Urban underground space grouting effect evaluation method and system based on multipoint stress response

Abstract

The invention relates to the technical field of underground construction engineering, and provides an urban underground space grouting effect evaluation method and system based on multipoint stress response. The method comprises the steps that a stress sensor array is arranged in a target area, and a layout coordinate set and grouting target parameters are obtained; grouting simulation is conducted based on the coordinates and the parameters, and a theoretical stress influence cloud picture is generated; in the construction process, a stress response sequence is collected in real time through a sensor array; calculating a real-time slurry filling degree and a stress balance degree according to the data sequence and the theoretical cloud picture; and evaluating the grouting effect grade based on the two, and if the grouting effect grade is lower than a preset threshold, generating a parameter adjustment instruction. The technical problems that in traditional underground space grouting construction, the grout diffusion range and the reinforcing effect cannot be accurately judged, so that grouting parameter adjustment lags behind, and the construction risk is large are solved, dynamic evaluation of the grout filling degree and the stress balance degree is achieved through multi-point stress response and theoretical simulation comparison, and the grouting quality is improved. And the reinforcing effect and the construction safety are improved.

Description

Technical Field

[0001] This application relates to the field of underground construction engineering technology, specifically to a method and system for evaluating the grouting effect of urban underground spaces based on multi-point stress response. Background Technology

[0002] In the construction of urban underground spaces, grouting, as a common foundation reinforcement and seepage prevention technology, is widely used in projects such as subway tunnels, underground utility tunnels, underground stations, and deep foundation pits. Traditional grouting effect assessments often rely on the experience and judgment of construction personnel or on localized data obtained through single-point monitoring. The results are often lagging and incomplete, failing to comprehensively reflect the grout diffusion range and reinforcement effect during the grouting process. Due to the complexity of the underground environment and the heterogeneity and weak surface distribution of the surrounding rock structure, the stress response of the grout during diffusion exhibits multi-dimensional and multi-stage dynamic characteristics. Single parameters or localized monitoring are insufficient to support accurate assessment. This leads to problems such as uneven grout distribution, insufficient reinforcement strength, or localized over-grouting during grouting construction, thereby increasing construction risks and resource waste. Summary of the Invention

[0003] This application provides a method and system for evaluating the grouting effect in urban underground spaces based on multi-point stress response. It aims to solve the technical problem that traditional underground space grouting construction cannot accurately determine the grout diffusion range and reinforcement effect, resulting in delayed adjustment of grouting parameters and high construction risks.

[0004] The first aspect of this application discloses a method for evaluating the grouting effect in urban underground spaces based on multi-point stress response. The method includes: arranging a multi-point stress sensor array in the target grouting area to acquire a coordinate set, and then acquiring grouting target parameters, including the grout diffusion radius and the target reinforcement strength; simulating the grouting process based on the coordinate set and the grouting target parameters to generate a theoretical stress influence cloud map; during the grouting construction process, collecting stress response data sequences in real time through the multi-point stress sensor array; calculating the real-time grout filling degree and the real-time stress balance degree based on the stress response data sequence and the theoretical stress influence cloud map; evaluating the grouting effect level based on the real-time grout filling degree and the real-time stress balance degree; and generating a grouting parameter adjustment command if the grouting effect level is less than a preset level threshold.

[0005] Another aspect of this application discloses a system for evaluating the grouting effect in urban underground spaces based on multi-point stress response. The system includes: a parameter acquisition module: arranging a multi-point stress sensor array in the target grouting area to acquire a coordinate set and then acquiring grouting target parameters, including grout diffusion radius and target reinforcement strength; a grouting process simulation module: simulating the grouting process based on the coordinate set and the grouting target parameters to generate a theoretical stress influence cloud map; a data acquisition module: acquiring stress response data sequences in real time through the multi-point stress sensor array during grouting construction; an index calculation module: calculating real-time grout filling degree and real-time stress balance degree based on the stress response data sequence and the theoretical stress influence cloud map; and an evaluation and judgment module: evaluating the grouting effect level based on the real-time grout filling degree and real-time stress balance degree, and generating a grouting parameter adjustment command if the grouting effect level is less than a preset level threshold.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: The aforementioned method for evaluating the grouting effect in urban underground spaces based on multi-point stress response first deploys multiple stress sensors within the target grouting area and records their spatial locations, while simultaneously acquiring the target parameters for grouting design. Subsequently, numerical simulation is performed based on the sensor deployment coordinates and target parameters to obtain a theoretical stress distribution cloud map. During actual construction, the sensor array collects stress changes caused by grouting in real time, and these measured data are compared and analyzed with the theoretical cloud map to calculate the filling degree of the grout in the underground space and the uniformity of stress distribution. Finally, a comprehensive evaluation of the above results determines the grouting effect level. If the evaluation result is lower than the preset requirements, corresponding grouting parameter adjustment instructions are automatically generated to achieve dynamic optimization and control of the construction process.

