Application in water conservancy project construction monitoring method

Abstract

The present invention belongs to the technical field of engineering monitoring, and relates to a method for monitoring construction of water conservancy projects, including: collecting monitoring data from a water conservancy project construction site; inputting the monitoring data into a structural stability prediction model for analysis to generate a safety factor assessment value; inputting the monitoring data into a seepage risk prediction model for analysis to generate a seepage risk probability; combining the safety factor assessment value and the seepage risk probability, presetting a threshold, and triggering a graded warning signal; in response to the graded warning signal, calling a multi-objective optimization algorithm to dynamically adjust the current construction parameters and generate an optimized construction plan; loading the optimized construction plan into a digital twin system for virtual construction simulation to obtain predicted performance parameters; comparing the deviation value of the predicted performance parameters with the measured monitoring data, and iteratively updating the parameters of the prediction model when the deviation exceeds the tolerance range. The present invention solves the problem that traditional methods are difficult to discover potential problems and make optimization adjustments before construction.

Description

Technical Field

[0001] The invention belongs to the technical field of engineering monitoring and relates to a monitoring method applied to the construction of water conservancy projects. Background Art

[0002] As an important infrastructure, the construction quality and safety of water conservancy projects are directly related to the safety of people's lives and property and the development of the national economy. During the construction of water conservancy projects, the safety and stability of the project structure face many challenges due to factors such as complex geological conditions, harsh construction environment and complex construction technology.

[0003] The traditional monitoring method for water conservancy project construction relies on manual inspections and simple monitoring equipment, which has problems such as low monitoring efficiency, inaccurate data, and untimely warnings. With the continuous expansion of the scale of water conservancy projects and the increase in construction difficulty, traditional monitoring methods can no longer meet the strict requirements of modern water conservancy projects for construction safety, quality and progress.

[0004] Based on the above problems, traditional methods lack virtual simulation means to predict the effects of construction plans after implementation, making it difficult to discover potential problems and make optimization adjustments before construction, which increases the risks in the construction process. Summary of the invention

[0005] In order to solve the problem that it is difficult to discover potential problems and make optimization adjustments before construction using traditional methods, the present invention provides a monitoring method for water conservancy project construction.

[0006] The method for monitoring water conservancy project construction includes the following steps: S1. Collect monitoring data of water conservancy project construction sites through distributed sensors, including structural deformation data, stress distribution data, crack extension data, and seepage pressure gradient data; S2. Combined with the preset structural stability prediction model, the monitoring data is input into the structural stability prediction model for analysis to generate a safety factor assessment value; S3. Combined with the preset seepage risk prediction model, the monitoring data is input into the seepage risk prediction model for analysis to generate the seepage risk probability; S4. Combine the safety factor assessment value and the seepage risk probability, pre-set the threshold value, and trigger the graded warning signal; S5. In response to the graded warning signal, a multi-objective optimization algorithm is called to dynamically adjust the current construction parameters and generate an optimized construction plan; S6. Load the optimized construction plan into the digital twin system for virtual construction simulation to obtain predicted performance parameters; S7. Compare the deviation values ​​of the predicted performance parameters with the measured monitoring data, and iteratively update the parameters of the prediction model when the deviation exceeds the tolerance range.

[0007] A further solution of the present invention, step S1, comprises the following steps: Collect structural deformation data, including three-dimensional displacement and displacement growth rate; Specifically, a Beidou monitoring station is arranged at a certain distance along the axis of the gravity dam on the top of the gravity dam to measure the cumulative amount of the three-dimensional displacement of the dam body at a certain construction stage, as well as the displacement growth rate of the dam body within a certain period of time; Collect stress distribution data and crack extension data inside concrete; Specifically, during the concrete pouring stage of the gravity dam, several groups of fiber grating sensors are arranged in a 45° cross network along the direction of the principal stress of the dam body, including strain sensors and distributed temperature measurement optical cables; Among them, the strain sensor is used to measure the stress distribution data inside the concrete, and the distributed temperature measurement optical cable is used to measure the crack expansion data inside the concrete; The osmotic pressure gradient data at different elevations are obtained by using an osmometer array; Specifically, three rows of monitoring holes are opened in the foundation corridor of the gravity dam. The row spacing of the three rows of monitoring holes is 5m, and the depth of each monitoring hole is 20m. A vibrating wire piezometer is installed in each monitoring hole at the elevation interval to calculate the seepage pressure gradient data at different elevations to meet the following formula: ; in, represents the osmotic pressure gradient, and Indicates the adjacent elevation piezometer readings, Indicates the height difference between the two piezometers.

