Advanced Sensors in Nondestructive Testing and Structural Health Monitoring

Dear Colleagues,

Advanced sensors play a crucial role in enhancing the accuracy, reliability, and efficiency of Nondestructive Testing (NDT) and Structural Health Monitoring (SHM). Traditional methods are often time-consuming, subjective, and may be inadequate to detect hidden defects or early signs of damage. In contrast, advanced sensors enable real-time, remote, and nonintrusive monitoring, allowing for early detection of structural issues and facilitating proactive maintenance and repair strategies. Collecting and analyzing data from advanced sensors provides valuable insights into the structural health and performance of critical assets such as bridges, pipelines, aircraft, and buildings.

The scope of this Special Issue encompasses various technologies used in advanced sensors, including but not limited to optical fibers, piezoelectric materials, wireless sensor networks, ultrasound, electromagnetic waves, and smart materials. We invite submissions that explore the design, development, simulation, and validation of advanced sensors. Additionally, we encourage researchers to share their work on practical applications of advanced sensors in SHM, including case studies that demonstrate their effectiveness in real-world scenarios. Both reviews and original research articles are welcome, contributing to the advancement of knowledge and implementation in this field.

Authors are encouraged to address topics such as: Topics include, but are not limited to, the following:

• Design and simulation of advanced sensors;
• Case studies and practical applications showcasing the use of advanced sensors in SHM;
• Integration of advanced sensor technologies into existing NDT and SHM frameworks;
• Data analysis and interpretation methods for extracting valuable insights from sensor data;
• Proactive maintenance and repair strategies enabled by advanced sensor monitoring.

Dr. Miguel A. Machado
Guest Editor

Manuscript Submission Information

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• advanced sensors
• Nondestructive Testing (NDT)
• Structural Health Monitoring (SHM)
• sensor technologies
• smart materials
• numerical simulation
• remote monitoring
• proactive maintenance
• structural integrity
• real-time monitoring
• wireless sensor networks
• terahertz Inspection
• eddy currents
• ultrasounds
• thermography

Title: Eddy currents probe design for NDT applications: a review
Authors: Miguel A. Machado
Affiliation: Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Lisboa, Portugal
Abstract: To be provided

Title: An on-line damage identification approach for substation structure based on acceleration sensor
Authors: Xinghuai Huang; Zhaodong Xu
Affiliation: Southeast University, Nanjing, China
Abstract: The development of damage identification methods that enable rapid implementation holds great promise for assessing structural integrity to avoid further damage or catastrophic failure. Here an on-line model updating approach is proposed to rapidly and simultaneously identify the mass, stiffness and damping properties of a structural model. The proposed approach facilitates identification of these unknown parameters using two steps: first, energy equilibrium equations are used to establish a relationship between structural energy and unknown parameters; second, the Kalman filter is adopted to obtain the unknown parameters in a short period of time. Numerical verification is conducted on a 158 degrees-of-freedom (DOFs) substation structure model with 324 unknown parameters based on a real-world structure. The results indicate that the proposed approach can simultaneously identify the model parameters for the mass, stiffness and damping of both the damaged and undamaged structures online, and also can effectively identify the stiffness damage and mass change, even in an environment with noise.

Title: DEVELOPMENT OF CONVOLUTION NEURAL NETWORKS TO IDENTIFY DAMAGE IN BRIDGE SPANS FROM LIGHTWEIGHT MOVING VEHICLE VIBRATIONS
Authors: George D. Manolis1*, Georgios Dadoulis2
Affiliation: 1Professor Honorarius; email: [email protected] 2Dipl. Ing., PhD candidate; email: [email protected]
Abstract: A convolution neural network is developed in this work for the purpose of detecting damage in bridges from measurement of their dynamic response to travelling loads. More specifically, damage detection via vibration testing relies on the development of damage-sensitive indicators, which are then used for reaching a decision regarding the existence/absence of damage, provided they have been retrieved from at least two distinct structural states. Damage indicators, however, exhibit a relatively low sensitivity regarding the onset of structural damage, despite considerable research efforts in vibration testing to rectify this problem. This is further exacerbated by the complex dynamic behavior of flexible structures and by the low amplitude vibrations induced by lightweight vehicles. To this end, a mathematical model is developed for interpreting the experimental data recovered from a simply supported, bridge span model that is traversed by a light mass classified as a moving load. Damage is introduced in the model in the form of springs corresponding to yielding end supports and to cracking of the beam’s lower flange. The presence of both a travelling load and of point springs modifies the dynamic properties of the beam and results in a time-dependent eigenvalue problem, which is numerically solved by discretizing the time axis in a series of time steps. Thus, families of numerically generated acceleration records are produced at select stations along the beam’s span, which are then used for training a convolution neural network. More specifically, these time histories are processed by using the Gabor transform to produce spectrograms that give a picture of damage location. Once the network has been trained, it is used for identifying damage from acceleration records produced from a series of experiments. These are conducted in parallel with the analysis and involve two otherwise identical beam sections, one intact and one artificially damaged, which are traversed by light sliding masses in the range of 10% as compared to the beam’s total mass. The difficulties that the convolution neural network faces in correctly identifying the presence/absence of damage in the model bridge are discussed, and steps taken to improve the quality of the results are proposed.