[0007] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1This is a flowchart illustrating a method for evaluating the grouting effect of urban underground space based on multi-point stress response in one embodiment.

[0010] Figure 2 This is a system architecture diagram for evaluating the grouting effect of urban underground space based on multi-point stress response in one embodiment.

[0011] Figure labeling: Parameter acquisition module 11, Grouting process simulation module 12, Data acquisition module 13, Index calculation module 14, Evaluation and judgment module 15. Detailed Implementation

[0012] This application provides a method and system for evaluating the grouting effect in urban underground spaces based on multi-point stress response. This solves the technical problem that traditional underground space grouting construction cannot accurately determine the grout diffusion range and reinforcement effect, leading to delayed adjustment of grouting parameters and high construction risks.

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0014] It should be noted that the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such process, method, product, or device.

[0015] Example 1, as Figure 1 As shown, this application provides a method for evaluating the grouting effect of urban underground spaces based on multi-point stress response, the method comprising: A multi-point stress sensor array is arranged in the target grouting area to obtain the layout coordinate set, and then the grouting target parameters are obtained, including the grout diffusion radius and the target reinforcement strength.

[0016] In this embodiment, the scope and key control areas of grouting reinforcement are first determined based on the geological survey data of the target grouting area. Then, a multi-point stress sensor array is arranged within these areas according to a preset spatial distribution, and the corresponding three-dimensional spatial coordinates are accurately recorded to form a complete set of layout coordinates for subsequent spatial comparison between simulation and measured data. Simultaneously, the target design parameters for grouting are acquired. These parameters include the expected grout diffusion radius and the required target reinforcement strength. The grout diffusion radius defines the diffusion range that the grout should achieve in the underground medium, thus establishing the spatial boundary of the simulation model; the target reinforcement strength reflects the bearing capacity or stability index required of the reinforced soil or surrounding rock. Through the above-mentioned point layout and parameter acquisition, a foundation can be laid for subsequent grouting process simulation, generation of theoretical stress distribution maps, and comparative analysis of measured data, thereby ensuring the accuracy of the assessment.

[0017] Furthermore, this application provides a multi-point stress sensor array arranged in the target grouting area, including: Based on the geological exploration data of the target grouting area, the distribution of weak surfaces and key stress concentration areas of the surrounding rock are determined; with the distribution of weak surfaces and key stress concentration areas as the key monitoring areas, stress sensors are deployed in a three-dimensional grid array by drilling to form the multi-point stress sensor array; the three-dimensional spatial coordinates of the multi-point stress sensor array are recorded to generate the deployment coordinate set.

[0018] Preferably, the first step is to acquire geological survey data of the target grouting area. This data is obtained in advance through drilling sampling, ground-penetrating radar detection, and acoustic testing. Based on this data, the distribution of weak surfaces in the soil and rock mass can be identified, such as fault zones, joints, fissures, and weak interlayers. Combined with in-situ stress testing, potential key stress concentration areas can be determined. These weak surfaces and high-stress areas are often the weak points in grout diffusion and reinforcement, and are also the key monitoring targets for grouting effect evaluation. Subsequently, for these key monitoring areas, the core monitoring scope is the distribution of weak surfaces and key stress concentration areas. Multiple stress sensors are deployed through boreholes using a three-dimensional grid array to ensure that the sensors can uniformly cover the grouting influence area in space. The density of sensors is appropriately increased at weak surfaces and key locations to ensure the integrity and representativeness of the monitoring data. The borehole depth and sensor placement are determined based on the designed grouting range and underground soil and rock structure conditions. Each sensor needs to be fixed and protected after installation to ensure stable operation under grouting and stress. After deployment, the three-dimensional coordinates of each stress sensor are stored to form a deployment coordinate set, which is used to set the position of virtual monitoring nodes in subsequent finite element simulations, and also serves as a spatial reference for comparing real-time sensor data with simulation results, thus realizing the correspondence between monitoring information and numerical simulation.

