Uncertainty bounds for multivariate machine learning predictions on high-strain brittle fracture

Great progress has been made in the dynamic mechanical properties of concrete which is usually assumed to be homogenous. In fact, concrete is a typical heterogeneous material, and the meso-scale structure with aggregates has a significant effect on its macroscopic mechanical properties of concrete. In this paper, concrete is regarded as a two-phase composite material, that is, a combination of aggregate inclusion and mortar matrix. To create the finite element (FE) models, the Monte Carlo method is used to place the aggregates as random inclusions into the mortar matrix of the cylindrical specimens. To validate the numerical simulations of such an inclusion-matrix model at high strain rates, the comparisons with experimental results using the split Hopkinson pressure bar are made and good agreement is achieved in terms of dynamic increasing factor. By performing more extensive FE predictions, the influences of aggregate size and content on the macroscopic dynamic properties (i.e., peak dynamic strength) of concrete materials subjected to high strain rates are further investigated based on the back-propagation (BP) artificial neural network method. It is found that the particle size of aggregate has little effect on the dynamic mechanical properties of concrete but the peak dynamic strength of concrete increases obviously with the content increase of aggregate. After detailed comparisons with FE simulations, machine learning predictions based on the BP algorithm show good applicability for predicting dynamic mechanical strength of concrete with different aggregate sizes and contents. Instead of FE analysis with complicated meso-scale aggregate pre-processing, time-consuming simulation and laborious post-processing, machine learning predictions reproduce the stress–strain curves of concrete materials under high strain rates and thus the constitutive behavior can be efficiently predicted.

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

In general, all BCC transition metals have been observed to twin under appropriate conditions. At the present time various experimental reports of solid solution effects on BCC metals have been made. Indications are that solid solution effects are important in the formation of twins. The formation of twins in metals and alloys may be explained in terms of dislocation mechanisms. It has been suggested that twins are nucleated by the achievement of local stress-concentration of the order of 15 to 45 times the applied stress. Prietner and Leslie have found that twins in BCC metals are nucleated at intersections of (110) and (112) or (112) and (112) type of planes.In this paper, observations are reported of a transmission microscope study of the iron manganese series under conditions in which twins both were and were not formed. High strain rates produced by shock loading provided the appropriate deformation conditions. The workhardening mechanisms of one alloy (Fe - 7.37 wt% Mn) were studied in detail.

The output of the ultramicrotomy process with its high strain levels is dependent upon the input, ie., the nature of the material being machined. Apart from the geometrical constraints offered by the rake and clearance faces of the tool, each material is free to deform in whatever manner necessary to satisfy its material structure and interatomic constraints. Noncrystalline materials appear to survive the process undamaged when observed in the TEM. As has been demonstrated however microtomed plastics do in fact suffer damage to the top and bottom surfaces of the section regardless of the sharpness of the cutting edge or the tool material. The energy required to seperate the section from the block is not easily propogated through the section because the material is amorphous in nature and has no preferred crystalline planes upon which defects can move large distances to relieve the applied stress. Thus, the cutting stresses are supported elastically in the internal or bulk and plastically in the surfaces. The elastic strain can be recovered while the plastic strain is not reversible and will remain in the section after cutting is complete.