Numerical and experimental studies of deflections of conventional and strengthened reinforced concrete bendable elements under short-term dynamic loading

The relevance of this study is conditioned by the technical complexity of the design solutions for construction projects of ground-based space infrastructure. It is associated with the possibility of special loads in the form of an air shock wave in the event of a launch abort, a fall of a fragment, an emergency shutdown of engines, an air shock wave from the indirect impact of nuclear weapons, seismic loads, accidental cargo falls, terrorist attacks, etc. Such impacts with a high degree of probability lead to damage to building structures and in the future, they need to be reinforced. These building structures must have survivability under special loads and deform without collapsing. Under the dynamic loading, the energy intensity of the bendable structures is important, to determine which it is necessary to know the magnitude of the acting force and deflections. The effective load in a wide class of problems refers to the initial data, and the determination of reliable values of the dynamic deflection of the bendable structure is an actual problem. The purpose of this study is to conduct a numerical and experimental investigation of the deflection of conventional and strengthened reinforced concrete structures under short-term dynamic loading. This study used the following research methods: measurements of deflections and loads by strain measurement, graphical analytic research using Microsoft Excel, numerical calculation in the environment of the Explicit Dynamics module of the Ansys software package. As a result of the study, experimental investigation of conventional and strengthened bendable reinforced concrete elements under short-term dynamic loading was carried out, the values of the effective force and deflections were obtained. The same experiment was modelled in the environment of the Explicit Dynamics module of the Ansys software package. A comparison of the deflection parameters was made, based on the results of numerical and physical experiments on the example of a specific design, which showed satisfactory convergence.

Explicit dynamics based numerical simulation approach for assessment of impact of relief hole on blast induced deformation pattern in an underground face blast

The breakage of rock mass by blasting has many challenges. The optimal breakage in an underground development face/tunnel blast is dominantly dependent on the relief area provided to the blast holes. The cut portion in the burn cut face blast is significantly important to achieve the controlled deformation due to the blast. This paper has discussed the impact of the number and diameter of the relief holes on the breakage pattern of the rock. The numerical simulation with varying numbers and diameter of relief hole was carried out for this purpose. Finite element modeller explicit dynamics of Ansys-Autodyn was used for the simulation work. The isosurface of non-deformed zone was plotted to compare the extent of deformation under varying conditions of relief holes. The analysis shows that the higher number of relief holes with optimum diameter gives more controlled deformation than single relief hole with larger diameter. The nearfield vibration was also recorded by placement of seismograph. The waveform analysis of the recorded vibration was carried out. The redesigning of the blasting pattern was done using the results of numerical simulation and waveform analysis. The redesigned pattern consists of four relief holes of 115 mm diameter. The blasting output with the revised design has resulted into the considerable improvements in the pull and reduction of overbreak. The revised pattern has addressed the issue of the socket formation at the site.

Representation learning for neural population activity with Neural Data Transformers

Neural population activity is theorized to reflect an underlying dynamical structure. This structure can be accurately captured using state space models with explicit dynamics, such as those based on recurrent neural networks (RNNs). However, using recurrence to explicitly model dynamics necessitates sequential processing of data, slowing real-time applications such as brain-computer interfaces. Here we introduce the Neural Data Transformer (NDT), a non-recurrent alternative. We test the NDT’s ability to capture autonomous dynamical systems by applying it to synthetic datasets with known dynamics and data from monkey motor cortex during a reaching task well-modeled by RNNs. The NDT models these datasets as well as state-of-the-art recurrent models. Further, its non-recurrence enables 3.9ms inference, well within the loop time of real-time applications and more than 6 times faster than recurrent baselines on the monkey reaching dataset. These results suggest that an explicit dynamics model is not necessary to model autonomous neural population dynamics.Codegithub.com/snel-repo/neural-data-transformers.