Civil structures deteriorate over time due to environmental conditions, chemical and physical processes within the material as well as dynamic/static loads. Even though a diagnosis before a catastrophic failure is very important, it is also needed to maintain the structure to reduce environmental impact related to the construction process. Almost half of the total greenhouse gases are produced by the construction industry and therefore, for a sustainable future, the life cycle of structures needs to be extended as much as possible with well maintenance.
In order to maintain the proper service of the structure, extend its life and reduce risks regarding the safety, structural damage assessment is crucial by reliable and robust methods; those at the same time do not impair the function of the structure. Consequently, non-destructive evaluation (NDE) plays an important role for inspecting and evaluating the damage and different NDE techniques have been developed and applied widely. With the advancing technologies in sensors, computing, data processing and most recently Artificial Intelligence, these techniques evolve and provide better results.
This Research Topic aims to gather research articles about state-of-the-art as well as cutting-edge advancements and best practices in the field of non-destructive testing and health monitoring of civil engineering structures. For this purpose, contributions related but not limited to following topics are welcome:
• Damage detection and evaluation
• Material characterization using NDT,
• Sensors, instrumentation, software for NDT and SHM applications
• Signal and image processing
• Structural assessment and structural health monitoring
• Ultrasonics, impact-echo, acoustic emission, ground penetrating radar, infrared thermography, etc.
• Linear/phased arrays, Total Focusing Method (TFM), Full Matrix Capture (FMC)
• Multiphysics modeling and simulation of building materials and NDT
• Integration of NDT methods, data fusion
• Artificial intelligence, machine learning, deep learning
• Big data, Internet of Things for NDE applications
• NDE 4.0
Civil structures deteriorate over time due to environmental conditions, chemical and physical processes within the material as well as dynamic/static loads. Even though a diagnosis before a catastrophic failure is very important, it is also needed to maintain the structure to reduce environmental impact related to the construction process. Almost half of the total greenhouse gases are produced by the construction industry and therefore, for a sustainable future, the life cycle of structures needs to be extended as much as possible with well maintenance.
In order to maintain the proper service of the structure, extend its life and reduce risks regarding the safety, structural damage assessment is crucial by reliable and robust methods; those at the same time do not impair the function of the structure. Consequently, non-destructive evaluation (NDE) plays an important role for inspecting and evaluating the damage and different NDE techniques have been developed and applied widely. With the advancing technologies in sensors, computing, data processing and most recently Artificial Intelligence, these techniques evolve and provide better results.
This Research Topic aims to gather research articles about state-of-the-art as well as cutting-edge advancements and best practices in the field of non-destructive testing and health monitoring of civil engineering structures. For this purpose, contributions related but not limited to following topics are welcome:
• Damage detection and evaluation
• Material characterization using NDT,
• Sensors, instrumentation, software for NDT and SHM applications
• Signal and image processing
• Structural assessment and structural health monitoring
• Ultrasonics, impact-echo, acoustic emission, ground penetrating radar, infrared thermography, etc.
• Linear/phased arrays, Total Focusing Method (TFM), Full Matrix Capture (FMC)
• Multiphysics modeling and simulation of building materials and NDT
• Integration of NDT methods, data fusion
• Artificial intelligence, machine learning, deep learning
• Big data, Internet of Things for NDE applications
• NDE 4.0