[0008] A further solution of the present invention, step S2, comprises the following steps: The structural deformation data, stress distribution data, crack extension data, and seepage pressure gradient data collected in step S1 are combined to construct a structural stability prediction model that satisfies the following formula: ; in, is the safety factor evaluation value, is the allowable tensile strength of concrete, represents the displacement threshold, is the temperature difference threshold of adjacent nodes, , , It is a fixed value given by the laboratory and design specifications; It represents the maximum tensile stress of concrete measured by strain sensor; represents the osmotic pressure gradient, calculated from the adjacent osmometer readings; It represents the influence coefficient of osmotic pressure, which is an empirical value; It represents the cumulative value of the three-dimensional displacement of the dam body, measured by the Beidou monitoring station; Indicates displacement growth rate, the rate of change during the Beidou monitoring period; Indicates the growth rate weight coefficient, which is an empirical value; Represents the measured temperature difference between adjacent nodes, measured using distributed temperature measurement optical cables.

[0009] A further solution of the present invention, step S3, comprises the following steps: The seepage risk prediction model is constructed by combining the structural deformation data, stress distribution data, crack extension data, and seepage pressure gradient data collected in step S1, satisfying the following formula: ; in, It represents the osmotic pressure gradient, calculated from the readings of adjacent osmometers; Represents the measured temperature difference between adjacent nodes, measured by distributed temperature measurement optical cable; It represents the cumulative value of the three-dimensional displacement of the dam body, measured by the Beidou monitoring station; Indicates displacement growth rate, the rate of change during the Beidou monitoring period; Indicates the growth rate weight coefficient, which is an empirical value; represents the probability of seepage risk; represents the osmotic pressure gradient safety threshold, is the temperature difference threshold of adjacent nodes, and It is a fixed value given by the laboratory and design specifications; , , Represents the weight coefficient, which is a fixed value given by the laboratory and design specifications.

[0010] A further solution of the present invention, step S4, comprises the following steps: The threshold value of the pre-set structural safety factor evaluation value is , the threshold of seepage risk probability is ; When the structural safety factor evaluation value And the probability of seepage risk When both conditions are met, the risk is extremely high, triggering a first-level warning. Construction must be stopped immediately and emergency measures must be initiated. The dual deterioration of structural safety and seepage risk is a necessary condition for triggering a first-level warning. The emergency measures for the first-level warning are to lower the reservoir water level to below the designed safety line and open the emergency flood discharge gates; stop all construction activities and evacuate on-site personnel to a safe area; carry out temporary grouting and drainage treatment in the crack area; set up backup Beidou monitoring points and increase the frequency of data collection; install temporary anti-seepage curtains or steel sheet piles to isolate the seepage channel; notify the project command center; form an expert group to conduct on-site investigation and submit a risk assessment report within 24 hours.

[0011] A further solution of the present invention, step S4, further comprises the following steps: When the structural safety factor evaluation value or seepage risk probability If any of the conditions are met, the second-level warning will be triggered; if the structural safety factor assessment value and the seepage risk probability both trigger the second-level warning multiple times within 2 consecutive hours, it will automatically upgrade to the first-level warning; The emergency measures for the second-level warning include starting local grouting reinforcement to limit load increase; increasing the monitoring frequency of piezometers and strain sensors; simulating red warning scenarios and testing emergency response procedures; restricting entry to the construction area to only necessary personnel and setting up cordons; and preparing emergency supplies.

[0012] A further solution of the present invention, step S4, further comprises the following steps: When the seepage risk probability When regional abnormal gradient distribution occurs, a level 3 warning is triggered; For the emergency measures of the third-level warning, the data review cycle is shortened to 15 minutes / time, and on-site inspections are carried out every 2 hours; check whether the sensors are false alarms; analyze the historical data trends to determine whether they are occasional fluctuations; notify the construction team to make emergency preparations and do not adjust the construction plan for the time being.

