Artificial Intelligence applied to Structural Health Monitoring (SHM) has provided considerable advantages in the accuracy and quality of the estimated structural integrity. Nevertheless, several challenges still need to be tackled in the SHM field, which extended the monitoring process beyond the mere data analytics and structural assessment task. Besides, one of the open problems in the field relates to the communication layer of the sensor networks since the continuous collection of long time series from multiple sensing units rapidly consumes the available memory resources, and requires complicated protocol to avoid network congestion. In this scenario, the present work presents a comprehensive framework for vibration-based diagnostics, in which data compression techniques are firstly introduced as a means to shrink the dimension of the data to be managed through the system. Then, neural network models solving binary classification problems were implemented for the sake of damage detection, also encompassing the influence of environmental factors in the evaluation of the structural status. Moreover, the potential degradation induced by the usage of low cost sensors on the adopted framework was evaluated: Additional analyses were performed in which experimental data were corrupted with the noise characterizing MEMS sensors. The proposed solutions were tested with experimental data from the Z24 bridge use case, proving that the amalgam of data compression, optimized (i.e., low complexity) machine learning architectures and environmental information allows to attain high classification scores, i.e., accuracy and precision greater than 96% and 95%, respectively.
ABSTRACT. The following theorem is proved and several fixed point theorems and coincidence theorems are derived as corollaries. Let C be a nonempty convex subset of a normed linear space X, f:C X a continuous function, g: C C continuous, onto and almost quasi-convex. Assume that C has a nonempty compact convex subset D such that the setThen there is a point Y0 G C such that I I g(0)-f(Yo)II d(f(Yo),C).
We present a new approach to the analysis of solvability properties for complementarity problems in a Hilbert space. This approach is based on the Skrypnik degree which, in the case of mappings in a Hilbert space, is essentially more general in comparison with the classical Leray-Schauder degree. Namely, the Skrypnik degree allows us to obtain some new results about solvability of complementarity problems in the infinite-dimensional case. The case of generalized solutions is also considered.
ABSTRACT. In this paper we prove a result of complementarity problem where compact condition is somewhat relaxed.KEY WORDS AND PHRASES: Complementarity problem, variational inequality, implicit complementarity problem 1991 AMS SUBJECT CLASSIFICATION CODES: Primary 47H10, Secondary 54H25Recently several interesting results have been given for complementarity problems As the complementarity problem, variational inequality and fixed point theory are closely related (equivalent to each other) that is why it has growing interest and varied applications. The applications in the field of economics, optimization, game theory, mechanics and engineering are even growing rapidly Here we will start with implicit complementarity problem and then derive results for complementarity theory. For terminology one is referred to
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