Wharf storage yard settlement control method

Abstract

The invention discloses a wharf storage yard settlement control method. The method comprises the following steps: S1, planning a monitoring point position in a storage yard, real-time ground settlement volume and settlement volume and pore water pressure of each soil layer at a corresponding position; s2, dividing the storage yard into a plurality of settlement control areas; s3, obtaining historical meteorological data of the storage yard, ground surface settlement volume data in each settlement control area, settlement volume data of each soil layer, pore water pressure and historical loading records, and constructing a storage yard settlement volume prediction model; s4, ground surface settlement prediction values of all the settlement control areas are obtained, and after the ground surface settlement prediction values are compared with the ground surface settlement control values, the settlement control areas to be reinforced are determined; and S5, obtaining a predicted value of the settlement amount of the soil layer in the to-be-reinforced settlement control area, designing a soil layer grouting scheme, and performing grouting enhancement. According to the method, through a dynamic partition monitoring and accurate response technology, the real-time performance and the coverage performance of settlement control of the wharf storage yard are remarkably improved, and the control purpose of accurate grouting of the soil layer is achieved.

Description

Technical Field

[0001] The present invention relates to the field of foundation settlement treatment. More specifically, the present invention relates to a method for controlling the settlement of a wharf yard. Background Art

[0002] As the core area for cargo loading, unloading and storage, the wharf yard is long-term affected by the stacking of materials and containers. Under the influence of continuous load, periodic surcharge, rainfall, groundwater level and other natural factors, the foundation soil is prone to uneven settlement problems. In the wharf yard projects located on the coast, complex geological conditions such as soft soil and backfill soil further exacerbate the risk of soil compression and deformation, resulting in local ground subsidence, crack expansion and instability of facility foundations. Such settlement not only threatens the structural safety of the yard, but may also affect the loading and unloading efficiency and even cause operation interruption. It is urgent to ensure long-term stability through effective control means. Currently, settlement monitoring and treatment technologies mostly rely on fixed monitoring points and static data analysis, making it difficult to capture local settlement mutations in a timely manner, with late warning. In addition, traditional methods have insufficient ability to synergistically analyze multi-source parameters and cannot accurately predict the settlement trend. Treatment measures often start after significant settlement occurs, missing the opportunity for early intervention.

[0003] Currently, in the treatment technologies for foundation settlement, some projects begin to predict settlement based on the yard load and reinforce the foundation according to the prediction results. However, the existing settlement prediction models mostly rely on a single parameter such as load intensity or surface settlement data, ignoring the influence of the coupling effect of multiple factors. During the process of foundation settlement, changes in meteorological conditions such as precipitation and temperature will change the soil moisture content and pore water pressure, and the differences in permeability and compression modulus of layered soils will also lead to increased differential settlement. However, traditional models are difficult to effectively integrate these dynamic parameters, with low utilization rate of historical data, and do not fully utilize data such as surcharge records and layered settlement to construct a comprehensive sample set, resulting in insufficient prediction accuracy, especially with significant errors under extreme weather or sudden load conditions.

[0004] The limitations of the existing foundation settlement control methods are not only reflected in insufficient prediction accuracy, but also in the difficulty of realizing closed-loop management from monitoring to intervention. Traditional methods rely on discrete monitoring points in the data collection stage, making it difficult to comprehensively capture the layered deformation characteristics inside the soil mass; in the analysis stage, there is a lack of effective integration of multi-source heterogeneous data, resulting in a single model input parameter and unable to accurately reflect the dynamic evolution law of soil mechanical behavior, while in the execution stage, it is difficult to achieve differential and precise reinforcement. For example, in the geological conditions of interbedded soft soil and sandy soil, the differences in compression modulus and permeability of different soil layers are significant. If a unified grouting diffusion radius and grouting volume are adopted, it may cause over-reinforcement of shallow soil and insufficient reinforcement of deep soil, and even trigger the risk of secondary settlement.

[0005] Therefore, there is an urgent need to construct a systematic method that integrates multi-dimensional monitoring data, dynamically predicts the settlement trend, and optimizes the control strategy in real time. This method should make full use of the acquired real-time data to construct a settlement prediction model, design differential grouting parameters for different risk areas, and through the closed-loop linkage between the prediction model and the execution system, accurately identify weak soil layers at the budding stage of settlement, realizing the integration of "monitoring - prediction - control", thereby improving the reinforcement efficiency, reducing the project cost, and minimizing the risk of operation interruption to the greatest extent. Summary of the Invention

[0006] An object of the present invention is to provide a method for controlling the settlement of a wharf yard, which integrates multi-dimensional monitoring data, dynamically predicts the settlement trend, and precisely performs stratum grouting to accurately identify weak soil layers at the budding stage of settlement, realizing the integration of "monitoring - prediction - control", thereby improving the reinforcement efficiency, reducing the project cost, and minimizing the risk of operation interruption to the greatest extent.

