Device and system for monitoring and predicting deformation of expressway tunnel

Abstract

The invention discloses a highway tunnel deformation monitoring and predicting device and system, and belongs to the field of road construction. The system comprises a data acquisition module, a data preprocessing module, a data storage module, a model training and evaluation module, a real-time prediction module, an early warning mechanism module, a decision support and maintenance module, a feedback and optimization module and a model training and evaluation module. The device comprises an arch frame and detection devices. Compared with a traditional monitoring mode, different sensors provide a real-time and accurate data basis, and the deep learning model can mine deeper structure health information from the data basis, so that the monitoring precision is improved, the intelligent decision-making capability is enhanced, and more comprehensive guarantee is provided for safe operation of the tunnel.

Description

Technical Field

[0001] The present invention belongs to the field of road construction, and in particular relates to a device and system for monitoring and predicting deformation of a highway tunnel. Background Art

[0002] Highway tunnels are passages designed specifically for automobile transportation. With the development of society, the economy, and production, the proliferation of expressways has posed higher standards for road construction, requiring straight routes, gentle slopes, and wide road surfaces. Consequently, tunnels have been increasingly used when roads pass through mountainous areas, replacing the traditional winding routes. Tunnel construction plays a crucial role in improving highway technology, shortening operating distances, increasing transportation capacity, and reducing accidents.

[0003] The risks caused by deformation of highway tunnels mainly include the following four aspects:

[0004] First, structural safety hazards: deformation may cause lining cracking and concrete spalling, and in severe cases, lead to partial or complete collapse, weakening the stability of the tunnel structure and affecting its long-term bearing capacity. Deformation cracks may cause groundwater to seep in, further corroding steel and concrete.

[0005] Second, there is the risk of traffic accidents: deformation may reduce tunnel clearance, causing overheight vehicles to collide with the roof or side walls. Arch settlement or floor bulges may cause road undulations, leading to loss of vehicle control. Furthermore, ventilation, lighting, fire protection and other ancillary facilities may fail due to deformation, affecting emergency response functions.

[0006] Third, traffic and economic impacts: Severe deformation requires closure for repairs, leading to traffic congestion and increased detour costs. The cost of repairing deep structural damage may far exceed the cost of routine maintenance.

[0007] Fourth, secondary hazards: Tunnel deformation may reflect the loosening of the surrounding rock, inducing landslides or rockfalls, and may also lead to blockage of drainage ditches, which may cause waterlogging in the tunnel.

[0008] At the same time, my country has also formulated a number of national standards and industry specifications for the design, construction, operation and maintenance of highway tunnels, clarified the requirements for deformation control, and controlled deformation throughout the entire cycle from design to operation and maintenance through national standard systems such as JTGD70 and JTGH12.

[0009] A Chinese patent with authorization announcement number CN109556529B discloses a tunnel deformation monitoring and analysis method based on grid projection point cloud processing technology. For the tunnel surface point cloud model obtained by a three-dimensional laser scanner, this method constructs a tunnel design surface model and performs unit grid processing, projects the unit grid onto the tunnel surface point cloud model, fits the point cloud within the projection range into a plane quadrilateral, calculates the distance from the centroid of the plane polygon to the projection center on the tunnel central axis, determines the difference in deformation direction of the point cloud in the projection area corresponding to the unit grid, screens deformation monitoring data, and colorizes the tunnel surface design model according to the magnitude of the deformation at the corresponding position of the unit grid, thereby achieving holistic analysis and visualization of tunnel deformation monitoring.

[0010] The above technical solution has the following defects:

[0011] In terms of monitoring, it is a method for monitoring and analyzing tunnel deformation under ideal conditions. However, in actual situations, it is still necessary to analyze whether deformation has occurred based on multiple factors such as the current construction environment and the current climatic conditions used.

