Mass concrete temperature monitoring method and device for super-wide-section open-cut tunnel construction

Abstract

The application provides a mass concrete temperature monitoring method and device for super-wide-section open-cut tunnel construction, and relates to the technical field of building engineering. The application constructs a three-dimensional model by collecting tunnel geometric parameters, performs grid division to generate a simulation model in combination with concrete material properties, calculates the temperature gradient modulus of nodes at each time step by simulating the whole process from pouring to cooling of the concrete, constructs a gradient extraction neighborhood with the initial temperature monitoring point as the center, extracts the maximum gradient strength in the neighborhood and calculates the gradient integral index, thereby screening out the candidate monitoring points, and finally selects the final monitoring points with the best representation by calculating the representative weight between the candidate monitoring points and the nodes, and arranges the temperature sensors, so that the scientific and dynamic monitoring of the concrete temperature field is realized, and the monitoring accuracy and efficiency are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of building engineering technology, specifically to a method and device for monitoring the temperature of large-volume concrete in ultra-wide cross-section open-cut tunnel construction. Background Technology

[0002] During the construction of ultra-wide cross-section open-cut tunnels, large-volume concrete structures, due to their large size, high cement content, and concentrated heat release from hydration, are prone to significant internal and external temperature differences and gradients after pouring, leading to temperature cracks and severely impacting the structure's durability and safety. Traditional temperature monitoring methods largely rely on empirical sensor placement, lacking a scientific theoretical basis for monitoring point selection. This makes it difficult to comprehensively capture areas of drastic temperature changes, resulting in monitoring blind spots and failing to accurately reflect the true temperature state of the structure, thus affecting the timeliness and effectiveness of temperature control measures. Furthermore, existing monitoring schemes do not pay sufficient attention to temperature gradient changes, making it difficult to achieve dynamic optimization of monitoring point placement based on the evolution characteristics of the temperature field, limiting monitoring efficiency and accuracy. Therefore, there is an urgent need for a method that combines numerical simulation and gradient analysis to achieve intelligent optimization of temperature monitoring points, thereby improving the targeting and reliability of temperature monitoring for large-volume concrete.

[0003] In the prior art, CN113591176A discloses an intelligent monitoring device and method for concrete temperature during the construction of a super-large span bridge main tower. The method includes: acquiring basic data of the concrete, wherein the basic data includes concrete component data and corresponding thermal conductivity data; setting thermal weights for the concrete component data based on the thermal conductivity data; calculating the overall thermal conductivity score and overall thermal conductivity coefficient of the concrete based on the thermal weights and the component data; acquiring the final shape data of the concrete; determining a target area on the concrete surface based on the final shape data; determining the number of temperature sensor pre-embedded points based on the overall thermal conductivity score and the target area; and setting temperature sensors based on the temperature sensor pre-embedded point data to complete the intelligent monitoring of the concrete temperature during the construction of the super-large span bridge main tower.

[0004] The main problems with the above scheme are: the core is to calculate a static overall heat conduction score based on the composition of concrete, which is essentially an empirical and static mix ratio estimation. It ignores key physical processes that evolve dynamically over time, such as the release of heat of hydration of concrete, structural heat dissipation conditions, and changes in ambient temperature. Its scientific validity and accuracy are low. It only determines the number of sensors based on the overall heat conduction score and the target area, but does not solve the key problem of where the sensors should be specifically placed inside the three-dimensional structure.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a method and device for monitoring the temperature of large-volume concrete in ultra-wide cross-section open-cut tunnel construction, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A method for monitoring the temperature of large-volume concrete in ultra-wide cross-section open-cut tunnel construction, comprising the following steps:

[0009] Step 1: Collect the geometric parameters of the cut-and-cover tunnel and the geometric parameters of the concrete pouring area inside, and construct a three-dimensional model of the tunnel structure;

[0010] Step 2: Obtain the material properties of concrete and import them into a 3D model. Mesh the 3D model to generate a simulation model of the tunnel. Set several initial temperature monitoring points in the simulation model.

[0011] Step 3: Run the simulation model of the tunnel to simulate the entire process of concrete pouring from the start to the temperature dropping to the ambient temperature, and obtain the temperature of each grid cell at each time step. Then calculate the temperature gradient vector of each grid cell at each time step, and generate the temperature gradient modulus of the node based on the temperature gradient vector of the grid cell.

[0012] Step 4: Construct a gradient extraction neighborhood for each initial temperature monitoring point. At each time step, find the maximum value of the temperature gradient modulus among all nodes contained in the gradient extraction neighborhood, and use it as the gradient intensity of the initial temperature monitoring point. Then, calculate the gradient integral exponent and select candidate monitoring points to generate a set of candidate monitoring points.

[0013] Step 5: Calculate the representative weights between each pair of candidate monitoring points and nodes, then select the final monitoring points, and install temperature sensors according to the locations of the final monitoring points to monitor the concrete temperature.

[0014] Furthermore, the principle upon which the three-dimensional model of the tunnel structure is constructed is as follows:

[0015] The geometric parameters of the cut-and-cover tunnel include the tunnel cross-sectional shape, cross-sectional width, and cross-sectional height;

[0016] The concrete pouring area within the cut-and-cover tunnel includes a bottom slab, a top slab, and side walls, and its geometric parameters include bottom slab thickness, bottom slab width, side wall height, side wall thickness, top slab thickness, and top slab width.

[0017] Based on the geometric parameters of the cut-and-cover tunnel, a three-dimensional, hollow tunnel cavity model is generated in finite element software. Within the tunnel cavity model, concrete solid models of the bottom slab, top slab, and sidewalls are constructed according to the geometric parameters of the pouring area. The combination of the two forms a complete three-dimensional model of the tunnel structure.