[0007] To achieve these and other advantages of the present invention, the present invention provides a method for controlling the settlement of a wharf yard, including the following steps: S1. Plan the monitoring points in the yard. Each monitoring point can at least obtain the surface settlement amount at this position in real time, the settlement amounts of each soil layer at the corresponding position, and the pore water pressure. S2. Based on the positions of the monitoring points, divide the yard into several settlement control areas, with at least one monitoring point in each settlement control area. S3. Obtain the historical meteorological data of the yard, the surface settlement amount data, the settlement amount data of each soil layer, the pore water pressure of each soil layer, and the historical stacking record in each settlement control area. Use the above data as sample set data to construct a yard settlement amount prediction model. S4. Use the meteorological information, stacking plan in the next cycle, and real-time data collected by the monitoring points to obtain the predicted surface settlement values of each settlement control area. After comparing with the surface settlement control values, confirm the settlement control areas to be reinforced. S5. Obtain the predicted settlement amount values of each soil layer in the settlement control area to be reinforced, and obtain the soil compression modulus of this soil layer. Design a soil layer grouting plan and perform grouting enhancement.

[0008] Preferably, in step S1, the monitoring points include surface settlement monitoring points, settlement gauges, and several pore water pressure gauges arranged close to each other. Among them, the settlement gauges are buried in the soil body to collect the settlement amount data of each soil layer below the corresponding position in real time. The pore water pressure gauges are arranged in each soil layer from top to bottom to collect the pore water pressure of each soil layer in the corresponding settlement control area in real time.

[0009] Preferably, in step S2, taking each monitoring point as the generation base point of the Thiessen polygon, a closed polygon area is constructed by calculating the perpendicular bisectors between adjacent monitoring points, so that the distance from any position within each Thiessen polygon area to its corresponding monitoring point is less than the distance to other monitoring points. Each Thiessen polygon area contains only one monitoring point, and the monitoring point is located at the geometric center of the polygon area.

[0010] Preferably, step S3 includes the following steps: S31. According to the monitoring records of the meteorological stations around the yard, the obtained meteorological data includes precipitation and temperature; S32. According to the stacking load information in the yard, obtain the stacking loads in each settlement control area in each time period; S33. Obtain the surface settlement amounts, the settlement amounts of each soil layer, and the initial pore water pressures of each soil layer in each time period in each settlement control area; S34. Use the meteorological data, the stacking loads, the initial pore water pressures of each soil layer, the settlement amounts of each soil layer, and the surface settlement amounts in each time period as sample set data to construct a yard settlement amount prediction model.

[0011] Preferably, the yard settlement amount prediction model adopts a BP neural network including an input layer, a hidden layer, and an output layer. The input layer of the BP neural network model includes multiple input neuron nodes, and multiple hidden neuron nodes are arranged on the hidden layer. Each input neuron node is respectively connected to each hidden neuron node; the hidden neuron nodes are all connected to the output layer. Taking the meteorological data, the stacking load, and the initial pore water pressures of each soil layer as the input values of the input neuron nodes, comparing the output results with the measured settlement amounts of each soil layer and the surface settlement amount, and optimizing the BP neural network model through the algorithm to obtain the yard settlement amount prediction model.

[0012] Preferably, the method for optimizing the BP neural network by the algorithm includes: A1: Taking the meteorological data including precipitation and temperature, the initial pore water pressures of each soil layer, and the stacking load as influencing factors, taking the total number of parameter types as the number of neuron nodes m, the settlement amounts of each soil layer and the surface settlement amount as the output value c, and the number of hidden layer nodes c1 is , where a is a random constant between 1 and 10; A2. Perform normalization processing on the sample set data, and its mathematical expression is: Among them, represents the sample data of the influencing factor, , are respectively the minimum value and the maximum value in the sample data. The influencing factor data after dimensionless processing; A3. Initialize the mapping relationship between the population particles and the weights and thresholds of the BP neural network, including particle dimension, initial velocity, population size, learning factor, and inertia weight; A4. Input the normalized input variables and output variables into the BP neural network model, calculate the fitness function value of the particle, and obtain the historical optimal fitness and global fitness of the particle. The fitness function value of the particle is the mean square error of the calculation result, and its function expression is: Where, represents the predicted value of the i-th sample, is the true value of the i-th sample, and n is the total number of calculation results of the neural network; A5. Perform iterative calculation on the particle fitness, update the historical optimal fitness and the global fitness according to the preset update conditions until the preset iteration end condition is satisfied; A6. Update the weights and thresholds of the BP neural network model to obtain an optimized BP neural network model for calculating the settlement of the wharf yard.

