Work

Real-Time Multi-Damage Detection and Risk Prioritisation for Aging Buildings Using YOLOv11 and a Damage Criticality Index

Year: 2025

Type: article

Abstract: Ageing building stock, shrinking budgets, and inspector shortages hinder timely façade safety inspections. This research develops an automated damage detection and risk prioritization system for aging concrete structures. Five YOLOv11 variants were trained on 130,838 high-resolution images from 25 Seoul districts to detect three critical damage types: cracks, exposed rebar, and spalling. The proposed framework integrates YOLOv11 detection with a novel Damage Criticality Index (DCI) that transforms five visual-spatial cues—area, multiplicity, confidence, density, and spread—into continuous severity scores, subsequently categorized into low, medium, and high risk via K-means clustering. YOLOv11x achieved 0.78 [email protected] at 101 FPS, enabling real-time processing suitable for field deployment. Field trials confirmed robust detection and consistent risk ranking in both uncluttered and cluttered urban environments, substantially reducing inspection time and minimizing missed defects compared to conventional manual methods. The framework provides scalable, data-driven support for city-wide monitoring and transparent, risk-prioritized maintenance of aging infrastructure.

Source: Sustainability

DOI: 10.3390/su17219390

Authors: Jongnam Ho, Yonghan Ahn, Hyunkyu Shin

Institutions: Hanyang University, Mokwon University

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896