ε–greedy automated indentation of cementitious materials for phase mechanical properties determination

Heat Treatment’s Vital Role: Elevating Orthodontic Mini-Implants for Superior Performance and Longevity—Pilot Study

“…Microindentation is a valuable technique for assessing the durability and mechanical properties of various materials. This technique is crucial for determining mechanical properties at different scales, aiding in material design and assessing durability against degradation [3].…”

Interpretable ensemble machine learning for the prediction of the expansion of cementitious materials under external sulfate attack

“…Deep Learning techniques and Convolutional Neural networks help assess concrete properties at various scales: from crack and defect detection [18][19][20] to concrete microscopic image analysis [21][22][23] or mechanical properties [24]. Gaussian processes, Bayesian techniques, and exploration-exploitation techniques close to reinforcement learning have been successfully employed to infer mechanical characteristics from microindentation and nanoindentation [25] or quantitatively estimate uncertainties concerning concrete properties such as susceptibility to sulfate degradation [26]. However, these techniques are relatively limited in terms of interpretability.…”

Open‐source deep learning‐based air‐void detection algorithm for concrete microscopic images

“…Due to the complex heterogeneous nature of concrete, phase separation remains a challenging task. The last advances in machine learning led to a better understanding of concrete properties, from strength 8 to shrinkage 9 or micromechanical properties, 10 and, more specifically, deep learning segmentation techniques have been fruitful in different fields 11 . For visual imagery, a convolutional neural network (CNN) might be used, combined with other techniques, to increase observation precision classifying or detecting objects of low contrast with a completely automated procedure.…”