[0012] At present, there are also methods such as installing distributed optical fibers in the tunnel for monitoring, or using Transformer models for prediction. However, traditional distributed optical fiber monitoring has significant limitations. Subsequent disassembly, maintenance and replacement may cause secondary crack damage to tunnels with a long service life. Transformer models usually require a large amount of labeled data for training, and the cost of data collection and labeling is high. Summary of the Invention

[0013] To address the above shortcomings, the present invention provides a highway tunnel deformation monitoring and prediction system, which includes a data acquisition module, a data preprocessing module, a data storage module, a model training and evaluation module, a real-time prediction module, an early warning mechanism module, a decision support and maintenance module, a feedback and optimization module, and a model training and evaluation module;

[0014] The data acquisition module is used to collect data from optical fiber sensors and external sensors. The data preprocessing module cleans and denoises the data collected by the data acquisition module and then transmits it to the data storage module for integration and storage. The model training and evaluation module is trained using the labeled data set obtained from the data storage module. The real-time prediction module is used to receive real-time data and use the trained model to predict the structural health status. The early warning mechanism module performs intelligent early warning based on the real-time prediction results and the set thresholds. The decision support and maintenance module provides maintenance decision support based on the early warning information and prediction results. The feedback and optimization module analyzes the model performance based on the comparison information between the collected actual monitoring data and the prediction results, and continues training to improve the prediction accuracy.

[0015] Furthermore, the data acquisition module includes a weather data acquisition unit for collecting wind speed, humidity, temperature and precipitation data and a structural health detection data acquisition unit for collecting strain, displacement and vibration data.

[0016] Furthermore, after receiving the real-time data, the real-time prediction module is divided into three preset condition judgment units according to the data information:

[0017] When the wind speed is greater than 20m / s, the strong wind weather unit is used to calculate the average deformation of the weather data and structural monitoring data collected by the data acquisition module, and the calculated average deformation is output to the early warning mechanism module;

[0018] When the relative humidity is less than 30% and the temperature is greater than 20°C, the dry weather unit is used to calculate the crack width based on the weather data and structural monitoring data collected by the data acquisition module, and the calculated crack width is output to the early warning mechanism module;

[0019] When the precipitation is greater than 5 mm / h and the temperature is less than 0°C, the rain and snow weather unit is used to calculate the average deformation of the weather data and structural monitoring data collected by the data acquisition module, and the calculated crack width is output to the early warning mechanism module.

[0020] The present invention also discloses a device applied to the above-mentioned highway tunnel deformation monitoring and prediction system, comprising an optical fiber sensor, a laser scanner, an ultrasonic sensor and an infrared thermal imager that cooperate with a data acquisition module to monitor the tunnel itself, and an environmental detection sensor for detecting the current environment;

[0021] The optical fiber sensor includes an embedded optical fiber sensor and an external light sensor. The embedded optical fiber sensor is embedded in the inner wall of the tunnel, and the external light sensor, laser scanner, ultrasonic sensor, infrared thermal imager and environmental detection sensor are all installed on the arch truss;

[0022] The width and height of the arched truss are adapted to the inner diameter of the tunnel. Its bottom is fixed to the top of the vehicle through a fixed page, and it moves from one end of the tunnel to the other to complete the data collection work;

[0023] The truss includes an arched beam at the top, and the arched beam is used to adapt to the curved surface of the tunnel;

[0024] Two supporting columns connected to the bottom of the arched beam, the supporting columns are used to provide vertical support to the arched beam;

[0025] The bottoms of the two support columns are fixed on a fixed base, which is used to be fixed to the top of the carrier;

[0026] A crossbeam installed between the two ends of the arched beam, and several diagonal web members connected between the arched beam and the crossbeam, the diagonal web members are used to increase stability.

[0027] Furthermore, the environmental detection sensors including optical fiber sensors, laser scanners, ultrasonic sensors and infrared thermal imagers that cooperate with the data acquisition module to monitor the tunnel itself are used to detect the current environment;

[0028] The optical fiber sensor includes an embedded optical fiber sensor and an external light sensor. The embedded optical fiber sensor is embedded in the inner wall of the tunnel, and the external light sensor, laser scanner, ultrasonic sensor, infrared thermal imager and environmental detection sensor are all installed on the arch truss;

[0029] The width and height of the arched truss are adapted to the inner diameter of the tunnel. Its bottom is fixed to the top of the vehicle through a fixed page, and it moves from one end of the tunnel to the other to complete the data collection work.

