Bearing Pressure Risk Prediction Algorithm Research and Simulation for Single Points of the Building Materials

Abstract:

The pressure testing of the building materials has been a hot topic of research in the field of architecture. Traditional building materials pressure testing methods all calculate the larger bearing pressure fragile area, and are difficult to be accurate to the very point. This is mainly because of the larger range of signal distribution, which avianizes the correlation of the signal. To address the problem mentioned above, a bearing pressure fragile support point positioning algorithm for the building materials is proposed. The algorithm The combines the quantum computing with the neuron model in neural network to form the quantum neurons, and then expands them into a quantum neural network to achieve the functions of the traditional neural network, enhance the optimization capability of computing the small surface area of the building materials and ensure that the bearing pressure fragile support area of the building materials is further reduced, and shorten the positioning range. Simulation results show that the proposed method has better positioning effect on bearing pressure fragile points computing of the building and higher positioning accuracy.

• Periodical: Applied Mechanics and Materials (Volumes 380-384)
• Pages: 1550-1554
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.380-384.1550
• Online since: August 2013
• Authors: Jian Wan
• Keywords: Building Materials Bearing Pressure Fragile Point, Neural Network (NN), Particle Swarm Optimization (PSO)

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