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In this paper, we compare different nanoclay-PEG composites and the influence of the input parameters especially the percentage of PEG and the clay size. Because of the facility of material elaboration, dried state with grinding, we adopted a complete experiments plan to obtain a maximum of robustness of the responses. For each sample, we made an XRD analysis to see if we obtain the intercalation of the PEG 6000 (Polyethylene Glycol 6000) within the clay sheets. The characterization adopted consists on the measurement of the shrinking of some cylinders we made, the liquidity and plasticity limits according to the Casagrande protocol used in geotechnical clays characterizations. We utilize also the methylen blue protocol to estimate the variation of the specific surface of ionic exchange of the clay sheets according to the PEG 6000 percentage and the clay sizes. SEM microscopy permits to visualize some of the phases detected by the XRD analysis. The TEM microscopy permits also to see the amorphous phases created by the grinding protocol which affects significantly the specific surface and the shrinking of the new materials. For each section, we made some conclusions with interpretation in order to integrate these results in civil engineering, classical/artisanal material construction and geotechnical fields.

Kmeans is one of the most algorithms that are utilized in data clustering. Number of metrics is coupled with kmeans in order cluster data targeting the enhancement of both locally clusters compactness and the globally clusters separation. Then, before the ultimate data assignment to their corresponding clusters, the selection of the optimal number of clusters should constitute a crucial step in the clustering process. The present work aims to build up a new clustering metric/heuristic that takes into account both space dispersion and inferential characteristics of the data to be clustered. Hence, in this paper, a Geometry-Inference based Clustering (GIC) heuristic is proposed for selecting the optimal numbers of clusters. The conceptual approach proposes the “Initial speed rate” as the main geometric parameter to be inferentially studied. After, the corresponding histograms are fitted by means of classical distributions. A clear linear behaviour regarding the distributions’ parameters was detected according to the number of optimal clusters k* for each of the 14 datasets adopted in this work. Finally, for each dataset, the optimal k* is observed to match with the change-points assigned as the intersection of two clearly salient lines. All fittings are tested using Khi2 tests showing excellent fitting in terms of p-values, and R² also for linear fittings. Then, a change-point algorithm is launched to select k*. To sum up, the GIC heuristic shows a full quantitative aspect, and is fully automated; no qualitative index or graphical techniques are used herein.

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