[0030] The truss includes an arched beam at the top, and the arched beam is used to adapt to the curved surface of the tunnel;

[0031] Two supporting columns connected to the bottom of the arched beam, the supporting columns are used to provide vertical support to the arched beam;

[0032] The bottoms of the two support columns are fixed on a fixed base, which is used to be fixed to the top of the carrier;

[0033] A crossbeam installed between the two ends of the arched beam, and several diagonal web members connected between the arched beam and the crossbeam, the diagonal web members are used to increase stability.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. Combining structural and environmental data collected by fiber optic sensors and a variety of different sensors to form a multidimensional data set. At the same time, using data cleaning and feature extraction techniques, the raw data is converted into a format suitable for model input;

[0036] 2. Initially, traditional sensor technology is used for preliminary real-time monitoring to identify obvious structural issues. Historical monitoring data is then used to train Transformer and composite neural network models to extract potential hidden features and patterns. Furthermore, based on real-time monitoring, the trained deep learning model is used for dynamic prediction to identify potential risks.

[0037] 3. Compared with traditional monitoring methods, different sensors provide a real-time and accurate data foundation, and deep learning models can extract deeper structural health information from it, which not only improves monitoring accuracy, but also enhances intelligent decision-making capabilities, providing more comprehensive protection for the safe operation of tunnels. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 Schematic diagram of the arch frame in Example 1 of the present invention.

[0039] Figure 2 Schematic diagram of the arch frame in Example 2 of the present invention.

[0040] In the figure: 1. Arched beam; 2. Support column; 3. Fixed base; 31. Base I; 32. Base II; 33. Positioning seat; 34. Limiting column; 35. T-shaped seat; 36. Telescopic rod; 4. Cross beam; 5. Diagonal brace. DETAILED DESCRIPTION

[0041] To facilitate understanding of the present invention, the apparatus of the present invention will be described more fully below with reference to the accompanying drawings. The drawings illustrate embodiments of the apparatus. However, the apparatus can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present invention.

[0042] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "installed," "connected," and "disposed" should be understood in a broad sense. For example, they may refer to fixed connection or disposition, detachable connection or disposition, or integral connection or disposition. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0043] Example 1

[0044] This embodiment provides a highway tunnel deformation monitoring and prediction system for detecting a tunnel in a highway section in central my country, which has a total length of 2,228 meters, a width of 8 meters, and a height of 11.20 meters.

[0045] This highway tunnel deformation monitoring and prediction system includes a data acquisition module, a data preprocessing module, a data storage module, a model training and evaluation module, a real-time prediction module, an early warning mechanism module, a decision support and maintenance module, a feedback and optimization module, and a model training and evaluation module.

[0046] Specifically, the functions and interaction methods of each module are as follows:

[0047] First, the data acquisition module collects data from fiber optic sensors and wireless sensors (wireless sensors are sensors with wireless transmission capabilities) to monitor parameters such as strain, temperature, humidity, and vibration in the tunnel in real time. The collected data is then sent to the data processing module via a data transmission interface (such as LoRa or Zigbee).

[0048] In detail, the data acquisition module includes a weather data acquisition unit for collecting wind speed (m / s), humidity (%), and precipitation (mm) data, and a structural health detection data acquisition unit for collecting strain, displacement, and vibration data;

[0049] Subsequently, the data processing module cleans and removes noise from the received data information, processes missing values, and then extracts features (such as strain rate, vibration energy, etc.) to form a data set for use in subsequent models;

[0050] The data storage module is responsible for storing the integrated historical data and real-time data, and provides data query and access interfaces. The historical data is marked by date for subsequent analysis modules to call. It should be noted that SQL or NoSQL databases can be used.

