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High Performance Concrete (HPC) Compressive Strength Prediction With Advanced Machine Learning Methods: Combinations of Machine Learning Algorithms With Bagging, Rotation Forest, and Additive Regression
Abstract
In this chapter, the authors realized prediction applications of concrete compressive strength values via generation of various hybrid models, which are based on decision trees as main a prediction method. This was completed by using different artificial intelligence and machine learning techniques. In respect to this aim, the authors presented a literature review. The authors explained the machine learning methods that they used as well as with their developments and structural features. Next, the authors performed various applications to predict concrete compressive strength. Then, the feature selection was applied to a prediction model in order to determine parameters that were primarily important for the compressive strength prediction model. The authors evaluated the success of both models with respect to correctness and precision prediction of values with different error metrics and calculations.
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