A method for predicting the horizontal ultimate bearing capacity of a negative pressure screw anchor

Abstract

The application discloses a negative pressure spiral anchor horizontal limit bearing capacity prediction method and relates to the technical field of geotechnical engineering, and comprises the following steps: generating construction resistance energy feature vectors by using sinking time sequence data, inversing soil improvement factors by an energy-soil property mapping model and correcting original soil parameters, and calculating and superimposing each partial resistance based on the corrected parameters and geometric parameters to obtain a prediction value. The application is used to solve the problem that the existing prediction method ignores the influence of construction disturbance on the remodeling of soil parameters, thereby causing the problem of insufficient bearing capacity prediction accuracy.

Description

Technical Field

[0001] This invention relates to the field of geotechnical engineering technology, and more specifically, to a method for predicting the horizontal ultimate bearing capacity of a negative pressure spiral anchor. Background Technology

[0002] Negative-pressure helical anchors, as a new type of composite anchoring foundation, are widely used in mooring engineering for floating platforms and underwater production systems due to their excellent pull-out resistance and ease of construction. During their service life, negative-pressure helical anchors must withstand strong and complex horizontal cyclic loads such as wind, waves, and currents for extended periods. Accurate prediction of their horizontal ultimate bearing capacity directly affects the overall stability and safety of the superstructure, making it a crucial aspect of marine engineering design.

[0003] In existing technologies, the prediction of the horizontal bearing capacity of such anchored foundations mainly follows the limit equilibrium method or Py curve method used in traditional pile foundation engineering. In engineering practice, designers usually calculate the passive earth resistance on the pile side based on Rankine or Coulomb earth pressure theory, and according to standards such as the "Code for Design of Building Pile Foundations", they assume that the anchor body is a rigid component and estimate its ultimate resistance by multiplying simple geometric parameters with the mechanical parameters of the foundation soil.

[0004] However, the aforementioned existing technologies have significant limitations when applied to large-diameter negative pressure spiral anchors. First, traditional earth pressure theory, based on the assumption of two-dimensional plane strain, ignores the three-dimensional spatial soil squeezing effect and wedge failure characteristics generated when the cylindrical sleeve moves in the soil, resulting in calculation results that are often far less than the actual bearing capacity, leading to material waste. Second, existing methods often treat the anchor body as absolutely rigid or absolutely flexible, failing to accurately reflect the "rigid-flexible" coupled deformation mechanism of anchor bodies with large length-to-diameter ratios in complex seabeds. Finally, traditional superposition algorithms do not consider the shielding effect of the external large-diameter sleeve on the internal central rod, and simply accumulating the resistance of each component leads to inflated prediction values, thus posing a hidden danger to engineering safety. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method for predicting the horizontal ultimate bearing capacity of a negative pressure spiral anchor. This method constructs a soil improvement factor based on the construction resistance energy characteristic vector of the construction process time series data, and uses this factor to correct the mechanical parameters of the undisturbed soil before superimposing and calculating the resistance of each component. This solves the problem that the bearing capacity prediction accuracy based on static geological reports is insufficient because the prior art ignores the reshaping effect of the construction settlement process on the soil strength.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for predicting the horizontal ultimate bearing capacity of a negative pressure spiral anchor includes the following steps: constructing a comprehensive installation specific energy index based on the installation sequence data of the negative pressure spiral anchor during the sinking construction process, and generating a construction resistance energy feature vector distributed along the depth; based on the construction resistance energy feature vector, obtaining a soil improvement factor using a preset energy-soil property mapping model; correcting the undisturbed soil mechanical parameters using the soil improvement factor; calculating each component resistance of the negative pressure spiral anchor based on the corrected soil parameters and the geometric parameters of the negative pressure spiral anchor, and superimposing the component resistances to obtain the predicted value of the horizontal ultimate bearing capacity of the negative pressure spiral anchor.

[0008] In a preferred embodiment, the instantaneous integrated installation energy is integrated at a depth or weighted average to obtain the average installation energy within the sleeve embedment range, which is used as the feature value of the construction resistance energy feature vector.

[0009] In a preferred embodiment, the step of inverting the soil improvement factor using a preset energy-soil mapping model includes the following steps: acquiring multiple sets of measured horizontal bearing capacity data of historical negative pressure spiral anchors and their corresponding historical installation time series data; calculating the historical comprehensive installation specific energy based on the historical installation time series data; and training a nonlinear regression model or a neural network model with the goal of minimizing the error between the predicted bearing capacity and the measured bearing capacity, and establishing a functional mapping relationship between the comprehensive installation specific energy and the soil improvement factor.

[0010] In a preferred embodiment, the step of correcting the undisturbed soil mechanical parameters using soil amendment factors includes: using formulas... Calculate the corrected severity Using formulas Calculate the corrected cohesion Using formulas Calculate the corrected internal friction angle ;in, These are the natural unit weight, undisturbed cohesion, and undisturbed internal friction angle of the undisturbed soil mechanical parameters, respectively. These are the soil improvement factors, namely, the density improvement factor, the cohesion improvement factor, and the internal friction angle increment.