[0018] Furthermore, the material properties of concrete include thermal conductivity, specific heat capacity, density, and heat of hydration. These material properties are then assigned to the concrete solid model in the 3D model. The mesh size is set, and the 3D model is divided into several mesh units according to the mesh size to generate a simulation model of the tunnel.

[0019] Furthermore, the principle underlying the calculation of the temperature gradient vector of the mesh cells is as follows:

[0020] The simulation covers the entire process of concrete pouring from the start to the temperature dropping to ambient temperature, and obtains the temperature value at the geometric center of the mesh element at each time step, along with... The formula used to calculate the temperature gradient components in the direction is:

[0021]

[0022]

[0023]

[0024] in, Indicates the first Each grid cell at time step along Temperature gradient along the axial direction Indicates the index of the grid cell. Indicates the index of the time step. Indicates the first Each grid cell at time step along Temperature gradient along the axial direction Indicates the first Each grid cell at time step along Temperature gradient along the axial direction Indicates the first The geometric center of each grid cell along Move in the positive direction of the axis Temperature value at a distance, These represent the horizontal, vertical, and angular coordinates of the geometric center of the grid cell, respectively. This represents a small distance variable, with a value of 5% of the grid size. Indicates the first The geometric center of each grid cell along movement in the negative direction of the axis Temperature value at a distance, , , , Similarly;

[0025] The temperature gradient vector is:

[0026]

[0027] in, Indicates the first Each grid cell at time step The temperature gradient vector.

[0028] Furthermore, the specific principle for generating the temperature gradient modulus of a node is as follows:

[0029] For any node, its temperature gradient vector is obtained by weighted averaging of the temperature gradient vectors of all mesh cells sharing that node, using the following formula:

[0030]

[0031] in, Represents a node At time step temperature gradient, Represents all containing nodes The set of grid cells, Represents the global index of the node. Indicates the first The volume of each grid cell;

[0032] The specific formula for calculating the temperature gradient modulus of any node is as follows:

[0033]

[0034] in, Represents a node At time step temperature gradient modulus They represent exist The directional component.

[0035] Furthermore, the principle for generating the set of candidate monitoring points is as follows:

[0036] Centered on each initial temperature monitoring point, and with a radius three times the average grid size (where the average grid size represents the average side length of all grid cells in the simulation model), a gradient extraction neighborhood is defined for each initial temperature monitoring point. At each time step, the maximum temperature gradient modulus of all nodes within the gradient extraction neighborhood is found and used as the gradient intensity of that initial temperature monitoring point at that time step. The formula for calculating the gradient integral exponent is as follows:

[0037]

[0038] in, Indicates the first Gradient integral exponent of each initial temperature monitoring point Indicates the index of the initial temperature monitoring point. Indicates the number of time steps. Indicates the step size of the time step. Indicates the first The initial temperature monitoring point at the first The gradient strength at each time step;

[0039] The principle for selecting candidate monitoring points is as follows: calculate the average and standard deviation of the gradient integral exponent of all initial temperature monitoring points, and set the selection threshold. ,in, This represents the average value of the gradient integral exponent for all initial temperature monitoring points. This represents the standard deviation of the gradient integral exponent for all initial temperature monitoring points. This represents the adjustment coefficient, and ;

[0040] If the gradient integral exponent of the initial temperature monitoring point is not less than the screening threshold, then this initial temperature monitoring point is set as a candidate monitoring point, and all such candidate monitoring points are retained to generate a candidate monitoring point set. .

[0041] Furthermore, the principle upon which the final monitoring points are selected is as follows:

[0042] The formula used to calculate the representative weight of each candidate monitoring point and each node in the candidate monitoring point set is as follows:

[0043]

[0044] in, Indicates the first The first alternative monitoring point and the first The representative weight of each node, Indicates the first The first alternative monitoring point and the first Euclidean distance between nodes Indicates the constant affecting the radius;

[0045] The principle for selecting the final monitoring point is as follows:

[0046] Let the final set of monitoring points be It is initially an empty set, and the set of remaining nodes is Its initial state was , Represents a set of nodes;

[0047] for Each of those not selected The alternative monitoring points are calculated to determine their impact on the current situation. The total representative weight is calculated using the following formula:

[0048]

[0049] in, Indicates the first The total representative weight of each candidate monitoring point to the current set of remaining nodes;

[0050] Select the candidate monitoring point with the highest total representative weight and add it to the list. and from Removed from, and from Remove all nodes that are fully represented by the candidate monitoring point with the largest total representative weight, when the following conditions are met. When, then the node is considered Alternate monitoring points Fully representative, among which, This represents the weighting adjustment coefficient. Indicates alternative monitoring points The maximum representative weight of other nodes;

[0051] Repeat the above steps until the final set of monitoring points is met. The preset maximum number of temperature sensors, or the remaining set of nodes, is reached. Stop when the set is empty, and output the value at this point. ;

[0052] according to A temperature sensor is installed at the midpoint to detect the concrete temperature.

[0053] This invention also provides a temperature monitoring device for large-volume concrete in ultra-wide cross-section open-cut tunnel construction. The device is used to implement the aforementioned method for monitoring the temperature of large-volume concrete in ultra-wide cross-section open-cut tunnel construction, specifically including:

[0054] The data acquisition module is used to collect the geometric parameters of the open-cut tunnel and the geometric parameters of the concrete pouring area inside, and to construct a three-dimensional model of the tunnel structure.

[0055] The model building module is used to obtain the material properties of concrete and import the three-dimensional model, mesh the three-dimensional model, generate a simulation model of the tunnel, and set several initial temperature monitoring points in the simulation model.