[0013] A further solution of the present invention, step S5, comprises the following steps: Optimized construction plan for the first level warning, Rapid crack treatment: quick setting polyurethane slurry is injected into leaking cracks, and double-liquid chemical grouting is used in key areas; anti-seepage geomembrane is laid and fixed with anchoring agent; Structural adjustment and reinforcement: installing adjustable transverse steel supports in areas where displacement exceeds the limit, and the steel supports are set up to limit deformation expansion; filter layer reconstruction: replacing the damaged drainage body and backfilling graded gravel and geotextile in layers; Optimized construction plan for secondary warning, The anti-seepage system was reinforced, auxiliary grouting holes were added downstream of the existing curtain, and the water-cement ratio of cement-bentonite slurry was adjusted to 1:1; the original drainage holes were flushed with high-pressure water guns, and PVC blind ditches were added to guide the seepage; Construction parameters were adjusted, concrete pouring strength was reduced to 70% of the design value, and heavy equipment was prohibited from approaching high-risk sections; earthwork operations were avoided during rainy periods to prevent external water infiltration from exacerbating seepage; Optimized construction plan for three-level warning, The construction process was optimized, the concrete mix ratio was adjusted, 6% silica fume + 1.5% water reducer was added, and the porosity was reduced to below 12%; the layered cooling technology was used to control the temperature rise of the concrete, and the temperature rise was controlled within the range of 20°C to reduce temperature cracks; Preventive maintenance: low-pressure grouting is performed on construction joints every 30 days to fill micro-cracks; water collection wells and drainage pipes are checked once a week to remove silt and ensure drainage efficiency.

[0014] A further solution of the present invention, step S6, comprises the following steps: A three-dimensional finite element prediction model of the dam body is constructed based on a neural network. The filling or pouring units are activated layer by layer according to the construction sequence to simulate the staged construction process of the dam body and construct a steady-state displacement cloud map after the construction is completed. The level set function is used to simulate arbitrary path cracks and crack length increments. , output displacement time history curve according to time step; input random distribution of parameters such as permeability coefficient k and porosity n, and statistically analyze the probability of permeability damage; substitute the steady-state displacement cloud map, displacement time history curve, and permeability damage probability into the three-dimensional finite element prediction model to predict key performance indicators; Key performance indicators include running virtual construction simulations and outputting future prediction data, including crack growth rates , seepage prediction risk probability , dam crest displacement .

[0015] A further solution of the present invention, step S7, comprises the following steps: Compare the deviation values ​​of the predicted performance parameters with the measured monitoring data to meet the following formula: Absolute Deviation: ; Relative deviation: ; in, It represents a parameter value predicted by the virtual construction model; is the corresponding parameter value actually monitored on site; is the absolute difference between the predicted value and the measured value; is the absolute deviation as a percentage of the measured value; If the crack growth rate The relative deviation , seepage prediction risk probability The relative deviation , dam crest displacement , all conditions are met, indicating that the prediction model only records anomalies and continues to observe; If the crack growth rate The relative deviation , seepage prediction risk probability The relative deviation , dam crest displacement If any of the conditions is met, it means that the construction should be stopped immediately and the structural stability prediction model and seepage risk prediction model should be calibrated.

[0016] In summary, the present invention includes the following beneficial technical effects: 1. By triggering graded warning signals based on pre-set thresholds, an alarm can be issued in a timely manner when the construction safety risk reaches a critical point, and a multi-objective optimization algorithm can be automatically called to adjust the construction parameters and generate an optimized construction plan; this effectively avoids safety accidents caused by improper construction and significantly improves the safety of the construction process; 2. By comparing the deviation values ​​of the predicted performance parameters with the measured monitoring data, the parameters of the prediction model are iteratively updated when the deviation exceeds the tolerance range to ensure the accuracy and reliability of the prediction results. The predictive maintenance strategy helps to discover and solve potential problems before construction, reducing the number of rework and corrections during the construction process, thereby optimizing construction efficiency and reducing costs; 3. It not only realizes real-time monitoring and early warning of the construction site, but also provides scientific and reasonable suggestions for optimizing the construction plan, and predicts the construction effect through virtual construction simulation; this management system that integrates monitoring, early warning, optimization and prediction has significantly improved the construction monitoring and management level of water conservancy projects, and provided strong guarantees for the safety, quality and progress of the project. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flow chart of a method for monitoring construction of a water conservancy project is disclosed.

[0018] Figure 2 A framework schematic diagram of a water conservancy project construction monitoring system is disclosed. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0020] The following is combined with Figure 1-Figure 2 The preferred embodiments of the present invention are described in detail.