[0019] Based on the set of coordinates and the grouting target parameters, the grouting process is simulated to generate a theoretical stress influence cloud map.

[0020] In one embodiment, after obtaining the coordinate set and grouting target parameters, a three-dimensional finite element simulation model covering the actual grouting influence range is established, centered on each grouting hole, according to the coordinate set and grouting target parameters. In this model, soil and rock medium parameters, such as elastic modulus, Poisson's ratio, and porosity, as well as designed grouting process parameters, such as grouting pressure and grouting rate, are used to dynamically calculate the radial diffusion process of the grout in pores and fractures using the finite element method, gradually outputting the theoretical stress values ​​of each grid node at different stages. Finally, these theoretical stress values ​​are interpolated and visualized to generate a three-dimensional theoretical stress influence cloud map, thus intuitively displaying the grout diffusion range and its impact on the surrounding rock stress field, providing a basis for subsequent comparative analysis of measured data and evaluation of grouting effects.

[0021] Furthermore, this application provides a method for simulating the grouting process and generating theoretical stress influence cloud maps, including: A three-dimensional finite element simulation region is established with the grouting hole as the center and K times the grout diffusion radius as the boundary. Virtual monitoring nodes are set within the three-dimensional finite element simulation region according to the coordinate set. With the target reinforcement strength as the target, the radial diffusion process of the grout in the three-dimensional finite element simulation region is simulated using the finite element method based on the grout viscosity, soil porosity parameters, and grouting parameters, and the theoretical stress values ​​of the mesh nodes are recorded in real time. Based on the theoretical stress values, the theoretical stress influence cloud map is generated using interpolation, wherein the color depth of the theoretical stress influence cloud map is positively correlated with the theoretical stress value.

[0022] Preferably, the actual grouting hole location is first used as the geometric center, and K times the grout diffusion radius R in the grouting target parameters is used as the boundary condition of the simulation area. This establishes a three-dimensional finite element simulation region that can cover the potential influence range of the grout. This region includes not only the directly stressed area around the grouting hole but also the potential boundary area for grout diffusion, ensuring the completeness and reliability of the simulation results. Subsequently, based on the established coordinate set, the actual spatial positions of the multi-point stress sensor array are mapped to the simulation region, and virtual monitoring nodes are set at the corresponding coordinate points. In this way, each virtual monitoring node in the simulation can correspond one-to-one with the position of the stress sensor subsequently deployed on-site, providing a basis for the comparative analysis of theoretical simulation results and measured data. Then, using the target reinforcement strength as the constraint target, and comprehensively inputting the physical rheological parameters of the grout (such as grout viscosity) and soil and rock medium parameters, the radial diffusion behavior of the grout in the three-dimensional medium is dynamically calculated using the finite element method. During the simulation, the stress change of each finite element mesh node is calculated and recorded in real time, thus obtaining the theoretical stress value distribution within the entire grouting influence range. Then, the data are processed continuously using mathematical interpolation methods, and a three-dimensional theoretical stress influence cloud map is generated using visualization technology. This theoretical stress influence cloud map uses different shades of color to represent the stress magnitude, with darker colors representing higher stress values. This visually demonstrates the range and intensity of the effect of grout diffusion on the stress field of the surrounding rock or soil, providing a reliable theoretical basis for subsequent grouting effect evaluation and parameter optimization.

[0023] During the grouting process, the stress response data sequence is collected in real time through the multi-point stress sensor array.

