Work

Automated image-based condition assessment of the built environment: A state-of-the-art investigation of damage characteristics and detection requirements

Year: 2025

Type: article

Abstract: Inspection activities are intended to detect damage, informing subsequent maintenance and repair actions. Considering the difficulties and limitations associated with traditional inspection methods, there have been growing interests in utilizing image processing and computer vision strategies. Despite several developments, however, the state of the practice still lacks necessary insights on how such advanced strategies should be utilized to realize their expected benefits, in terms of ease, coverage, and accuracy. Considering this critical gap, the current study systematically investigated various types of damage and how they can be evaluated in a condition assessment framework. For the automated detection, localization, and measurement of damage, various convolutional neural network, support vector machine, and classification-based methods were examined, including their advantages and limitations. This study’s recommendations are anticipated to assist researchers and practicing engineers with the proper selection and use of automated damage detection for improving how the built environment is inspected and maintained.

Source: Results in Engineering

DOI: 10.1016/j.rineng.2025.104978

Authors: Leila Farahzadi, Ibrahim Odeh, Mahdi Kioumarsi, Behrouz Shafei

Institutions: OsloMet – Oslo Metropolitan University, Iowa State University

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,940