Intelligent safety step monitoring method and system for tunnel construction safety control

Abstract

The invention relates to the technical field of tunnel safety monitoring, in particular to an intelligent safety step monitoring method and system for tunnel construction safety control, and the system comprises a sensor arrangement module, a data collection and transmission module, a safety step intelligent analysis and early warning module and a dynamic control module. According to the tunnel safety-oriented safety step pitch monitoring method and system, various high-precision sensors are arranged at key positions of the tunnel, and advanced data transmission and intelligent analysis technologies are utilized, so that key information such as the safety step pitch and the stress state of surrounding rock can be accurately obtained in real time. Once the safety step is close to or beyond a reasonable range, the system immediately triggers multi-channel early warning, so that constructors can know potential dangers in the first time and take countermeasures in time, serious safety accidents such as tunnel collapse are effectively avoided, and the life safety of the constructors is greatly guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel safety monitoring, and in particular to an intelligent safety step monitoring method and system for tunnel construction safety control. Background Art

[0002] The starting point of safe step management in tunnel construction is to ensure construction safety and prevent casualties caused by closing cave-ins; the core of step management is to control the distance between the heading face and the invert and secondary lining, and to control the length of the cycle excavation.

[0003] The safe step distance mainly involves the reasonable spacing between the tunnel face and the completed supporting structure behind (such as secondary lining, invert, etc.). Traditional safe step distance monitoring methods have many significant defects. On the one hand, it mainly relies on manual regular measurement and empirical judgment. Manual measurement is not only extremely inefficient and requires a lot of manpower and time costs, but also the accuracy of the measurement results is easily affected by human factors, such as the measurement personnel's proficiency, fatigue state, and the accuracy limitations of the measurement tools. On the other hand, empirical judgment lacks scientific quantitative basis and is difficult to adapt to complex and changeable geological conditions and construction conditions. Under complex geological conditions, the mechanical properties of the surrounding rock change rapidly and are difficult to predict. Traditional methods cannot capture the dynamic changes of the safe step distance in time, resulting in the inability to quickly issue effective warnings when the safe step distance actually exceeds the reasonable range.

[0004] This situation can easily lead to a series of serious safety accidents, such as tunnel collapse, large deformation of surrounding rock, etc., which pose a direct and huge threat to the life safety of construction workers, and will also lead to serious delays in the progress of the project, causing significant economic losses, including increased project repair costs, compensation for delayed construction, and waste of equipment and materials. In addition, it may also cause adverse effects at the social level, such as traffic congestion (if it involves traffic tunnels), damage to the surrounding environment, and a crisis of public confidence in engineering construction.

[0005] The field of tunnel construction urgently needs an intelligent safety step distance monitoring method that can monitor the changes in safety step distance in real time and accurately, and provide timely and effective early warning of potential safety risks. Summary of the invention

[0006] The purpose of the present invention is to provide an intelligent safety step monitoring method and system for tunnel construction safety control to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: an intelligent safety step distance monitoring system for tunnel construction safety control, comprising a sensor arrangement module, a data acquisition and transmission module, a safety step distance intelligent analysis and early warning module and a dynamic control module.

[0008] The sensor arrangement module is configured to install a laser distance measuring sensor on the tunnel face for measuring the distance between the tunnel face and the rear structure, install a displacement sensor on the lining trolley and the invert construction equipment for monitoring the position change thereof, and arrange a stress strain sensor in the tunnel surrounding rock for monitoring the stress state of the surrounding rock, and each sensor is connected to the data acquisition terminal via a wired or wireless method;

[0009] The data acquisition and transmission module, the data acquisition terminal is used to receive the data collected by the sensor, and after preliminary processing and integration of the data, use 4G / 5G communication technology to transmit the data in real time to the cloud server;

[0010] The safe step intelligent analysis and early warning module has a built-in data analysis model in the cloud server that comprehensively considers factors such as tunnel geological conditions, construction technology, and support parameters. Through big data analysis and machine learning algorithms, the model is used to analyze the received data to calculate the reasonable safe step range under the current construction state, and compare it with the actual monitoring data. When the actual safe step approaches or exceeds the reasonable range, an early warning mechanism including an audible and visual alarm at the construction site and a warning information push notification to the mobile phone APP of the construction management personnel is automatically triggered. The warning information includes the warning position, step deviation, and possible risk level.