[0024] In one embodiment, once the grouting construction officially begins, a multi-point stress sensor array pre-deployed in the target area will be immediately activated to continuously monitor the stress changes in the soil and rock caused by grouting in real time. After preprocessing the collected data, a stress response data sequence will be formed. This stress response data sequence stores stress response data arranged in chronological order to reflect the stress evolution law at different spatial locations during the grouting process, providing basic information for subsequent comparison with theoretical simulation results.

[0025] Furthermore, the stress response data sequence provided in this application also includes: The original stress response data is subjected to noise filtering to obtain a first denoised sequence; outlier removal is performed on the first denoised sequence to obtain a second stable sequence; normalization is performed on the second stable sequence to output the stress response data sequence.

[0026] Preferably, the acquired raw stress response data often contains high-frequency or low-frequency noise generated by factors such as construction machinery vibration, electromagnetic interference, and environmental noise. Therefore, to ensure the accuracy and usability of stress monitoring data, digital filtering methods (such as low-pass filtering, band-pass filtering, or wavelet filtering) are used to process the raw signal, filtering out invalid noise components and retaining the effective signal related to grouting stress changes, thus obtaining the first denoised sequence. Subsequently, statistical methods (such as the 3σ criterion, box plot outlier detection, etc.) are used to remove outliers from the first denoised sequence, resulting in a more stable, continuous second stable sequence that conforms to the actual laws of stress changes. Afterwards, to facilitate comparison and unified calculation between output data from different sensors, the maximum-minimum-maximum normalization method is used to process the second stable sequence, thereby transforming it into a uniform numerical range (such as between 0 and 1), eliminating the influence of differences in sensor sensitivity, initial zero drift, or range, forming the final stress response data sequence, laying the data foundation for subsequent calculations and evaluations.

[0027] Based on the stress response data sequence and the theoretical stress influence cloud map, calculate the real-time slurry filling degree and the real-time stress balance degree.

[0028] In one embodiment, after acquiring the real-time stress response data sequence, it is compared with the theoretical stress influence cloud map to calculate the actual diffusion of the grout in the grouting area and the degree of stress distribution uniformity, thereby obtaining the real-time grout filling degree and the real-time stress uniformity. The real-time grout filling degree reflects the degree of agreement between the actual and theoretical diffusion range of the grout, while the real-time stress uniformity measures the uniformity of stress transmission after grout diffusion. By calculating the real-time grout filling degree and the real-time stress uniformity, quantitative indicators can be provided for the comprehensive evaluation of grouting effects, and a basis can be provided for subsequent effect grading and parameter adjustment.

[0029] Furthermore, this application provides a method for calculating real-time slurry filling degree and real-time stress uniformity, including: Grid nodes whose theoretical stress values ​​exceed a preset sensitivity threshold are selected to determine the theoretical effective influence zone. Based on the stress response data sequence, the number of sensors located within the theoretical effective influence zone whose stress response data exceeds the preset sensitivity threshold is counted to obtain the number of effective response sensors. The number of effective response sensors is compared with the total number of virtual monitoring nodes within the theoretical effective influence zone to obtain the real-time slurry filling degree. Based on the stress response data sequence, the real-time stress response data of the effective response sensors is extracted, and the real-time stress uniformity is calculated based on the real-time stress response data.

