Conclusions
This review paper presented the significance of emerging AI methods for structural engineering applications during the last decade. The survey indicated that among the numerous AI methods, pattern recognition (PR), machine learning (ML), and deep learning (DL) have been increasingly adapted and used for SHM and damage identification, optimization, modeling concrete properties, structural identification, earthquake engineering, etc. Yet, the common use of the noted methods has been for interpreting sensor data in SHM. The survey revealed that ML, PR, and DL algorithmic techniques have the ability to learn complicated interrelations among the contributing parameters, and thus allow solving a diversity of problems that are difficult, or not possible, to solve with traditional methods. Based on the literature survey, potential research avenues for employing PR, ML, and DL were also presented. Considering the emerging use of wireless sensor networks (e.g., self-powered sensor networks), ML- and PR-based models could become the next generation approaches to conduct non-destructive structural and material evaluation in SHM. This review showed that ML methods are able to discover hidden information about the structure’s performance by learning the influence of various damage or degrading mechanisms and the data collected from sensors, leading to reliable and efficient SHM frameworks. The literature further suggests that ML and DL techniques could also be applied to the computational mechanics domain, such as to optimize processes in the finite element method to enhance computational efficiency. These methods can also be used to solve complex problems through the novel concept of the Internet of Things (IoT).