[0011] In a preferred embodiment, the calculation of each component resistance of the negative pressure spiral anchor includes: determining the bending stiffness of the negative pressure spiral anchor based on the geometric parameters, and obtaining the soil elastic modulus from the undisturbed soil mechanical parameters; calculating the anchor-soil relative stiffness coefficient using the bending stiffness and soil elastic modulus; calculating the effective bearing depth of the negative pressure spiral anchor based on the anchor-soil relative stiffness coefficient; and calculating the lateral passive earth resistance of the sleeve using passive earth pressure theory integration within the effective bearing depth range based on the corrected soil parameters, as one of the component resistances.

[0012] In a preferred embodiment, the individual resistance components include at least the lateral passive soil resistance of the sleeve, the horizontal resistance of the anchor plate, and the bottom shear and overturning resistance; wherein the bottom shear and overturning resistance are calculated based on the soil shear strength provided by the annular contact area at the bottom of the sleeve and the overturning component generated by the negative pressure suction.

[0013] In a preferred embodiment, calculating the effective bearing depth of the negative pressure helical anchor includes: determining an empirical reduction factor based on the relative stiffness coefficient of the anchor soil; calculating the product of the empirical reduction factor and the relative stiffness coefficient of the anchor soil, and comparing the product with the sleeve length in the geometric parameters; and selecting the smaller of the two as the effective bearing depth.

[0014] In a preferred embodiment, after obtaining the predicted value of the horizontal ultimate bearing capacity, a construction quality feedback step is further included: setting a design bearing capacity threshold and a minimum energy consumption standard; comparing the predicted value of the horizontal ultimate bearing capacity with the design bearing capacity threshold; if the predicted value is lower than the threshold, further retrieving the construction resistance energy feature vector; if the average installation specific energy shown by the construction resistance energy feature vector is lower than the minimum energy consumption standard, generating a prompt message to indicate that the soil compaction is insufficient or there is a risk of soil disturbance and softening during the settlement process.

[0015] In a preferred embodiment, the geometric parameters include at least the outer diameter of the sleeve, the sleeve length, the diameter of the anchor plate, and the sleeve wall thickness for calculating stiffness; the undisturbed soil mechanical parameters include at least the natural unit weight, undisturbed cohesion, undisturbed internal friction angle, and soil elastic modulus; the installation timing data includes the advancing torque, negative pressure suction value, penetration rate, and rotation speed collected at a preset sampling frequency.

[0016] The technical effects and advantages of the negative pressure spiral anchor horizontal ultimate bearing capacity prediction method of this invention are as follows:

[0017] This invention constructs a comprehensive installation energy index based on the installation sequence data during the negative pressure spiral anchor penetration construction process, and generates a construction resistance energy characteristic vector distributed along the depth. Based on a preset energy-soil property mapping model, a soil improvement factor is obtained, and this factor is used to correct the undisturbed soil mechanical parameters. Combined with the geometric parameters of the negative pressure spiral anchor, each component resistance is calculated and vector-superimposed, thereby achieving high-precision prediction of the horizontal ultimate bearing capacity driven by actual construction data. Furthermore, it breaks through the limitations of traditional design relying solely on static geological reports. By quantifying the mapping relationship between penetration energy and soil parameter evolution, it helps to truly reflect the reshaping effect of construction disturbance or compaction on soil strength. This effectively solves the technical problems of existing technologies that neglect the influence of the construction process, resulting in large deviations between predicted bearing capacity and actual engineering values, and difficulty in controlling safety redundancy. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the method for predicting the horizontal ultimate bearing capacity of a negative pressure spiral anchor provided in an embodiment of the present invention.

[0019] Figure 2 A schematic diagram of the construction resistance energy feature vector provided in an embodiment of the present invention.

[0020] Figure 3 This is a schematic diagram showing the comparison curves of the horizontal ultimate bearing capacity with burial depth provided in an embodiment of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0022] Example 1, Figure 1 The present invention provides a method for predicting the horizontal ultimate bearing capacity of a negative pressure spiral anchor, comprising the following steps:

[0023] Step S1: Obtain the geometric parameters and undisturbed soil mechanical parameters of the negative pressure spiral anchor to be predicted, and obtain the installation sequence data of the negative pressure spiral anchor during the entire sinking construction process.

[0024] It should be noted that this step forms the data foundation for subsequent energy analysis and bearing capacity prediction. In practice, the geometric parameters are mainly used to determine the structural characteristics of the helical anchor and subsequent stiffness calculations. These parameters include at least the outer diameter of the sleeve, the sleeve length, the anchor disc diameter, and the sleeve wall thickness used for stiffness calculation. These parameters are usually directly read from the design drawings, CAD model data, or prefabricated construction parameter tables of the negative pressure helical anchor and entered into the system. The undisturbed soil mechanical parameters reflect the geological conditions of the site before construction and include at least the natural unit weight of the soil, undisturbed cohesion, undisturbed internal friction angle, and elastic modulus of the soil. These parameters are generally obtained based on the geotechnical engineering investigation report of the construction site or on-site in-situ testing and laboratory geotechnical test data.