[0011] The dynamic control module can automatically adjust the construction progress when the safe step distance exceeds the standard, such as reducing the excavation speed of the heading face and giving priority to the secondary lining or invert construction. It can also combine the surrounding rock stress and strain monitoring data to automatically give suggestions for optimizing support parameters when the surrounding rock stability is affected by the safe step distance, including increasing the anchor rod length, increasing the spacing between steel arch frames, etc., and generate a construction guidance plan for construction personnel to carry out support and strengthening operations.

[0012] Preferably, in the sensor arrangement module, the installation position of the laser ranging sensor on the tunnel face should ensure that it can fully and accurately measure the distance to the rear structure, and its measurement accuracy should be not less than ±1 mm; the installation of the displacement sensor on the lining trolley and the invert arch construction equipment should be located at the key node, and its measurement accuracy should be not less than 0.001 mm; the arrangement of the stress and strain sensor in the tunnel surrounding rock should select representative positions according to the geological structure and stress distribution characteristics, and its measurement range should cover the stress and strain change range that may occur in the surrounding rock during the construction process.

[0013] Preferably, in the data acquisition and transmission module, the data acquisition terminal performs preliminary processing and integration of data, including removing outliers and noise interference, and uses encryption technology to ensure data security when transmitting data using 4G / 5G communication technology to prevent data leakage or tampering.

[0014] Preferably, in the safe step intelligent analysis and early warning module, the training data of the data analysis model comes from a large number of tunnel engineering cases with different geological conditions, construction processes and support parameters, and the model can continuously self-learn and optimize according to new monitoring data to improve the accuracy of the safe step calculation and the reliability of the early warning.

[0015] Preferably, in the dynamic control module, the construction progress is adjusted by automatically controlling the operating parameters of the construction equipment and rationally allocating the work tasks of the construction personnel, and in the process of implementing the dynamic control, the changes in the safe step distance and the surrounding rock state are continuously monitored.

[0016] An intelligent safety step distance monitoring method for tunnel safety comprises the following steps:

[0017] S1: Sensor installation and initialization step: determine the installation position of sensors in the tunnel face, secondary lining, invert and surrounding rock according to the tunnel design drawings and geological survey report, install sensors according to the installation specifications and perform calibration and initialization settings, including setting measurement accuracy and data acquisition frequency;

[0018] S2: Data collection and transmission step, starting the sensor to collect data at a preset frequency and transmitting it to the data collection terminal. The data collection terminal verifies and organizes the data and then transmits it to the cloud server through the 4G / 5G network. The transmission process uses encryption technology;

[0019] S3: Intelligent analysis and early warning step. After receiving the data, the cloud server calls the data analysis model for processing, obtains a reasonable safe step range and compares it with the actual monitoring data. If the difference exceeds the preset threshold, the early warning program is triggered, and the early warning information is sent through multiple channels and the detailed information of the early warning event is recorded;

[0020] S4: Dynamic control step. After receiving the warning information, the construction management personnel organize the construction personnel to implement dynamic control measures according to the warning content and system suggestions, including adjusting the construction progress and performing operations according to the optimized support plan. During the implementation process, the safe step distance and surrounding rock state changes are continuously monitored, and the monitoring data is fed back to the cloud server for re-analysis until the safe step distance meets the requirements and the surrounding rock remains stable.