[0013] Preferably, step S5 includes the following steps: S51. Conduct static cone penetration tests on the settlement control area to be strengthened to obtain the thickness h i and compression modulus E i and other data of each soil layer; S52. Calculate the settlement contribution ratio of each soil layer according to the predicted settlement value S i of each soil layer in the yard settlement prediction model. The formula for the settlement contribution ratio of the soil layer is: , accumulate the contribution ratios of each soil layer from large to small, and select the soil layer with a cumulative contribution ≥ 70% as the grouting target layer; S53. Obtain the difference L1 between the predicted surface settlement value L and the surface settlement control value, and obtain the corresponding settlement adjustment value L i according to the settlement amount ratio of each selected grouting target layer in all grouting target layers; S54. Back-calculate the target compression modulus E i ’ of the grouting target layer. The calculation formula is ; S55. Obtain the grout modulus E g of the grouting material, the grout modulus ratio , and calculate the grouting volume ratio of the grouting target layer; S56. Design the grouting plan in the settlement control area to be strengthened according to the calculation results and conduct grouting.

[0014] Preferably, step S56 includes the following steps: S561. Calculate the area S of the settlement control area to be reinforced a , and calculate the total grouting volume ; S562. Calculate the effective diffusion radius R of the grouting material in the soil layer according to the characteristics of the grouting material; S563. Calculate the grouting volume of a single hole , where b is the slurry filling rate and n is the soil layer porosity. For obtaining the soil layer porosity, sampling and analysis can be carried out near the static cone penetration test points; S564. Obtain the number N of grouting holes and the designed hole positions in the settlement control area to be reinforced; S565. Carry out grouting according to the design results.

[0015] Preferably, the calculation formula for the effective diffusion radius R is , where P is the designed grouting pressure, P0 is the pore water pressure of the grouting target layer, t is the grouting time of a single hole, k is the permeability coefficient of the grouting target layer , and

[0016] The present invention has at least the following beneficial effects: First, through the dynamic zoning monitoring and precise response technology, the present invention significantly improves the real-time performance and coverage of the settlement control of the wharf yard. Based on the divided settlement control areas, the surface settlement, layered settlement and pore water pressure data are collected in real time with the monitoring points as the center. Combining with the meteorological data and the stacking plan, a prediction model is formed, and the reinforcement area is determined according to the prediction results to achieve precise soil layer grouting.

[0017] Second, the present invention proposes a targeted grouting technology based on the soil layer contribution ratio, realizing the efficient utilization of resources and scientific decision-making. By analyzing the settlement contribution ratio of each soil layer, grouting is preferentially carried out on the key layers with larger cumulative contributions, and the target compression modulus is inversely calculated by combining the predicted settlement difference to dynamically design the grouting operation. This method avoids the problems of insufficient reinforcement or over-reinforcement caused by traditional empirical operations, and reduces the cost of foundation treatment materials.

[0018] Other advantages, objectives and features of the present invention will be partially reflected by the following description and partially understood by those skilled in the art through the research and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flowchart of a method for controlling the settlement of a wharf yard in a technical solution of the present invention; Figure 2 is a flowchart of a method for formulating the design of soil body grouting in a technical solution of the present invention; Figure 3 Schematic diagram of the installation of monitoring equipment at the monitoring point in an embodiment of the present invention; Figure 4 Schematic diagram of the zoning of the settlement control area in an embodiment of the present invention. Detailed implementation manners

[0020] The following further elaborates the present invention in detail in conjunction with the accompanying drawings and specific implementation manners, so that those skilled in the art can implement it according to the text of the specification.

[0021] It should be understood that terms such as "having", "comprising", and "including" used herein do not exclude the presence or addition of one or more other elements or their combinations.

[0022] It should be noted that the experimental methods described in the following implementation schemes are all conventional methods unless otherwise specified, and the reagents and materials can be obtained from commercial channels unless otherwise specified; in the description of the present invention, it should be noted that unless otherwise clearly defined and limited, the terms "installation", "connection", and "setting" should be understood in a broad sense. For example, it can be fixedly connected and set, or detachably connected and set, or integrally connected and set. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations. The orientation or positional relationship indicated by terms such as "lateral", "longitudinal", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention.