[0056] The simulation calculation module is used to run the simulation model of the tunnel, simulate the entire process of concrete pouring from the start to the temperature dropping to the ambient temperature, obtain the temperature of each node at each time step, calculate the temperature gradient vector of each grid cell at each time step, and generate the temperature gradient modulus of the node based on the temperature gradient vector of the grid cell.

[0057] The monitoring point screening module is used to construct the gradient extraction neighborhood of each initial temperature monitoring point as the center, find the maximum value of the temperature gradient modulus among all nodes contained in the gradient extraction neighborhood at each time step, and use it as the gradient intensity of the initial temperature monitoring point. Then, the gradient integral exponent is calculated and candidate monitoring points are screened to generate a set of candidate monitoring points.

[0058] The monitoring point output module is used to calculate the representative weights between each pair of candidate monitoring points and nodes, and then select the final monitoring point. Temperature sensors are installed according to the location of the final monitoring point to monitor the concrete temperature.

[0059] Compared with the prior art, the beneficial effects of the present invention are:

[0060] This invention generates a simulation model based on geometric parameters and material properties to simulate the entire process of temperature field evolution in large-volume concrete during hydration heat release, heat dissipation, and interaction with the environment. This makes the analysis results more engineering-oriented and can capture local high-risk areas that are easily overlooked by traditional methods. Most existing monitoring technologies only focus on the absolute temperature value or temperature difference at specific points, lacking consideration of the spatial rate of change of the temperature field. However, the temperature gradient is the direct driving force for thermal stress and temperature cracks in concrete. This invention calculates the temperature gradient vector of each grid cell and the temperature gradient modulus of each node at each time step, realizing a leap from point temperature monitoring to full-field gradient field evolution analysis. It can track the evolution trajectory of the gradient with changes in hydration heat release and heat dissipation conditions, identify the time and location when the gradient reaches its peak, and capture structural temperature risks to the greatest extent by identifying and prioritizing monitoring of high gradient areas, significantly improving monitoring efficiency.

[0061] This invention also considers the spatiotemporal cumulative effect of temperature by constructing a gradient extraction neighborhood. First, a spatial influence range is defined for each initial monitoring point. Then, the maximum gradient modulus of all nodes in the neighborhood is integrated over the entire time history. This ensures that the selected monitoring point not only represents the gradient at its location but also the cumulative effect of the drastic temperature changes experienced by its surrounding area throughout its entire life cycle. This identifies long-term, large-scale high-risk areas. By calculating the gradient integral exponent and setting a statistical threshold, the points with the most significant temperature gradient cumulative effect over the entire time history can be automatically selected from a large number of initial points. This allows limited monitoring resources to be prioritized for deployment in areas that are most indicative of the overall structural temperature control safety and have the most sufficient risk exposure. When selecting the final monitoring points, two factors are considered simultaneously: the large gradient integral exponent of the monitoring point itself and the spatial dispersion of the selected point group. This aims to achieve accurate monitoring while covering as large a range as possible. Representative weights are used to integrate these two points, ultimately generating a sensor installation scheme that balances monitoring accuracy and coverage. Attached Figure Description

[0062] Figure 1 This is a schematic diagram of the method flow of an embodiment of the present invention;

[0063] Figure 2 This is a schematic diagram illustrating the change of concrete pouring temperature over time in an embodiment of the present invention.

[0064] Figure 3 This is a schematic diagram of the device module in an embodiment of the present invention. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0066] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0067] Example:

[0068] Please see Figures 1 to 3 The present invention provides a technical solution:

[0069] A method for monitoring the temperature of large-volume concrete in ultra-wide cross-section open-cut tunnel construction, comprising the following steps:

[0070] Step 1: Collect the geometric parameters of the cut-and-cover tunnel and the geometric parameters of the concrete pouring area inside, and construct a three-dimensional model of the tunnel structure;

[0071] In this embodiment, the principle upon which the three-dimensional model of the tunnel structure is constructed is as follows:

[0072] The geometric parameters of the cut-and-cover tunnel include the tunnel cross-sectional shape, cross-sectional width, and cross-sectional height;

[0073] The concrete pouring area within the cut-and-cover tunnel includes a bottom slab, a top slab, and side walls, and its geometric parameters include bottom slab thickness, bottom slab width, side wall height, side wall thickness, top slab thickness, and top slab width.

[0074] In finite element method (FEM) software, a hollow 3D tunnel cavity model is generated based on the tunnel's cross-sectional shape and dimensions. This model represents only the internal spatial outline of the tunnel and is not assigned material properties. It is used to locate the positions of concrete components. Inside the cavity model, concrete pouring areas are defined, specifically including the bottom slab, top slab, and sidewalls. The bottom slab is located at the bottom of the cavity, with its thickness along the vertical direction and its width matching the bottom of the cavity. The sidewalls are located on both sides of the cavity, with their height along the vertical direction and their thickness along the horizontal direction. The top slab is located at the top of the cavity, with its thickness along the vertical direction and its width matching the top of the cavity. These three structures form an integrated box-shaped structure. The concrete pouring areas represent specific parts or areas of the tunnel structure that require concrete pouring during construction. These areas bear the main loads and structural functions. Different concrete pouring components have different thicknesses, exposure conditions, and heat dissipation paths, so they are modeled separately. After determining the pouring areas, concrete is poured to form the bottom slab, top slab, and sidewalls.

[0075] Based on the geometric parameters of the cut-and-cover tunnel, a three-dimensional, hollow tunnel cavity model is generated in finite element software. Within the tunnel cavity model, concrete solid models of the bottom slab, top slab, and sidewalls are constructed according to the geometric parameters of the pouring area. The combination of the two forms a complete three-dimensional model of the tunnel structure.