[0025] Furthermore, to capture the energy input characteristics during construction, it is necessary to acquire installation timing data for the entire penetration construction process. This timing data includes the precession torque, negative pressure suction value, penetration rate, and rotation speed, collected at a preset sampling frequency. In terms of specific hardware configuration, the precession torque and rotation speed can be monitored in real time using a non-contact torque-speed sensor mounted on the output shaft of the power head to avoid cable tangling and ensure measurement accuracy. The negative pressure suction value can be acquired using an industrial-grade pressure transmitter located at the vacuum pump outlet or the top cover of the sleeve. The penetration rate can be measured using a displacement sensor combined with time differentiation or directly using a velocity sensor. To ensure the continuity and accuracy of the subsequent construction resistance energy feature vector construction, the data acquisition system sets the preset sampling frequency to 10Hz to 50Hz. It should be noted that, given the involvement of multi-source heterogeneous data acquisition on-site, to ensure strict timing alignment of the sensor signals, the aforementioned sensor signals are preferably acquired synchronously using the same multi-channel data acquisition instrument, or interpolated and aligned at the software level based on a unified system timestamp to eliminate time errors caused by hardware response delays. Furthermore, considering the complex mechanical vibration and noise interference typically present at construction sites, the acquisition step may specifically include a signal preprocessing stage to ensure the accuracy and signal-to-noise ratio of the acquired installation timing data. Preferably, before substituting the raw data into the calculation model, a low-pass filter (e.g., a Butterworth filter with a cutoff frequency of 5Hz) or a moving average algorithm is used to smooth and denoise the penetration rate and negative pressure suction values. This preferred data acquisition and processing method effectively eliminates high-frequency noise, thereby recording the true, stable, and high signal-to-noise ratio complete dynamic response process of the negative pressure spiral anchor from contact with the soil until it penetrates to the design depth, preventing subsequent calculation model convergence difficulties due to data noise.

[0026] The data acquisition and preprocessing in this step not only effectively solves the shortcomings of traditional prediction methods that rely solely on static geological parameters, but also eliminates the adverse effects of environmental noise on prediction accuracy through rigorous data cleaning, providing a reliable data benchmark for subsequent accurate quantification of construction disturbances.

[0027] Step S2: Based on the installation time sequence data, construct a comprehensive installation specific energy index and generate a construction resistance energy feature vector distributed along the depth.

[0028] Specifically, this step aims to transform discrete, multi-source mechanical response data into an energy index that can uniformly characterize the soil resistance properties. Based on the time-series data obtained in the preceding steps, the rotational kinetic energy input and vertical potential energy input during the sinking process of the helical anchor are superimposed using the principle of energy conservation. This embodiment introduces the concept of "instantaneous comprehensive installation specific energy," which is calculated at any sampling time using the following formula:

[0029] (1)

[0030] In the formula, This represents the energy consumed per unit volume of soil when it is displaced or disturbed, and the unit is 1. ; Precession torque, in units of ; Angular velocity, in units of ; Penetration rate, unit: ; This is the effective downward force converted from negative pressure suction. (Variable) Defined as the characteristic horizontal projected area of ​​the helical anchor, specifically referring to the annular projected area at the bottom of the negative pressure helical anchor sleeve. This formula, from a physical perspective, normalizes the mechanical rotation work and negative pressure auxiliary work, thereby shielding the interference of construction speed on energy consumption evaluation. This is to prevent interference from construction stoppages or extremely slow creep states (i.e., ... This causes the denominator to approach zero, resulting in divergent calculation results. Therefore, a minimum rate threshold needs to be set. (This embodiment is preferred) When the real-time detected penetration rate is lower than this threshold, the system determines that moment as an invalid data point, and the calculated value at that moment... The value is not included in subsequent statistical analysis.

[0031] The effective downward force converted from negative pressure suction Calculated using the following formula:

[0032] (2)

[0033] in, This refers to the real-time negative pressure suction value in the installation timing data. The pressure-bearing area of ​​the top cover of the sleeve; The negative pressure transmission efficiency coefficient, taking into account the performance of the sealing ring and the loss along the way, is usually between 0.80 and 0.95.

[0034] After calculating the instantaneous values, in order to obtain a spectrum with engineering guidance significance, time-domain-spatial mapping and data resampling operations need to be performed. First, using the formula... (Or use direct readings from displacement sensors) All time-sampling based data Data points are mounted to the depth coordinate axis The above completes the transformation from the time domain to the depth domain.

[0035] Furthermore, the instantaneous comprehensive installation energy is integrated at depth or weighted averaged to obtain the average installation energy within the sleeve embedment range, which serves as the key feature value of the construction resistance energy feature vector. As a preferred embodiment, the step of integrating or weighting the instantaneous comprehensive installation energy at depth can be implemented by calculating the total energy consumption using the depth integration method. As another, more preferred embodiment, to obtain refined features along the depth distribution, a sliding window algorithm is specifically used to implement the weighted averaging process. Specifically, a preset depth step size (e.g., ...) is used... or Divide the window and calculate all valid values ​​within that window. The arithmetic mean (i.e., a weighted average with equal weights) or Gaussian weighted average of the values ​​is used to obtain a smooth, depth-distributed average installation energy curve, which generates the construction resistance energy characteristic vector.