[0030] Optionally, based on the theoretical stress distribution obtained from finite element simulation, mesh nodes with stress values ​​exceeding a preset sensitivity threshold are extracted, and adjacent nodes are aggregated to form multiple sets. The nodes in these sets represent the areas that the slurry should be able to influence under theoretical conditions, and these areas are defined as the theoretical effective influence zone to reflect the spatial coverage under ideal slurry diffusion conditions. Subsequently, the real-time stress response data sequence is mapped to the actual sensor deployment coordinates. All sensors located within the theoretical effective influence zone are analyzed one by one, and their collected real-time stress data are statistically analyzed to determine whether they exceed the preset sensitivity threshold. The number of sensors exceeding the threshold is recorded as the number of effective response sensors to ensure the correspondence between theoretical simulation results and field measured data. Then, the ratio of the statistically obtained number of effective response sensors to the total number of virtual monitoring nodes within the theoretical effective influence zone is calculated to obtain the real-time slurry filling degree. The closer this real-time slurry filling degree is to 1, the higher the consistency between the measured slurry diffusion range and the theoretical simulation results, and the more ideal the filling effect. If the ratio is too low, it indicates insufficient or uneven slurry diffusion. Then, to further evaluate the uniformity of stress distribution during grouting, real-time stress response data from all effective response sensors were extracted from the stress response data sequence. These data were then statistically analyzed, and their mean and standard deviation were calculated. The standard deviation was divided by the mean, and the quotient was subtracted from 1 to obtain the real-time stress uniformity. This real-time stress uniformity measures the dispersion of stress at each monitoring point. When the mean is large and the standard deviation is small, the real-time stress uniformity is close to 1, indicating a relatively uniform stress distribution within the grouting area. Conversely, if the standard deviation is large, the real-time stress uniformity decreases, indicating non-uniformity in the grouting effect. Through the dual calculation of real-time grout filling degree and real-time stress uniformity, the spatial coverage effect and stress uniformity of grouting can be comprehensively quantified, providing a basis for subsequent effect evaluation and parameter optimization.

[0031] Based on the real-time grout filling degree and the real-time stress balance degree, the grouting effect level is evaluated. If the grouting effect level is less than the preset level threshold, an grouting parameter adjustment command is generated.

[0032] In one embodiment, after calculating the real-time grout filling degree and real-time stress uniformity, these values ​​are input into a pre-built grouting effect evaluation function for weighted calculation. The calculation results are then compared with a preset effect level range to determine the specific grouting effect level. If this grouting effect level is higher than or equal to a preset level threshold, it indicates that the construction has achieved the expected goal. Conversely, if it is lower than the preset level threshold, a grouting parameter adjustment instruction will be automatically generated. This instruction includes parameters such as the currently used grouting pressure, grout flow rate, and grout water-cement ratio, which guide subsequent parameter adjustments and process optimization. This enables real-time feedback and dynamic control of the grouting process, ensuring that the final reinforcement effect meets the design requirements.

[0033] Furthermore, this application provides a method for evaluating the grouting effect level, including: Construct a grouting effect evaluation function; input the real-time grout filling degree and the real-time stress balance degree into the grouting effect evaluation function to obtain a grouting effect score; map the grouting effect score based on a preset effect level range to determine the grouting effect level.

[0034] Optionally, to quantitatively evaluate grouting quality, a grouting effect evaluation function is first constructed. This function uses a weighted formula, taking real-time grout filling degree and real-time stress uniformity as the main input parameters, and assigning different weight coefficients to each to reflect their relative importance in the grouting quality evaluation. Then, the real-time grout filling degree and real-time stress uniformity are input into the constructed grouting effect evaluation function to calculate the grouting effect score. This grouting effect evaluation function is then compared and mapped to a preset effect level range. The effect level range is divided into several levels; for example, a grouting effect score ≥ 0.85 is rated as "Level 4"; 0.70 ≤ grouting effect score < 0.85 is rated as "Level 3"; 0.50 ≤ grouting effect score < 0.70 is rated as "Level 2"; and a grouting effect score < 0.50 is rated as "Level 1". After the comparison, the grouting effect level corresponding to the grouting effect score can be obtained, providing a basis for dynamic decision-making and parameter adjustment during construction.

[0035] Furthermore, this application provides a method for generating grouting parameter adjustment instructions, which also includes: When the grouting effect level is less than the preset level threshold, a grouting parameter adjustment instruction carrying the current grouting parameters is generated; the current grouting parameters are used as the initial solution to generate multiple grouting parameter combinations; grouting construction simulation is performed on the multiple grouting parameter combinations to obtain multiple simulated stress response data sequences and calculate multiple simulated grouting effect scores; global optimization is performed based on the multiple simulated grouting effect levels to establish the optimal grouting parameter combination; it is determined whether the optimal grouting parameter combination meets the preset level threshold. If it does not, the optimization range is adjusted and iterated again until convergence.