[0076] The cavity model represents the hollow space inside the tunnel, that is, the internal area that can be passed through after the tunnel is completed. It provides a geometric reference for the overall shape and internal contour of the tunnel, and is used to determine the location and range of concrete components. Based on the geometric parameters of the concrete components, solid models are created at the corresponding positions inside the cavity model. The cavity model and the solid model are combined into a complete three-dimensional geometry. The final three-dimensional model is a shell structure with filling.

[0077] Step 2: Obtain the material properties of concrete and import them into a 3D model. Mesh the 3D model to generate a simulation model of the tunnel. Set several initial temperature monitoring points in the simulation model.

[0078] In this embodiment, the material properties of concrete include thermal conductivity, specific heat capacity, density, and heat of hydration. The concrete solid model in the three-dimensional model is given the above material properties, the mesh size is set, and the three-dimensional model is divided into several mesh units according to the mesh size to generate a simulation model of the tunnel.

[0079] The material properties of concrete used in different concrete components were collected. The material properties used in this invention include thermal conductivity, specific heat capacity, density, and heat of hydration. Thermal conductivity reflects the material's ability to conduct heat, determining the rate at which heat is transferred within the concrete. A higher thermal conductivity allows heat to be transferred more easily from high-temperature areas to low-temperature areas, resulting in a smaller temperature difference between the inside and outside. Specific heat capacity represents the heat rise per unit mass of concrete. The required energy absorption determines the rate of temperature change when concrete absorbs or releases heat. A higher specific heat capacity results in a smaller temperature rise and slower temperature change for the same amount of heat. Density is used in conjunction with specific heat capacity to calculate volumetric heat capacity, reflecting the temperature rise per unit volume of concrete. The required heat; heat of hydration represents the heat released during the reaction of cement and water, which is the main heat source for the increase in internal temperature of concrete. The heat of hydration curve reflects the change of heat of hydration over time. In the completed three-dimensional model, the above material properties are assigned to each concrete component.

[0080] Mesh generation discretizes a continuous 3D model into a finite number of mesh elements. The mesh size is much smaller than the geometric dimensions of each component, typically taking the minimum thickness of the component. For areas such as edges and corners, smaller mesh sizes are used to ensure simulation accuracy, taking the minimum thickness of the component. In the 3D model, different concrete components have different thicknesses, so the mesh element size obtained by meshing the 3D model is not uniform.

[0081] The principle for setting initial temperature monitoring points is as follows: ensure that the initial temperature monitoring points can cover the concrete components in the area to be tested, and focus on deploying them in key structural and process areas. Key structural areas include geometric abrupt changes, such as locations where the cross-sectional width changes, connections between concrete components, and areas where the thickness changes abruptly; the core area of ​​large-volume concrete, such as the center of the cross-section and the area with the greatest thickness; and key process areas, including the junctions of different pouring batches and areas with dense reinforcement. Initial temperature monitoring points are set based on an expert scoring method.

[0082] As shown in Table 1, the changes in concrete temperature over time after pouring concrete during the simulation process are reflected. The temperature rises rapidly within 0 to 24 hours, indicating a violent early hydration reaction. The peak occurs at approximately 44 to 48 hours, which is a typical time characteristic of heat release from hydration in large-volume concrete. The highest temperature reaches 63℃, which is much higher than the initial temperature and is a high-risk period for cracking. The temperature begins to drop after 48 hours. The temperature drops rapidly in the early stage and then tends to level off in the later stage. After 168 hours, the temperature approaches the ambient temperature.

[0083] Table 1. Variation of Concrete Pouring Temperature over Time

[0084]

[0085] Step 3: Run the simulation model of the tunnel to simulate the entire process of concrete pouring from the start to the temperature dropping to the ambient temperature, and obtain the temperature of each grid cell at each time step. Then calculate the temperature gradient vector of each grid cell at each time step, and generate the temperature gradient modulus of the node based on the temperature gradient vector of the grid cell.

[0086] In this embodiment, the principle underlying the calculation of the temperature gradient vector of the grid cells is as follows:

[0087] The simulation covers the entire process of concrete pouring from the start to the temperature dropping to ambient temperature, and obtains the temperature value at the geometric center of the mesh element at each time step, along with... The formula used to calculate the temperature gradient components in the direction is:

[0088]

[0089]

[0090]

[0091] in, Indicates the first Each grid cell at time step along Temperature gradient along the axial direction Indicates the index of the grid cell. Indicates the index of the time step. Indicates the first Each grid cell at time step along Temperature gradient along the axial direction Indicates the first Each grid cell at time step along Temperature gradient along the axial direction Indicates the first The geometric center of each grid cell along Move in the positive direction of the axis Temperature value at a distance, These represent the horizontal, vertical, and angular coordinates of the geometric center of the grid cell, respectively. This represents a small distance variable, with a value of 5% of the grid size. Indicates the first The geometric center of each grid cell along movement in the negative direction of the axis Temperature value at a distance, , , , Similarly;

[0092] The temperature gradient vector is:

[0093]

[0094] in, Indicates the first Each grid cell at time step The temperature gradient vector.

[0095] The model uses finite element simulation to model the entire process from pouring to the temperature dropping to ambient temperature, outputting the temperature value at the geometric center of each mesh element at each time step during this period; the temperature gradient describes the rate and direction of temperature change in space, and is divided into three-dimensional spaces according to the coordinate system direction. In three directions, the central difference method is used to calculate the temperature gradient components of each grid cell in each direction. The temperature gradient component represents the change in temperature per unit length. For a continuous concrete block, in an ideal local region that ignores boundary effects and special internal heat sources, the temperature field near a point should be relatively symmetrical. Based on the geometric center of the grid cell, small distance variables are intercepted in each direction by symmetrically taking points, and the central difference method is used for calculation. In three-dimensional space, after calculating the temperature gradient components of a grid cell in three directions, they can be directly combined to obtain the temperature gradient components of the grid cell at one time step.