[0021] Preferably, in the intelligent analysis and early warning step, the data analysis model first performs classification and feature extraction when processing data, and then performs comprehensive analysis and calculation in combination with tunnel-related parameter information.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] 1. This intelligent safety step distance monitoring method and system for tunnel construction safety control can obtain key information such as safety step distance and surrounding rock stress state in real time and accurately by arranging multiple high-precision sensors at key locations in the tunnel and using advanced data transmission and intelligent analysis technology. Once the safety step distance approaches or exceeds the reasonable range, the system immediately triggers a multi-channel warning, allowing construction personnel to know the potential danger at the first time, so that they can take countermeasures in time, effectively avoid the occurrence of serious safety accidents such as tunnel collapse, and greatly protect the lives of construction personnel.

[0024] 2. The intelligent safety step monitoring method and system for tunnel construction safety control, combined with surrounding rock stress and strain monitoring data, automatically generates support parameter optimization suggestions that are highly targeted and scientific. Construction personnel can perform support and reinforcement operations in accordance with these suggestions to better adapt to the changes in surrounding rock under complex geological conditions, enhance the stability of tunnel support structures, fundamentally reduce safety risks caused by insufficient support, and further improve the overall safety of tunnel construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0026] Figure 1 This is a diagram of the architecture of the safety step distance monitoring system of the present invention;

[0027] Figure 2 It is a schematic diagram of the cyclic operation of the mechanized supporting equipment in the tunnel of the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] See also Figure 1-Figure 2 The present invention provides a technical solution: an intelligent safety step distance monitoring system for tunnel construction safety control, including a sensor arrangement module, a data acquisition and transmission module, a safety step distance intelligent analysis and early warning module and a dynamic control module.

[0030] Sensor arrangement module: a laser distance measuring sensor is installed on the tunnel face to measure the distance between the tunnel face and the rear structure; a displacement sensor is installed on the lining trolley and the invert construction equipment to monitor their position changes; a stress and strain sensor is arranged in the tunnel surrounding rock to monitor the stress state of the surrounding rock, and each sensor is connected to the data acquisition terminal via wired or wireless means;

[0031] In the sensor layout module, the installation position of the laser ranging sensor on the tunnel face should ensure that it can fully and accurately measure the distance to the rear structure, and its measurement accuracy should be no less than ±1 mm; the installation of the displacement sensor on the lining trolley and the invert arch construction equipment should be located at the key nodes, and its measurement accuracy should be no less than 0.001 mm; the layout of the stress and strain sensor in the tunnel surrounding rock should select representative positions based on the geological structure and stress distribution characteristics, and its measurement range should cover the stress and strain change range that may occur in the surrounding rock during the construction process.

[0032] Data acquisition and transmission module: The data acquisition terminal is used to receive data collected by sensors, and after preliminary processing and integration of the data, it uses 4G / 5G communication technology to transmit the data in real time to the cloud server;

[0033] In the data collection and transmission module, the data collection terminal performs preliminary processing and integration of data, including removing outliers and noise interference, and uses encryption technology to ensure data security when transmitting data using 4G / 5G communication technology to prevent data leakage or tampering.

[0034] The intelligent analysis and early warning module for safe step distance has a built-in data analysis model on the cloud server that comprehensively considers factors such as tunnel geological conditions, construction technology, and support parameters. Through big data analysis and machine learning algorithms, the model is used to analyze the received data to calculate the reasonable safe step distance range under the current construction status and compare it with the actual monitoring data. When the actual safe step distance approaches or exceeds the reasonable range, the early warning mechanism is automatically triggered. The early warning information includes the warning position, step distance deviation, and possible risk level.

[0035] Remove outliers from sensor data. For example, for laser ranging sensor data, if the difference between a certain measurement value and the measurement value at adjacent time points exceeds a certain threshold (such as 5 meters, set based on experience), the value is considered an outlier and is deleted or corrected (methods such as mean substitution can be used).

[0036] For displacement sensor and stress strain sensor data, a similar method is used to set the abnormal value judgment threshold according to the accuracy range of the sensor and the fluctuation of historical data.