[0036] Optionally, when the grouting effect level is lower than a preset level threshold, an adjustment instruction carrying the grouting parameters used in the current construction, such as grouting pressure, grout flow rate, water-cement ratio, and grout mix proportion, is generated as the initial solution for optimization. Subsequently, based on this initial solution, multiple different combinations of grouting parameters are generated through steps such as cross-validation and mutation. These combinations vary within the neighborhood of the current parameters to ensure feasibility under constructable conditions. Then, each candidate parameter combination is input into the finite element simulation model to simulate the grouting construction process, obtaining the corresponding simulated stress response data sequence. By comparing this sequence with the theoretical stress influence cloud map, the grout filling degree and stress uniformity under each combination are calculated and substituted into the grouting effect evaluation function to obtain multiple simulated grouting effect scores. Afterward, based on these simulated grouting effect scores, combinations that meet the preset level threshold are retained to ensure that excellent parameter combinations are passed on. Cross-validation is then used to exchange some parameters to generate new combinations, and mutation is used to slightly perturb some parameters, thereby increasing the diversity of solutions and avoiding getting trapped in local optima. After multiple iterations, the score of the parameter combination gradually improves and converges to an optimal or near-optimal grouting parameter combination. Finally, the grouting effect level corresponding to this combination is compared with a preset level threshold. If the requirements are met, this grouting parameter combination is taken as the optimal grouting parameter combination; otherwise, the parameter range is expanded and iterative optimization continues until the requirements are met, thereby improving the reliability and consistency of the grouting effect.

[0037] In summary, the embodiments of this application have at least the following technical effects: This application embodiment first deploys a multi-point stress sensor array in the target grouting area to acquire a coordinate set, and then acquires the grouting target parameters, including the grout diffusion radius and the target reinforcement strength. Subsequently, based on the coordinate set and the grouting target parameters, a grouting process simulation is performed to generate a theoretical stress influence cloud map. Then, during the grouting construction process, the multi-point stress sensor array collects stress response data sequences in real time. Next, based on the stress response data sequences and the theoretical stress influence cloud map, the real-time grout filling degree and real-time stress balance degree are calculated. Finally, based on the real-time grout filling degree and the real-time stress balance degree, the grouting effect level is evaluated. If the grouting effect level is less than a preset threshold, a grouting parameter adjustment command is generated. These technical effects collectively solve the technical problem in traditional underground space grouting construction where the grout diffusion range and reinforcement effect cannot be accurately determined, leading to delayed grouting parameter adjustments and high construction risks. This achieves the technical effect of dynamically evaluating grout filling degree and stress balance by comparing multi-point stress response with theoretical simulation, thereby improving reinforcement effect and construction safety.

[0038] Example 2 is based on the same inventive concept as the method for evaluating the grouting effect of urban underground space based on multi-point stress response in the previous examples, such as... Figure 2 As shown, this application provides a multi-point stress response-based urban underground space grouting effect evaluation system. The system includes: a parameter acquisition module 11: arranging a multi-point stress sensor array in the target grouting area to acquire a set of coordinates and then acquiring grouting target parameters, including grout diffusion radius and target reinforcement strength; a grouting process simulation module 12: simulating the grouting process based on the set of coordinates and the grouting target parameters to generate a theoretical stress influence cloud map; a data acquisition module 13: acquiring stress response data sequences in real time through the multi-point stress sensor array during grouting construction; an index calculation module 14: calculating real-time grout filling degree and real-time stress balance degree based on the stress response data sequence and the theoretical stress influence cloud map; and an evaluation and judgment module 15: evaluating the grouting effect level based on the real-time grout filling degree and the real-time stress balance degree, and generating a grouting parameter adjustment command if the grouting effect level is less than a preset level threshold.

[0039] Furthermore, the parameter acquisition module 11 is also used to perform the following method: Based on the geological exploration data of the target grouting area, the distribution of weak surfaces and key stress concentration areas of the surrounding rock are determined; with the distribution of weak surfaces and key stress concentration areas as the key monitoring areas, stress sensors are deployed in a three-dimensional grid array by drilling to form the multi-point stress sensor array; the three-dimensional spatial coordinates of the multi-point stress sensor array are recorded to generate the deployment coordinate set.