[0023] As Figures 1-4 shown, the present invention provides a method for controlling the settlement of a wharf yard, including the following steps: S1. Plan the monitoring points in the yard. Each monitoring point can at least obtain the surface settlement at that location, the settlement of each soil layer at the corresponding location, and the pore water pressure in real time. Specifically, each monitoring point can at least obtain the surface settlement at that location, the settlement of each corresponding soil layer, and the pore water pressure in real time. The monitoring points need to be arranged in key locations such as soft soil areas, backfilled areas, load concentration areas, and soil layer interfaces in combination with the geological exploration report and the load distribution map to ensure the representativeness of spatial coverage. Each monitoring point needs to integrate three types of sensors: the surface settlement monitor measures the vertical displacement of the surface in real time, and the settlement value of the point can be accurately obtained through a level or a GNSS receiver; the multi-point settlement gauge can use a magnetic ring settlement pipe, which is buried according to the soil layer depth, and the compression amount of each soil layer is recorded by magnetic induction; the pore water pressure gauge can use a vibrating wire sensor, which is installed in different soil layers in layers to monitor the dynamic change of the pore water pressure.

[0024] S2. Based on the location of the monitoring points, divide the yard into several settlement control areas, with at least one monitoring point in each settlement control area. Specifically, the zoning needs to be carried out based on the actual distribution of the monitoring points. The Thiessen polygon can be used for the plane division algorithm. Through zoning, the yard can be divided into multiple independent units, and the monitoring data in each unit can more accurately represent the settlement of the area.

[0025] S3. Obtain the historical meteorological data of the yard, the surface settlement data, the settlement data of each soil layer, the pore water pressure of each soil layer, the historical load record in each settlement control area. The above data are used as sample set data to construct a yard settlement prediction model. Specifically, by integrating multi-source historical and real-time data, a multi-parameter prediction model that can comprehensively reflect the yard settlement mechanism is constructed. The historical meteorological data are the time series data such as precipitation, temperature, and humidity obtained from the meteorological stations around the yard. The settlement monitoring data are the surface settlement in each settlement control area, the soil layer compression recorded by the multi-point settlement gauge, and the pore water pressure value monitored by the pore water pressure gauge. The data sampling frequency can be once a day. The historical load record is the load type, distribution location, duration, and weight information in the yard operation log, which is quantified into the relationship between the load and time in a spatial grid. It is necessary to normalize the multi-source heterogeneous data.

[0026] S4. Use the meteorological information, surcharge plan, and real-time data collected at the monitoring points in the next cycle to obtain the predicted surface settlement values for each settlement control area. After comparing with the surface settlement control values, identify the settlement control areas to be strengthened. Specifically, through dynamic prediction and threshold comparison, real-time identify the settlement risk areas beyond the safe range, providing a decision-making basis for targeted strengthening. First, integrate the meteorological forecast data, surcharge plan, and the settlement amounts and pore water pressure data collected in real time at each monitoring point in the next cycle, and input them into the prediction model constructed in S3 to generate the predicted surface settlement values for each settlement control area in the future. Subsequently, compare the predicted values with the preset surface settlement control values area by area, and screen out the control areas with over-limit predicted values or abnormal trends and mark them as the areas to be strengthened.

[0027] S5. Obtain the predicted soil layer settlement values for each soil layer in the settlement control area to be strengthened, and obtain the soil compression modulus of the soil layer. Design the soil layer grouting plan and carry out grouting reinforcement.

[0028] In this technical solution, through the dynamic zoning monitoring and precise response technology, the real-time performance and coverage of the settlement control of the wharf yard are significantly improved. Based on the divided settlement control areas, the surface settlement, layered settlement, and pore water pressure data are collected in real time with the monitoring points as the center. Combining the meteorological data and the surcharge plan, a prediction model is formed, and the reinforcement area is determined according to the prediction results to achieve precise soil layer grouting.

[0029] In another technical solution, in step S1, the monitoring points include surface settlement monitoring points, layered settlement gauges, and several pore water pressure gauges arranged closely. Among them, the layered settlement gauges are buried in the soil body to collect the soil layer settlement amount data of each soil layer below the corresponding position in real time. The pore water pressure gauges are arranged in each soil layer from top to bottom to collect the pore water pressure of each soil layer in the settlement control area in real time. In this technical solution, the magnetic ring type settlement measuring instrument can be used for the layered settlement gauge. Through the drilling and hole-leading method, the layered settlement pipe pre-installed with magnetic rings is vertically buried into the soil body. The magnetic rings and positioning rings are sleeved on the settlement pipe according to the designed depth, and gradually lowered to the bottom of the drilling hole to ensure that the magnetic rings are bonded and fixed with the surrounding soil layers. Based on the synchronous settlement of the magnetic rings with the soil layers, the sensor measures the depth difference between the magnetic rings and the pipe orifice through electromagnetic induction, and converts the settlement amounts of each soil layer by combining the elevation change of the pipe orifice. The vibrating wire type pore water pressure gauge can be used for the pore water pressure gauge and buried by layered drilling. After drilling to the target depth in each soil layer, first fill in medium-coarse sand to form a permeable layer, then vertically place the osmometer, and finally backfill the original soil and compact it.

