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Article summary:
1. This paper proposed a two-dimensional convolutional neural network (2-D CNN) integrated with electromechanical admittance (EMA) to detect compressive stress and load-induced damages of concrete cubic structure.
2. An experimental investigation was conducted on a concrete specimen under compressive test to monitor the structural states from initial loading to fatal failure.
3. The proposed approach possessed excellent accuracy and efficiency for quantification on compressive stress and damage state changes in the specimen.
Article analysis:
The article is generally reliable and trustworthy, as it provides detailed information about the proposed two-dimensional convolutional neural network (2-D CNN) integrated with electromechanical admittance (EMA) for detecting compressive stress and load-induced damages of concrete cubic structure, as well as an experimental investigation conducted on a concrete specimen under compressive test to monitor the structural states from initial loading to fatal failure. The article also presents results that demonstrate that EMA signature is sensitive to stress variations in concrete and its cracking, expansion and propagation, as well as the proposed approach possessing excellent accuracy and efficiency for quantification on compressive stress and damage state changes in the specimen.
The article does not appear to have any biases or one-sided reporting, unsupported claims, missing points of consideration, missing evidence for the claims made, unexplored counterarguments, promotional content or partiality. It also notes possible risks associated with using this method for detecting compressive stress and load-induced damages of concrete cubic structure. Furthermore, both sides of the argument are presented equally throughout the article.