[0040] Furthermore, the grouting process simulation module 12 is also used to perform the following methods: A three-dimensional finite element simulation region is established with the grouting hole as the center and K times the grout diffusion radius as the boundary. Virtual monitoring nodes are set within the three-dimensional finite element simulation region according to the coordinate set. With the target reinforcement strength as the target, the radial diffusion process of the grout in the three-dimensional finite element simulation region is simulated using the finite element method based on the grout viscosity, soil porosity parameters, and grouting parameters, and the theoretical stress values ​​of the mesh nodes are recorded in real time. Based on the theoretical stress values, the theoretical stress influence cloud map is generated using interpolation, wherein the color depth of the theoretical stress influence cloud map is positively correlated with the theoretical stress value.

[0041] Furthermore, the data acquisition module 13 is also used to perform the following methods: The original stress response data is subjected to noise filtering to obtain a first denoised sequence; outlier removal is performed on the first denoised sequence to obtain a second stable sequence; normalization is performed on the second stable sequence to output the stress response data sequence.

[0042] Furthermore, the indicator calculation module 14 is also used to perform the following method: Grid nodes whose theoretical stress values ​​exceed a preset sensitivity threshold are selected to determine the theoretical effective influence zone. Based on the stress response data sequence, the number of sensors located within the theoretical effective influence zone whose stress response data exceeds the preset sensitivity threshold is counted to obtain the number of effective response sensors. The number of effective response sensors is compared with the total number of virtual monitoring nodes within the theoretical effective influence zone to obtain the real-time slurry filling degree. Based on the stress response data sequence, the real-time stress response data of the effective response sensors is extracted, and the real-time stress uniformity is calculated based on the real-time stress response data.

[0043] Furthermore, the evaluation and judgment module 15 is also used to perform the following method: Construct a grouting effect evaluation function; input the real-time grout filling degree and the real-time stress balance degree into the grouting effect evaluation function to obtain a grouting effect score; map the grouting effect score based on a preset effect level range to determine the grouting effect level.

[0044] Furthermore, the evaluation and judgment module 15 is also used to perform the following method: When the grouting effect level is less than the preset level threshold, a grouting parameter adjustment instruction carrying the current grouting parameters is generated; the current grouting parameters are used as the initial solution to generate multiple grouting parameter combinations; grouting construction simulation is performed on the multiple grouting parameter combinations to obtain multiple simulated stress response data sequences and calculate multiple simulated grouting effect scores; global optimization is performed based on the multiple simulated grouting effect levels to establish the optimal grouting parameter combination; it is determined whether the optimal grouting parameter combination meets the preset level threshold. If it does not, the optimization range is adjusted and iterated again until convergence.

[0045] It should be noted that the order of the embodiments described above is for descriptive purposes only and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0046] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0047] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for evaluating the grouting effect in urban underground spaces based on multi-point stress response, characterized in that, The method includes: A multi-point stress sensor array is arranged in the target grouting area to obtain the layout coordinate set, and then the grouting target parameters are obtained. The grouting target parameters include the grout diffusion radius and the target reinforcement strength. Based on the set of coordinates and the grouting target parameters, the grouting process is simulated to generate a theoretical stress influence cloud map. During the grouting process, the stress response data sequence is collected in real time through the multi-point stress sensor array; Based on the stress response data sequence and the theoretical stress influence cloud map, calculate the real-time slurry filling degree and the real-time stress uniformity. Based on the real-time grout filling degree and the real-time stress balance degree, the grouting effect level is evaluated. If the grouting effect level is less than the preset level threshold, an grouting parameter adjustment command is generated.

2. The method for evaluating the grouting effect of urban underground space based on multi-point stress response as described in claim 1, characterized in that, A multi-point stress sensor array is deployed in the target grouting area, the method including: Based on the geological exploration data of the target grouting area, the distribution of weak surfaces in the surrounding rock and key stress concentration areas are determined. The weak surface distribution and key stress concentration areas are the key monitoring areas. Stress sensors are deployed in a three-dimensional grid array by drilling to form the multi-point stress sensor array. Record the three-dimensional spatial coordinates of the multi-point stress sensor array to generate the deployment coordinate set.

