EXPERIMENTAL STUDY ON A BUCKLING BEHAVIOR OF A WOODEN SINGLE-LAYER SPACE FRAME WITH A COMPRESSIVE STRAIN INCLINED TO THE GRAIN

Application of strain based design to pipelines in arctic or seismic areas has recently been recognized as important. So far, there has been much study performed on tensile strain limit and compressive strain limit. However, the relationship between bending buckling (compressive strain limit) and tensile strain limit has not been discussed. A model using actual stress strain curves suggests that the tensile strain limit increases as Y/T rises under uniaxial tensile stress because a pipe manufacturer usually raises TS instead of lowering YS to achieve low Y/T. Under bending of a pipe with a high D/t, an increase in compressive strain on intrados of a bent pipe at the maximum bending moment (ε-cp*) improves the tensile strain limit because the tensile strain limit is controlled by the onset of buckling or ε-cp* which is increased by lowering Y/T. On the other hand, under bending of a pipe with a low D/t, the tensile strain limit may not be influenced by improvement of buckling behavior because tensile strain on the extrados is already larger than the tensile limit at ε-cp*. Finally, we argue that the balance of major linepipe properties is important. Efforts other than the strict demands for pipe properties are also very important and inevitable to improve the strain capacity of a pipeline.

Download Full-text

Elastic Buckling Behavior of Single Layer Latticed Cylindrical Roofs Under Snow Loading.

Journal of Snow Engineering of Japan ◽

10.4106/jsse.11.291 ◽

1995 ◽

Vol 11 (4) ◽

pp. 291-302 ◽

Cited By ~ 1

Author(s):

Seishi YAMADA ◽

Takashi TAGUCHI

Keyword(s):

Single Layer ◽

Elastic Buckling ◽

Buckling Behavior ◽

Snow Loading

Download Full-text

NUMERICAL SIMULATION AND EXPERIMENTAL STUDY OF A HARDWARE PULSE NEURAL NETWORK WITH MEMRISTOR SYNAPSES

Tyumen State University Herald Physical and Mathematical Modeling Oil Gas Energy ◽

10.21684/2411-7978-2021-7-2-223-235 ◽

2021 ◽

Vol 7 (2) ◽

pp. 223-235

Author(s):

Alexander N. BUSYGIN ◽

Andrey N. BOBYLEV ◽

Alexey A. GUBIN ◽

Alexander D. PISAREV ◽

Sergey Yu. UDOVICHENKO

Keyword(s):

Neural Network ◽

Numerical Simulation ◽

Experimental Study ◽

Single Layer ◽

Electrical Circuit ◽

Learning Curves ◽

Spiking Neural Network ◽

New Associations ◽

Strong Artificial Intelligence ◽

Comparison Of The Results

This article presents the results of a numerical simulation and an experimental study of the electrical circuit of a hardware spiking perceptron based on a memristor-diode crossbar. That has required developing and manufacturing a measuring bench, the electrical circuit of which consists of a hardware perceptron circuit and an input peripheral electrical circuit to implement the activation functions of the neurons and ensure the operation of the memory matrix in a spiking mode. The authors have performed a study of the operation of the hardware spiking neural network with memristor synapses in the form of a memory matrix in the mode of a single-layer perceptron synapses. The perceptron can be considered as the first layer of a biomorphic neural network that performs primary processing of incoming information in a biomorphic neuroprocessor. The obtained experimental and simulation learning curves show the expected increase in the proportion of correct classifications with an increase in the number of training epochs. The authors demonstrate generating a new association during retraining caused by the presence of new input information. Comparison of the results of modeling and an experiment on training a small neural network with a small crossbar will allow creating adequate models of hardware neural networks with a large memristor-diode crossbar. The arrival of new unknown information at the input of the hardware spiking neural network can be related with the generation of new associations in the biomorphic neuroprocessor. With further improvement of the neural network, this information will be comprehended and, therefore, will allow the transition from weak to strong artificial intelligence.

Download Full-text