Crack risk assessment method and related equipment

Abstract

The invention provides a crack risk assessment method and related equipment. The method comprises the following steps: constructing a concrete structure geometric model and performing mesh generation to obtain a node set and a topological graph; spatial configuration identification and thermal attribute assignment are carried out, an inter-node thermal coupling matrix is constructed, and a node temperature time sequence is predicted; performing gradient sensitivity analysis to screen key temperature measuring points, acquiring measuring point data and converting the measuring point data into a continuous temperature field; the temperature field is adjusted through a heat conduction equation and boundary conditions, and a dynamic three-dimensional temperature field including future temperature data is obtained in combination with a prediction algorithm; thermal stress is calculated based on the dynamic temperature field, the crack risk level is determined in combination with the temperature gradient, and concrete structure cracks are evaluated. According to the embodiment of the invention, through modeling, gradient analysis and three-dimensional temperature field generation, the thermal stress and the risk index are calculated, the crack risk is accurately evaluated, and scientific and efficient support is provided for construction optimization of the concrete structure.

Description

Technical Field

[0001] This application relates to the field of crack risk assessment technology, and in particular to a crack risk assessment method and related equipment. Background Technology

[0002] In the field of civil engineering, concrete structures are widely used in major infrastructure projects such as bridges, tunnels, dams, and high-rise buildings due to their excellent load-bearing capacity and ease of construction. However, during actual service, concrete structures often face complex thermal environments, especially under the influence of factors such as early hydration heat release, changes in ambient temperature, or fires. These factors can cause drastic changes in the internal temperature field distribution, inducing thermal stress concentration and cracking, which seriously affects the durability and safety of the structure.

[0003] In the prior art, Chinese patent application number 202410002998.2 discloses a concrete monitoring and early warning method and system based on temperature analysis. This technology collects real-time temperature data of concrete through sensors and inputs it into the BIM model of the main construction structure to generate a temperature distribution cloud map. Combined with a preset temperature risk threshold, the system identifies risks, calculates risk coefficients, and issues a temperature warning signal when the threshold is exceeded. However, the generation of its temperature distribution cloud map relies on linear interpolation of sensor data, failing to consider the nonlinear characteristics of the actual heat diffusion process. Furthermore, risk judgment is still based on a single temperature threshold comparison, without constructing a thermo-stress coupling model or regional gradient analysis, resulting in low warning accuracy and lag. It is evident that traditional temperature measurement systems often use point-based linear interpolation or simplified isothermal surface methods, which are insufficient to accurately reflect the non-uniform temperature distribution inside concrete and cannot perform structural-level thermal field analysis. Moreover, current temperature control systems often rely on setting temperature difference thresholds for simple judgments, without considering structural stress distribution and thermo-mechanical coupling behavior, easily leading to misjudgments or missed judgments.

[0004] Furthermore, the placement of concrete temperature monitoring points typically relies on experience or manual estimation, leading to issues such as insufficient point representativeness, sensor redundancy, or omission of key areas, thus affecting the quality of temperature field reconstruction. In existing technologies, the placement of concrete temperature monitoring points often depends on experience or manual estimation, resulting in problems such as insufficient point representativeness, sensor redundancy, or omission of key areas, impacting the quality of temperature field reconstruction. Traditional temperature monitoring systems often employ linear interpolation or simplified isothermal surface methods, which struggle to accurately reflect the non-uniform temperature distribution within concrete and are incapable of structural-level thermal field analysis. Additionally, current temperature control systems often rely on setting temperature difference thresholds for simple judgments, failing to consider structural stress distribution and thermo-mechanical coupling behavior, easily leading to misjudgments or omissions. Summary of the Invention

[0005] In view of this, the purpose of this application is to propose a crack risk assessment method and related equipment.

[0006] To achieve the above objectives, this application provides a crack risk assessment method, comprising: A geometric model is constructed based on the concrete structure to be evaluated. The geometric model is then subjected to mesh decomposition and topological analysis to obtain the discretized node set and topological graph structure. Based on the discretized node set and the topology graph structure, spatial configuration identification and thermal attribute assignment are performed to obtain the inter-node thermal coupling matrix, and the temperature time series of the nodes are predicted based on the inter-node thermal coupling matrix. Gradient sensitivity analysis was performed on the temperature time series to obtain key temperature measurement points; Data of key measuring points are obtained based on the aforementioned key temperature measuring points; The key measurement point data is converted into a continuous temperature field. The continuous temperature field is adjusted based on the heat conduction control equation and boundary conditions. The adjusted temperature field is then predicted using a prediction algorithm to obtain the restored dynamic three-dimensional temperature field. The dynamic three-dimensional temperature field includes temperature data in the future time dimension. Based on the dynamic three-dimensional temperature field, the thermal stress of the concrete structure is calculated; By combining the thermal stress and the calculated temperature gradient, the risk level of the cracks is determined in order to assess the cracks in the concrete structure.

[0007] In one possible implementation, the step of constructing a geometric model based on the concrete structure to be evaluated, performing mesh decomposition and topological analysis on the geometric model to obtain a discretized node set and topological graph structure includes: The geometric model is constructed based on the concrete structure to be evaluated; The geometric model is subjected to mesh decomposition and discretization to obtain the discretized node set; Based on the structural entities between the discretized node sets, the connection relationships between nodes are constructed to obtain the topological graph structure.