[0037] Unify the data from different sensors to the same dimension and value range. For example, for distance data (the distance between the tunnel face and the rear structure, etc.), normalize it to the [0,1] interval, the formula is: Where x is the original data, x min and x max are the minimum and maximum values ​​of this type of data respectively, and x1 is the normalized data.

[0038] Parameterization of geological conditions:

[0039] According to the geological survey report, geological factors such as rock type and degree of joint and fissure development are quantified. For example, rock types are divided into soft rock (assigned value 1), hard rock (assigned value 3), medium-strength rock (assigned value 2), etc.; for the degree of joint and fissure development, it is divided into high (assigned value 0.8), medium (assigned value 0.5), low (assigned value 0.2), etc. according to indicators such as crack density and width.

[0040] To calculate the comprehensive geological parameter G, it is assumed that the weighted average method is adopted, such as G = 0.6 × rock type assignment + 0.4 × joint and fissure development degree assignment.

[0041] Construction process parameterization:

[0042] For the excavation method, if it is drill and blast excavation, assign a value of 1; if it is shield excavation, assign a value of 2.

[0043] The efficiency of the support installation process is quantified based on the time required for installation of each meter of tunnel support. For example, if the installation time per meter of support is t hours, the reciprocal 1 / t can be calculated as the support installation efficiency parameter S.

[0044] Quantification of support parameters:

[0045] For anchor rods, the support strength coefficient R is calculated using the formula: Where L is the anchor length (meters), d is the anchor diameter (centimeter), n is the number of anchors per square meter, and k1 is an empirical coefficient (such as 0.5).

[0046] For steel arches, the support stiffness coefficient R1 is calculated using the formula: Where I is the moment of inertia of the steel arch section (m 4 ), is the steel arch spacing (meters), and k2 is the empirical coefficient (such as 1.2).

[0047] Using the multiple linear regression model, it is assumed that the relationship between the safe step distance D and the above parameters is:

[0048] D=a×G+b×s+c×R+d×R1+e

[0049] Among them, a, b, c, d, and e are regression coefficients obtained through big data analysis and training.

[0050] A large amount of existing tunnel construction data (including the actual values ​​of safe step distances under different geological conditions, construction techniques and support parameters and the corresponding parameter values) is used for model training. The regression coefficient is solved using methods such as the least squares method to minimize the sum of squares of the errors between the safe step distance predicted by the model and the actual safe step distance.

[0051] A part of known tunnel construction data (data not involved in training) is used as a test set and input into the trained model to calculate evaluation indicators such as the root mean square error (RMSE) and mean absolute error (MAE) between the safe step length predicted by the model and the actual safe step length.

[0052]

[0053] Where D 1,i is the predicted safe step length of the ith sample, D 2,i is the actual safe step size of the i-th sample, and n is the number of test samples.

[0054] During the tunnel construction process, sensor data is obtained in real time and after the above preprocessing, feature extraction and parameterization steps, it is substituted into the trained model to calculate the reasonable safe step distance D under the current construction state. T .

[0055] D T The safety step distance D from the actual monitoring R For comparison, if |D T -D R |>The set threshold (such as 2 meters, set according to engineering experience and safety standards) triggers the early warning mechanism.

[0056] In the safe step intelligent analysis and early warning module, the training data of the data analysis model comes from a large number of tunnel engineering cases with different geological conditions, construction processes and support parameters. The model can continuously self-learn and optimize according to new monitoring data to improve the accuracy of safe step calculation and the reliability of early warning.

[0057] The dynamic control module can automatically adjust the construction progress when the safe step distance exceeds the standard, such as reducing the excavation speed of the heading face and giving priority to the secondary lining or invert construction. It can also combine the surrounding rock stress and strain monitoring data to automatically give suggestions for optimizing support parameters when the surrounding rock stability is affected by the safe step distance, including increasing the anchor length, increasing the spacing between steel arch frames, etc., and generate a construction guidance plan for construction personnel to carry out support and reinforcement operations.

