Unravelling the Relations between and Predictive Powers of Different Testing Variables in High Performance Concrete Experiments: The Data-Driven Analytical Methods

Abstract

This study proposes and applies a systematic data analysis methodology to analyse experimental data for high-performance concrete (HPC) samples with different admixtures for offshore fan foundation grouting materials uses. In contrast with other relevant research, including experimental studies, the materials physics and chemistry studies, or cementitious material portfolio determination studies, this data-driven analysis provides a deep exploration of the experimental variables associated with the test data. To offer complete and in-depth perspectives, several methods are employed for the data analyses, including correlation analysis, cosine similarity analysis, simple linear regression (SLR) modelling, and heat map and heat-based tabularised visualisations; the outcome is a proposed methodology that is easily implementable. The results from these methods are validated using a pairwise comparison approach (PCA) to avoid unnecessary interference between data variables. There are several potential contributions from this work, including insights for cohered groups of variables, techniques for double check and ‘third check’, an established ‘knowledge base’ consisting of 504 SLR predictive models with their effectiveness (significance) and prediction accuracy (data-model fitness) used in practical applications, an alternative visualisations of the results, three data transforms which can be omitted in a future analysis, and three valuable theory-linking perspectives (e.g., for the relationships between destructive and non-destructive tests with respect to the variable categories). The implication that some variables are interchangeable will make future experiments less labour intensive and time consuming for pre-project HPC material testing.