[0021] Attached Figure 1 The present invention proposes a method for monitoring water conservancy project construction, comprising the following steps: S1. Collect monitoring data of water conservancy project construction sites through distributed sensors, including structural deformation data, stress distribution data, crack extension data, and seepage pressure gradient data; S2. Combined with the preset structural stability prediction model, the monitoring data is input into the structural stability prediction model for analysis to generate a safety factor assessment value; S3. Combined with the preset seepage risk prediction model, the monitoring data is input into the seepage risk prediction model for analysis to generate the seepage risk probability; S4. Combine the safety factor assessment value and the seepage risk probability, pre-set the threshold value, and trigger the graded warning signal; S5. In response to the graded warning signal, a multi-objective optimization algorithm is called to dynamically adjust the current construction parameters and generate an optimized construction plan; S6. Load the optimized construction plan into the digital twin system for virtual construction simulation to obtain predicted performance parameters; S7. Compare the deviation values ​​of the predicted performance parameters with the measured monitoring data, and iteratively update the parameters of the prediction model when the deviation exceeds the tolerance range.

[0022] In one embodiment of the present invention, step S1 includes the following steps: 1. Collect structural deformation data, including three-dimensional displacement and displacement growth rate; Specifically, a Beidou monitoring station is arranged at a certain distance along the axis of the gravity dam on the top of the gravity dam to measure the cumulative amount of the three-dimensional displacement of the dam body at a certain construction stage, as well as the displacement growth rate of the dam body within a certain period of time; For example, one Beidou monitoring station is arranged at a distance of 20 m on the top of the gravity dam along the axis of the gravity dam; The accumulated horizontal displacement of the downstream side of the dam body during a certain construction phase was measured. ; The displacement growth rate of the dam body was measured within 3 hours. .

[0023] 2. Collect stress distribution data and crack extension data inside concrete; Specifically, during the concrete pouring stage of the gravity dam, several groups of fiber grating sensors are arranged in a 45° cross network along the direction of the principal stress of the dam body. The fiber grating sensors include strain sensors and distributed temperature measurement optical cables; Among them, the strain sensor is used to measure the stress distribution data inside the concrete, and the distributed temperature measurement optical cable is used to measure the crack expansion data inside the concrete; Exemplarily, during the concrete pouring stage, 6 groups of FBG strain sensors and distributed temperature measurement optical cables are arranged in a 45° cross network along the direction of the principal stress of the dam body, and the distributed temperature measurement optical cable includes 12 sensor nodes; The maximum tensile stress of concrete was measured 24 hours after pouring. ; Detected temperature difference of adjacent nodes of distributed temperature measurement optical cable According to the experimental determination, the threshold of the temperature difference limit of adjacent nodes is , combined with The measurement results are automatically marked as suspected crack areas.

[0024] 3. Use osmometer array to obtain osmotic pressure gradient data at different elevations; Specifically, three rows of monitoring holes are opened in the foundation corridor of the gravity dam. The row spacing of the three rows of monitoring holes is 5m, and the depth of each monitoring hole is 20m. A vibrating wire piezometer is installed in each monitoring hole at the elevation interval to calculate the seepage pressure gradient data at different elevations to meet the following formula: ; in, represents the osmotic pressure gradient, and Indicates the adjacent elevation piezometer readings, It indicates the height difference between two piezometers; For example, the piezometer reading installed at 150m above the gravity dam is , the piezometer reading installed at the gravity dam elevation of 145m At this time, the height difference between the two piezometers is , substitute it into the formula and calculate, ; In one embodiment of the present invention, step S2 comprises the following steps: The structural deformation data, stress distribution data, crack extension data, and seepage pressure gradient data collected in step S1 are combined to construct a structural stability prediction model that satisfies the following formula: ; in, is the safety factor evaluation value, is the allowable tensile strength of concrete, represents the displacement threshold, is the temperature difference threshold of adjacent nodes, , , It is a fixed value given by the laboratory and design specifications; It represents the maximum tensile stress of concrete measured by strain sensor; represents the osmotic pressure gradient, calculated from the adjacent osmometer readings; It represents the influence coefficient of osmotic pressure, which is an empirical value; It represents the cumulative value of the three-dimensional displacement of the dam body, measured by the Beidou monitoring station; Indicates displacement growth rate, the rate of change during the Beidou monitoring period; Indicates the growth rate weight coefficient, which is an empirical value; Represents the measured temperature difference between adjacent nodes, measured using distributed temperature measurement optical cables.

[0025] For example, set the parameters allowed by concrete materials , , ; ; ; The measured data, , , , ; ; Substituting into the formula, we can calculate: .

