Smart and Automated Infrastructure Management: A Deep Learning Approach for Crack Detection in Bridge Images

Hina Inam, Naeem Ul Islam, Muhammad Usman Akram, Fahim Ullah

Sustainability 2023 article cited by 73

Source Open-access PDF More in Engineering & Materials

importance 6/10 ai assisted Infrastructure Maintenance and Monitoring infrastructure inspection stage: research

Problem

Manual visual inspection of bridges for cracks is costly, time-consuming and error-prone, and no automated reliable system exists, especially in developing countries.

Why it matters

Cracks indicate the structural health of critical bridges, so missed damage endangers public safety.

What the AI did

Proposed a two-phased deep-learning-based framework for automatic crack and damage detection in bridge images.

Results

not stated in source

Impact

Evidence supports only that a framework was proposed; performance and deployment are not stated.

Record

Venue	Sustainability
Published	2023
Work type	article
Authors	Hina Inam, Naeem Ul Islam, Muhammad Usman Akram, Fahim Ullah
Citations	73 (OpenAlex, at import time)
References	86
Model or method	deep learning framework; specific architecture not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.3390/su15031866
Open access	gold
Keywords	deep learning, crack detection, bridges, structural health monitoring, automation

Importance rating

6 / 10

significant — thousands to millions affected

Crack detection affects structural health of critical bridges and public safety; abstract notes infrastructure management lags in tech adoption, especially developing countries, but cites no scale.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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