Real-time monitoring system of construction of large sunk well foundations based on remote wireless transmission technology

Abstract

The invention discloses a real-time monitoring system of construction of large sunk well foundations based on a remote wireless transmission technology. The real-time monitoring system comprises a data acquisition unit, a server and a client side, wherein the data acquisition unit comprises a sunk well gesture monitoring module used for monitoring a three-dimensional coordinate and a tilting rateof a sunk well, a sunk well deflection monitoring module used for monitoring the deflection of the sunk well, a sunk well stress monitoring module used for monitoring concrete stress and steel bar stress of the sunk well and a sunk well inner and outer water level monitoring module used for monitoring difference between inner water level and outer water level of the sunk well; the server is connected with the data acquisition unit through a wireless transmitter and is used for storing monitoring values output by the data acquisition unit according to the time sequence; the client side is in communication connection with the server and is used for obtaining a construction data table and alarm information according to the monitoring values. Through the real-time monitoring system, the problems that artificial acquisition is high in error rate and the real-time acquisition cannot be achieved are solved; the result data is visualized, so that the problems that the sunk well construction control information presentation is not visual and the presented information is incomplete are solved.

Description

technical field [0001] The invention relates to the technical field of caisson construction. More specifically, the present invention relates to a real-time monitoring system for large-scale caisson foundation construction based on remote wireless transmission technology. Background technique [0002] The caisson is a shaft-shaped structure. It excavates the soil in the well, relies on its own gravity to overcome the friction of the well wall and sinks to the design elevation, and then seals the bottom with concrete and fills the well hole to make it a bridge pier or other structures. The basics. [0003] The caisson construction monitoring refers to the process of collecting the data of the caisson itself and surrounding structures during the construction of the caisson. The current caisson construction monitoring is mostly carried out manually by the staff at the construction site when the construction progress is certain, and then the collected raw data is processed int...

AI Technical Summary

Problems solved by technology

[0004] 1. The workload of manual collection is large, and data processing is prone to errors

[0005] Since caisson construction involves the collection of parameters such as the stress, water level, and disturbance of the bottom and wall of the caisson, and the slope rate of surrounding buildings, both the collection frequency and the number of collections are very large, and manual collection is adopted. Inefficient and very error-prone when processing data

[0006] 2. Only the data at the time of collection, the data in other time periods cannot be queried, and the continuous change of data cannot be grasped

[0007] As a result of manual collection, only the data at the time of collection can be consulted, and the data of other time periods needs to be searched from the archives, which is difficult to find in the first time, and the results of manual collection cannot be observed intuitively Continuous change of data

[0008] 3. Unable to realize real-time monitoring and early warning

[0009] Since there is a certain lag in the manual collection from the collection to the calculation of the results, and the frequency of manual collection is not high, the construction personnel cannot obtain the monitoring results in real time, so it is impossible to give early warning of possible conditions during construction.

[0010] 4. The result display is not intuitive

[0011] The existing calculation results are often in the form of Excel tables. The table data is relatively general and the operation is cumbersome. Both the input speed and the reading speed are relatively slow, and it is impossible to intuitively see the changes in the monitoring results from the Excel table.

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