[0008] In one possible implementation, the step of identifying spatial configurations and assigning thermal attributes based on the discretized node set and the topology graph structure to obtain a thermal coupling matrix between nodes, and predicting the temperature time series of the nodes based on the thermal coupling matrix between nodes, includes: The physical connection medium between any two adjacent nodes in the discretized node set is analyzed to identify the spatial configuration and obtain the spatial configuration. By assigning thermal property values ​​to the material thermal property parameters corresponding to different spatial configurations, different thermal conductivity coefficients are obtained; The inter-node thermal coupling matrix is ​​constructed based on the different thermal conductivity coefficients. The temperature time series of the nodes is predicted based on the inter-node thermal coupling matrix.

[0009] In one possible implementation, the method further includes: Based on the spatial thermal coupling characteristics between nodes in the discretized node set, a heat conduction correction term is introduced to correct the temperature time series.

[0010] In one possible implementation, the step of performing gradient sensitivity analysis on the temperature time series to obtain key temperature measurement points includes: Gradient sensitivity analysis was performed on the temperature time series to obtain a candidate set of key points; Based on the candidate set of key points, redundant information is eliminated using the mutual information algorithm, and the key temperature measurement points are obtained by maximizing the representativeness score.

[0011] In one possible implementation, the process of converting the key measurement point data into a continuous temperature field, adjusting the continuous temperature field based on the heat conduction control equation and boundary conditions, and predicting the adjusted temperature field using a prediction algorithm to obtain a reconstructed dynamic three-dimensional temperature field includes: Spatial interpolation calculations are performed on the key measurement point data to obtain the continuous temperature field; The constraints for spatial interpolation are determined based on the heat conduction control equation. The boundary conditions are determined based on the collected real-time ambient temperature and temperature change parameters. The continuous temperature field is adjusted according to the constraints and boundary conditions to obtain the adjusted temperature field; Based on the adjusted temperature field, the temperature-time relationship is determined using a hydration thermodynamics model. Based on the aforementioned change relationship, the adjusted temperature field is predicted in the future using the aforementioned prediction algorithm, thereby obtaining the restored dynamic three-dimensional temperature field.

[0012] In one possible implementation, determining the risk level of cracks by combining the thermal stress and the calculated temperature gradient to assess cracks in the concrete structure includes: The maximum principal stress is determined based on the aforementioned thermal stress; The risk index is calculated based on the maximum principal stress and the temperature gradient. Based on the magnitude of the risk index, the risk level of the crack is determined in order to assess the cracks in the concrete structure.

[0013] Based on the same inventive concept, embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the crack risk assessment method as described in any of the above.

[0014] Based on the same inventive concept, embodiments of this application also provide a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute any of the crack risk assessment methods described above.

[0015] Based on the same inventive concept, this application also provides a computer program product, which includes computer program instructions for causing the computer program product to execute any of the crack risk assessment methods described above.

[0016] As can be seen from the above, the crack risk assessment method and related equipment provided in this application construct a geometric model based on the concrete structure to be assessed, perform mesh decomposition and topological analysis on the geometric model to obtain a discretized node set and topological graph structure; perform spatial configuration identification and thermal attribute assignment based on the discretized node set and topological graph structure to obtain a thermal coupling matrix between nodes, and predict the temperature time series of the nodes based on the thermal coupling matrix between nodes; perform gradient sensitivity analysis on the temperature time series to obtain key temperature measurement points; acquire key measurement point data based on the key temperature measurement points; convert the key measurement point data into a continuous temperature field, adjust the continuous temperature field based on the heat conduction control equation and boundary conditions, and predict the adjusted temperature field using a prediction algorithm to obtain a restored dynamic three-dimensional temperature field; the dynamic three-dimensional temperature field includes temperature data in the future time dimension; calculate the thermal stress of the concrete structure based on the dynamic three-dimensional temperature field; and determine the crack risk level by combining the thermal stress and the calculated temperature gradient to assess the cracks in the concrete structure. This application's embodiments accurately discretize the structure and establish inter-node connections through geometric model construction, mesh generation, and topology analysis, laying the foundation for spatial configuration identification and thermal property assignment. Based on the thermal coupling matrix, the time series of node temperatures is predicted, and the prediction results are optimized by combining heat conduction correction terms, improving the accuracy and spatiotemporal consistency of temperature data. Gradient sensitivity analysis is used to screen key temperature measurement points, reducing monitoring costs and ensuring data representativeness. Continuous processing of key measurement point data, combined with heat conduction equations, boundary conditions, and prediction algorithms, generates a dynamic three-dimensional temperature field covering future temperature evolution trends, providing a reliable basis for thermal stress calculation. Based on thermal stress and temperature gradient, a risk index is calculated, and combined with graded assessment, crack risk levels are determined, accurately locating high-risk areas. The overall technical effect is to provide an efficient, scientific, and economical crack risk assessment method that combines accuracy, reliability, and practicality, providing important technical support for temperature control management and construction optimization of concrete structures. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the crack risk assessment method according to an embodiment of this application; Figure 2 This is a schematic diagram illustrating the selection process of key temperature measurement points in an embodiment of this application; Figure 3 This is a schematic diagram of the temperature acquisition system framework according to an embodiment of this application; Figure 4 This is a schematic diagram of the electronic device structure according to an embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0020] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application 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 after 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 only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0021] It is understood that before using the technical solutions of the various embodiments in this disclosure, users will be informed of the type, scope of use, and usage scenarios of the personal information involved in an appropriate manner, and user authorization will be obtained.

