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Prestressed steel member monitoring method based on machine learning

A technology of prestressed steel and machine learning, which is applied in the field of real-time monitoring of steel structures through machine learning, can solve problems such as untimely risk handling, and achieve the effect of improving lag and inefficiency

Pending Publication Date: 2020-11-03
BEIJING UNIV OF TECH
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  • Abstract
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  • Claims
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AI Technical Summary

Problems solved by technology

[0005] In view of the deficiencies in the above-mentioned prior art, the present invention aims to propose an intelligent structure detection and monitoring method to realize intelligent monitoring and early warning of steel structures, improve the authenticity of structural health monitoring and accurate judgment of hidden risks in structures , to detect abnormal points in time, so as to solve the problem of untimely risk treatment

Method used

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  • Prestressed steel member monitoring method based on machine learning

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Embodiment

[0030] Step 1: Install structural stress and strain monitoring devices on the steel members to monitor the steel structure in real time, including wireless sensor networks, strain sensor components and displacement sensors, install environmental monitoring devices at specific parts of the structure, and monitor data through the communication network subsystem In fact, it is sent to the cloud data server.

[0031] Step 2, through the stress and strain monitoring device, the structure is monitored by the wireless sensor network, strain sensors and displacement sensors, and the health status of the structure can be sensed in real time, and transmitted to the terminal through the network; Monitor the displacement of the working base point of the part, so as to check the stability of each base point;

[0032] Step 3, through the measurement of each sensor, the monitoring data is uploaded to the sensor module in the data acquisition and monitoring subsystem, which is used to collect...

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Abstract

The invention discloses a prestressed steel member monitoring method based on machine learning. A structure detection device, a data acquisition subsystem, a database management subsystem, a safety assessment and early warning subsystem and a user interface subsystem are included. Structural performance changes can be accurately and effectively monitored through machine learning to obtain monitoring data, the monitoring data and structural theoretical data are compared and analyzed, and therefore safety early warning is conducted, a decision-making technical basis is provided for the construction process and normal structure reinforcement, and the construction safety is improved. The method has great value, economic benefit and social benefit for realizing maximization of structural efficiency and ensuring structural use safety.

Description

technical field [0001] The invention belongs to a prestressed steel structure monitoring method based on machine learning, and in particular relates to a system for real-time monitoring of steel structures through machine learning. Background technique [0002] In recent years, the quality and safety of steel structure engineering during installation and use after installation has always been the focus of the industry, because the building structure will be affected by weather (wind, temperature and humidity, solar radiation, precipitation), natural disasters and the surrounding environment. By establishing a finite element model to represent these factors, and performing structural analysis and calculation based on machine learning, the accuracy and timeliness of calculation and analysis are greatly improved. [0003] During the actual implementation of the project, because the external environmental conditions of the building structure are constantly changing, the analysis...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N20/00G06N3/08G01B21/02G01B21/32G01D21/02G01L5/00G08C17/02H04L29/08H04W4/38
CPCG06N20/00G06N3/08H04W4/38G08C17/02H04L67/12G01L5/0028G01B21/32G01B21/02G01D21/02
Inventor 刘占省蒋安桐杜修力王宇波邢泽众史国梁
Owner BEIJING UNIV OF TECH
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