[0058] In the dynamic control module, the construction progress is adjusted by automatically controlling the operating parameters of the construction equipment and rationally allocating the work tasks of the construction personnel. In the process of dynamic control, the changes in the safe step distance and the surrounding rock conditions are continuously monitored.

[0059] Specific implementation steps:

[0060] Three laser rangefinders were evenly arranged on the face to ensure that the distance between the face and the structures at different positions behind could be fully and accurately measured; displacement sensors were installed at key nodes of the lining trolley and the invert construction equipment, with a total of five displacement sensors installed; and stress and strain sensors were installed at 10 representative locations in the surrounding rock according to the geological structure and stress distribution characteristics. After installation, all sensors were strictly calibrated and initialized, with the measurement accuracy of the laser rangefinder calibrated to ±0.05 meters, the accuracy of the displacement sensor set to ±0.01 millimeters, the measurement range of the stress and strain sensor reasonably set according to the expected stress conditions of the surrounding rock, and the data acquisition frequency was uniformly set to once every 5 minutes.

[0061] During the construction process, the sensor continuously collects data at a set frequency and sends the data to the data acquisition terminal via wireless transmission. The data acquisition terminal quickly processes the received data, removes outliers and noise interference, and transmits the data to the cloud server in real time using the 5G network. The data analysis model in the cloud server is trained and optimized based on the geological conditions of the tunnel (such as the tunnel mainly passes through soft rock formations, the rock strength is low, and the joints and fissures are relatively developed), the construction process (drilling and blasting excavation, support using anchor rods and steel arches, lining using concrete pouring technology) and support parameters (anchor rod length is 3 meters, diameter is 22 mm, spacing is 1 meter; steel arch spacing is 0.8 meters). When the model analysis finds that the actual safe step distance between the face and the secondary lining exceeds the reasonable range by 2 meters at a certain moment, the system immediately triggers an early warning.

[0062] "The safe step distance between the face and the secondary lining exceeds the reasonable range by 2 meters. It is located at tunnel mileage K5+300. The risk level is medium. Please take immediate measures." Based on the early warning information, the management personnel quickly issued instructions to reduce the excavation speed of the face and dispatched additional personnel and equipment to speed up the construction of the secondary lining. At the same time, based on the stress and strain data of the surrounding rock, the system recommended increasing the anchor length to 3.5 meters and the spacing between steel arch frames to 0.6 meters. The construction personnel carried out the work in accordance with the optimized support plan. During the operation, they continuously monitored the safe step distance and the surrounding rock status, and fed back the monitoring data to the cloud server for re-analysis every 30 minutes. After a period of adjustment and construction, the safe step distance gradually returned to a reasonable range, and the stability of the surrounding rock was effectively guaranteed, ensuring the safety and smooth progress of the tunnel construction.

[0063] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0064] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent safety step distance monitoring system for tunnel construction safety control, comprising a sensor arrangement module, a data acquisition and transmission module, a safety step distance intelligent analysis and early warning module and a dynamic control module, characterized in that: The sensor arrangement module is configured to install a laser distance measuring sensor on the tunnel face for measuring the distance between the tunnel face and the rear structure, install a displacement sensor on the lining trolley and the invert construction equipment for monitoring the position change thereof, and arrange a stress strain sensor in the tunnel surrounding rock for monitoring the stress state of the surrounding rock, and each sensor is connected to the data acquisition terminal via a wired or wireless method; The data acquisition and transmission module, the data acquisition terminal is used to receive the data collected by the sensor, and after preliminary processing and integration of the data, use 4G / 5G communication technology to transmit the data in real time to the cloud server; The safe step intelligent analysis and early warning module has a built-in data analysis model in the cloud server that comprehensively considers factors such as tunnel geological conditions, construction technology, and support parameters. Through big data analysis and machine learning algorithms, the model is used to analyze the received data to calculate the reasonable safe step range under the current construction state, and compare it with the actual monitoring data. When the actual safe step approaches or exceeds the reasonable range, an early warning mechanism including an audible and visual alarm at the construction site and a warning information push notification to the mobile phone APP of the construction management personnel is automatically triggered. The warning information includes the warning position, step deviation, and possible risk level. The dynamic control module can automatically adjust the construction progress when the safe step distance exceeds the standard, such as reducing the excavation speed of the heading face and giving priority to the secondary lining or invert construction. It can also combine the surrounding rock stress and strain monitoring data to automatically give suggestions for optimizing support parameters when the surrounding rock stability is affected by the safe step distance, including increasing the anchor rod length, increasing the spacing between steel arch frames, etc., and generate a construction guidance plan for construction personnel to carry out support and strengthening operations.

