High-performance concrete strength predicting method based on memristor-gradient reducing method neural network

A high-performance concrete, gradient descent method, applied in neural learning methods, biological neural network models, instruments, etc., to achieve the effect of high strength prediction accuracy and low consumption

Active Publication Date: 2019-10-22
HOHAI UNIV
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Problems solved by technology

Fortunately, the advent of memristors offers the possibility to solve this problem

Method used

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  • High-performance concrete strength predicting method based on memristor-gradient reducing method neural network
  • High-performance concrete strength predicting method based on memristor-gradient reducing method neural network
  • High-performance concrete strength predicting method based on memristor-gradient reducing method neural network

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Embodiment 1

[0086] The present invention excavates 104 groups from the literature (see literature C.H.Lim, Y.S.Yoon, J.H.Kim. Genetic algorithm in mixproportioning of high-performance concrete [J]. Cement and Concrete Research, 2004, 34 (3): 409-420.) Data, 92 groups are randomly selected as training samples, and the remaining 12 groups are used as prediction samples. The 104 groups of specific sample data values ​​are shown in Table 1.

[0087] Table 1 sample data

[0088]

[0089]

[0090]

[0091]

[0092] (1) In order to train the neural network established in combination with the memristor, and considering the need to test the learned neural network, randomly select 12 groups (2, 10, 17, 30, 40, 48, 55, 63 , 75, 83, 93, 101 groups) as test data, and the remaining 92 data as training samples. For convenience, the screened test data are numbered 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12 in sequence.

[0093] (2) Establish as figure 2 The memristor-gradient descent neur...

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Abstract

The invention discloses a high-performance concrete strength predicting method based on a memristor-gradient reducing method neural network. The method comprises the steps of acquiring high-performance concrete experiment data, establishing a database, and obtaining a training sample and a predicting sample; according to the memristor and a traditional gradient reducing learning algorithm, establishing a memristor-gradient reducing method neural network; training the established memristor-gradient reducing method neural network by means of the obtained training sample, and obtaining a trainedmemristor-gradient reducing method neural network; inputting a predicting sample into the trained memristor-gradient reducing method neural network, and predicting the high-performance concrete strength; outputting the strength value of to-be-predicted high-performance concrete, and performing performance evaluation on a predicting result. The method of the invention can accurately predict the strength of the high-performance concrete, thereby satisfying a building engineering requirement. The method can be applied for predicting the high-performance concrete strength in actual engineering.

Description

technical field [0001] The invention belongs to the field of high-performance concrete strength prediction, and in particular relates to a high-performance concrete strength prediction method based on a memristor-gradient descent method neural network. Background technique [0002] As one of the most widely used and consumed modern engineering structure building materials in the world, concrete plays an important role in the process of economic development and social progress. High-performance concrete is a building material with many special properties such as high workability, high strength, high volume stability and high durability. In the past ten years, high-performance concrete has been widely used in a large number of large-scale projects such as nuclear reactors, cross-sea bridges, nuclear waste containers, and submarine tunnels. Strength is an important mechanical property to measure the quality of high-performance concrete, how to accurately predict it has become ...

Claims

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Application Information

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IPC IPC(8): G16C60/00G06N3/08
CPCG06N3/084G16C60/00
Inventor 邱林梁英杰
Owner HOHAI UNIV
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