[0096] The specific principle behind generating the temperature gradient modulus of nodes is as follows:

[0097] For any node, its temperature gradient vector is obtained by weighted averaging of the temperature gradient vectors of all mesh cells sharing that node, using the following formula:

[0098]

[0099] in, Represents a node At time step temperature gradient, Represents all containing nodes The set of grid cells, Represents the global index of the node. Indicates the first The volume of each grid cell;

[0100] The temperature gradient vector of a node is a three-dimensional vector that reflects the rate and direction of temperature change at that node in three-dimensional space. The direction of the vector indicates the direction of the fastest temperature change. A node is usually shared by multiple mesh elements, and its temperature gradient vector is a volume-weighted average of the temperature gradient vectors of all mesh elements containing that node. The temperature gradient of each node is a concentrated representation of the temperature gradients of the mesh elements sharing that node. The larger the volume of a cell, the larger the physical space it occupies around the node, the greater the thermal inertia it contains, and the greater the influence of the internal physical state of the cell on the node, thus contributing more to the node's temperature gradient. This means that the larger the volume of a cell, the greater its contribution to the temperature gradient at the node, because it represents more material and stronger thermal inertia. At nodes shared by different mesh elements, the temperature gradients from different elements may not be equal. To obtain a unique and continuous gradient value at the node, it is necessary to smooth the temperature gradients from different mesh elements. In actual engineering simulations, the mesh is often non-uniform, for example, with local refinement near structural abrupt changes or boundaries. If a simple arithmetic average is used, small cells and large cells will have the same weight at the nodes, which will distort the reality of the gradient distribution. Therefore, a volume-weighted average is used to avoid gradient calculation errors caused by uneven mesh density.

[0101] The specific formula for calculating the temperature gradient modulus of any node is as follows:

[0102]

[0103] in, Represents a node At time step temperature gradient modulus They represent exist The directional component.

[0104] The temperature gradient modulus of a node is the Euclidean norm of the temperature gradient vector, reflecting the intensity of temperature change at that node. The larger the modulus, the more significant the temperature difference around the node, i.e., the more uneven the temperature distribution, which easily forms local thermal stress concentration zones and is a high-risk area for temperature cracks in concrete. By calculating the gradient modulus of all nodes at each time step, the areas with the largest temperature gradient can be automatically identified. These areas are the focus of temperature control monitoring. Areas with high gradient modulus are the areas with the most drastic temperature changes, and sensors should be deployed in these areas first to improve the targeting of monitoring and the effectiveness of early warning.

[0105] Step 4: Construct a gradient extraction neighborhood for each initial temperature monitoring point. At each time step, find the maximum value of the temperature gradient modulus among all nodes contained in the gradient extraction neighborhood, and use it as the gradient intensity of the initial temperature monitoring point. Then, calculate the gradient integral exponent and select candidate monitoring points to generate a set of candidate monitoring points.

[0106] In this embodiment, the principle for generating the set of candidate monitoring points is as follows:

[0107] Centered on each initial temperature monitoring point, and with a radius three times the average grid size (where the average grid size represents the average side length of all grid cells in the simulation model), a gradient extraction neighborhood is defined for each initial temperature monitoring point. At each time step, the maximum temperature gradient modulus of all nodes within the gradient extraction neighborhood is found and used as the gradient intensity of that initial temperature monitoring point at that time step. The formula for calculating the gradient integral exponent is as follows:

[0108]

[0109] in, Indicates the first Gradient integral exponent of each initial temperature monitoring point Indicates the index of the initial temperature monitoring point. Indicates the number of time steps. Indicates the step size of the time step. Indicates the first The initial temperature monitoring point at the first The gradient strength at each time step;

[0110] The temperature distribution inside concrete is not an isolated point, but a continuously changing field. A high temperature gradient at a certain point often indicates a high temperature gradient in the surrounding area. The goal of this step is to select the most representative monitoring point from the initial temperature monitoring points. These initial temperature monitoring points are set based on experience or structural characteristics, and their locations are not necessarily exactly grid nodes or points with high gradients. By dividing the initial temperature monitoring points into gradient extraction neighborhoods, the maximum temperature gradient within the neighborhood is obtained. This ensures that even if the monitoring point itself is not at the highest gradient, the most dangerous gradient state in that area can still be captured, thus enhancing the representativeness of the monitoring points. The side lengths of each grid cell in the tunnel simulation model are obtained, and the average of these side lengths is calculated to obtain the average grid size of the tunnel simulation model. Using a multiple of the average grid size as the radius ensures that the neighborhood size matches the discretization degree of the model. If the radius is too small, the gradient extraction neighborhood may only contain a very few nodes, resulting in insufficient representativeness. If the radius is too large, the gradient extraction neighborhood may cover too many irrelevant areas, losing locality. Typically, the radius is two to four times the average grid size; in this scheme, it is set to three times.

[0111] In the formula for calculating the gradient integral exponent, the gradient strength... Indicates the first At each time step, using the initial temperature monitoring point The maximum temperature gradient modulus of all nodes in the neighborhood of the initial temperature monitoring point is extracted, reflecting the drastic temperature change around that point. The gradient integral exponent is the interval between adjacent time points during the simulation process. It is used to transform discrete time steps into continuous time integrals, summing up the gradient intensities of all time steps over the entire time range. This reflects the cumulative effect of the intensity of the temperature gradient over time in the area surrounding the initial monitoring point throughout the temperature evolution process. The larger the gradient integral exponent, the more likely the area has been in a high gradient state for a long time, making it a high-risk area for temperature cracks. It considers not only the gradient intensity but also its duration, providing a more comprehensive assessment of the regional temperature risk. The higher the gradient integral exponent, the more representative the temperature gradient evolution process of the surrounding area is, making it suitable as a monitoring point.

