Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model

• Create by: Administrator
• Research
• 10/01/2017

This research carries out a comparative study to investigate a machine learning solution that employs the Gaussian Process Regression (GPR) for modeling compressive strength of high-performance concrete (HPC). This machine learning approach is utilized to establish the nonlinear functional mapping between the compressive strength and HPC ingredients. To train and verify the aforementioned prediction model, a data set containing 239 HPC experimental tests, recorded from an overpass construction project in Danang City (Vietnam), has been collected for this study. Based on experimental outcomes, prediction results of the GPR model are superior to those of the Least Squares Support Vector Machine and the Artificial Neural Network. Furthermore, GPR model is strongly recommended for estimating HPC strength because this method demonstrates good learning performance and can inherently express prediction outputs coupled with prediction intervals.

1. Introduction:

In construction industry, high-performance concrete (HPC) has been widely used in high-rise building/infrastructure projects for its superior strength, durability, and workability which exceed those of normal concrete [1, 2]. Generally, special ingredients are employed to make these specially designed concretes to satisfy a combination of performance requirements. Furthermore, the compressive strength is often considered as the most important property of HPC; other concrete properties such as elastic modulus, water tightness, and impermeability appear to have direct relationships with compressive strength [3]. Hence, the compressive strength is commonly utilized as the main...

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