Ε–greedy automated indentation of cementitious materials for phase mechanical properties determination. (May 2022)

• Record Type: Journal Article
• Authors: Hilloulin, B.; Lagrange, M.; Duvillard, M.; Garioud, G.
• Is Part Of: Cement & concrete composites. Volume 129(2022)
• Publication Date: 2022-05
• Subjects: Indentation -- Machine learning -- Phase properties -- ε–greedy strategy -- Material characterization -- Cementitious materials; Composite-reinforced concrete -- Periodicals; Concrete -- Periodicals; Composite materials -- Periodicals; Composites de ciment -- Périodiques; Béton -- Périodiques; Composites -- Périodiques; Béton léger -- Périodiques; Cement composites; Composite materials; Composite-reinforced concrete; Concrete; Lightweight concrete; Periodicals; Electronic journals; 620.135
• DOI: 10.1016/j.cemconcomp.2022.104465
• Languages: English
• ISSNs: 0958-9465

Abstract

Abstract: Microindentation and nanoindentation tests are very useful to assess the micromechanical properties of materials. However, statistical indentation tests require large matrices of indents for heterogeneous materials, which can be time-consuming. Given that specific properties of only one phase of interest can be looked for, a novel procedure is proposed to locate indents automatically. The procedure uses an ε–greedy strategy to determine the next indents to perform. The strategy is informed by the nature of predicted indents using unsupervised clustering of the indentation curves and Gaussian Process Classification. The influence of several parameters has been assessed, and the best combination performance has been quantified in the case of the microindentation of a cementitious mortar sample (500 mN) and the nanoindentation of a cement paste (1 mN). The proportion of indents in the phase of interest increased by around 20% in both cases, paving the way for faster indentation experiments. Highlights: ε–greedy strategies can automatically locate the next indents to perform in a phase of interest during indentation. The technique is used for cementitious materials at the micro- and the nanoscale. The proportion of indents in the phase of interest can increase by around 20%. Indentation curves are interpreted by unsupervised learning and Gaussian processes. The algorithm's parameters can be tuned to increase the global performance.