Work

Hybrid data generation and deep learning for GPR-based reconstruction of robotic-built underground structures

Year: 2025

Type: article

Abstract: There is substantial potential for future underground construction operations to be performed by autonomous robots. This paper proposes a 360-degree digital reconstruction framework for robotic-built underground structures using in-pipe rotating ground penetrating radar (GPR). Unlike conventional ground-level applications, placing GPR inside pipes significantly reduces signal attenuation by shortening the distance to the target, enhancing imaging accuracy. To overcome limited data, this paper proposes a high-fidelity in-pipe GPR generator that combines calibrated synthetic data with real-world pipe reflections, clutter, and random noises. Besides, a ‘stochastic-ellipse-union’ method models robot-constructed structures mathematically, ensuring dataset diversity. Moreover, a optimized 2D digital antenna model, calibrated to 97 % accuracy using a genetic algorithm, reduces radargram generation time by 99.2 % compared to traditional 3D methods. Benchmark tests among seven DL models identified ResNet101-enhanced U-Net as optimal, achieving an intersection-over-union score of 0.937, proving the effectiveness of the framework in reconstructing robotic-built underground structures.

Source: Automation in Construction

DOI: 10.1016/j.autcon.2025.106275

Authors: Haibing Wu, Brian Sheil

Institution: University of Cambridge

Language: English

Topic: Geophysical Methods and Applications

Subfield: Ocean Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $5,490