2. The intelligent safety step monitoring system for tunnel construction safety control according to claim 1 is characterized by: In the sensor arrangement module, the installation position of the laser ranging sensor on the tunnel face should ensure that it can fully and accurately measure the distance to the rear structure, and its measurement accuracy should be no less than ±1 mm; the installation of the displacement sensor on the lining trolley and the invert arch construction equipment should be located at the key node, and its measurement accuracy should be no less than 0.001 mm; the arrangement of the stress and strain sensor in the tunnel surrounding rock should select representative positions according to the geological structure and stress distribution characteristics, and its measurement range should cover the stress and strain change range that may occur in the surrounding rock during the construction process.

3. The intelligent safety step monitoring system for tunnel construction safety control according to claim 1 is characterized by: In the data collection and transmission module, the data collection terminal performs preliminary processing and integration of data, including removing outliers and noise interference, and uses encryption technology to ensure data security when transmitting data using 4G / 5G communication technology to prevent data leakage or tampering.

4. The intelligent safety step monitoring system for tunnel construction safety control according to claim 1 is characterized by: In the safe step intelligent analysis and early warning module, the training data of the data analysis model comes from a large number of tunnel engineering cases with different geological conditions, construction processes and support parameters, and the model can continuously self-learn and optimize according to new monitoring data to improve the accuracy of safe step calculation and the reliability of early warning.

5. The intelligent safety step monitoring system for tunnel construction safety control according to claim 1 is characterized by: In the dynamic control module, the construction progress is adjusted by automatically controlling the operating parameters of the construction equipment and rationally allocating the work tasks of the construction personnel. In the process of implementing the dynamic control, the changes in the safe step distance and the surrounding rock state are continuously monitored.

6. An intelligent safety step distance monitoring method for tunnel safety according to any one of claims 1 to 5, characterized in that: The following steps are involved: S1: Sensor installation and initialization step: determine the installation position of sensors in the tunnel face, secondary lining, invert and surrounding rock according to the tunnel design drawings and geological survey report, install sensors according to the installation specifications and perform calibration and initialization settings, including setting measurement accuracy and data acquisition frequency; S2: Data collection and transmission step, starting the sensor to collect data at a preset frequency and transmitting it to the data collection terminal. The data collection terminal verifies and organizes the data and then transmits it to the cloud server through the 4G / 5G network. The transmission process uses encryption technology; S3: Intelligent analysis and early warning step. After receiving the data, the cloud server calls the data analysis model for processing, obtains a reasonable safe step range and compares it with the actual monitoring data. If the difference exceeds the preset threshold, the early warning program is triggered, and the early warning information is sent through multiple channels and the detailed information of the early warning event is recorded; S4: Dynamic control step. After receiving the warning information, the construction management personnel organize the construction personnel to implement dynamic control measures according to the warning content and system suggestions, including adjusting the construction progress and performing operations according to the optimized support plan. During the implementation process, the safe step distance and surrounding rock state changes are continuously monitored, and the monitoring data is fed back to the cloud server for re-analysis until the safe step distance meets the requirements and the surrounding rock remains stable.

7. The intelligent safety step monitoring method and system for tunnel construction safety control according to claim 6 is characterized by: In the intelligent analysis and early warning step, the data analysis model first performs classification and feature extraction when processing data, and then performs comprehensive analysis and calculation in combination with tunnel-related parameter information.

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