A comprehensive structural condition assessment and structural safety of civil infrastructure (bridges, roads, buildings, etc.) is not possible without evaluating the material properties (steel, concrete, timer, masonry, etc.) and detecting surface and subsurface defects. Nondestructive evaluation and testing (NDE and T) methods such as impact echo, ground penetrating radar, ultrasonic surface waves, infrared thermography, etc. have been used for this task for the past half century. Despite decades of effort for NDE and T implementation in structural engineering, though, their applications are still somewhat limited, since the raw data associated with these techniques require user expertise. Introducing automation to NDE and T data analysis could alleviate this issue and would pave the way for more popular implementations of the NDE and T techniques in future. Using artificial intelligence and signal and image processing, especially deep learning models, has shown substantial advantages over traditional data analysis; however, they are either relatively new to the practice or are completely untested for NDE and T techniques. This issue could be associated to the lack of available and reliable ground truth. The editor invites the civil and structural engineering community (researchers, engineers, NDE and T manufacturers, and users) to submit their solutions to the aforementioned issues. The scope of this Special Issue includes but is not limited to the followings:\u00a0Data papers \u2013 Introducing and sharing annotated NDE and T datasets (with reliable ground truth);\u00a0Technical papers \u2013 Implementing of artificial intelligence for NDE and T data analysis;\u00a0Technical papers \u2013 Decision making using data fusion techniques for two or more NDE and T methods;\u00a0Technical papers \u2013 Data augmentation techniques to generate realistic NDE and T data for deep learning implementation;\u00a0Review papers \u2013 History of using artificial intelligence for NDE and T techniques, challenges, and potentials.
\nKeywords: Ground-penetrating radar (GPR);\u00a0Impact echo (IE);\u00a0Ultrasonic surface waves (USW);\u00a0Infrared thermography (IRT);\u00a0Signal processing;\u00a0Image processing;\u00a0Deep learning;\u00a0Ground truth;\u00a0NDE;\u00a0NDT.<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"template":"","format":"standard","topic":[192,430,15,644,576,597,598,676,661,567,667,660,677,551,593,672],"country":[55],"nsj_language":[57],"class_list":["post-1327","call_for_papers","type-call_for_papers","status-publish","format-standard","hentry","topic-artificial-intelligence","topic-big-data","topic-civil-engineering","topic-data-analysis-processes","topic-data-management","topic-data-sensing-and-analysis","topic-decision-support-systems","topic-infrastructures","topic-innovative-techniques","topic-methodology","topic-methods-for-diagnostics","topic-nondestructive-tests","topic-structural-dynamics","topic-structural-engineering","topic-traditional-techniques","topic-urban-infrastructures","country-switzerland","nsj_language-english"],"acf":[],"yoast_head":"\n