[0022] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose, based on the prompt message, whether to provide personal information to the software or hardware such as electronic devices, applications, servers, or storage media performing the operations of this disclosed technical solution.

[0023] As an optional but not limited implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0024] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0025] As described in the background section, concrete structures are widely used in infrastructure such as bridges, tunnels, and dams. However, due to factors such as heat of hydration and changes in ambient temperature, the internal temperature field is prone to drastic changes, leading to thermal stress concentration and cracking, which affects the durability of the structure. In existing technologies, the placement of temperature measurement points relies on manual experience, resulting in insufficient representativeness of the points and affecting the quality of temperature field reconstruction. Traditional methods use linear interpolation or simplified isothermal surfaces, which are difficult to accurately reflect non-uniform temperature distributions. Crack risk assessment relies solely on temperature difference judgments, without considering thermo-mechanical coupling behavior, which is prone to misjudgment. This application proposes a spatial temperature field reconstruction and crack risk intelligent assessment system, which improves the accuracy of crack assessment and construction safety by automatically selecting key measurement points and employing high-precision temperature field modeling and thermo-mechanical coupling analysis.

[0026] Based on the above considerations, this application proposes a crack risk assessment method. A geometric model is constructed based on the concrete structure to be assessed. The geometric model is then meshed, discretized, and subjected to topological analysis to obtain a discretized node set and topological graph structure. Spatial configuration identification and thermal attribute assignment are performed based on the discretized node set and topological graph structure to obtain a thermal coupling matrix between nodes. The temperature time series of the nodes is predicted based on this thermal coupling matrix. Gradient sensitivity analysis is performed on the temperature time series to obtain key temperature measurement points. Key measurement point data is acquired based on these key temperature measurement points. The key measurement point data is converted into a continuous temperature field. This continuous temperature field is adjusted based on the heat conduction control equation and boundary conditions. A prediction algorithm is then used to predict the adjusted temperature field, resulting in a reconstructed dynamic three-dimensional temperature field. This dynamic three-dimensional temperature field includes temperature data in the future time dimension. Based on the dynamic three-dimensional temperature field, the thermal stress of the concrete structure is calculated. Combining the thermal stress and the calculated temperature gradient, the crack risk level is determined to assess the cracks in the concrete structure. This application's embodiments predict temperature sequences through geometric modeling, mesh generation, and thermal coupling matrix analysis. Key measuring points are then selected using gradient analysis. A dynamic three-dimensional temperature field is generated using the heat conduction equation and boundary conditions, and thermal stress and risk indices are calculated to determine the crack risk level. This method is accurate, efficient, scientific, and economical, providing reliable technical support for crack risk assessment and construction optimization in concrete structures.

[0027] The technical solutions of the embodiments of this application will be described in detail below through specific examples.

[0028] refer to Figure 1 The crack risk assessment method of this application includes the following steps: Step S101: Construct a geometric model based on the concrete structure to be evaluated, perform mesh decomposition and topological analysis on the geometric model, and obtain the discretized node set and topological graph structure. Step S102: Based on the discretized node set and the topology graph structure, spatial configuration identification and thermal attribute assignment are performed to obtain the inter-node thermal coupling matrix, and the temperature time series of the nodes is predicted based on the inter-node thermal coupling matrix. Step S103: Perform gradient sensitivity analysis on the temperature time series to obtain key temperature measurement points; Step S104: Obtain key measurement point data based on the key temperature measurement points; Step S105: The key measurement point data is converted into a continuous temperature field. The continuous temperature field is adjusted based on the heat conduction control equation and boundary conditions. The adjusted temperature field is then predicted using a prediction algorithm to obtain the restored dynamic three-dimensional temperature field. The dynamic three-dimensional temperature field includes temperature data in the future time dimension. Step S106: Calculate the thermal stress of the concrete structure based on the dynamic three-dimensional temperature field; Step S107: Combining the thermal stress and the calculated temperature gradient, determine the risk level of the cracks to assess the cracks in the concrete structure.

[0029] Regarding step S101, in some embodiments, the step of constructing a geometric model based on the concrete structure to be evaluated, performing mesh decomposition and topological analysis on the geometric model to obtain a discretized node set and a topological graph structure includes: constructing the geometric model based on the concrete structure to be evaluated; performing mesh decomposition and discretization on the geometric model to obtain the discretized node set; and constructing the connection relationship between nodes based on the structural entities between the discretized node sets to obtain the topological graph structure.

[0030] refer to Figure 2 This is a schematic diagram of the key temperature measurement point selection process in an embodiment of this application.

