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About this Research Topic

Manuscript Submission Deadline 08 September 2022
Manuscript Extension Submission Deadline 08 October 2022

During the life cycle of bridges, structures may be damaged, have reduced operational efficiency and life expectancy because of adverse impacts such as corrosion, overload, environmental loads, natural disasters, etc. This can cause safety hazards for people. Structural Health Monitoring (SHM) based on nondestructive methods became a central focus point and has received increasing attention from the scientific community in recent decades. Numerous successful applications of damage detection applying nondestructive damage identification methods have been reported in the literature. To detect any defects occurring to structures, current approaches to SHM are conducted for the entire structure instead of focusing solely on suspicious locations. As a result, the amount of collected data is rapidly growing. This poses huge challenges to the data acquisition and analysis techniques. With the outstanding development of science and technology, in recent years, Artificial Intelligence has been impacting all areas of work. Numerous applied researches of ML have been conducted in many fields such as disease diagnosis, architectural design, and so on.

Therefore, this project will focus on developing an effective tool using novel Deep Learning (DL) models to deal with big data obtained to monitor structure health. Signals obtained from sensors are converted into numerical and image data. Physical characteristics of the intact and damaged structure are identified, located, and quantified based on the obtained data and the ability of DL.


Suitable research papers for this topic can include but not be limited to the following:

• bridges and dams;
• structural mechanics;
• mechanical behaviour;
• vibration and dynamics;
• composite structures.

Keywords: Structural Health Monitoring using Machine Learning and Optimization Algorithms.


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

During the life cycle of bridges, structures may be damaged, have reduced operational efficiency and life expectancy because of adverse impacts such as corrosion, overload, environmental loads, natural disasters, etc. This can cause safety hazards for people. Structural Health Monitoring (SHM) based on nondestructive methods became a central focus point and has received increasing attention from the scientific community in recent decades. Numerous successful applications of damage detection applying nondestructive damage identification methods have been reported in the literature. To detect any defects occurring to structures, current approaches to SHM are conducted for the entire structure instead of focusing solely on suspicious locations. As a result, the amount of collected data is rapidly growing. This poses huge challenges to the data acquisition and analysis techniques. With the outstanding development of science and technology, in recent years, Artificial Intelligence has been impacting all areas of work. Numerous applied researches of ML have been conducted in many fields such as disease diagnosis, architectural design, and so on.

Therefore, this project will focus on developing an effective tool using novel Deep Learning (DL) models to deal with big data obtained to monitor structure health. Signals obtained from sensors are converted into numerical and image data. Physical characteristics of the intact and damaged structure are identified, located, and quantified based on the obtained data and the ability of DL.


Suitable research papers for this topic can include but not be limited to the following:

• bridges and dams;
• structural mechanics;
• mechanical behaviour;
• vibration and dynamics;
• composite structures.

Keywords: Structural Health Monitoring using Machine Learning and Optimization Algorithms.


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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