Nondestructive evaluation (NDE) techniques are used in many industries to evaluate the properties of components and inspect for flaws and anomalies in structures without altering the part’s integrity or causing damage to the component being tested. This includes monitoring materials’ condition (Material State Awareness (MSA)) and health of structures (Structural Health Monitoring (SHM)). NDE techniques are highly valuable tools to help prevent potential losses and hazards arising from the failure of a component while saving time and cost by not compromising its future usage. On the other hand, Artificial Intelligence (AI) and Machine Learning (ML) techniques are useful tools which can help automating data collection and analyses, providing new insights, and potentially improving detection performance in a quick and low effort manner with great cost savings. This paper presents a survey on state of the art AI-ML techniques for NDE and the application of related smart technologies including Machine Vision (MV) and Digital Twins in NDE.

Safety is the top priority for every transportation system. Although various aspects of transportation infrastructure’s safety have been studied, in-motion monitoring and detection of defect is still a big concern. Understanding the trend of anomalies, and how to monitor undesired conditions are of high interest in transportation. In this study, the technology of Distributed Acoustic Sensing (DAS) for in-motion rail condition monitoring is studied through experimental testing and simulation modeling. DAS uses fiber optic cables along the track to detect any anomaly indicator. DAS permit the measurement of a desired parameter as a function of length along the fiber. Despite any conventional Nondestructive Testing (NDT) technique where the coverage or scanning area of the sensors are very limited, DAS provides a full, fast and accurate coverage of all section under the test. The objective of this research is to provide an assessment of anomaly detection and monitoring techniques based on DAS for transportation investigation. It presents the experimental evaluations and numerical simulations on the current methodologies in DAS systems. DAS was used to evaluate the transportation traffic condition in a rural area by connecting an available underground dark fiber to the DAS interrogator and system as well as simulated traffic condition in smaller scale in a parking lot. COMSOL Multiphysics software was used to model the interaction of ambient vibration with the fiber optic. Results show that the condition of the transportation can be monitored and detected by DAS with an appropriate accuracy. DAS information can be used for traffic condition monitoring, object tracking and flaw detections in the transportation lines.

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