Machine learning-based classification of quality grades for concrete vibration behaviour.

The vibration behaviour of the vibrating rods is one of the key factors for the quality of concrete vibration that is essential for the long-term safety of concrete structures. Although many vibration operation regulations have been widely applied, evaluating method for concrete vibration samples is relatively rare. To fill this gap, this paper proposes a concrete vibration data acquisition system and attempts to perform a quality analysis of samples employing machine learning that amalgamates various parameters. The experimental results demonstrate that the data collection system to identify the vibration state achieves an accuracy of 93.75% and an accuracy of 90.3% in classifying vibration quality levels. The proposed method can classify and evaluate concrete quality levels, which strongly supports the visualization of concrete vibration process and the implementation of autonomous robot vibration. In the future, improving the reliability of the system and enhancing the accuracy of algorithms will be the focused. • A method for datafication conventional concrete vibration behaviour is proposed. • The concrete vibration data is sampled in order to classify with shallow learning. • The performance capabilities of the six algorithms in concrete samples were compared. • The identified framework proposed attains a stage recognition accuracy of 93.75%. • The proposed method strongly supports the implementation of autonomous vibration. [ABSTRACT FROM AUTHOR]