[0036] Figure 2 The diagram displays the characteristic vector of construction resistance energy within a depth range of 0m to 12m. The gray dashed lines in the diagram represent the energy characteristics obtained solely through the formula. The calculated raw instantaneous discrete data shows severe high-frequency sawtooth fluctuations due to construction vibrations. The solid line in the figure represents the average installation specific energy curve after processing with the sliding window algorithm described in this embodiment. From Figure 2 It can be clearly observed that the SIE value shows a step-like upward trend with increasing depth, and a significant energy mutation peak appears at a depth of 8.5m. This proves that the construction resistance energy feature vector of the present invention can accurately capture the subtle changes in the lithology of the formation.

[0037] This specific weighted averaging process not only solves the singularity problem of the mathematical model at zero rate, but also eliminates the random fluctuations of the original data through spatial resampling technology. It successfully constructs a construction resistance energy feature vector that can truly reflect the characteristics of soil impedance changes along the depth direction, providing a high-precision and high-stability input basis for subsequent soil parameter inversion.

[0038] Step S3: Based on the construction resistance energy feature vector, the soil improvement factor is obtained by inversion using the preset energy-soil property mapping model.

[0039] Specifically, the core of this step lies in using artificial intelligence algorithms to establish a quantitative mapping between "sinking energy input" and "soil parameter variation." To ensure the accuracy and interpretability of this mapping relationship, this embodiment adopts a two-stage strategy of "inversion labeling first, followed by supervised learning" to construct a pre-set energy-soil property mapping model. First, a sample database for model training and validation is constructed. To ensure the model's generalization ability and prediction accuracy, the sample database contains several sets of negative pressure spiral anchor engineering case data under different geological conditions. The geological conditions preferably cover typical soil types such as soft clay, silty clay, and sand. Each set of case data typically includes geological survey parameters, time-series data of the entire sinking construction process, and the corresponding horizontal ultimate bearing capacity calibration value. It should be noted that the horizontal ultimate bearing capacity calibration value can be obtained through on-site static load tests, or through indoor model tests or high-fidelity numerical simulations (such as finite element analysis), thereby providing reliable ground truth labels for subsequent supervised learning.

[0040] In the first stage of model building, namely the historical data annotation (sample construction) stage, this step aims to establish true label values ​​for each set of historical data for supervised learning. For each set of historical data in the sample library, given its physical geometric parameters, undisturbed soil parameters, and measured bearing capacity, an optimization objective function is constructed using a mechanical calculation model, and parameter inversion is performed using a global optimization algorithm (such as a genetic algorithm) or a nonlinear least squares method. Specifically, the algorithm automatically searches for a set of optimal soil improvement factors within a preset feasible parameter domain, which specifically includes a density improvement factor. Cohesion modifier and the increase in internal friction angle The optimal solution minimizes the error between the theoretical bearing capacity calculated by substituting into the formula and the measured bearing capacity, and is therefore marked as the "true label value" corresponding to this historical sample.

[0041] After data annotation is completed, the second stage, the mapping model training stage, begins. This stage aims to minimize the error between the predicted and measured bearing capacity. To reduce the convergence difficulty of direct end-to-end training, this embodiment employs a supervised learning strategy as an indirect implementation method, with the following steps:

[0042] 1. Dataset Construction and Preprocessing

[0043] First, construct paired training datasets. Input vectors The dimensions are set to 5, specifically including: the statistical characteristics of the Integrated Installation Energy (SIE) (specifically, the mean, peak value, and standard deviation of the entire depth are selected) and the soil parameters of the foundation (such as the void ratio and liquidity index of the undisturbed soil). Output vector The dimension is set to 3, corresponding to three soil improvement factors. The actual label value.

[0044] To eliminate the interference of data with different dimensions on the model weights and accelerate convergence, the Min-Max normalization method is used to map the input features and output labels to the [0, 1] interval. The dataset is randomly divided into training, validation, and test sets in a 7:2:1 ratio.

[0045] 2. Model Architecture Construction

[0046] This embodiment preferably uses a lightweight backpropagation (BP) neural network as the core prediction algorithm. This network comprises an input layer, hidden layers, and an output layer.

[0047] Input layer: The number of nodes is the same as the dimension of the input vector.

[0048] Hidden layer: configured as a single or two-layer structure. In this preferred embodiment, a single hidden layer structure is used, with 12 nodes. The hidden layer neurons employ the Sigmoid activation function to introduce non-linear features and adapt to the numerical range of the modification factor.

[0049] Output layer: There are 3 nodes, corresponding to three soil improvement factors to be predicted. The output layer uses the Linear activation function.

[0050] It should be noted that if a support vector machine (SVR) model is used, the radial basis function (RBF) is preferred as the kernel function, the penalty coefficient is set to 6, and the kernel parameter Gamma is set to auto mode.