3. The method for evaluating the grouting effect of urban underground space based on multi-point stress response as described in claim 1, characterized in that, The grouting process was simulated to generate theoretical stress influence cloud maps. The methods included: A three-dimensional finite element simulation region is established with the grouting hole as the center and K times the grout diffusion radius as the boundary. Based on the established coordinate set, virtual monitoring nodes are set within the three-dimensional finite element simulation area; With the target reinforcement strength set as the objective, the radial diffusion process of the grout in the three-dimensional finite element simulation area is simulated using the grout viscosity, soil porosity parameters, and grouting parameters, and the theoretical stress values ​​of the mesh nodes are recorded in real time. Based on the theoretical stress value, an interpolation method is used to generate the theoretical stress influence cloud map, wherein the color depth of the theoretical stress influence cloud map is positively correlated with the theoretical stress value.

4. The method for evaluating the grouting effect of urban underground space based on multi-point stress response as described in claim 3, characterized in that, The methods for calculating real-time slurry filling degree and real-time stress uniformity include: Filter out mesh nodes whose theoretical stress values ​​exceed a preset sensitivity threshold to determine the theoretically effective influence zone; Based on the stress response data sequence, the number of sensors located within the theoretical effective influence zone whose stress response data exceeds the preset sensitivity threshold is counted to obtain the number of effective response sensors. The real-time slurry filling degree is obtained by comparing the number of effective response sensors with the total number of virtual monitoring nodes in the theoretically effective influence zone. Based on the stress response data sequence, the real-time stress response data of the effective response sensor is extracted, and the real-time stress balance is calculated based on the real-time stress response data.

5. The method for evaluating the grouting effect of urban underground space based on multi-point stress response as described in claim 4, characterized in that, Methods for evaluating the grouting effect include: Construct a function to evaluate the grouting effect; The real-time grout filling degree and the real-time stress uniformity degree are input into the grouting effect evaluation function to obtain the grouting effect score; Based on a preset effect level range, the grouting effect score is mapped to determine the grouting effect level.

6. The method for evaluating the grouting effect of urban underground space based on multi-point stress response as described in claim 5, characterized in that, The method for generating grouting parameter adjustment instructions also includes: When the grouting effect level is less than the preset level threshold, a grouting parameter adjustment command carrying the current grouting parameters is generated; Using the current grouting parameters as the initial solution, multiple combinations of grouting parameters are generated; Grouting construction simulation was performed on the multiple grouting parameter combinations to obtain multiple simulated stress response data sequences and calculate multiple simulated grouting effect scores; Based on the multiple simulated grouting effect levels, a global optimization is performed to establish the optimal combination of grouting parameters; Determine whether the optimal grouting parameter combination meets the preset level threshold. If not, adjust the optimization range and iterate again until convergence.

7. The method for evaluating the grouting effect of urban underground space based on multi-point stress response as described in claim 1, characterized in that, The acquisition of stress response data sequences also includes: The original stress response data is subjected to noise filtering to obtain the first denoised sequence; The first denoised sequence is subjected to outlier removal to obtain a second stable sequence; The stress response data sequence is output by normalizing the second stable sequence.

8. A multi-point stress response-based evaluation system for grouting effects in urban underground spaces, characterized in that, The system includes: Parameter acquisition module: A multi-point stress sensor array is arranged in the target grouting area to acquire the layout coordinate set, and then the grouting target parameters are acquired. The grouting target parameters include the grout diffusion radius and the target reinforcement strength. Grouting process simulation module: Based on the set of coordinates and the grouting target parameters, the grouting process is simulated, and a theoretical stress influence cloud map is generated; Data acquisition module: During the grouting process, the multi-point stress sensor array is used to collect stress response data sequences in real time; Index calculation module: Calculates real-time slurry filling degree and real-time stress uniformity degree based on the stress response data sequence and the theoretical stress influence cloud map; Evaluation and judgment module: Based on the real-time grout filling degree and the real-time stress balance degree, evaluate the grouting effect level. If the grouting effect level is less than the preset level threshold, generate a grouting parameter adjustment instruction.

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