Long-Standing Themes and Future Prospects for the Inspection and Maintenance of Façade Falling Objects from Tall Buildings

The increasing number of accidents arising from falling objects from the façade of tall buildings has attracted much attention globally. To regulators, a preventive approach based on a mandatory periodic façade inspection has been deemed as a necessary measure to maintain the functionality and integrity of the façade 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 façade to detect latent defects and anomalies. The methods proposed include laser scanning, image-based sensing and infrared thermography to support the automatic façade visual inspection. This paper aims to review and analyse the state-of-the-art literature on the automated inspection of building façades, 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 façade falling objects, legislations, practices and the effectiveness of various inspection techniques are discussed. Various diagnostic, inspection and analytical methods which support façade inspection and maintenance are analysed with discussion on the potential future research in this field.

Medienart:

E-Artikel

Erscheinungsjahr:

2022

Erschienen:

2022

Enthalten in:

Zur Gesamtaufnahme - volume:22

Enthalten in:

Sensors (Basel, Switzerland) - 22(2022), 16 vom: 14. Aug.

Sprache:

Englisch

Beteiligte Personen:

Chew, Michael Y L [VerfasserIn]
Gan, Vincent J L [VerfasserIn]

Links:

Volltext

Themen:

3D reconstruction
Automated inspection
Building façade
Computer vision
Deep learning
Design optimisation
Information modelling
Journal Article
Laser scanning
Review

Anmerkungen:

Date Completed 29.08.2022

Date Revised 30.08.2022

published: Electronic

Citation Status MEDLINE

doi:

10.3390/s22166070

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM345342259