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Multi-factor safety grading method and system for underground large-space construction

A multi-factor, large-space technology, applied in the direction of instruments, biological neural network models, data processing applications, etc., can solve problems such as human safety assessment errors, construction safety hazards, false data reporting, etc., to improve prediction efficiency, avoid human intervention, The effect of reducing security risks

Pending Publication Date: 2020-08-25
SOUTHWEST JIAOTONG UNIV +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In addition, due to the complex relationship between the input and disturbance factors of the geotechnical engineering system and the output, it shows very complex high-order nonlinear characteristics. The nonlinear description itself is an unsolved problem. It can be seen that the geotechnical work Difficulty of Characterization Research
Due to the lack of understanding of the internal working mechanism of the geotechnical system, the artificial safety assessment mainly used for multiple indicators of the safety state during construction has great errors and contingencies, and there are also artificial false data, which leads to greater safety in construction. Hidden problems

Method used

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  • Multi-factor safety grading method and system for underground large-space construction
  • Multi-factor safety grading method and system for underground large-space construction
  • Multi-factor safety grading method and system for underground large-space construction

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Embodiment 1

[0027] figure 1 It shows an exemplary multi-factor safety classification method for underground large space construction of the present invention, including:

[0028] Step 1011, determine the construction monitoring index and its threshold, including two parts:

[0029] 1. Select an existing indicator. National and local standards can be integrated to select reasonable and available control indicators, and when determining these indicators, it is necessary to propose control indicators for all risk factors that are in line with the project based on the actual project situation.

[0030] 2. Reduction of existing indicators (for special cases such as large spans). Carry out numerical experiments and model experiments, and determine the reduction factor through certain field experiments.

[0031] The specific monitoring indicators and thresholds need to be considered comprehensively according to the actual project.

[0032] Step 1012, the method for determining the security l...

Embodiment 2

[0303] image 3 It shows a multi-factor safety prediction system for underground large space construction according to an exemplary embodiment of the present invention, that is, an electronic device 310 (such as a computer server with a program execution function), which includes at least one processor 311, a power supply 314, and a A memory 312 and an input-output interface 313 that are communicatively connected to the at least one processor 311; the memory 312 stores instructions that can be executed by the at least one processor 311, and the instructions are executed by the at least one processor 311 to Enable the at least one processor 311 to perform the method disclosed in any of the preceding embodiments; the input and output interface 313 may include a display, a keyboard, a mouse, and a USB interface for input and output data; the power supply 314 is used for electronic Device 310 provides electrical power.

[0304]Those skilled in the art can understand that all or p...

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Abstract

The invention discloses a multi-factor safety grading method and system for underground large-space construction. The method comprises the steps of establishing an underground large-space constructionmulti-factor safety grading comparison table; collecting data samples associated with the security levels according to the comparison table; constructing a security classification BP neural network,training the security classification BP neural network by utilizing the data samples associated with the security levels, and enabling the security classification BP neural network to meet the following conditions: when parameters are input into an input layer, an output layer of the security classification BP neural network automatically outputting a security level judgment result; and inputtinga data sample collected in real time in construction into the safety grading BP neural network so as to predict the construction safety level in real time. By establishing the corresponding safety grading comparison table and adopting the BP neural network to establish the construction safety grading model, safety grading evaluation can be carried out on underground large-space construction beforeconstruction without depending on the internal working mechanism of a geotechnical system, and therefore real-time prediction and alarm are carried out in construction.

Description

technical field [0001] The invention relates to the technical field of civil engineering construction, in particular to a multi-factor safety classification method and system for underground large space construction. Background technique [0002] At present, the existing monitoring systems for the complex environment of urban underground large space construction are independent of each other, and the monitoring data has a high rate of false reporting. The multi-indicator graded evaluation standard for the construction safety status of large underground spaces in cities provides a strong guarantee for the construction safety of large underground spaces in my country. [0003] In addition, the practice of geotechnical engineering shows that in most cases, engineers and technicians can only measure and grasp the appearance of rock and soil, such as the pressure of bearing plates, ground subsidence, etc., and although they have a certain degree of understanding of its working mec...

Claims

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/08G06N3/04
CPCG06Q10/06393G06Q10/067G06Q50/08G06N3/045
Inventor 肖清华雷升祥王立新李聪明何亚涛李储军汪珂韩翔宇熊强邱泽民
Owner SOUTHWEST JIAOTONG UNIV