[0026] In one embodiment of the present invention, step S3 includes the following steps: The seepage risk prediction model is constructed by combining the structural deformation data, stress distribution data, crack extension data, and seepage pressure gradient data collected in step S1, satisfying the following formula: ; in, It represents the osmotic pressure gradient, calculated from the readings of adjacent osmometers; Represents the measured temperature difference between adjacent nodes, measured by distributed temperature measurement optical cable; It represents the cumulative value of the three-dimensional displacement of the dam body, measured by the Beidou monitoring station; Indicates displacement growth rate, the rate of change during the Beidou monitoring period; Indicates the growth rate weight coefficient, which is an empirical value; represents the osmotic pressure gradient safety threshold, is the temperature difference threshold of adjacent nodes, and It is a fixed value given by the laboratory and design specifications; , , Represents the weight coefficient, which is a fixed value given by the laboratory and design specifications.

[0027] In one embodiment of the present invention, step S4 comprises the following steps: The threshold value of the pre-set structural safety factor evaluation value is , the threshold of seepage risk probability is ; When the structural safety factor evaluation value And the probability of seepage risk When both conditions are met, the risk is extremely high, triggering a first-level warning. Construction must be stopped immediately and emergency measures must be initiated. The dual deterioration of structural safety and seepage risk is a necessary condition for triggering a first-level warning. The emergency measures for the first-level warning are to lower the reservoir water level to below the designed safety line and open the emergency flood discharge gate; stop all construction activities and evacuate on-site personnel to a safe area; carry out temporary grouting (quick-setting materials) and drainage treatment in the crack area; Set up spare Beidou monitoring points and increase the frequency of data collection (1 time / minute); install temporary anti-seepage curtains or steel sheet piles to isolate the seepage channel; notify the project command center; form an expert group to conduct on-site inspection and submit a risk assessment report within 24 hours.

[0028] When the structural safety factor evaluation value or seepage risk probability If any of the conditions are met, the second-level warning will be triggered; if the structural safety factor assessment value and the seepage risk probability both trigger the second-level warning multiple times within 2 consecutive hours, it will automatically upgrade to the first-level warning; The emergency measures for the second-level warning include starting local grouting reinforcement to limit the increase in load; increasing the monitoring frequency of piezometers and strain sensors (5 minutes / time); simulating red warning scenarios and testing emergency response procedures; restricting access to the construction area to only necessary personnel and setting up cordons; and preparing emergency supplies (sandbags, drainage pumps, etc.).

[0029] When the seepage risk probability Regional abnormal gradient distribution (adjacent monitoring points When the difference is greater than 0.3), a level 3 warning is triggered; The emergency measures for the third-level warning are as follows: the data review cycle is shortened to 15 minutes per time, and on-site inspections are conducted every 2 hours; check whether the sensors have false alarms (such as fiber breakage, electromagnetic interference); analyze historical data trends to determine whether they are occasional fluctuations; notify the construction team to make emergency preparations and not adjust the construction plan for the time being.

[0030] In one embodiment of the present invention, step S5 comprises the following steps: Optimized construction plan for the first level warning, Rapid crack treatment, inject quick-setting polyurethane slurry into leaking cracks, and use double-liquid chemical grouting in key areas; lay anti-seepage geomembrane (thickness ≥ 1.5mm) and fix it with anchoring agent; Structural adjustment and reinforcement: install adjustable transverse steel supports (spacing ≤3m) in areas where displacement exceeds the limit. The steel supports are installed to limit the expansion of deformation. Reconstruct the filter layer, that is, replace the damaged drainage body and backfill with graded gravel and geotextile in layers.

[0031] Optimized construction plan for secondary warning, The anti-seepage system was reinforced, and auxiliary grouting holes were added downstream of the existing curtain, and the water-cement ratio of cement-bentonite slurry was adjusted to 1:1; the original drainage holes were flushed with high-pressure water guns, and PVC blind ditches (200mm in diameter) were added to guide the seepage; The construction parameters were adjusted, the concrete pouring strength was reduced to 70% of the design value, and heavy equipment was prohibited from approaching high-risk sections; earthwork operations were avoided during rainy periods to prevent the infiltration of external water and aggravated seepage.

[0032] Optimized construction plan for three-level warning, The construction process was optimized, the concrete mix ratio was adjusted, 6% silica fume + 1.5% water reducer was added, and the porosity was reduced to below 12%; the layered cooling technology was used to control the temperature rise of the concrete, and the temperature rise was controlled within the range of 20°C to reduce temperature cracks; Preventive maintenance: low-pressure grouting is performed on construction joints every 30 days to fill micro-cracks; water collection wells and drainage pipes are checked once a week to remove silt and ensure drainage efficiency.

