Work

Beyond Corrosion: Autonomous Real-Time Detection of Structural Cracks and Degradation Using Robotic Sensing and Digital Twins

Year: 2025

Type: article

Abstract: Structural crack detection and environmental hazard monitoring are essential in non-destructive testing (NDT) for civil infrastructure. This paper presents a real-time, AI-powered multi-sensor dashboard that receives data from a mobile climbing robot. The system integrates optical, thermal infrared (IR), and gas sensing, each supported by dedicated deep learning models. AI inference is performed server-side to classify surface cracks, segment IR-based crack regions, and detect gas anomalies. Models include MobileNetV2 for crack classification, U-Net for IR segmentation, and a gas classifier using MQ-135 data. The dashboard fuses predictions with visual overlays and quantitative measurements, enabling faster, safer, and more robust infrastructure inspection.

Source: e-Journal of Nondestructive Testing

DOI: 10.58286/31663

Authors: Ghezal Ahmad Zia, Cemal Hekim, Sabine Kruschwitz

Institution: Federal Institute For Materials Research and Testing

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: diamond

Funders: European Commission, HORIZON EUROPE Framework Programme