Abstract
In the present study, data generated from nanoindentation were used in order to reconstruct the surface constituent phases of mortar grids through machine learning algorithms. Specifically, the K-Means algorithm (unsupervised learning) was applied to two 49 measurement (7 × 7) datasets with information about the modulus (E) and hardness (H) in order to discover the underlying structure of the data. The resulting clusters from K-Means were then evaluated and values range assigned so as to signify the various constituent phases of the mortar. Furthermore, another dataset from nanoindentation containing information about E, H, and the surface colour of the measured area (obtained from an optical microscope) was used as the training set in order to develop a random forests model (supervised learning), which predicts the surface colour from the E and H values. Colour predictions on the two 7 × 7 mortar grids were made and then possible correlations between the clusters, signifying constituent phases, and the predicted colours were examined. The groupings of data in the clusters (phases) corresponded to a unique surface colour. Finally, the constituent phases of the mortar grids were reconstructed in contour plots by assigning the corresponding cluster of the K-Means algorithm to each measurement (position in the grid).
Highlights
Instrumentation requirements in the characterization field are changing rapidly in terms of quality assurance, desired accuracies, and measurement speed
In the present study, data generated from nanoindentation were used in order to reconstruct the surface constituent phases of mortar grids through machine learning algorithms
The resulting clusters from K-Means were evaluated and values range assigned so as to signify the various constituent phases of the mortar. Another dataset from nanoindentation containing information about E, H, and the surface colour of the measured area was used as the training set in order to develop a random forests model, which predicts the surface colour from the E and H values
Summary
Instrumentation requirements in the characterization field are changing rapidly in terms of quality assurance, desired accuracies, and measurement speed. The nanoindentation technique’s use for characterizing cement-based materials is growing rapidly as it can help identify the local elastic properties and the hardness at micro-scale and at nanoscale [30,31]. This rapid growth can be further justified from the fact that nanoindentation is highly localized and, provides a big number of elastic, plastic, and fracture properties without being destructive [32]. Data science and machine learning ease the efficient mining and potential for further processing of large material data sets, resulting in the extraction and identification of high-value material knowledge, towards design, quality, and manufacturing This is accomplished by using linkages of process-structure-property (PSP) information, with the main focus of data transformations to be in the forward direction (process → structure → properties). Using machine learning, potential alternate materials available in nature could be investigated to replace Portland cement (with a great potential being sustainable and longer-lasting alternatives), without requiring a huge amount of energy to manufacture
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