[0031] like Figure 2 As shown, in this embodiment, a three-dimensional geometric model of the target concrete structure is input, supporting Computer-Aided Design (CAD), Standard for the Exchange of Product Model Data (STEP), and three-dimensional computer-aided design software (SolidWorks, SW). The model is then meshed and discretized to construct a spatial temperature analysis network. Let the discretized node set be:

[0032] in, This represents the i-th temperature field analysis node, which has definite spatial coordinates. The connections between nodes are constructed based on structural entities (concrete entities, steel reinforcement cages), forming a topological graph structure. .

[0033] Furthermore, in step S102, spatial configuration identification and thermal attribute assignment are performed based on the discretized node set and the topology graph structure to obtain the inter-node thermal coupling matrix, and the temperature time series of the nodes is predicted based on the inter-node thermal coupling matrix.

[0034] In some embodiments, the step of identifying spatial configurations and assigning thermal properties based on the discretized node set and the topology graph structure to obtain a thermal coupling matrix between nodes, and predicting the temperature time series of nodes based on the thermal coupling matrix between nodes, includes: analyzing the physical connection medium between any two adjacent nodes in the discretized node set to identify spatial configurations; assigning thermal properties to the material thermal parameters corresponding to different spatial configurations to obtain different thermal conductivity coefficients; constructing the thermal coupling matrix between nodes based on the different thermal conductivity coefficients; and predicting the temperature time series of nodes based on the thermal coupling matrix between nodes.

[0035] In some embodiments, the method further includes: introducing a heat conduction correction term to correct the temperature time series based on the spatial thermal coupling characteristics between nodes in the discretized node set.

[0036] In this embodiment, spatial configuration identification and thermal property assignment are performed on the discretized structural topology. First, heat conduction path identification between nodes is completed: analyzing any two adjacent nodes... , The physical connection medium between them is classified as: 1. the concrete solid contact area; 2. the concrete-reinforcement composite interface; 3. the hole, boundary or insulation zone.

[0037] Next, the thermal conductivity parameters are calculated: different thermal conductivity coefficients are assigned based on the material's thermophysical properties. Construct the thermal coupling matrix between nodes:

[0038] in, , These are the thermal conductivity coefficients of concrete and steel reinforcement, respectively.

[0039] For each node, a method based on finite difference, finite element, or data-driven approaches is employed. Within the set time interval The internal temperature response is predicted to obtain the initial time series: .

[0040] Based on this, considering the spatial thermal coupling characteristics between nodes, a heat conduction correction term is added to improve the accuracy of temperature prediction: .

[0041] Furthermore, in step S103, gradient sensitivity analysis is performed on the temperature time series to obtain key temperature measurement points.

[0042] In some embodiments, performing gradient sensitivity analysis on the temperature time series to obtain key temperature measurement points includes: performing gradient sensitivity analysis on the temperature time series to obtain a candidate set of key points; and based on the candidate set of key points, using a mutual information algorithm to eliminate redundant information and using a maximum representativeness score to obtain the key temperature measurement points.

[0043] In this embodiment, gradient sensitivity analysis is performed on the temperature response of each node to quantify its degree of response to structural temperature rise: Time thermal gradient: Spatial thermal gradient:

[0044] According to the set threshold , A set of nodes with highly active thermal responses is selected to form a candidate set C of key points. Within candidate set C, a mutual information algorithm is applied to eliminate redundant information and enhance representativeness. Mutual information matrix:

[0045] Representative scoring function:

[0046] By maximizing the representativeness score, a set of nodes with the highest information coverage, physical saliency, and uniform distribution is ultimately selected. This serves as the output of key temperature measurement points.

[0047] Furthermore, in step S104, key measurement point data is obtained based on the key temperature measurement points.

[0048] In this embodiment, a temperature acquisition system is used to obtain data from key measurement points.

[0049] refer to Figure 3 This is a schematic diagram of the temperature acquisition system framework according to an embodiment of this application.

[0050] like Figure 3As shown, the system consists of three main parts: temperature sensor nodes, communication relay terminals, and communication receiver terminals, forming a wireless temperature monitoring network with wide coverage, low power consumption, and high stability. Its design goal is to solve the problems of traditional wired temperature measurement systems, such as cumbersome wiring, susceptibility to construction interference, and insufficient scalability, ensuring high-precision, long-term temperature monitoring and data transmission even in complex construction environments.

[0051] Specifically, at the front end, the temperature sensor node is the basic unit of the system. Each node consists of a temperature sensor, a microcontroller unit (MCU), a LoRa communication module (LoRa), a power supply circuit, and a parameter setting interface. The temperature sensor is deployed at different depths and key temperature measurement points within the concrete structure, enabling real-time capture of dynamic changes in internal temperature. The MCU is responsible for digitizing the analog signals acquired by the sensor, as well as formatting the data and encapsulating the communication protocol, ensuring the accuracy and consistency of information transmission. The LoRa communication module handles long-distance wireless transmission, and its low-power characteristics significantly extend the node's lifespan. The power supply circuit provides stable power support, using a battery and a sleep algorithm to achieve long-cycle operation. The parameter setting interface is used for external configuration and maintenance, including adjusting parameters such as sampling frequency and node address, thereby enhancing the system's flexibility and adaptability.