[0051] 3. Model Training and Parameter Optimization

[0052] During training, the mean squared error (MSE) between the predicted and true label values ​​is used as the loss function. The Adam optimizer or particle swarm optimization (PSO) algorithm is preferred for parameter optimization.

[0053] The specific training steps are as follows:

[0054] Initialization: Set the initial learning rate to 0.01, the maximum number of epochs to 1000, and the batch size to 32.

[0055] Forward propagation: Input the normalized training set data into the network and calculate the output values ​​of the hidden layers and the output layer.

[0056] Backpropagation: Calculate the gradient of the loss function MSE, and update the weight matrix and bias terms in the network using gradient descent. If a particle swarm optimization algorithm is used, all weights and biases of the network are encoded as particle position vectors. Set the population size to 30, and iteratively update the velocity and position of the particles to search for the global optimum that minimizes the MSE.

[0057] Early stopping mechanism: During training, the model performance is evaluated on the validation set every 50 epochs. If the MSE of the validation set does not decrease significantly within 10 consecutive detection epochs, training is terminated early to prevent overfitting.

[0058] Since the true label value itself is obtained based on the principle of minimizing bearing capacity error, when the model's MSE on the validation set converges to a preset threshold (such as...), the model will be able to achieve the desired result. When this is achieved, it is equivalent to minimizing the error between the predicted bearing capacity and the measured bearing capacity.

[0059] 4. Model Deployment and Inference

[0060] After the model training is complete, the network weight parameters are saved. For the negative pressure spiral anchor project to be predicted, the system extracts features from the construction resistance energy feature vector data generated in step S2, normalizes it, and inputs it into the trained model. The model outputs the normalized prediction result, and finally, after inverse normalization processing, the actual physical value of the soil improvement factor under the current working condition can be directly obtained.

[0061] Through this "inversion annotation-driven" modeling method, the present invention successfully solves the technical pain point of "difficulty in directly measuring soil disturbance parameters" in geotechnical engineering, and realizes intelligent dynamic correction of soil parameters based on construction big data.

[0062] Step S4: Use the soil improvement factor to correct the mechanical parameters of the undisturbed soil to obtain corrected soil parameters that reflect the compaction or disturbance effect of the soil around the sleeve.

[0063] Specifically, this step aims to establish a quantitative relationship between the original geological parameters and the actual soil state after construction disturbance. The original parameters are mathematically corrected using the three improvement factors obtained in the previous step. The correction process follows this logic: First, considering the change in soil density, the formula... Calculate the corrected unit weight; secondly, considering the change in soil cohesion, use the formula... Calculate the corrected cohesion; finally, considering the changes in inter-particle friction in the soil, use the formula... Calculate the corrected internal friction angle. In the above formula, These represent the undisturbed soil mechanical parameters provided in the geological exploration report, namely, natural unit weight, undisturbed cohesion, and undisturbed internal friction angle; while These represent the corresponding parameters after correction.

[0064] It should be noted that this embodiment fully considers the nonlinear response characteristics of complex geological conditions. Although large-diameter anchors typically produce a significant compaction effect in conventional soil layers (where the improvement factor is positive), in some highly sensitive soft soils (such as thixotropic clay), strong construction disturbances may disrupt the original structural connections of the soil, leading to a decrease in local strength, i.e., a "softening effect." Therefore, this application allows the improvement factor in its algorithm design. Negative values ​​are used to fully accommodate the special working condition of "disturbance softening" and ensure that the prediction model is not distorted. In addition, after obtaining the corrected soil parameters using the above formula, as a preferred data verification and protection mechanism, this system can further impose physical boundary constraints on the calculation results.

[0065] Specifically, to prevent statistical prediction bias in artificial intelligence models from causing parameter values ​​to exceed physical limits, the system will determine whether the results calculated by the formula are within a reasonable range: for example, if the corrected internal friction angle is calculated using the formula... Exceeding the preset limit of soil internal friction angle (e.g.) If ), the system will automatically truncate it and assign a value. If the corrected cohesion If the calculation result is less than 0, it is forcibly set to 0. This optimized post-processing step ensures that the parameters input into the subsequent mechanical model are always within a reasonable physical range, without changing the fundamental mathematical logic of the soil improvement factor correcting the original parameters.

[0066] To illustrate the effect of this correction step more intuitively, let's take a typical compactable soft clay site construction as an example: Assume that the geological survey provides the original cohesion... Based on the high integrated installation energy (SIE) fingerprint spectrum during the sinking process, the system obtains the cohesion improvement factor at that location through mapping model inversion. After verification that the physical boundaries were not exceeded, the calculation yielded... .this The strength enhancement quantitatively characterizes the contribution of the compaction effect to the shear strength of the soil. Through the bidirectional correction mechanism and physical boundary constraints introduced in this step, this invention successfully constructs a parameter correction system that can reflect the construction enhancement effect, prevent softening risks, and has extremely high robustness, effectively solving the technical problem of predicting failure under extreme geological conditions using traditional methods.

