Desert sand concrete strength prediction method, device and equipment and storage medium
A concrete strength and strength prediction technology, applied in the direction of measuring devices, biological neural network models, computer material science, etc., can solve the problems of cumbersome operation steps, high cost, and low efficiency, and achieve cumbersome operation steps, high cost, and low efficiency Effect
Pending Publication Date: 2022-03-25
CHINA RAILWAY 20 BUREAU GRP
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[0003] The embodiment of the present invention provides a desert sand concrete strength prediction method, device, equipment and storage medium, which solves the problems of cumbersome operation steps, high cost and low efficiency when performing strength detection in the prior art
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[0047] It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.
[0048]The main solution of the embodiment of the present invention is to obtain the historical detection data of the strength of desert sand concrete; for the different types of desert sand concrete, obtain the characteristic text values corresponding to the different types of desert sand concrete, and map the The feature text value is converted into a corresponding four-dimensional vector; the neural network model is trained by using the historical detection data to obtain a trained neural network model; the strength of the desert sand concrete to be tested is predicted by using the trained neural network model to obtain The strength prediction value of the desert sand concrete to be tested.
[0049] The traditional strength testing method in the prior art generally detects the strength value by manually operating ...
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The invention discloses a desert sand concrete strength prediction method, device and equipment and a storage medium, and belongs to the technical field of strength prediction.The method comprises the steps that historical detection data of desert sand concrete strength is obtained; for the different types of desert sand concrete, feature text values corresponding to the different types of desert sand concrete are obtained, and the feature text values are converted into corresponding four-dimensional vectors according to a preset mapping mode; training a neural network model by using the four-dimensional vector and the historical detection data to obtain a trained neural network model; and utilizing the trained neural network model to carry out strength prediction on to-be-detected desert sand concrete to obtain a strength prediction value of the to-be-detected desert sand concrete. According to the method, the neural network model is trained by using the historical detection data of the desert sand concrete, and the strength of the desert sand concrete can be predicted by using the neural network model after training is completed, so that efficient and accurate strength prediction of the desert sand concrete is realized.
Description
technical field [0001] The invention relates to the technical field of strength prediction, in particular to a desert sand concrete strength prediction method, device, equipment and storage medium. Background technique [0002] In related technologies, the traditional strength detection method generally detects the strength value by manipulating testing machines and other equipment, the operation steps are cumbersome, the cost is high and the efficiency is low, and it focuses on the detection of the strength value of ordinary concrete. Contents of the invention [0003] The embodiment of the present invention solves the problems of cumbersome operation steps, high cost and low efficiency in strength detection in the prior art by providing a desert sand concrete strength prediction method, device, equipment and storage medium. [0004] According to a first aspect of the present invention, a method for predicting the strength of desert sand concrete is provided, comprising: ...
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Login to View More IPC IPC(8): G16C20/70G16C60/00G06N3/02G01N33/38
CPCG16C20/70G16C60/00G06N3/02G01N33/383
Inventor 廖太昌杨星智常小娟姚鑫杨文萃谢江胜袁杰杨阳蔡小平帖锋斌
Owner CHINA RAILWAY 20 BUREAU GRP