TITLE: Learning from Sparse Datasets: Predicting Concrete's Strength by Machine Learning 

ABSTRACT: Despite enormous efforts over the last decades to establish the relationship
between concrete proportioning and strength, a robust knowledge-based model for
accurate concrete strength predictions is still lacking. As an alternative to
physical or chemical-based models, data-driven machine learning (ML) methods
offer a new solution to this problem. Although this approach is promising for
handling the complex, non-linear, non-additive relationship between concrete
mixture proportions and strength, a major limitation of ML lies in the fact
that large datasets are needed for model training. This is a concern as
reliable, consistent strength data is rather limited, especially for realistic
industrial concretes. Here, based on the analysis of a large dataset (>10,000
observations) of measured compressive strengths from industrially-produced
concretes, we compare the ability of select ML algorithms to "learn" how to
reliably predict concrete strength as a function of the size of the dataset.
Based on these results, we discuss the competition between how accurate a given
model can eventually be (when trained on a large dataset) and how much data is
actually required to train this model.
Despite enormous efforts over the last decades to establish the relationship
between concrete proportioning and strength, a robust knowledge-based model for
accurate concrete strength predictions is still lacking. As an alternative to
physical or chemical-based models, data-driven machine learning (ML) methods
offer a new solution to this problem. Although this approach is promising for
handling the complex, non-linear, non-additive relationship between concrete
mixture proportions and strength, a major limitation of ML lies in the fact
that large datasets are needed for model training. This is a concern as
reliable, consistent strength data is rather limited, especially for realistic
industrial concretes. Here, based on the analysis of a large dataset (>10,000
observations) of measured compressive strengths from industrially-produced
concretes, we compare the ability of select ML algorithms to "learn" how to
reliably predict concrete strength as a function of the size of the dataset.
Based on these results, we discuss the competition between how accurate a given
model can eventually be (when trained on a large dataset) and how much data is
actually required to train this model.
Learning from Sparse Datasets: Predicting Concrete's Strength by Machine Learning
http://arxiv.org/abs/2004.14407v1