[0052] In the middle layer, the communication relay plays a crucial role, its core consisting of a dual-mode communication unit combining LoRa and cellular network (4G). This relay receives temperature data transmitted via LoRa from various front-end sensor nodes and then reliably uploads the data to the host computer via 4G, enabling remote monitoring and cross-regional information exchange. Due to the complex environment and numerous obstacles at construction sites, this dual-mode design effectively solves the problems of high signal attenuation and limited transmission range in single-mode communication, significantly improving the system's adaptability and reliability in large-volume concrete construction.

[0053] At the back end, the communication receiver mainly consists of a 4G module and a host computer system. The host computer not only undertakes the task of receiving and storing temperature data, but also integrates the three-dimensional temperature field modeling and risk assessment algorithm proposed in this application. Through this module, data modeling and analysis can be carried out in real time after receiving temperature measurement data, outputting the three-dimensional temperature distribution inside the structure and the evolution trend of key thermal parameters, and further providing a basis for crack risk prediction and construction process optimization.

[0054] Furthermore, after acquiring the key measurement point data, regarding step S105, in some embodiments, converting the key measurement point data into a continuous temperature field, adjusting the continuous temperature field based on the heat conduction control equation and boundary conditions, and predicting the adjusted temperature field using a prediction algorithm to obtain the restored dynamic three-dimensional temperature field includes: performing spatial interpolation calculation on the key measurement point data to obtain the continuous temperature field; determining the constraints of the spatial interpolation based on the heat conduction control equation; determining the boundary conditions based on the collected real-time ambient temperature and temperature change parameters; adjusting the continuous temperature field according to the constraints and boundary conditions to obtain the adjusted temperature field; determining the temperature change relationship with time using a hydration thermodynamics model based on the adjusted temperature field; and predicting the future of the adjusted temperature field using the prediction algorithm based on the change relationship to obtain the restored dynamic three-dimensional temperature field.

[0055] In this embodiment, during the construction of large-volume concrete structures, the internal temperature distribution exhibits significant spatiotemporal nonuniformity, making it difficult to comprehensively reflect the overall temperature field using only a limited number of key measurement points. Therefore, after completing the layout of key temperature points and the acquisition of measured data, this application uses the measured temperature sequence as input, combined with the concrete heat conduction equation, interpolation algorithm, and boundary condition coupling strategy, to achieve spatiotemporal reconstruction and prediction of the overall temperature field of the structure.

[0056] Specifically, for temperature data from discrete sampling points, spatial interpolation and inversion techniques are used to reconstruct it into a continuous data structure. In the spatial interpolation process, an inverse distance weighting method is employed.

[0057] in, This represents the temperature at location x to be estimated. For known measuring points Temperature value, is the Euclidean distance between the point to be estimated and the known points, and p is the weight exponent (default value is 2).

[0058] To improve reconstruction accuracy, this application introduces constraints from the heat conduction control equation during the interpolation calculation process:

[0059] Where ρ is the density of concrete, c is the specific heat capacity, and k is the thermal conductivity. This represents the heat release term during hydration. Using a finite difference discretization method, this partial differential equation is coupled and corrected with the interpolation field to reflect the physical mechanism of heat transfer within the concrete.

[0060] Furthermore, this application considers the external influences of the construction environment and further superimposes boundary conditions during the temperature field reconstruction process. Specifically, the concrete surface temperature is not only constrained by measured sensors but also needs to consider convection and radiation effects. A more accurate three-dimensional temperature field value can be obtained by collecting real-time ambient temperature and acquiring temperature change parameters from weather forecasts. The boundary heat flux can be expressed as:

[0061] in, The convective heat transfer coefficient is... The concrete surface temperature Let ε be the ambient air temperature, ε be the surface emissivity, and σ be the Stefan-Boltzmann constant. The ambient radiation temperature is used. This boundary condition effectively improves the spatiotemporal accuracy of three-dimensional temperature field simulations.

[0062] In the time dimension, this application uses measured temperature sequences combined with hydration thermodynamics models and time series prediction methods to extrapolate and predict the temperature evolution of several future time windows, ensuring that the three-dimensional temperature field can not only reflect the current state, but also show the future evolution trend.

[0063] Furthermore, for steps S106 and S107, the thermal stress of the concrete structure is calculated based on the dynamic three-dimensional temperature field; the risk level of the cracks is determined by combining the thermal stress and the calculated temperature gradient, so as to assess the cracks in the concrete structure.

[0064] In this embodiment, traditional large-volume concrete temperature control systems often use a single temperature difference threshold (e.g., the internal and surface temperature difference does not exceed 25°C) as a risk criterion. However, such empirical methods fail to fully consider the stress distribution characteristics caused by non-uniform temperature environments within the concrete structure, and even more so, fail to reflect the essential impact of thermo-mechanical coupling behavior on crack risk, thus posing a risk of misjudgment or omission. To address this, this application proposes a crack risk model based on a combination of thermo-mechanical coupling analysis and regional gradient threshold judgment, and introduces a thermal stress inversion mechanism to achieve a more physically grounded risk assessment and feedback control.