[0030] In another technical solution, in step S2, taking each monitoring point as the generation base point of the Thiessen polygon, a closed polygon area is constructed by calculating the perpendicular bisectors between adjacent monitoring points, so that the distance from any position within each Thiessen polygon area to its corresponding monitoring point is less than the distance to other monitoring points. Each Thiessen polygon area contains only one monitoring point, and this monitoring point is located at the geometric center of the polygon area. In this technology, the Thiessen polygon is used to divide the settlement control area, aiming to ensure that the monitoring data of each partition can accurately represent the soil behavior of its covered area, and the monitoring data is closer to the real settlement trend compared with the conventional area division method.

[0031] In another technical solution, step S3 includes the following steps: S31. According to the monitoring records of the meteorological stations around the yard, the obtained meteorological data includes precipitation and temperature. Specifically, the coupled analysis of meteorological data and soil mechanical parameters has been widely verified in the geotechnical engineering field. There is a strong correlation between precipitation and temperature changes and soil settlement.

[0032] S32. According to the stacking load information in the yard, obtain the stacking loads of each settlement control area in each time period. Specifically, based on the existing soil constitutive model, there is a strong relationship between the stacking load on the ground and the settlement of the stratum. According to the stacking load records of the yard, the stacking loads of each settlement control area at each time period and time node can be basically accurately obtained.

[0033] S33. Obtain the surface settlement amount, the settlement amounts of each soil layer, and the initial pore water pressure of each soil layer in each settlement control area in each time period. Specifically, the permeability and compression modulus of layered soils vary significantly, and the layered monitoring data can support the coupled analysis of multiple soil layers.

[0034] S34. Use the meteorological data in each time period, the stacking loads in each settlement control area, the initial pore water pressure of each soil layer, the settlement amounts of each soil layer, and the surface settlement amount as sample set data to construct a yard settlement amount prediction model. Specifically, by integrating meteorological, load, and soil response data, a highly adaptable settlement prediction model is constructed. Machine learning is good at dealing with high-dimensional non-linear relationships. Through the optimization of hidden layer nodes and the iteration of the particle swarm algorithm, the model can dynamically learn the complex associations between parameters, and the error is significantly reduced compared with traditional statistical models.

[0035] Through systematic data collection and the integration of multidisciplinary theories, a closed-loop analysis framework of environment-load-soil response is constructed, solving the problems of data fragmentation and one-sided models in traditional methods, and laying a foundation for accurate settlement control.

[0036] In another technical solution, the method for optimizing the BP neural network of the algorithm includes: A1: With meteorological data including precipitation and temperature, initial pore water pressure of each soil layer, and stacking load as influencing factors, the total number of parameter types is used as the number of neuron nodes m, and the settlement amounts of each soil layer and the ground surface settlement amount are used as output values c. The number of hidden layer nodes c1 is , where a is a random constant between 1 and 10; A2. Normalize the data in the sample set, and its mathematical expression is: Among them, represents the sample data of the influencing factors, , are the minimum and maximum values in the sample data respectively, is the dimensionless processed influencing factor data; A3. Initialize the mapping relationship between the population particles and the weights and thresholds of the BP neural network, including particle dimension, initial velocity, population size, learning factor, and inertia weight; A4. Input the normalized input variables and output variables into the BP neural network model, calculate the fitness function value of the particle, and obtain the historical optimal fitness and global fitness of the particle. The fitness function value of the particle is the mean square error of the calculation result, and its function expression is: Among them, represents the predicted value of the i-th sample, is the true value of the i-th sample, and n is the total number of calculation results of the neural network; A5. Iteratively calculate the particle fitness, and update the historical optimal fitness and the global fitness according to the preset update conditions until the preset iteration end condition is met; A6. Update the weights and thresholds of the BP neural network model to obtain an optimized BP neural network model for calculating the settlement amount of the wharf yard.

[0037] In another technical solution, step S5 includes the following steps: S51. Conduct a static cone penetration test on the settlement control area to be strengthened to obtain the thickness h i of each soil layer and the compression modulus E i and other data. Specifically, the static cone penetration test method refers to the existing geotechnical engineering investigation specifications. The compression characteristics of different soil layers are significantly different, and the static cone penetration test inversely calculates E i through the penetration resistance of the probe and the measured pore water pressure.

[0038] S52. Calculate the settlement contribution ratio of each soil layer according to the predicted settlement value S i of each soil layer in the yard settlement prediction model. The formula for the settlement contribution ratio of the soil layer is: Accumulate the contribution ratios of each soil layer from large to small, and select the soil layer when the cumulative contribution is ≥ 70% as the grouting target layer. Specifically, the total settlement is the superposition of the compression amounts of each layer, and the proportion of the main control layer being ≥ 70% conforms to the principle of engineering economy, avoiding the waste of resources in grouting the entire soil layer, and focusing on the key layer positions to improve the reinforcement efficiency.

