prefix":"10.1007","volume":"84","author":[{"given":"Mehmet","family":"Aydin","sequence":"first","affiliation":[]},{"given":"Prabal Datta","family":"Barua","sequence":"additional","affiliation":[]},{"given":"Sreenivasulu","family":"Chadalavada","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9677-5684","authenticated-orcid":false,"given":"Sengul","family":"Dogan","sequence":"additional","affiliation":[]},{"given":"Turker","family":"Tuncer","sequence":"additional","affiliation":[]},{"given":"Subrata","family":"Chakraborty","sequence":"additional","affiliation":[]},{"given":"Rajendra U.","family":"Acharya","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,17]]},"reference":[{"key":"19163_CR1","doi-asserted-by":"publisher","unstructured":"Chukwuka OJ, Ren J, Wang J, Paraskevadakis D (2023) A comprehensive research on analyzing risk factors in emergency supply chains. 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