[0051] The model training and evaluation module then uses the labeled data set (historical monitoring data and status) obtained from the data storage module to train the prediction model through a combination of Transformer and composite neural networks to evaluate the model's performance and accuracy. By combining fiber optic sensing technology and wireless sensor networks with Transformer models and composite neural networks, a comprehensive big data monitoring and prediction platform is formed, which can effectively improve the overall effectiveness of tunnel detection and prediction. Traditional sensors provide a real-time and accurate data foundation, while deep learning models can extract deeper structural health information from it. Therefore, this combination solution has significant advantages in innovation, effectiveness, and real-time performance. This fusion not only improves monitoring accuracy, but also enhances intelligent decision-making capabilities, providing more comprehensive protection for the safe operation of tunnels.

[0052] The real-time prediction module receives real-time data from the data acquisition module and pre-processed data and uses the trained model to predict the structural health status. The prediction results are passed to the early warning mechanism module for subsequent decision support;

[0053] After receiving the real-time data, the real-time forecast module divides the current weather conditions into three preset conditions (which can be set as condition judgment units) according to the data information:

[0054] When the wind speed is greater than 20m / s, the strong wind weather unit is used to calculate the average deformation (applying the wind load calculation model) from the weather data and structural monitoring data collected by the data acquisition module, as follows:

[0055]

[0056] The average deformation is as follows:

[0057]

[0058] Where N is the number of detected points;

[0059] The calculated average deformation is output to the early warning mechanism module;

[0060] When the relative humidity is less than 30% and the temperature is greater than 20°C, the dry weather unit is used to calculate the crack width based on the weather data and structural monitoring data collected by the data acquisition module (crack collection is based on laser point cloud data, and cracks are identified by calculating the density change or change rate of the points. The model uses the soil moisture and settlement model) as follows:

[0061] Crack width = maximum crack length - minimum crack length;

[0062] The calculated crack width is output to the early warning mechanism module;

[0063] When the precipitation is greater than 5 mm / h and the temperature is less than 0°C, the rain and snow weather unit is used to calculate the average deformation (using the water flow and soil deformation model) from the weather data and structural monitoring data collected by the data acquisition module as follows:

[0064] Calculate the displacement of each point and, combined with the current humidity and temperature, focus on the changes in the flooded area, and output the calculated crack width to the early warning mechanism module;

[0065] The early warning mechanism module provides intelligent early warnings based on real-time prediction results and set thresholds (generating early warning information and sending notifications to relevant personnel, which can be achieved through existing technologies, such as downloading an APP that cooperates with the system on a mobile phone);

[0066] The thresholds are:

[0067] Calculation results corresponding to strong wind weather units:

[0068] If the average deformation exceeds 5mm, an early warning is triggered. If the vibration frequency of the tunnel wall exceeds 1.0Hz, it indicates that the structure is affected and also triggers an early warning.

[0069] Calculation results for dry weather cells:

[0070] If the crack width exceeds 2mm, an early warning will be triggered. If the local temperature exceeds 30°C (using image processing technology to identify abnormal temperature areas through infrared images), it indicates the risk of deformation under dry conditions and also triggers an early warning.

[0071] Calculation results corresponding to rain and snow weather units:

[0072] If the average deformation exceeds 5mm and is accompanied by abnormal temperature and humidity, an early warning is triggered and an area of moisture accumulation is detected. If its temperature is below 0°C, the tunnel may need to be temporarily closed (depending on the staff's decision).

[0073] The decision support and maintenance module provides maintenance decision support based on early warning information and prediction results, and recommends possible maintenance measures and inspection frequencies. At the same time, it is connected to the feedback loop of the data acquisition module to ensure that the monitoring strategy can be dynamically adjusted according to actual conditions;

[0074] The feedback and optimization module is used to collect comparative information between actual monitoring data and prediction results, analyze model performance, and regularly retrain and optimize the model to improve prediction accuracy. Finally, the optimized model is returned to the model training and evaluation module to form a cyclic improvement mechanism.