[0065] Based on the temperature field reconstruction, this application performs inverse calculations of the thermal stress in concrete structures using a thermo-mechanical coupling equation. The thermal stress can be expressed as:

[0066] in, For stress components, Here is the elastic stiffness matrix. Let α be the total strain, α be the coefficient of linear expansion, and ΔT be the temperature change. This is the Kronecker delta function. Through the above equations, the correspondence between the temperature field and the stress field can be obtained, enabling the inversion from temperature measurement data to the structural stress state.

[0067] Furthermore, in some embodiments, determining the risk level of the crack by combining the thermal stress and the calculated temperature gradient to assess the cracks in the concrete structure includes: determining the maximum principal stress based on the thermal stress; calculating a risk index based on the maximum principal stress and the temperature gradient; and determining the risk level of the crack based on the magnitude of the risk index to assess the cracks in the concrete structure.

[0068] In this embodiment, the present application introduces a regional temperature gradient threshold into the crack risk criterion. Traditional methods rely solely on the global maximum temperature difference, while this application calculates the temperature gradient within a local region: and its modulus | T| is compared with a set threshold. When the local temperature gradient exceeds the critical value, it indicates that there is strong thermal inhomogeneity in the region. Combined with the thermal stress inversion results, potential crack risk areas can be located more accurately.

[0069] In addition, this application constructs a comprehensive evaluation model based on a risk index:

[0070] in, To calculate the maximum principal stress, For the tensile strength of concrete, For local temperature differences, For empirical or standard critical temperature difference, , This is a weighting coefficient. By setting risk levels (e.g., RI < 0.6 is the safe range, RI 0.6–0.9 is the warning range, and RI > 0.9 is the high-risk range), the risk of cracks can be classified and determined.

[0071] Finally, to ensure the system's engineering practicality, this application further proposes an intelligent feedback control strategy. When the risk index reaches the warning threshold, the system adds temporary temperature monitoring nodes in high-risk areas to improve local monitoring accuracy, automatically controls the cooling pipe water flow adjustment, and provides multi-dimensional control suggestions such as strengthening insulation measures, optimizing construction timing, and increasing the density of local monitoring. Specifically, based on the crack risk distribution, the cooling pipe flow rate or velocity can be adjusted to reduce the local temperature gradient; insulation materials can be added to mitigate the impact of temperature differences on the structure. The construction plan is optimized based on the risk distribution to avoid critical construction phases during high-risk periods.

[0072] As can be seen from the above embodiments, the crack risk assessment method described in this application constructs a geometric model based on the concrete structure to be assessed, performs mesh decomposition and topological analysis on the geometric model to obtain a discretized node set and topological graph structure; performs spatial configuration identification and thermal attribute assignment based on the discretized node set and topological graph structure to obtain a thermal coupling matrix between nodes, and predicts the temperature time series of the nodes based on the thermal coupling matrix between nodes; performs gradient sensitivity analysis on the temperature time series to obtain key temperature measurement points; acquires key measurement point data based on the key temperature measurement points; converts the key measurement point data into a continuous temperature field, adjusts the continuous temperature field based on the heat conduction control equation and boundary conditions, and predicts the adjusted temperature field using a prediction algorithm to obtain a restored dynamic three-dimensional temperature field; the dynamic three-dimensional temperature field includes temperature data in the future time dimension; calculates the thermal stress of the concrete structure based on the dynamic three-dimensional temperature field; and determines the crack risk level by combining the thermal stress and the calculated temperature gradient to assess the cracks in the concrete structure. This application's embodiments construct a geometric model of the concrete structure to be evaluated, perform mesh generation and topological analysis, ensuring that complex structures can be accurately discretized into node sets and topological graph structures. This provides a foundation for subsequent spatial configuration identification and thermal attribute assignment. By identifying spatial configurations and assigning thermal attributes based on the discretized node sets and topological graph structures, a thermal coupling matrix between nodes is constructed. The matrix is ​​used to predict the temperature time series of nodes, effectively quantifying the heat conduction relationship and temperature change trend between nodes, improving the accuracy of temperature prediction. Simultaneously, gradient sensitivity analysis can screen key temperature measurement points, reducing the number of monitoring points, lowering costs, and improving representativeness. By converting key measurement point data into a continuous temperature field and adjusting it based on the heat conduction control equation and boundary conditions, a dynamic three-dimensional temperature field is generated using a prediction algorithm, providing spatiotemporally consistent temperature field data for subsequent crack risk assessment. The dynamic three-dimensional temperature field covers temperature data in the future time dimension and can predict the evolution trend of the temperature field. Based on the dynamic temperature field, thermal stress is calculated, and combined with temperature gradients, crack risk levels are determined, quantifying crack risk and locating high-risk areas. This provides a scientific basis for crack assessment of concrete structures, improving the accuracy and reliability of the assessment. The overall approach has strong applicability and engineering value, and can be widely applied to crack risk control and construction optimization of large-volume concrete structures.

