MACHINE LEARNING METHOD FOR ESTIMATING CONCRETE DENSITY

Evaluating concrete density is a key element in building projects, altering the structural design and quality control, as concrete density has a specific impact on strength, usefulness, longevity on concrete structures. It is essential to accurately estimate the concrete strength using machine learning techniques because current methods of measuring concrete density are labour and time-intensive and may not be applicable in all circumstances. The use of machine learning approach for computing concrete density using the characteristics of the concrete mix. A collection of concrete mix designs and their accompanying densities is used in the recommended method for machine learning. Using a range of criteria, including the mean absolute error or the coefficient of determination. We evaluate the model's effectiveness. Our results show that the suggested method can accurately forecast concrete density to a great degree. The non-destructive testing data-based machine learning technique for calculating concrete density. To determine the correlation between non-destructive testing data and concrete density, the suggested method applies a random forest regression algorithm. The method is validated using the experimental results come from a concrete testing facility, and the findings demonstrate that the suggested approach can precisely and effectively predict concrete density. The method provides a practical and reliable alternative to traditional methods, and has potential applications in the construction industry for real-time monitoring and quality control