[0039] S53. Obtain the difference L1 between the predicted value L of the ground surface settlement and the ground surface settlement control value, and obtain the corresponding settlement adjustment value L according to the proportion of the settlement amount of each selected grouting target layer in all the grouting target layers. i .

[0040] S54. Back-calculate the target compression modulus E' of the grouting target layer, and the calculation formula is i ; The grouting foundation uses external pressure or electrochemistry principle to uniformly inject the prepared chemical slurry or cement slurry into the voids of the medium through a grouting pump and a conduit, so as to discharge the water and gas in the voids by filling, permeating and compressing, etc., and fill its position, so that it combines with the soil body to undergo a physical and chemical reaction. During the process from the injection of the slurry to its consolidation and hardening, the water in the slurry will penetrate into the surrounding strata, and the contact surface between the slurry and the strata will undergo consolidation. For soft soil foundations, grouting can increase the elastic modulus of the soil body, thereby effectively reducing the settlement.

[0041] S55. Obtain the grouting body modulus E of the grouting material, and the ratio of the grouting body modulus g , calculate the grouting volume ratio of the grouting target layer , .

[0042] S56. Design the grouting plan in the settlement control area to be reinforced according to the calculation results, and carry out grouting.

[0043] In another technical solution, step S56 includes the following steps: S561. Calculate the area S of the settlement control area to be reinforced, calculate the total grouting volume a , ; S562. According to the characteristics of the grouting material, calculate the effective diffusion radius R of the grouting material in the soil layer. Specifically, quantify the diffusion range of the grouting material in the soil layer to guide the subsequent single-hole grouting volume and the calculation of the hole spacing.

[0044] S563. Calculate the single-hole grouting volume , where b is the slurry filling rate and n is the soil layer porosity. Specifically, based on the cylindrical diffusion model, combined with the correction of the porosity and the filling rate, ensure that the slurry fully fills the soil pores.

[0045] S564. Obtain the number N of grouting holes and the designed hole positions in the settlement control area to be reinforced.

[0046] S565. Grout according to the design results.

[0047] In another technical solution, the calculation formula for the effective diffusion radius R is , where P is the designed grouting pressure, P0 is the pore water pressure of the grouting target layer, t is the single-hole grouting time, k is the permeability coefficient of the grouting target layer, is the viscosity of the grouting material. In this technical solution, the formula for the effective diffusion radius is based on the spherical diffusion model theory, considering the pressure gradient, fluid resistance and time effect, conforming to the laws of fluid mechanics. By dynamically quantifying the slurry diffusion range, it scientifically guides the design of grouting hole spacing and avoids insufficient or redundant slurry coverage caused by traditional empirical estimation.

[0048] The following are specific on-site examples: Taking a wharf yard in a certain country as an example, the soil layers in the yard are, from top to bottom, 4m of backfill soil, 4m of silty clay, and 7m of sandy soil, and the settlement control target is 1mm / month.

[0049] S1. Set 12 monitoring points in the yard. Install a cement pier at each monitoring point, and install a GNSS receiver on it to monitor the surface settlement, and 1 magnetic ring type settlement detector, with three magnetic rings buried in the corresponding soil layers, and three vibrating wire piezometers are respectively set in the corresponding soil layers.

[0050] S2. Import the coordinates of the 12 monitoring points and the yard boundary into the GIS software to generate 12 Thiessen polygons, and each Thiessen polygon is a settlement control area.

[0051] S3. Use the meteorological information collected by the surrounding meteorological station, the meteorological information is the periodic precipitation, periodic average temperature, periodic average humidity, the periodic stacking load information of each settlement control area, the surface settlement amount at the end of the period, the pore water pressure of each soil layer at the beginning of the period, and the settlement amount of each soil layer at the end of the period. After normalizing the above data, it is used as the model database. Among them, the input layer has 7 nodes, the hidden layer has 9 nodes, and the output layer has 4 nodes. Optimize the mapping relationship between the optimized population particles and the weights and thresholds of the BP neural network, including particle dimension, initial velocity, population size, learning factor, and inertia weight; input the normalized input variables and output variables into the BP neural network model, calculate the fitness function value of the particles, and obtain the historical optimal fitness and global fitness of the particles. The constructed neural network model curve is stable and has good stability, and the MSE value of the model meets the usage requirements.

[0052] S4. Now predict the settlement of each settlement control area in the next month. According to the feedback information from the surrounding weather stations, the predicted values of temperature, precipitation, and humidity can be obtained. According to the stacking plan of the yard, the predicted load values of the stacking in each settlement control area can be estimated. The monitoring points can collect the pore water pressure in each soil layer at present. Input the above information into the prediction model obtained in S3. The predicted settlement of a settlement control area with an area of 2000 m 2 in the next month is 1.5 mm, exceeding the settlement control target. This settlement control area is taken as the settlement control area to be reinforced.