[0112] The principle for selecting candidate monitoring points is as follows: calculate the average and standard deviation of the gradient integral exponent of all initial temperature monitoring points, and set the selection threshold. ,in, This represents the average value of the gradient integral exponent for all initial temperature monitoring points. This represents the standard deviation of the gradient integral exponent for all initial temperature monitoring points. This represents the adjustment coefficient, and ;

[0113] If the gradient integral exponent of the initial temperature monitoring point is not less than the screening threshold, then this initial temperature monitoring point is set as a candidate monitoring point, and all such candidate monitoring points are retained to generate a candidate monitoring point set. .

[0114] The screening threshold is used to select the points that best represent high-risk areas from numerous initial temperature monitoring points for focused monitoring. Selecting only the points with the highest gradient integral index might overlook areas that, while having lower peak values, are consistently exposed. Selecting too many points defeats the purpose of screening, leading to wasted resources. Therefore, the statistical distribution characteristics of the gradient integral index of all initial temperature monitoring points are used to define high-risk areas. The screening threshold defines a high-risk boundary, selecting monitoring points with gradient integral indices significantly higher than the average level. When the threshold is small, the screening threshold is low, and more candidate points are selected, which may include more medium-risk areas. It aims for comprehensive coverage and is suitable for situations with low risk tolerance and sufficient monitoring resources. When the threshold is large, the screening threshold is high, resulting in fewer candidate points. Only the areas with the most prominent risks are retained. This approach is suitable for situations where resources are limited or where it is necessary to focus on the most critical risks. The threshold is determined by expert scoring based on actual needs. The value of .

[0115] Step 5: Calculate the representative weights between each pair of candidate monitoring points and nodes, then select the final monitoring points, and install temperature sensors according to the locations of the final monitoring points to monitor the concrete temperature.

[0116] In this embodiment, the principle for selecting the final monitoring point is as follows:

[0117] The formula used to calculate the representative weight of each candidate monitoring point and each node in the candidate monitoring point set is as follows:

[0118]

[0119] in, Indicates the first The first alternative monitoring point and the first The representative weight of each node, Indicates the first The first alternative monitoring point and the first Euclidean distance between nodes Indicates the constant affecting the radius;

[0120] The representative weight between candidate monitoring points and nodes is used to evaluate how well a sensor can represent the temperature state at a node if it is deployed there. This is reflected by the gradient integral exponent and distance. The higher the gradient integral exponent, the more important the candidate monitoring point is and the greater its influence on surrounding nodes. This is a Gaussian decay function used to describe the linear decrease in the representativeness of a monitoring point to its surrounding nodes as distance increases. This represents the Euclidean distance between two points; the greater the distance, the faster the decay. It is an influencing radius constant used to control the decay rate. The larger the value, the slower the decay, and the wider the representative range of the candidate monitoring points. The smaller the value, the faster the decay, indicating that the representative range of the candidate monitoring points is more concentrated. Its value is 0.5 times the radius of the gradient extraction neighborhood. exist The maximum value is taken at time, as The increase gradually approaches zero, indicating that the closer the distance to the candidate monitoring point, the stronger the representativeness.

[0121] The principle for selecting the final monitoring point is as follows:

[0122] Let the final set of monitoring points be It is initially an empty set, and the set of remaining nodes is Its initial state was , Represents a set of nodes;

[0123] for Each of those not selected The alternative monitoring points are calculated to determine their impact on the current situation. The total representative weight is calculated using the following formula:

[0124]

[0125] in, Indicates the first The total representative weight of each candidate monitoring point to the current set of remaining nodes;

[0126] Select the candidate monitoring point with the highest total representative weight and add it to the list. and from Removed from, and from Remove all nodes that are fully represented by the candidate monitoring point with the largest total representative weight, when the following conditions are met. When, then the node is considered Alternate monitoring points Fully representative, among which, This represents the weighting adjustment coefficient. Indicates alternative monitoring points The maximum representative weight for other nodes;

[0127] Repeat the above steps until the final set of monitoring points is met. The preset maximum number of temperature sensors, or the remaining set of nodes, is reached. Stop when the set is empty, and output the value at this point. ;

[0128] The total weight represents the sum of all remaining nodes not covered by the candidate monitoring points and the current candidate monitoring point. The weights of the representatives are added together, which is equivalent to evaluating if the selection... As the final monitoring point, it represents the temperature state of the remaining nodes. The candidate monitoring point with the highest total representation weight is preferentially selected and added to the final monitoring point set because it represents the maximum total node weight in the current state, and temperature data collection at this point provides the most comprehensive reflection of the temperature field state. Each time a candidate monitoring point is selected from... Add to After that, first in The candidate monitoring point is removed from the initial selection to avoid duplicate selection. Then, nodes that are sufficiently representative of the candidate monitoring point are removed from the remaining node set. This is because the candidate monitoring point best reflects the temperature characteristics of these nodes, and there is no need to consider other candidate monitoring points. In this step, "sufficiently representative" is defined as follows: For each candidate monitoring point Find its maximum representative weight among all nodes, i.e. ,Will With representative weight adjustment coefficient Multiplication is used as a threshold to measure the performance of nodes. Alternate monitoring points To what extent is it representative, and has it reached the required level? A sufficiently high proportion of one's own best representative ability; simply reaching this proportion indicates... It can fully reflect The temperature characteristics, therefore from Remove from middle; usually The range of values ​​is Determined based on control accuracy The value of , The larger, the more it means Must be very close Only nodes that are very close to the monitoring point and almost equally important will be included in the coverage area. The result is a smaller coverage area, higher accuracy, and potentially denser monitoring point deployment. The smaller, the better. Even with lower values, coverage is still possible, indicating that monitoring points can cover a wider area, including nodes that are slightly farther away or less important. This results in a broad representativeness for a single monitoring point and a smaller required number of points. Specific monitoring points are determined based on expert scoring. Value selection. The output continues until all nodes have a backup monitoring point to reflect their temperature, or until the maximum number of monitoring points is reached (the maximum number of temperature sensors to be deployed). , The final monitoring point can reflect the temperature characteristics of the high gradient region and ensure sufficient coverage.