[0067] Step S5: Determine the effective bearing depth of the negative pressure spiral anchor and calculate the component resistance.

[0068] Specifically, this step aims to address the "rigid-flexible" coupled deformation characteristics exhibited by large-diameter negative-pressure helical anchors under horizontal loads. The system first needs to determine the relative stiffness relationship between the anchor body and the soil. Based on the aforementioned geometric parameters, the bending stiffness of the negative-pressure helical anchor is determined. (in For anchor body modulus, (e.g., moment of inertia of the cross section), and obtain the horizontal foundation reaction modulus of the soil. Considering that conventional geological reports typically only provide the soil's elastic modulus. Then parameter conversion is required: if the input parameter is only Then, the empirical conversion formula is used. Perform the conversion (where) The coefficient is an empirical value, typically taken as 0.5 to 1.0, where D is the outer diameter of the sleeve. Based on this, the relative stiffness coefficient T of the anchor soil is calculated using the aforementioned bending stiffness and the converted foundation reaction modulus.

[0069] The formula for calculating the relative stiffness coefficient of the anchor soil is as follows:

[0070] (3)

[0071] in, The elastic modulus of the anchor material (usually a constant for steel anchors). ); The moment of inertia (T) is calculated using the outer diameter and wall thickness of the sleeve. This coefficient T physically reflects the deformation capacity of the anchor body relative to the surrounding soil and is a key basis for subsequently determining the effective bearing depth.

[0072] After determining the relative stiffness coefficient, the effective bearing depth of the negative pressure spiral anchor was further determined. Following the "stiffness control cutoff principle," the empirical reduction factor is first determined based on the relative stiffness coefficient T of the anchor soil. Specifically, the empirical reduction factor It is positively correlated with the relative stiffness coefficient T: when (When exhibiting flexible characteristics), The preferred value is 2.5; when When exhibiting rigid characteristics, The preferred value is 4.0; when T is between 2.0 and 5.0, it can be determined by linear interpolation. The specific value of the coefficient. Next, calculate the coefficient. The product of the product with the relative stiffness coefficient T is taken as the critical elastic depth; then, this product is numerically compared with the actual embedded length L of the sleeve, and the smaller of the two values ​​is selected as the final effective stress depth. This means that for ultra-long flexible anchors, the system only calculates... The soil resistance within the depth range is thus consistent with the actual deformation characteristics.

[0073] Finally, based on the corrected soil parameters obtained in step S4 ( ), at the determined effective force depth Within the calculated range, the lateral passive earth resistance of the sleeve, the horizontal resistance of the anchor plate, and the bottom shear and overturning resistance are calculated. It is important to note that the traditional Rankine earth pressure theory is based on the assumption of two-dimensional plane strain; direct application of this theory will result in calculated values ​​far less than the actual values. Considering that the movement of a cylindrical sleeve in the soil is a typical three-dimensional spatial soil squeezing problem, and its resistance mechanism includes lateral shear and spatial wedge effects, this invention introduces a three-dimensional shape magnification factor. (The value is typically between 3.0 and 5.0, depending on the aspect ratio, or using an empirical coefficient from the Broms method). The corrected lateral passive earth resistance of the sleeve is calculated using the integral method. The specific calculation formula is as follows:

[0074] (4)

[0075] in, The corrected passive earth pressure intensity at depth z is expressed as follows:

[0076] (5)

[0077] The anchor plate horizontal resistance The calculation formula is as follows. Since the anchor disc of a helical anchor is typically buried in deep soil, this invention treats it as a deeply buried horizontal plate for calculation. The corrected cohesion obtained in step S4 is used... and deep foundation bearing capacity coefficient (In saturated cohesive soils, the maximum value is usually taken as 9.0) Calculation :

[0078] (6)

[0079] in, It is the projected area of ​​the anchor plate perpendicular to the direction of force, which is the product of the anchor plate diameter and thickness.

[0080] The bottom shear and anti-overturning force The shear strength of the soil, primarily determined by the annular area at the bottom of the sleeve, and the negative pressure suction, constitute the main components. The calculation formula is as follows:

[0081] (7)

[0082] in This refers to the annular contact area at the bottom of the sleeve. It is important to note the suction anti-tipping component. The value of is determined using the following strategy in this step: To ensure long-term operational safety, the system forcibly sets under the default "long-term drainage loading condition" calculation mode. This means ignoring the long-term contribution of negative pressure suction; only when it is a "short-term non-drainage loading condition" and the sensor confirms that the internal seal of the sleeve is intact, is it permissible to calculate this contribution based on the measured residual negative pressure value. The specific calculation steps are as follows:

[0083] Step A: Obtain the reading of the pore water pressure sensor installed inside the sleeve top cover, calculate the difference between it and the external hydrostatic pressure, and obtain the real-time residual negative pressure value. ;

[0084] Step B: Calculate the ultimate pull-out force of the soil plug using formula (8). (To prevent excessive suction from damaging the plug and causing it to be pulled out);

[0085] (8)

[0086] in Where L is the cross-sectional area of ​​the sleeve's inner surface, and L is the actual embedded length of the sleeve. If the measured suction force generates a tensile force... Exceed 80% then take Use this as the effective suction limit; otherwise, take the measured value.

