Numerous monitoring and detection approaches have been developed to provide practical means for early warning against structural damage or any type of anomaly. Damage may accumulate on structures due to different environmental and human-induced factors. While successful monitoring provides resolute and staunch information on the health, serviceability, integrity and safety of structures maintaining continuous performance of a structure depends highly on monitoring the occurrence, formation and propagation of damage. Monitoring structural damage is extremely important for sustaining and preserving the service life of civil structures.
A number of challenges towards the development of robotic platforms have also been discussed. The third stream of literature highlights different algorithms for the surface- and sub-surface-level analysis of bridges that have been developed by studies in the past. The second stream of literature examines myriad sensors used for the development of robotic platforms for the NDE of bridges. The first stream relates to technological robotic platforms developed for NDE of bridges. In order to provide an in-depth examination of the state-of-the-art, the current research will examine the three major research streams. The review methodology will be discussed in sufficient depth, which will provide insights regarding some of the primary aspects of the review methodology followed by this review-based study. Some of the salient features of this review-based study will be discussed in the light of the existing surveys and reviews that have been published in the recent past, which will enable the clarification regarding the novelty of the present review-based study. This review-based study will examine some of the recent developments in the field of autonomous robotic platforms for NDE and the structural health monitoring (SHM) of bridges. To facilitate this process, different sensors for data collection and techniques for data analyses have been used to effectively carry out this task in an automated fashion. The traditional inspection of civil infrastructure mostly relies on visual inspection using human inspectors. The non-destructive evaluation (NDE) of civil infrastructure has been an active area of research in recent decades. We illustrate the advantages, disadvantages, and application range of these techniques, and compare them with each other to provide some guidance for HEA study. The first-principles calculations are based on quantum mechanics and several open source databases, and it can also provide the finer atomic information for the thermodynamic analysis of CALPHAD and machine learning. The empirical model and the machine learning are both based on summary and analysis, while the latter is more believable for the use of multiple algorithms. Here we present and discuss four different calculation methods that are usually applied to accelerate the development of novel HEA compositions, that is, empirical models, first-principles calculations, calculation of phase diagrams (CALPHAD), and machine learning.
In order to explore the huge compositional and microstructural spaces more effectively, high-throughput calculation techniques are put forward, overcoming the time-consuming and laboriousness of traditional experiments. High-entropy alloys (HEAs) open up new doors for their novel design principles and excellent properties.
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