[0129] according to A temperature sensor is installed at the midpoint to detect the concrete temperature.

[0130] Please see Figure 3 The present invention also provides a temperature monitoring device for large-volume concrete in ultra-wide cross-section open-cut tunnel construction. The device is used to implement the above-mentioned method for temperature monitoring of large-volume concrete in ultra-wide cross-section open-cut tunnel construction, specifically including:

[0131] The data acquisition module is used to collect the geometric parameters of the open-cut tunnel and the geometric parameters of the concrete pouring area inside, and to construct a three-dimensional model of the tunnel structure.

[0132] The model building module is used to obtain the material properties of concrete and import the three-dimensional model, mesh the three-dimensional model, generate a simulation model of the tunnel, and set several initial temperature monitoring points in the simulation model.

[0133] The simulation calculation module is used to run the simulation model of the tunnel, simulate the entire process of concrete pouring from the start to the temperature dropping to the ambient temperature, obtain the temperature of each node at each time step, calculate the temperature gradient vector of each grid cell at each time step, and generate the temperature gradient modulus of the node based on the temperature gradient vector of the grid cell.

[0134] The monitoring point screening module is used to construct the gradient extraction neighborhood of each initial temperature monitoring point as the center, find the maximum value of the temperature gradient modulus among all nodes contained in the gradient extraction neighborhood at each time step, and use it as the gradient intensity of the initial temperature monitoring point. Then, the gradient integral exponent is calculated and candidate monitoring points are screened to generate a set of candidate monitoring points.

[0135] The monitoring point output module is used to calculate the representative weights between each pair of candidate monitoring points and nodes, and then select the final monitoring point. Temperature sensors are installed according to the location of the final monitoring point to monitor the concrete temperature.

[0136] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0137] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0138] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0139] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for monitoring the temperature of large-volume concrete in ultra-wide cross-section open-cut tunnel construction, characterized in that, The specific steps include: Step 1: Collect the geometric parameters of the cut-and-cover tunnel and the geometric parameters of the concrete pouring area inside, and construct a three-dimensional model of the tunnel structure; Step 2: Obtain the material properties of concrete and import them into a 3D model. Mesh the 3D model to generate a simulation model of the tunnel. Set several initial temperature monitoring points in the simulation model. Step 3: Run the simulation model of the tunnel to simulate the entire process of concrete pouring from the start to the temperature dropping to the ambient temperature, and obtain the temperature of each grid cell at each time step. Then calculate the temperature gradient vector of each grid cell at each time step, and generate the temperature gradient modulus of the node based on the temperature gradient vector of the grid cell. Step 4: Construct a gradient extraction neighborhood for each initial temperature monitoring point. At each time step, find the maximum value of the temperature gradient modulus among all nodes contained in the gradient extraction neighborhood, and use it as the gradient intensity of the initial temperature monitoring point. Then, calculate the gradient integral exponent and select candidate monitoring points to generate a set of candidate monitoring points. Step 5: Calculate the representative weights between each pair of candidate monitoring points and nodes, and then select the final monitoring points. Install temperature sensors according to the locations of the final monitoring points to monitor the concrete temperature. The principle underlying the calculation of the temperature gradient vector of a mesh cell is as follows: The simulation covers the entire process of concrete pouring from the start to the temperature dropping to ambient temperature, and obtains the temperature value at the geometric center of the mesh element at each time step, along with... The formula used to calculate the temperature gradient components in the direction is: in, Indicates the first Each grid cell at time step along Temperature gradient along the axial direction Indicates the index of the grid cell. Indicates the index of the time step. Indicates the first Each grid cell at time step along Temperature gradient along the axial direction Indicates the first Each grid cell at time step along Temperature gradient along the axial direction Indicates the first The geometric center of each grid cell along Move in the positive direction of the axis Temperature value at a distance, These represent the horizontal, vertical, and angular coordinates of the geometric center of the grid cell, respectively. This represents a small distance variable, with a value of 5% of the grid size. Indicates the first The geometric center of each grid cell along movement in the negative direction of the axis Temperature value at a distance, , , , Similarly; The temperature gradient vector is: in, Indicates the first Each grid cell at time step The temperature gradient vector.

2. The method for monitoring the temperature of large-volume concrete in ultra-wide cross-section open-cut tunnel construction according to claim 1, characterized in that: The principle underlying the construction of the three-dimensional model of the tunnel structure in step 1 is as follows: The geometric parameters of the cut-and-cover tunnel include the tunnel cross-sectional shape, cross-sectional width, and cross-sectional height; The concrete pouring area within the cut-and-cover tunnel includes a bottom slab, a top slab, and side walls, and its geometric parameters include bottom slab thickness, bottom slab width, side wall height, side wall thickness, top slab thickness, and top slab width. Based on the geometric parameters of the cut-and-cover tunnel, a three-dimensional, hollow tunnel cavity model is generated in finite element software. Within the tunnel cavity model, concrete solid models of the bottom slab, top slab, and sidewalls are constructed according to the geometric parameters of the pouring area. The combination of the two forms a complete three-dimensional model of the tunnel structure.