[0033] In one embodiment of the present invention, step S6 comprises the following steps: A three-dimensional finite element prediction model of the dam body is constructed based on a neural network. The filling or pouring units are activated layer by layer according to the construction sequence to simulate the staged construction process of the dam body and construct a steady-state displacement cloud map after the construction is completed. The level set function is used to simulate arbitrary path cracks and crack length increments. , output displacement time history curve according to time step; input random distribution of parameters such as permeability coefficient k and porosity n, and statistically analyze the probability of permeability damage; substitute the steady-state displacement cloud map, displacement time history curve and permeability damage probability into the three-dimensional finite element prediction model to predict key performance indicators; Key performance indicators include running virtual construction simulations and outputting future prediction data, including crack growth rates , seepage prediction risk probability , dam crest displacement .

[0034] In one embodiment of the present invention, step S7 includes the following steps: Compare the deviation values ​​of the predicted performance parameters with the measured monitoring data to meet the following formula: Absolute Deviation: ; Relative deviation: ; in, Indicates a parameter value predicted by the virtual construction model (including crack growth rate, seepage prediction risk probability, dam crest displacement); is the corresponding parameter value actually monitored on site; is the absolute difference between the predicted value and the measured value; is the absolute deviation as a percentage of the measured value; If the crack growth rate The relative deviation , seepage prediction risk probability The relative deviation , dam crest displacement , all conditions are met, indicating that the prediction model only records anomalies and continues to observe; If the crack growth rate The relative deviation , seepage prediction risk probability The relative deviation , dam crest displacement If any of the conditions is met, it means that the construction should be stopped immediately and the structural stability prediction model and seepage risk prediction model should be calibrated.

[0035] Attached Figure 2 , the present invention also proposes to be applied to a water conservancy project construction monitoring system, including: a monitoring data acquisition module, a safety factor evaluation value generation module, a seepage risk probability generation module, a graded warning signal triggering module, a construction scheme optimization module, a prediction performance parameter generation module, and a prediction model updating module; Monitoring data acquisition module, used to collect monitoring data at the construction site of water conservancy projects, including structural deformation data, stress distribution data, crack extension data, and seepage pressure gradient data; A safety factor evaluation value generation module is used to input monitoring data into a structural stability prediction model for analysis and generate a safety factor evaluation value; A seepage risk probability generation module is used to input monitoring data into a seepage risk prediction model for analysis and generate a seepage risk probability; The graded warning signal triggering module combines the safety factor assessment value and the seepage risk probability to preset a threshold value for triggering the graded warning signal; The construction plan optimization module, in response to the graded warning signal, calls the multi-objective optimization algorithm to dynamically adjust the current construction parameters to generate an optimized construction plan; The predicted performance parameter generation module is used to load the optimized construction plan into the digital twin system for virtual construction simulation to obtain the predicted performance parameters; The prediction model update module compares the deviation between the prediction performance parameters and the measured monitoring data, and iteratively updates the parameters of the prediction model when the deviation exceeds the tolerance range.

[0036] The modules described above may be implemented in whole or in part through software, hardware, or a combination thereof, supporting processors embedded in or independent of a computer device in hardware form, and also supporting memories stored in a computer device in software form, so that the processor can call and execute operations corresponding to the modules described above.

[0037] It should be noted that the user information (including but not limited to user device information and personal information, etc.) and data (including but not limited to data used for analysis, stored data and displayed data, etc.) involved in the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards.

[0038] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention is described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for monitoring water conservancy project construction, characterized in that: The following steps are involved: S1. Collect monitoring data of water conservancy project construction sites through distributed sensors, including structural deformation data, stress distribution data, crack extension data, and seepage pressure gradient data; S2. Combined with the preset structural stability prediction model, the monitoring data is input into the structural stability prediction model for analysis to generate a safety factor assessment value; S3. Combined with the preset seepage risk prediction model, the monitoring data is input into the seepage risk prediction model for analysis to generate the seepage risk probability; S4. Combine the safety factor assessment value and the seepage risk probability, pre-set the threshold value, and trigger the graded warning signal; S5. In response to the graded warning signal, a multi-objective optimization algorithm is called to dynamically adjust the current construction parameters and generate an optimized construction plan; S6. Load the optimized construction plan into the digital twin system for virtual construction simulation to obtain predicted performance parameters; S7. Compare the deviation values ​​of the predicted performance parameters with the measured monitoring data, and iteratively update the parameters of the prediction model when the deviation exceeds the tolerance range.

