Alanazi Abdulrahman M
Department of Electrical Engineering, College of Applied Engineering, King Saud University, Riyadh, 11362, Saudi Arabia.
Sci Rep. 2025 Aug 25;15(1):31275. doi: 10.1038/s41598-025-16978-3.
Non-destructive testing is a cornerstone of structural integrity assessment that enables internal evaluation of materials without inflicting damage. Among various imaging methods, Ultrasonic Model-Based Iterative Reconstruction (UMBIR) has gained attention for its ability to enhance ultrasonic imaging by incorporating physical and statistical priors. Notably, its extension, Multi-Frequency Ultrasonic Model-Based Iterative Reconstruction (MF-UMBIR) to process multi-frequency datasets have shown improved accuracy over traditional techniques. However, its performance has some limitations in highly complex structures. This paper presents Deep Multi-Agent Consensus Equilibrium (Deep-MACE) method, a novel reconstruction framework that integrates multi-frequency forward model agents with a deep learning prior using the consensus equilibrium formulation. By combining data consistency across different excitation frequencies with the expressive power of a learned U-Net prior, Deep-MACE enables high-fidelity imaging in acoustically heterogeneous environments. The primary objective is to reconstruct images that reveal structural damage, such as corroded rebars and delaminations, thereby supporting non-invasive Structural Health Monitoring (SHM) of steel-lined concrete structures. Experimental results demonstrate that both UMBIR and MF-UMBIR suffer from limitations in defect visibility and robustness when applied to steel-lined concrete structures. In contrast, Deep-MACE consistently produces clearer reconstructions, successfully identifying all internal rebars with fewer artifacts and improved spatial resolution. These results highlight the potential of integrating deep priors into multi-agent frameworks for advanced ultrasonic non-destructive testing.
无损检测是结构完整性评估的基石，它能够在不造成损坏的情况下对材料进行内部评估。在各种成像方法中，基于超声模型的迭代重建（UMBIR）因其通过纳入物理和统计先验知识来增强超声成像的能力而受到关注。值得注意的是，其扩展方法——用于处理多频率数据集的基于多频率超声模型的迭代重建（MF-UMBIR），相比传统技术显示出更高的精度。然而，其性能在高度复杂的结构中存在一些局限性。本文提出了深度多智能体共识均衡（Deep-MACE）方法，这是一种新颖的重建框架，它使用共识均衡公式将多频率正向模型智能体与深度学习先验知识相结合。通过将不同激励频率下的数据一致性与学习到的U-Net先验知识的表达能力相结合，Deep-MACE能够在声学异质环境中实现高保真成像。主要目标是重建能够揭示结构损伤（如锈蚀钢筋和分层）的图像，从而支持对钢衬混凝土结构进行非侵入式结构健康监测（SHM）。实验结果表明，当应用于钢衬混凝土结构时，UMBIR和MF-UMBIR在缺陷可见性和鲁棒性方面都存在局限性。相比之下，Deep-MACE始终能产生更清晰的重建结果，成功识别出所有内部钢筋，伪像更少且空间分辨率更高。这些结果突出了将深度先验知识集成到多智能体框架中用于先进超声无损检测的潜力。