[0053] S51. Conduct a static cone penetration test on this area. For the silty sand layer, according to the penetration test results, the compression modulus E3 = 10 Mpa and the actual thickness is 6.5 m.

[0054] S52. According to the predicted settlement values of each soil layer, the settlement of the silty sand layer S3 = 1.2 mm, which is 80% of the total settlement. Therefore, it is confirmed that only the silty sand layer will be grouted and reinforced.

[0055] S53. Since only the silty sand layer is grouted and reinforced, the corresponding settlement adjustment value L3 = 0.5 mm.

[0056] S54. Back-calculate the target compression modulus of the grouting target layer to be Mpa.

[0057] S55. The compression modulus of the grouting cement slurry is 40 Mpa, and the modulus ratio of the grouting body is 4. The grouting volume ratio of the silty sand layer is .

[0058] S561. The total grouting volume is 2031 m 3 .

[0059] S562. Calculate the effective diffusion radius R. According to the pore water pressure gauge and the static cone penetration test results, the permeability coefficient k = 5×10 -6 at this time, the pore water pressure P0 = 0.18 Mpa, the grouting time t = 3600 s, and the viscosity of the grouting material = 0.95. It can be known that = 3 m.

[0060] S563. Conduct in-situ sampling at the static cone penetration points and measure the porosity of the silty sand layer in the laboratory to be 0.3. According to the grouting experience, the slurry filling rate b is 0.9, and the single-hole grouting is .

[0061] S564. The number of grouting holes N = 41 holes.

[0062] S565. Drill holes in the settlement control area to be reinforced according to the number of grouting holes and perform pressure grouting. The grouting volume and time are strictly carried out in accordance with the design.

[0063] It should be noted that although the above steps are described in a specific order, it does not mean that the steps must be executed in the above specific order. In fact, some of these steps can be executed concurrently or even the order can be changed as long as the required functions can be achieved. The equipment quantity and processing scale described here are used to simplify the description of the present invention, and the application, modification and variation of the present invention are obvious to those skilled in the art.

[0064] Although the embodiments of the present invention have been disclosed as above, it is not limited to the applications listed in the specification and embodiments. It can be fully applied to various fields suitable for the present invention. For those familiar with the field, additional modifications can be easily achieved. Therefore, without departing from the general concept defined by the claims and the equivalent scope, the present invention is not limited to the specific details and the illustrations shown and described here.

Claims

1. A method for controlling the settlement of a terminal yard, characterized in that, It includes the following steps: S1. Plan the monitoring points in the yard. Each monitoring point can at least obtain the surface settlement amount at this position, the settlement amounts of each soil layer at the corresponding position, and the pore water pressure in real time; S2. Divide the yard into several settlement control areas based on the positions of the monitoring points. There is at least one monitoring point in each settlement control area; S3. Obtain the historical meteorological data of the yard, the surface settlement amount data, the settlement amount data of each soil layer, the pore water pressure of each soil layer, the historical stacking record in each settlement control area. The above data are used as sample set data to construct a yard settlement amount prediction model; S4. Use the meteorological information, stacking plan in the next cycle collected, and the real-time data collected by the monitoring points to obtain the predicted surface settlement values of each settlement control area. After comparing with the surface settlement control values, confirm the settlement control areas to be reinforced; S5. Obtain the predicted settlement amount values of each soil layer in the settlement control area to be reinforced, obtain the soil compression modulus in this soil layer, design a soil layer grouting plan and carry out grouting reinforcement.

2. The method for controlling the settlement of the terminal yard as claimed in claim 1, wherein In step S1, the monitoring points include surface settlement monitoring points, settlement gauges and several pore water pressure gauges arranged closely. Among them, the settlement gauges are buried in the soil body to collect the settlement amount data of each soil layer below the corresponding position in real time. The pore water pressure gauges are arranged in each soil layer from top to bottom to collect the pore water pressure of each soil layer in the settlement control area where they belong in real time.

3. The method for controlling the settlement of the terminal yard as described in claim 2, wherein In step S2, taking each monitoring point as the generation base point of the Thiessen polygon, construct a closed polygon area by calculating the perpendicular bisectors between adjacent monitoring points, so that the distance from any position within each Thiessen polygon area to its corresponding monitoring point is less than the distance to other monitoring points. Each Thiessen polygon area contains only one monitoring point, and this monitoring point is located at the geometric center of the polygon area.