[0075] like Figure 1 As shown, in order to meet the hardware requirements of the data acquisition module, the present invention also includes the following devices:

[0076] The data acquisition module monitors the tunnel itself using fiber optic sensors, laser scanners (scanning accuracy of ±1mm, scanning range of 30m, sampling frequency of 1000Hz), ultrasonic sensors (detection to a depth of 0.2m, sampling frequency of 200Hz), and infrared thermal imagers (resolution of 640x480 pixels, detection range of 5-150°C), as well as environmental monitoring sensors (such as traditional meteorological sensors) for monitoring the current environment.

[0077] The fiber optic sensors include embedded fiber optic sensors and external light sensors. The embedded fiber optic sensors are embedded into the inner wall of the tunnel, while the external light sensors, laser scanners, ultrasonic sensors, infrared thermal imagers and environmental detection sensors are all installed on the arch trusses.

[0078] The width and height of the arched truss are adapted to the inner diameter of the tunnel. Its bottom is fixed to the top of the vehicle through a fixed page, and it moves from one end of the tunnel to the other to complete the data collection work;

[0079] The truss includes an arched beam at the top, and the arched beam 1 is used to adapt to the curved surface of the tunnel;

[0080] Two support columns 2 connected to the bottom of the arched beam 1, the support columns 2 are used to provide vertical support for the arched beam 1;

[0081] The bottoms of the two support columns 2 are fixed on a fixed base 3, and the fixed base 3 is used to be fixed to the top of the carrier;

[0082] A crossbeam 4 is installed between the two ends of the arched beam 1, and a plurality of diagonal web members 5 are connected between the arched beam 1 and the crossbeam 4. The diagonal web members 5 are used to increase stability.

[0083] During operation, the vehicle moves at a speed of 0.5 m / s (1.8 km / h), which strikes a balance between detection accuracy and efficiency. Laser scanning collects 1,000 points per second (including distance and height data for each point). For every 0.5 m of movement, the laser scan generates 1,000 points of data, forming a cross-sectional point cloud. Ultrasonic sensors collect 200 data points per second to determine the density and internal defects of the concrete. Infrared thermal imaging generates a thermal image with 640 x 480 pixels (307,200 pixels) per frame, enabling real-time monitoring of temperature distribution within the tunnel.

[0084] Example 2

[0085] The difference from Example 1 is that Figure 2 As shown, the arched beam 1 can be adjusted in real time according to different tunnel sizes, and can be adapted to multiple tunnels of different sizes on the same highway, achieving the effect of having one beam for multiple measurements and reducing the investment of additional funds.

[0086] Its specific structure is:

[0087] The arched beam 1 is a segmented structure, with each arched beam segment fixed by a hinge, and the support column 2 is an electric hydraulic cylinder;

[0088] The fixed base 3 includes a base I31, a base II32 and a positioning base 33. The midlines of the bottom surfaces of the bases I31 and II32 are provided with card slots. The four end corners of the positioning base 33 are provided with screw holes for fixing with the carrier. Telescopic rods 36 are fixed to both ends of the positioning base 33. The top of the telescopic rod 36 is inserted into the card slot at the top. The bases I31 and II32 are penetrated by two parallel limit columns 34 to increase stability and ensure the level of the bases I31 and II32. The middle ends of the two limit columns 34 are fixed to the bases I31 and II32. The base I31 and the base II32 are fixed to the middle of the positioning base 33 through the T-seat 35 (the T-seat 35 is a T-shaped profile with two holes in the middle for the limit columns 34 to pass through). The base I31 and the base II32 are moved towards or away from each other by a driving device. The driving device in this embodiment adopts an electric trolley, that is, the two electric trolleys are respectively installed on the top of the two telescopic rods 36 and are engaged with the slide rails in the card slot. The two telescopic rods 36 can also be fixed by installing electric cylinders on both sides of the T-seat 35, which will not be described in detail here.

[0089] It should be noted that the structure described in the present invention can be implemented in a variety of different forms and is not limited to the described embodiments. Any equivalent transformations made by ordinary technicians in this field using the contents of the present invention description and drawings, or directly or indirectly applied to other related technical fields, such as the loading and unloading of other items, are included in the scope of protection of the present invention.