2. The method for monitoring water conservancy project construction according to claim 1, characterized in that: Step S1 includes the following steps: Collect structural deformation data, including three-dimensional displacement and displacement growth rate; Specifically, a Beidou monitoring station is arranged at a certain distance along the axis of the gravity dam on the top of the gravity dam to measure the cumulative amount of the three-dimensional displacement of the dam body at a certain construction stage, as well as the displacement growth rate of the dam body within a certain period of time; Collect stress distribution data and crack extension data inside concrete; Specifically, during the concrete pouring stage of the gravity dam, several groups of fiber grating sensors are arranged in a 45° cross network along the direction of the principal stress of the dam body, including strain sensors and distributed temperature measurement optical cables; Among them, the strain sensor is used to measure the stress distribution data inside the concrete, and the distributed temperature measurement optical cable is used to measure the crack expansion data inside the concrete; The osmotic pressure gradient data at different elevations are obtained by using an osmometer array; Specifically, three rows of monitoring holes are opened in the foundation corridor of the gravity dam. The row spacing of the three rows of monitoring holes is 5m, and the depth of each monitoring hole is 20m. A vibrating wire piezometer is installed in each monitoring hole at the elevation interval to calculate the seepage pressure gradient data at different elevations to meet the following formula: ； in, represents the osmotic pressure gradient, and Indicates the adjacent elevation piezometer readings, Indicates the height difference between the two piezometers.

3. The method for monitoring water conservancy project construction according to claim 2, characterized in that: Step S2 includes the following steps: The structural deformation data, stress distribution data, crack extension data, and seepage pressure gradient data collected in step S1 are combined to construct a structural stability prediction model that satisfies the following formula: ； in, is the safety factor evaluation value, is the allowable tensile strength of concrete, represents the displacement allowable threshold, is the temperature difference threshold of adjacent nodes, , , It is a fixed value given by the laboratory and design specifications; It represents the maximum tensile stress of concrete measured by strain sensor; represents the osmotic pressure gradient, calculated from the adjacent osmometer readings; It represents the influence coefficient of osmotic pressure, which is an empirical value; It represents the cumulative value of the three-dimensional displacement of the dam body, measured by the Beidou monitoring station; Indicates displacement growth rate, the rate of change during the Beidou monitoring period; Indicates the growth rate weight coefficient, which is an empirical value; Represents the measured temperature difference between adjacent nodes, measured using distributed temperature measurement optical cables.

4. The method for monitoring water conservancy project construction according to claim 3 is characterized in that: Step S3 includes the following steps: The seepage risk prediction model is constructed by combining the structural deformation data, stress distribution data, crack extension data, and seepage pressure gradient data collected in step S1, satisfying the following formula: ； in, It represents the osmotic pressure gradient, calculated from the readings of adjacent osmometers; Represents the measured temperature difference between adjacent nodes, measured by distributed temperature measurement optical cable; It represents the cumulative value of the three-dimensional displacement of the dam body, measured by the Beidou monitoring station; Indicates displacement growth rate, the rate of change during the Beidou monitoring period; Indicates the growth rate weight coefficient, which is an empirical value; represents the probability of seepage risk; represents the osmotic pressure gradient safety threshold, is the temperature difference threshold of adjacent nodes, and It is a fixed value given by the laboratory and design specifications; , , Represents the weight coefficient, which is a fixed value given by the laboratory and design specifications.

5. The method for monitoring water conservancy project construction according to claim 4 is characterized in that: Step S4 includes the following steps: The threshold value of the pre-set structural safety factor evaluation value is , the threshold of seepage risk probability is ; When the structural safety factor evaluation value And the probability of seepage risk When both conditions are met, the risk is extremely high, triggering a first-level warning. Construction must be stopped immediately and emergency measures must be initiated. The dual deterioration of structural safety and seepage risk is a necessary condition for triggering a first-level warning. The emergency measures for the first-level warning are to lower the reservoir water level to below the designed safety line and open the emergency flood discharge gates; stop all construction activities and evacuate on-site personnel to a safe area; carry out temporary grouting and drainage treatment in the crack area; set up backup Beidou monitoring points and increase the frequency of data collection; install temporary anti-seepage curtains or steel sheet piles to isolate the seepage channel; notify the project command center; form an expert group to conduct on-site investigation and submit a risk assessment report within 24 hours.