4. The method for controlling the settlement of a terminal yard as claimed in claim 1, wherein, Step S3 includes the following steps: S31. According to the monitoring records of the meteorological station around the yard, the obtained meteorological data include precipitation and temperature; S32. According to the stacking information in the yard, obtain the stacking loads in each settlement control area in each time period; S33. Obtain the surface settlement amount, the settlement amount of each soil layer, and the initial pore water pressure of each soil layer in each settlement control area in each time period; S34. Use the meteorological data in each time period, the stacking loads in each settlement control area, the initial pore water pressure of each soil layer, the settlement amount of each soil layer, and the surface settlement amount as sample set data to construct a yard settlement amount prediction model.

5. The method for controlling the settlement of a terminal yard as described in claim 4, characterized in that, The yard settlement amount prediction model adopts a BP neural network including an input layer, a hidden layer and an output layer. The input layer of the BP neural network model includes multiple input neuron nodes. There are multiple hidden neuron nodes arranged on the hidden layer. Each of the input neuron nodes is respectively connected to each of the hidden neuron nodes; the hidden neuron nodes are all connected to the output layer. Take the meteorological data, stacking load, and the initial pore water pressure of each soil layer as the input values of the input neuron nodes, compare the output result with the measured settlement amounts of each soil layer and the surface settlement amount, and optimize the algorithm of the BP neural network model to obtain the yard settlement amount prediction model.

6. The method for controlling the settlement of a terminal yard as claimed in claim 5, wherein, The method for optimizing the BP neural network by the algorithm includes: A1: Taking meteorological data including precipitation and temperature, initial pore water pressure of each soil layer, and stacking load as influencing factors, taking the total number of parameter types as the number of neuron nodes m, and the settlement amounts of each soil layer and the ground surface settlement amount as output values c, the number of hidden layer nodes c1 is , where a is a random constant between 1 and 10; A2. Normalize the data in the sample set, and its mathematical expression is: Among them, represents the sample data of influencing factors, , are respectively the minimum and maximum values in the sample data, is the data of influencing factors after dimensionless processing; A3. Initialize the mapping relationship between the population particles and the weights and thresholds of the BP neural network, including particle dimension, initial velocity, population size, learning factor, and inertia weight; A4. Input the normalized input variables and output variables into the BP neural network model, calculate the fitness function value of the particles, and obtain the historical optimal fitness and global fitness of the particles. The fitness function value of the particles is the mean square error of the calculation result, and its function expression is: Among them, represents the predicted value of the i-th sample, is the true value of the i-th sample, and n is the total number of calculation results of the neural network; A5. Iteratively calculate the particle fitness, and update the historical optimal fitness and the global fitness according to the preset update conditions until the preset iteration end condition is met; A6. Update the weights and thresholds of the BP neural network model to obtain an optimized BP neural network model for calculating the settlement of the wharf yard.

7. The method for controlling the settlement of a terminal yard as claimed in claim 1, wherein, Step S5 includes the following steps: Perform static cone penetration tests on the settlement control area to be reinforced to obtain the thickness h of each soil layer i and compression modulus E i and other data; S52. Predictive value S of settlement of each soil layer according to the yard settlement prediction model i Calculate the settlement contribution ratio of each soil layer. The formula for the settlement contribution ratio of the soil layer is: , accumulate the contribution ratios of each soil layer from large to small, and select the soil layer with a cumulative contribution ≥ 70% as the grouting target layer; S53. Obtain the difference value L1 between the predicted ground settlement value L and the ground settlement control value, and obtain the corresponding settlement adjustment value L according to the proportion of the settlement amount of each selected grouting target layer in all grouting target layers. i ; S54. Inverse calculation of the target compression modulus E of the grouting target layer i ’, and the calculation formula is ; S55. Obtain the grouting body modulus E of the grouting material g , the ratio of the grouting body modulus , calculate the grouting volume ratio of the grouting target layer ; S56. Design a grouting plan in the reinforcement settlement control area according to the calculation results and perform grouting.

8. The method for controlling the settlement of the terminal yard as claimed in claim 7, wherein Step S56 includes the following steps: S561. Calculate the area S of the settlement control area to be strengthened a , calculate the total grouting volume ; S562. Calculate the effective diffusion radius R of the grouting material in the soil layer according to the characteristics of the grouting material; S563. Calculation of single-hole grouting volume , where b is the slurry filling rate and n is the soil layer porosity; S564. Obtain the number N of grouting holes and the designed hole positions in the settlement control area to be reinforced; S565. Perform grouting according to the design results.

9. The method for controlling the settlement of a terminal yard as described in claim 8, characterized in that, The calculation formula for the effective diffusion radius R is , where P is the designed grouting pressure, P0 is the pore water pressure of the grouting target layer, t is the single-hole grouting time, k is the permeability coefficient of the grouting target layer, and μ is the viscosity of the grouting material.

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