[0073] Specifically, by constructing a geometric model of the concrete structure to be evaluated, the spatial information of the complex structure can be fully expressed. Mesh discretization transforms the geometric model into a finite set of nodes, discretizing the continuous structure into computable units, facilitating subsequent heat conduction analysis and calculation. Based on the physical structural entities between the node sets, the connection relationships between nodes are constructed, forming a topological graph structure that accurately reflects the internal spatial configuration of the concrete and the physical interactions between nodes, providing a reliable spatial foundation for heat conduction analysis between nodes and the construction of the thermal coupling matrix. This process effectively improves the accuracy of heat conduction analysis of concrete structures and the completeness of model data, providing precise geometric and spatial data support for temperature field reconstruction and crack risk assessment. Simultaneously, the discretization of structural entities and the construction of topological graph relationships enhance the adaptability of this method to complex geometric structures, enabling its widespread application in crack risk assessment of large-volume concrete structures such as bridges, dams, and tunnels.

[0074] By accurately analyzing and assigning values, a thermal coupling matrix between nodes is constructed, and the node temperature time series is predicted, laying a solid foundation for subsequent temperature field reconstruction and crack risk assessment. By analyzing the physical connection medium between any two adjacent nodes and identifying the spatial configuration, the heat conduction path within the concrete and the physical relationships between nodes can be accurately reflected. Thermal property values ​​are assigned to the material thermophysical parameters corresponding to different spatial configurations, and the thermal conductivity coefficients between nodes are constructed, enabling the thermal coupling matrix to accurately characterize the intensity and relationship of heat conduction between nodes. Predicting the node temperature time series based on the thermal coupling matrix can effectively quantify the temperature change trend of each node, improving the accuracy and reliability of temperature prediction. The technical effect of this claim lies in providing high-quality input data for subsequent temperature field reconstruction through accurate description of heat conduction relationships, while simultaneously enhancing the sensitivity and adaptability of the entire crack risk assessment method to complex heat conduction behavior.

[0075] By considering the spatial thermal coupling characteristics between nodes and using a heat conduction correction term to compensate for the deficiencies in the initial temperature time series, the prediction results not only reflect the temperature changes of the nodes themselves but also the impact of heat exchange between adjacent nodes on the target node. This correction process can dynamically reflect the coupling effects in the actual heat conduction path, thereby more accurately capturing the complex heat conduction behavior in concrete structures. The technical benefits also include optimizing the spatiotemporal consistency of the node temperature series, providing more accurate input data for subsequent temperature field reconstruction, enhancing the adaptability of crack risk assessment to heat conduction coupling effects, and providing reliable data support for temperature control management and risk analysis in complex construction environments.

[0076] By performing gradient sensitivity analysis on temperature time series data, the response of nodes to temperature changes can be quantified, identifying nodes with high thermal sensitivity and initially screening a set of candidate key points. This process significantly reduces redundancy in monitoring point deployment and improves resource utilization. The mutual information algorithm is used to eliminate redundant information in the candidate key point set, and the selection of key monitoring points is further optimized by maximizing representativeness scores, ensuring that the selected monitoring points comprehensively cover the main characteristics of temperature field changes. The effect of this technology is that, through scientific sensitivity analysis and information optimization, it improves the quality of temperature monitoring data, providing high-quality input for subsequent temperature field reconstruction and crack risk assessment, while reducing monitoring costs and enhancing its flexibility and practicality in complex construction environments.

[0077] By using spatial interpolation to make discrete measurement point data continuous, gaps in temperature information between measurement points can be filled, providing a preliminary overall distribution of the temperature field. Constraints are introduced based on the heat conduction control equation, coupling the interpolation results with the physical heat conduction mechanism, making the reconstructed temperature field more consistent with actual heat transfer patterns. Boundary conditions are determined by combining real-time ambient temperature and external temperature change parameters, adjusting the continuous temperature field and significantly improving its spatial accuracy, especially in boundary regions. A hydration thermodynamic model is used to describe the temperature change over time, and a prediction algorithm is combined to dynamically predict the adjusted temperature field, generating a dynamic three-dimensional temperature field that includes a future time dimension, reflecting the evolution trend of the temperature field. The technical effect is the accurate spatiotemporal reconstruction of the temperature field, providing scientifically reliable data support for subsequent thermal stress calculations and crack risk assessments, while enhancing the method's adaptability to complex temperature distributions and external environmental influences.

[0078] This technology enables precise assessment of crack risk in concrete structures through quantitative analysis. Determining the maximum principal stress based on thermal stress effectively identifies stress concentration areas within the structure, which are typically high-risk areas prone to cracking. Combining temperature gradient calculations with the risk index comprehensively reflects the complex physical mechanisms of crack formation by considering the combined effects of thermal stress and temperature gradients. The crack risk level is determined by the magnitude of the risk index, allowing for graded assessment of crack risk, clarifying the scope of high-risk areas, and providing scientific support for implementing targeted temperature control measures or optimizing construction techniques. The advantages of this technology lie in improving the accuracy and reliability of crack risk assessment, while providing a quantitative basis for engineering safety management. This application has strong practicality and engineering guidance value.

[0079] It should be noted that the method in this embodiment can be executed by a single device, such as a computer or server. The method can also be applied in a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method in this embodiment, and the multiple devices will interact with each other to complete the method described.

[0080] It should be noted that the above description describes some embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0081] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the crack risk assessment method described in any of the above embodiments.