[0087] Step C: Calculate the final suction anti-overturning component using formula (9). ;

[0088] (9)

[0089] In the formula, This is the horizontal-vertical coupling conversion coefficient. This coefficient reflects the efficiency of vertical suction in indirectly improving horizontal bearing capacity by suppressing the overturning moment of the anchor body, based on the length-to-diameter ratio of the anchor body. , The value is usually set between 0.1 and 0.25 (the larger the aspect ratio, the more obvious the leverage effect, and the larger the value).

[0090] Through this step of the calculation, the soil pressure varying along the depth is accumulated into the total resistance by using the integration method. Combined with the three-dimensional magnification factor and the corrected soil parameters, the technical problem of the traditional two-dimensional theoretical model's calculation values ​​being too conservative and unable to truly reflect the three-dimensional spatial resistance characteristics of large-diameter anchors is successfully solved.

[0091] Step S6: Bearing capacity superposition prediction and construction quality feedback.

[0092] Specifically, this step aims to comprehensively consider the collaborative working mechanism of each component of the negative pressure spiral anchor in the soil, output the final predicted bearing capacity value, and evaluate the construction quality in real time based on this. The system uses the superposition principle to vector-superimpose the resistance components calculated in the previous steps. When constructing the superposition model, this embodiment specifically considers the unique coaxial structure of the negative pressure spiral anchor, namely the "sleeve-center rod." Given that the center rod is located inside the large-diameter sleeve, its lateral resistance is usually unable to function independently or contributes very little due to the shielding effect of the outer sleeve. Therefore, to ensure the calculation results are on the safe side and to simplify the model, this step, in the preferred embodiment, ignores the lateral resistance of the center rod and only calculates the contributions of the sleeve, anchor plate, and bottom.

[0093] Corrected prediction of horizontal ultimate bearing capacity Calculated using the following formula:

[0094] (10)

[0095] Furthermore, to visually demonstrate the advantages of this invention compared to existing technologies, the bearing capacity prediction curve calculated by this invention is compared with the curve calculated by the traditional two-dimensional Rankine theory based on uncorrected parameters. The results are as follows: Figure 3 As shown.

[0096] Figure 3 The graph shows a comparison of the horizontal ultimate bearing capacity as a function of burial depth. The dashed line represents the result calculated using the traditional method, while the solid line represents the result calculated using the present invention. It can be seen that the difference between the two methods gradually widens with increasing burial depth. At a design depth of 12m, the traditional method calculates a value of 4800kN, while the present invention predicts a value of 6675kN, an increase of approximately 39%. This improvement is mainly attributed to the present invention's quantification of the soil compaction enhancement effect (manifested by the improvement factor) and the three-dimensional spatial wedge effect (manifested by...). (This demonstrates that) the present invention can effectively tap the load-bearing potential of large-diameter anchors and avoid material waste caused by overly conservative designs.

[0097] Furthermore, to achieve intelligent construction quality control, after obtaining the predicted horizontal ultimate bearing capacity, a construction quality feedback step is also required. This is achieved by setting a design bearing capacity threshold (…). ) and minimum energy consumption standards ( ). The calculated and Compare; if the predicted value Greater than or equal to If the predicted value is below the threshold, the anchor point construction is deemed qualified. Conversely, if the predicted value is below the threshold, a source diagnosis is performed, further retrieving the construction resistance energy feature vector generated in step S2. If the system finds that the average installation specific energy displayed by the construction resistance energy feature vector is simultaneously below the minimum energy consumption standard, it is determined that the soil properties in the area are extremely poor or the construction has not achieved the expected compaction effect. At this time, a red warning window will pop up on the control terminal at the construction site, generating specific prompt information, clearly indicating that "the soil compaction degree is insufficient or there is a risk of soil disturbance and softening during the settlement process," and recommending remedial measures such as "in-situ grouting reinforcement" or "starting the re-laying process" to the construction personnel based on the suggestions of the built-in expert database. Through the optimization and hierarchical safety control of this step, the present invention successfully constructs a highly reliable bearing capacity prediction system that conforms to the geotechnical mechanics shielding principle and takes into account the long-term operational safety.

