Visual inspections have been typically used in condition assessment of infrastructure. However, they are based on human judgment and their interpretation of data can differ from acquired results. In psychology, this difference is called cognitive bias which directly affects Structural Health Monitoring (SHM)-based decision making. Besides, the confusion between condition state and safety of a bridge is another example of cognitive bias in bridge monitoring. Therefore, integrated computer-based approaches as powerful tools can be significantly applied in SHM systems. This paper explores the relationship between the use of advanced computational intelligence and the development of SHM solutions through conducting an infrastructure monitoring methodology. Artificial Intelligence (AI)-based algorithms, i.e., Artificial Neural Network (ANN), hybrid ANN-based Imperial Competitive Algorithm, and hybrid ANN-based Genetic Algorithm, are developed for damage assessment using a lab-scale composite bridge deck structure. Based on the comparison of the results, the employed evolutionary algorithms could improve the prediction error of the pre-developed network by enhancing the learning procedure of the ANN.

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In this paper, a cutting-edge method for creating control interfaces for intention detection in active transfemoral prosthetic devices is presented. Given the current trend in the prosthetics industry, a review of the literature over the last two decades has found a number of control algorithms utilized for intention detection implementation. Scientific publications, books, and online resources were evaluated for published material on knee prosthesis. Based on the materials, three areas of scientific inquiry involving control interfaces for intention detection in active prosthetic legs have been identified. The studies were assessed using the Downs and Black checklist, and their control techniques as well as the performance assessment for these control interfaces are described. After screening, 211 studies were retrieved and examined, however only 39 publications were included and examined in this review. An active prosthetic leg's control strategy framework and goal output were examined in fifteen (15) papers. In two (2) papers, conventional control methods for transfemoral prosthetic legs were examined. Eight (8) further research looked at the potential implementation of intent detection in the transfemoral prosthetic leg, while fourteen (14) papers explored the active prosthetic leg's machine learning algorithm. As a result, our research showed that using a less complex sensory system to control an active transfemoral prosthetic limb is possible when paired with a creative approach and control algorithm that can translate the restricted sensor data into a larger set of relevant data. Therefore, an effective sensory system (practicality and quality of the sensory input) and intention detection algorithm were required for an active transfemoral prosthetic limb. It is considered a complex activity because of the connected biomechanics of human gait, which the brain governs unconsciously. No prosthetic device has been able to replace a human limb in the same way that the original one would operate due to the ongoing challenges in prosthetic technology. Even with something as simple as walking, there are several components to the difficulty that may be broken down into different study areas. No matter how little, this calls for input from many different domains of knowledge. Considerations for future development might be based on this current analysis of the intention-detecting control interface of active knee prosthetic devices and prosthetic technology. This evaluation of the literature includes not only a study of the viability of control interfaces that support intention detection for an active prosthetic limb, but it also suggests a framework for categorizing various works in the subject.