6. The method for monitoring water conservancy project construction according to claim 5, characterized in that: Step S4 further includes the following steps: When the structural safety factor evaluation value or seepage risk probability If any of the conditions are met, the second-level warning will be triggered; if the structural safety factor assessment value and the seepage risk probability both trigger the second-level warning multiple times within 2 consecutive hours, it will automatically upgrade to the first-level warning; The emergency measures for the second-level warning include starting local grouting reinforcement to limit load increase; increasing the monitoring frequency of piezometers and strain sensors; simulating red warning scenarios and testing emergency response procedures; restricting entry to the construction area to only necessary personnel and setting up cordons; and preparing emergency supplies.

7. The method for monitoring water conservancy project construction according to claim 6, characterized in that: Step S4 further includes the following steps: When the seepage risk probability When regional abnormal gradient distribution occurs, a level 3 warning is triggered; The emergency measures for the third-level warning are as follows: the data review cycle is shortened to 15 minutes per time, and on-site inspections are conducted every 2 hours; check whether the sensors are reporting false alarms; analyze historical data trends to determine whether they are occasional fluctuations; notify the construction team to make emergency preparations and not adjust the construction plan for the time being.

8. The method for monitoring water conservancy project construction according to claim 7, characterized in that: Step S5 includes the following steps: Optimized construction plan for the first level warning, Rapid crack treatment: quick setting polyurethane slurry is injected into leaking cracks, and double-liquid chemical grouting is used in key areas; anti-seepage geomembrane is laid and fixed with anchoring agent; Structural adjustment and reinforcement: installing adjustable transverse steel supports in areas where displacement exceeds the limit, and the steel supports are set up to limit deformation expansion; filter layer reconstruction: replacing the damaged drainage body and backfilling graded gravel and geotextile in layers; Optimized construction plan for secondary warning, The anti-seepage system was reinforced, auxiliary grouting holes were added downstream of the existing curtain, and the water-cement ratio of cement-bentonite slurry was adjusted to 1:1; the original drainage holes were flushed with high-pressure water guns, and PVC blind ditches were added to guide the seepage; Construction parameters were adjusted, concrete pouring strength was reduced to 70% of the design value, and heavy equipment was prohibited from approaching high-risk sections; earthwork operations were avoided during rainy periods to prevent external water infiltration from exacerbating seepage; Optimized construction plan for three-level warning, The construction process was optimized, the concrete mix ratio was adjusted, 6% silica fume + 1.5% water reducer was added, and the porosity was reduced to below 12%; the layered cooling technology was used to control the temperature rise of the concrete, and the temperature rise was controlled within the range of 20°C to reduce temperature cracks; Preventive maintenance: low-pressure grouting is performed on construction joints every 30 days to fill micro-cracks; water collection wells and drainage pipes are checked once a week to remove silt and ensure drainage efficiency.

9. The method for monitoring water conservancy project construction according to claim 7, characterized in that: Step S6 includes the following steps: A three-dimensional finite element prediction model of the dam body is constructed based on a neural network. The filling or pouring units are activated layer by layer according to the construction sequence to simulate the staged construction process of the dam body and construct a steady-state displacement cloud map after the construction is completed. The level set function is used to simulate arbitrary path cracks and crack length increments. , output displacement time history curve according to time step; input random distribution of parameters such as permeability coefficient k and porosity n, and statistically analyze the probability of permeability damage; substitute the steady-state displacement cloud map, displacement time history curve, and permeability damage probability into the three-dimensional finite element prediction model to predict key performance indicators; Key performance indicators include running virtual construction simulations and outputting future prediction data, including crack growth rates , seepage prediction risk probability , dam crest displacement .

10. The method for monitoring water conservancy project construction according to claim 9, characterized in that: Step S7 includes the following steps: Compare the deviation values ​​of the predicted performance parameters with the measured monitoring data to meet the following formula: Absolute Deviation: ; Relative deviation: ; in, It represents a parameter value predicted by the virtual construction model; is the corresponding parameter value actually monitored on site; is the absolute difference between the predicted value and the measured value; is the absolute deviation as a percentage of the measured value; If the crack growth rate The relative deviation , seepage prediction risk probability The relative deviation , dam crest displacement , all conditions are met, indicating that the prediction model only records anomalies and continues to observe; If the crack growth rate The relative deviation , seepage prediction risk probability The relative deviation , dam crest displacement If any of the conditions is met, it means that the construction should be stopped immediately and the structural stability prediction model and seepage risk prediction model should be calibrated.

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