[0098] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0099] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0100] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0101] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0102] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0103] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for predicting the horizontal ultimate bearing capacity of a negative pressure spiral anchor, characterized in that, Includes the following steps: Based on the installation sequence data of the negative pressure spiral anchor during the sinking construction process, a comprehensive installation specific energy index is constructed, and a construction resistance energy characteristic vector distributed along the depth is generated. Based on the construction resistance energy feature vector, the soil improvement factor is obtained by inversion using a preset energy-soil property mapping model. The mechanical parameters of undisturbed soil are corrected using soil amendment factors; Based on the corrected soil parameters and the geometric parameters of the negative pressure spiral anchor, the resistance of each component of the negative pressure spiral anchor is calculated, and the resistance of each component is superimposed to obtain the predicted value of the horizontal ultimate bearing capacity of the negative pressure spiral anchor. The construction of the comprehensive installation energy ratio index includes: Calculate the instantaneous integrated installation energy ratio based on the construction sequence data; The instantaneous comprehensive installation energy is integrated or weighted averaged along the depth to obtain the average installation energy corresponding to each depth interval, forming the construction resistance energy feature vector. Among them, the instantaneous integrated installation specific energy is the power consumed per unit projected area at time t, and the calculation method is as follows: In the formula, The instantaneous integrated installation specific energy at time t; Let t be the precession torque. Angular velocity of rotation This is the effective downward force converted from negative pressure suction. For penetration rate, The characteristic horizontal projected area of ​​the spiral anchor; The inversion yields soil improvement factors, including: Obtain multiple sets of historical negative pressure spiral anchor measured horizontal bearing capacity data and their corresponding historical installation time sequence data; Calculate the historical comprehensive installation ratio based on the aforementioned historical installation time sequence data; With the goal of minimizing the error between the predicted bearing capacity and the measured bearing capacity, a nonlinear regression model or a neural network model is trained to establish a functional mapping relationship between the comprehensive installation specific energy and the soil improvement factor.

2. The method for predicting the horizontal ultimate bearing capacity of a negative pressure spiral anchor according to claim 1, characterized in that, The method of correcting the mechanical parameters of undisturbed soil using soil improvement factors includes: Using formula Calculate the corrected severity ; Using formula Calculate the corrected cohesion ; Using formula Calculate the corrected internal friction angle ; in, These are the natural unit weight, undisturbed cohesion, and undisturbed internal friction angle of the undisturbed soil mechanical parameters, respectively. These are the soil improvement factors, namely, the density improvement factor, the cohesion improvement factor, and the internal friction angle increment.

3. The method for predicting the horizontal ultimate bearing capacity of a negative pressure spiral anchor according to claim 1, characterized in that, The calculation of each component resistance of the negative pressure spiral anchor includes: The bending stiffness of the negative pressure spiral anchor is determined based on the geometric parameters, and the elastic modulus of the soil in the undisturbed soil mechanical parameters is obtained. The relative stiffness coefficient of the anchor soil is calculated using bending stiffness and soil elastic modulus; The effective bearing depth of the negative pressure spiral anchor is calculated based on the relative stiffness coefficient of the anchor soil. Based on the corrected soil parameters, within the effective stress depth range, the lateral passive earth resistance of the sleeve is calculated using the passive earth pressure theory integral, and is used as one of the sub-items of resistance.

4. The method for predicting the horizontal ultimate bearing capacity of a negative pressure spiral anchor according to claim 1, characterized in that, The individual resistance items include at least the lateral passive soil resistance of the sleeve, the horizontal resistance of the anchor plate, and the bottom shear and overturning resistance. The bottom shear and overturning resistance are calculated based on the soil shear strength provided by the annular contact area at the bottom of the sleeve and the overturning resistance component generated by the negative pressure suction.

5. The method for predicting the horizontal ultimate bearing capacity of a negative pressure spiral anchor according to claim 3, characterized in that, Calculate the effective bearing depth of the negative pressure spiral anchor, including: The empirical reduction factor is determined based on the relative stiffness coefficient of the anchor soil. Calculate the product of the empirical reduction factor and the relative stiffness coefficient of the anchor soil, and compare the product with the sleeve length in the geometric parameters; The smaller of the two values ​​is selected as the effective force-bearing depth.

6. The method for predicting the horizontal ultimate bearing capacity of a negative pressure spiral anchor according to claim 1, characterized in that, After obtaining the predicted value of the horizontal ultimate bearing capacity, a construction quality feedback step is also included: Set design load-bearing capacity thresholds and minimum energy consumption standards; Compare the predicted horizontal ultimate bearing capacity with the design bearing capacity threshold; If the predicted value is lower than the threshold, the construction resistance energy feature vector is further retrieved. If the average installation energy ratio shown by the construction resistance energy characteristic vector is lower than the minimum energy consumption standard, a prompt message is generated, indicating that the soil compaction is insufficient or there is a risk of soil disturbance and softening during the sinking process.

7. The method for predicting the horizontal ultimate bearing capacity of a negative pressure spiral anchor according to claim 1, characterized in that, The effective downward force converted from negative pressure suction Calculated using the following formula: in, This refers to the real-time negative pressure suction value in the installation timing data. The pressure-bearing area of ​​the top cover of the sleeve. This is the negative pressure transmission efficiency coefficient.

8. The method for predicting the horizontal ultimate bearing capacity of a negative pressure spiral anchor according to claim 1, characterized in that, The geometric parameters include at least the outer diameter of the sleeve, the length of the sleeve, the diameter of the anchor plate, and the sleeve wall thickness used to calculate the stiffness; the undisturbed soil mechanical parameters include at least the natural unit weight, undisturbed cohesion, undisturbed internal friction angle, and soil elastic modulus. The installation timing data includes the rotational torque, negative pressure suction value, penetration rate, and rotation speed, all collected at a preset sampling frequency.

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