3. The method for monitoring the temperature of large-volume concrete in ultra-wide cross-section open-cut tunnel construction according to claim 1, characterized in that: In step 2, the material properties of concrete include thermal conductivity, specific heat capacity, density, and heat of hydration. These material properties are assigned to the concrete solid model in the three-dimensional model. The mesh size is set, and the three-dimensional model is divided into several mesh units according to the mesh size to generate a simulation model of the tunnel.

4. The method for monitoring the temperature of large-volume concrete in ultra-wide cross-section open-cut tunnel construction according to claim 1, characterized in that: The specific principle behind generating the temperature gradient modulus of the nodes in step 3 is as follows: For any node, its temperature gradient vector is obtained by weighted averaging of the temperature gradient vectors of all mesh cells sharing that node, using the following formula: in, Represents a node At time step temperature gradient, Represents all containing nodes The set of grid cells, Represents the global index of the node. Indicates the first The volume of each grid cell; The specific formula for calculating the temperature gradient modulus of any node is as follows: in, Represents a node At time step temperature gradient modulus They represent exist The directional component.

5. The method for monitoring the temperature of large-volume concrete in ultra-wide cross-section open-cut tunnel construction according to claim 4, characterized in that: The principle behind generating the candidate monitoring point set in step 4 is as follows: Centered on each initial temperature monitoring point, and with a radius three times the average grid size (where the average grid size represents the average side length of all grid cells in the simulation model), a gradient extraction neighborhood is defined for each initial temperature monitoring point. At each time step, the maximum temperature gradient modulus of all nodes within the gradient extraction neighborhood is found and used as the gradient intensity of that initial temperature monitoring point at that time step. The formula for calculating the gradient integral exponent is as follows: in, Indicates the first Gradient integral exponent of each initial temperature monitoring point Indicates the index of the initial temperature monitoring point. Indicates the number of time steps. Indicates the step size of the time step. Indicates the first The initial temperature monitoring point at the first The gradient strength at each time step; The principle for selecting candidate monitoring points is as follows: calculate the average and standard deviation of the gradient integral exponent of all initial temperature monitoring points, and set the selection threshold. ,in, This represents the average value of the gradient integral exponent for all initial temperature monitoring points. This represents the standard deviation of the gradient integral exponent for all initial temperature monitoring points. This represents the adjustment coefficient, and ; If the gradient integral exponent of the initial temperature monitoring point is not less than the screening threshold, then this initial temperature monitoring point is set as a candidate monitoring point, and all such candidate monitoring points are retained to generate a candidate monitoring point set. .

6. The method for monitoring the temperature of large-volume concrete in ultra-wide cross-section open-cut tunnel construction according to claim 5, characterized in that: The principle upon which the final monitoring point was selected is as follows: The formula used to calculate the representative weight of each candidate monitoring point and each node in the candidate monitoring point set is as follows: in, Indicates the first The first alternative monitoring point and the first The representative weight of each node, Indicates the first The first alternative monitoring point and the first Euclidean distance between nodes, Indicates the constant affecting the radius; The principle for selecting the final monitoring point is as follows: Let the final set of monitoring points be It is initially an empty set, and the set of remaining nodes is Its initial state was , Represents a set of nodes; for Each of those not selected The alternative monitoring points are calculated to determine their impact on the current situation. The total representative weight is calculated using the following formula: in, Indicates the first The total representative weight of each candidate monitoring point to the current set of remaining nodes; Select the candidate monitoring point with the highest total representative weight and add it to the list. and from Removed from, and from Remove all nodes that are fully represented by the candidate monitoring point with the largest total representative weight, when the following conditions are met. When, then the node is considered Alternate monitoring points Fully representative, among which, This represents the weighting adjustment coefficient. Indicates alternative monitoring points The maximum representative weight for other nodes; Repeat the above steps until the final set of monitoring points is met. The preset maximum number of temperature sensors, or the remaining set of nodes, is reached. Stop when the set is empty, and output the value at this point. ; according to A temperature sensor is installed at the midpoint to detect the concrete temperature.

7. A temperature monitoring device for large-volume concrete in ultra-wide cross-section open-cut tunnel construction, characterized in that: The device is used to implement the method for monitoring the temperature of large-volume concrete in ultra-wide cross-section open-cut tunnel construction as described in any one of claims 1-6, specifically including: The data acquisition module is used to collect the geometric parameters of the open-cut tunnel and the geometric parameters of the concrete pouring area inside, and to construct a three-dimensional model of the tunnel structure. The model building module is used to obtain the material properties of concrete and import the three-dimensional model, mesh the three-dimensional model, generate a simulation model of the tunnel, and set several initial temperature monitoring points in the simulation model. The simulation calculation module is used to run the simulation model of the tunnel, simulate the entire process of concrete pouring from the start to the temperature dropping to the ambient temperature, obtain the temperature of each node at each time step, calculate the temperature gradient vector of each grid cell at each time step, and generate the temperature gradient modulus of the node based on the temperature gradient vector of the grid cell. The monitoring point screening module is used to construct the gradient extraction neighborhood of each initial temperature monitoring point as the center, find the maximum value of the temperature gradient modulus among all nodes contained in the gradient extraction neighborhood at each time step, and use it as the gradient intensity of the initial temperature monitoring point. Then, the gradient integral exponent is calculated and candidate monitoring points are screened to generate a set of candidate monitoring points. The monitoring point output module is used to calculate the representative weights between each pair of candidate monitoring points and nodes, and then select the final monitoring point. Temperature sensors are installed according to the location of the final monitoring point to monitor the concrete temperature.

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