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This paper proposes an efficient methodology to monitor the formation of cracks in concrete after non-destructive ultrasonic testing of a structure. The objective is to be able to automatically detect the initiation of cracks early enough…
The paper is devoted to the development of the method of laser shock peening (LSP) of metals. To optimize the mode of LSP for Ti-6Al-4V specimens a deep learning model for predicting residual stresses by laser shock peening was developed.…
Cracks on concrete surface are typically clear warning signs of a potential threat to the integrity and serviceability of structure. The techniques based on image processing can effectively detect the cracks from images. These techniques…
Monitoring the energy release during fatigue tests of common engineering materials has been shown to give relevant information on fatigue properties, reducing the testing time and material consumption. During a static tensile test, it is…
In this paper, a machine learning-based framework is developed to quickly evaluate the structural safety of trusses. Three numerical examples of a 10-bar truss, a 25-bar truss, and a 47-bar truss are used to illustrate the proposed…
The process of damage identification in Structural Health Monitoring (SHM) gives us a lot of practical information about the current status of the inspected structure. The target of the process is to detect damage status by processing data…
The dynamic experimental and numerical analysis of cracked beams has been studied with the aim of quantifying the influence of depth crack on the dynamic response of steel beams. Artificial Neural Method ANN has been used where a numerical…
