{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T19:53:28Z","timestamp":1783540408343,"version":"3.55.0"},"reference-count":126,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The increasing number of accidents arising from falling objects from the fa\u00e7ade of tall buildings has attracted much attention globally. To regulators, a preventive approach based on a mandatory periodic fa\u00e7ade inspection has been deemed as a necessary measure to maintain the functionality and integrity of the fa\u00e7ade of tall buildings. Researchers worldwide have been working towards a predictive approach to allow for the assessment of the likely failure during some future period, by measuring the condition of the fa\u00e7ade to detect latent defects and anomalies. The methods proposed include laser scanning, image-based sensing and infrared thermography to support the automatic fa\u00e7ade visual inspection. This paper aims to review and analyse the state-of-the-art literature on the automated inspection of building fa\u00e7ades, with emphasis on the detection and maintenance management of latent defects and anomalies for falling objects from tall buildings. A step-by-step holistic method is leveraged to retrieve the available literature from databases, followed by the analyses of relevant articles in different long-standing research themes. The types and characteristics of fa\u00e7ade falling objects, legislations, practices and the effectiveness of various inspection techniques are discussed. Various diagnostic, inspection and analytical methods which support fa\u00e7ade inspection and maintenance are analysed with discussion on the potential future research in this field.<\/jats:p>","DOI":"10.3390\/s22166070","type":"journal-article","created":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T23:44:03Z","timestamp":1660607043000},"page":"6070","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Long-Standing Themes and Future Prospects for the Inspection and Maintenance of Fa\u00e7ade Falling Objects from Tall Buildings"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7936-5157","authenticated-orcid":false,"given":"Michael Y. L.","family":"Chew","sequence":"first","affiliation":[{"name":"Department of the Built Environment, National University of Singapore, Singapore 117566, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vincent J. L.","family":"Gan","sequence":"additional","affiliation":[{"name":"Department of the Built Environment, National University of Singapore, Singapore 117566, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1111\/mice.12578","article-title":"Fa\u00e7ade defects classification from imbalanced dataset using meta learning-based convolutional neural network","volume":"35","author":"Guo","year":"2020","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chew, M.Y. (Int. J. Build. Pathol. Adapt., 2021). Fa\u00e7ade inspection for falling objects from tall buildings in Singapore, Int. J. Build. Pathol. 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