[0082] Figure 4 This embodiment illustrates a more specific hardware structure of an electronic device, which may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0083] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0084] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0085] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0086] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0087] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0088] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0089] The electronic devices described above are used to implement the corresponding crack risk assessment methods in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0090] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the crack risk assessment method as described in any of the above embodiments.

[0091] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0092] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the crack risk assessment method as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0093] Based on the same inventive concept, corresponding to the crack risk assessment method described in any of the above embodiments, this disclosure also provides a computer program product, which includes computer program instructions. In some embodiments, the computer program instructions can be executed by one or more processors of a computer to cause the computer and / or the processor to perform the crack risk assessment method. Corresponding to the execution entity for each step in each embodiment of the crack risk assessment method, the processor executing the corresponding step can belong to the corresponding execution entity.

[0094] The computer program product of the above embodiments is used to cause the computer and / or the processor to execute the crack risk assessment method as described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0095] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in the details for the sake of brevity.

[0096] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0097] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0098] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

Claims

1. A crack risk assessment method, characterized in that, include: A geometric model is constructed based on the concrete structure to be evaluated. The geometric model is then subjected to mesh decomposition and topological analysis to obtain the discretized node set and topological graph structure. Based on the discretized node set and the topology graph structure, spatial configuration identification and thermal attribute assignment are performed to obtain the inter-node thermal coupling matrix, and the temperature time series of the nodes are predicted based on the inter-node thermal coupling matrix. Gradient sensitivity analysis was performed on the temperature time series to obtain key temperature measurement points; Data of key measuring points are obtained based on the aforementioned key temperature measuring points; The key measurement point data is converted into a continuous temperature field. The continuous temperature field is adjusted based on the heat conduction control equation and boundary conditions. The adjusted temperature field is then predicted using a prediction algorithm to obtain the restored dynamic three-dimensional temperature field. The dynamic three-dimensional temperature field includes temperature data in the future time dimension. Based on the dynamic three-dimensional temperature field, the thermal stress of the concrete structure is calculated; By combining the thermal stress and the calculated temperature gradient, the risk level of the cracks is determined in order to assess the cracks in the concrete structure.

2. The method according to claim 1, characterized in that, The process involves constructing a geometric model based on the concrete structure to be evaluated, performing mesh decomposition and topological analysis on the geometric model, and obtaining the discretized node set and topological graph structure, including: The geometric model is constructed based on the concrete structure to be evaluated; The geometric model is subjected to mesh decomposition and discretization to obtain the discretized node set; Based on the structural entities between the discretized node sets, the connection relationships between nodes are constructed to obtain the topological graph structure.

3. The method according to claim 1, characterized in that, The process of spatial configuration identification and thermal attribute assignment based on the discretized node set and the topology graph structure to obtain the inter-node thermal coupling matrix, and predicting the node temperature time series based on the inter-node thermal coupling matrix, includes: The physical connection medium between any two adjacent nodes in the discretized node set is analyzed to identify the spatial configuration and obtain the spatial configuration. By assigning thermal property values ​​to the material thermal property parameters corresponding to different spatial configurations, different thermal conductivity coefficients are obtained; The inter-node thermal coupling matrix is ​​constructed based on the different thermal conductivity coefficients. The temperature time series of the nodes is predicted based on the inter-node thermal coupling matrix.

4. The method according to claim 3, characterized in that, The method further includes: Based on the spatial thermal coupling characteristics between nodes in the discretized node set, a heat conduction correction term is introduced to correct the temperature time series.

5. The method according to claim 1, characterized in that, The gradient sensitivity analysis of the temperature time series, to obtain key temperature measurement points, includes: Gradient sensitivity analysis was performed on the temperature time series to obtain a candidate set of key points; Based on the candidate set of key points, redundant information is eliminated using the mutual information algorithm, and the key temperature measurement points are obtained by maximizing the representativeness score.

6. The method according to claim 1, characterized in that, The process of converting the key measurement point data into a continuous temperature field, adjusting the continuous temperature field based on the heat conduction control equation and boundary conditions, and predicting the adjusted temperature field using a prediction algorithm to obtain the restored dynamic three-dimensional temperature field includes: Spatial interpolation calculations are performed on the key measurement point data to obtain the continuous temperature field; The constraints for spatial interpolation are determined based on the heat conduction control equation. The boundary conditions are determined based on the collected real-time ambient temperature and temperature change parameters. The continuous temperature field is adjusted according to the constraints and boundary conditions to obtain the adjusted temperature field; Based on the adjusted temperature field, the temperature-time relationship is determined using a hydration thermodynamics model. Based on the aforementioned change relationship, the adjusted temperature field is predicted in the future using the aforementioned prediction algorithm, thereby obtaining the restored dynamic three-dimensional temperature field.

7. The method according to claim 1, characterized in that, The method of combining the thermal stress and the calculated temperature gradient to determine the risk level of the cracks in order to assess the cracks in the concrete structure includes: The maximum principal stress is determined based on the aforementioned thermal stress; The risk index is calculated based on the maximum principal stress and the temperature gradient. Based on the magnitude of the risk index, the risk level of the crack is determined in order to assess the cracks in the concrete structure.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 7.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1 to 7.

10. A computer program product comprising computer program instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1 to 7.

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