Claims

1. A highway tunnel deformation monitoring and prediction system, characterized by: It includes data acquisition module, data preprocessing module, data storage module, model training and evaluation module, real-time prediction module, early warning mechanism module, decision support and maintenance module, feedback and optimization module, and model training and evaluation module; The data acquisition module is used to collect data from optical fiber sensors and external sensors. The data preprocessing module cleans and denoises the data collected by the data acquisition module and then transmits it to the data storage module for integration and storage. The model training and evaluation module is trained using the labeled data set obtained from the data storage module. The real-time prediction module is used to receive real-time data and use the trained model to predict the structural health status. The early warning mechanism module performs intelligent early warning based on the real-time prediction results and the set thresholds. The decision support and maintenance module provides maintenance decision support based on the early warning information and prediction results. The feedback and optimization module analyzes the model performance based on the comparison information between the collected actual monitoring data and the prediction results, and continues training to improve the prediction accuracy.

2. The highway tunnel deformation monitoring and prediction system according to claim 1, characterized in that: The data acquisition module includes a weather data acquisition unit for collecting wind speed, humidity, temperature and precipitation data and a structural health detection data acquisition unit for collecting strain, displacement and vibration data.

3. The highway tunnel deformation monitoring and prediction system according to claim 1, characterized in that: After receiving real-time data, the real-time prediction module is divided into three preset condition judgment units according to the data information: When the wind speed is greater than 20m / s, the strong wind weather unit is used to calculate the average deformation of the weather data and structural monitoring data collected by the data acquisition module, and the calculated average deformation is output to the early warning mechanism module; When the relative humidity is less than 30% and the temperature is greater than 20°C, the dry weather unit is used to calculate the crack width based on the weather data and structural monitoring data collected by the data acquisition module, and the calculated crack width is output to the early warning mechanism module; When the precipitation is greater than 5 mm / h and the temperature is less than 0°C, the rain and snow weather unit is used to calculate the average deformation of the weather data and structural monitoring data collected by the data acquisition module, and the calculated crack width is output to the early warning mechanism module.

4. A highway tunnel deformation monitoring and prediction device, used in the highway tunnel deformation monitoring and prediction system according to any one of claims 1 to 3, characterized in that: It includes fiber optic sensors, laser scanners, ultrasonic sensors and infrared thermal imagers that work with data acquisition modules to monitor the tunnel itself, and environmental detection sensors for detecting the current environment; The optical fiber sensor includes an embedded optical fiber sensor and an external light sensor. The embedded optical fiber sensor is embedded in the inner wall of the tunnel, and the external light sensor, laser scanner, ultrasonic sensor, infrared thermal imager and environmental detection sensor are all installed on the arch truss; The width and height of the arched truss are adapted to the inner diameter of the tunnel. Its bottom is fixed to the top of the vehicle through a fixed page, and it moves from one end of the tunnel to the other to complete the data collection work; The truss includes an arched beam at the top, and the arched beam is used to adapt to the curved surface of the tunnel; Two supporting columns connected to the bottom of the arched beam, the supporting columns are used to provide vertical support to the arched beam; The bottoms of the two support columns are fixed on a fixed base, which is used to be fixed to the top of the carrier; A crossbeam installed between the two ends of the arched beam, and a plurality of diagonal web members connected between the arched beam and the crossbeam.

5. The highway tunnel deformation monitoring and prediction device according to claim 4, characterized in that: The arched beam is a segmented structure, each arched beam segment is fixed by a hinge, and the supporting column is an electric hydraulic cylinder; The fixed base includes a base I, a base II and a positioning base. A slot is provided on the midlines of the bottom surfaces of the base I and the base II. Screw holes for fixing to the carrier are provided at the four end corners of the positioning base. Telescopic rods are fixed at both ends of the positioning base, and the top ends of the telescopic rods are inserted into the slots at the tops. The base I and the base II are penetrated by two limiting columns. The middle ends of the two limiting columns are fixed to the middle part of the positioning base by T-rods. The base I and the base II move towards or away from each other through a driving device.

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