Method and device for predicting pile-forming strength of high-pressure jet grouting pile

Abstract

The invention provides a method and device for predicting the pile-forming strength of a high-pressure jet grouting pile, and relates to the technical field of pile-forming strength prediction.The method comprises the steps that construction parameters are collected in real time through a multi-source sensor, a construction parameter matrix is constructed, and the soil layer boundary depth and the dielectric constant are obtained through geological radar scanning so as to distribute static weight factors; and calculating an intensity correction coefficient based on geological parameters of the core sample, finally establishing an intensity prediction model fusing the dynamic weight, the slurry solidification time, the temperature influence factor and the geological correction coefficient, and setting a three-level early warning mechanism. The core innovation of the scheme is that construction parameters and geological characteristics are dynamically coupled, the model precision is remarkably improved through double calibration of geological radar and core data, and the problem that a traditional method is insufficient in adaptability to soil layer difference is solved through introduction of a geological correction coefficient; and the real-time closed-loop optimization of the construction process is realized through the multi-sensor time synchronization application.

Description

Technical Field

[0001] The present invention relates to the technical field of pile strength prediction, and in particular to a method and device for predicting the strength of a high-pressure jet grouting pile. Background Art

[0002] As a foundation reinforcement technology, high-pressure rotary jet grouting piles are widely used in foundation projects under complex strata such as soft soil and sand layers. The pile strength directly affects the stability and durability of the structure. In traditional construction processes, strength predictions mainly rely on empirical formulas or laboratory mix tests, which make it difficult to reflect the dynamic coupling effect of actual construction parameters and geological conditions in real time. Especially in strata where the soil interface changes significantly, existing methods often ignore the differences in geological parameters such as the dielectric properties and permeability coefficient of the soil layer, resulting in large deviations in the prediction results, and prone to problems such as insufficient local strength or waste of slurry. In addition, the adjustment of construction parameters usually lags behind actual needs, lacks a closed-loop optimization mechanism based on real-time data, and is difficult to adapt to the refined construction requirements under complex working conditions.

[0003] In recent years, with the advancement of sensor technology, some studies have attempted to predict pile quality by monitoring construction parameters. However, these approaches are often limited to analyzing a single data source, failing to effectively integrate multidimensional information such as geological radar scanning and core sample testing. Furthermore, they inadequately consider the influence of dynamic factors such as slurry temperature and setting time. Existing prediction models generally suffer from poor adaptability and delayed early warning, making them unable to meet the requirements of intelligent high-pressure jet grouting pile construction. Therefore, a pile strength prediction method that can integrate multi-source real-time data, dynamically correct for geological influences, and implement closed-loop control is urgently needed to improve construction accuracy and efficiency in complex strata.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and device for predicting the strength of high-pressure rotary jet grouting piles to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: A method for predicting the strength of high-pressure jet grouting piles, comprising the following steps: Step 1: A multi-source sensor system installed on the jet grouting machine collects construction parameters in real time. The construction parameters include nozzle injection pressure, slurry flow rate, drill rod lifting speed, drill rod rotation speed, and slurry temperature. The construction parameters are aligned using a time window and stored as a construction parameter matrix. Step 2: Use geological radar to continuously scan along the pile axis to obtain radar feedback data. Analyze the radar feedback data to obtain the soil profile, boundary depth and dielectric constant of different soil layers, and assign a static weight factor to each soil layer. Step 3: Drill core samples in the middle of each soil layer and measure the geological parameters of each core sample to construct a geological parameter vector and calculate the strength correction coefficient of each soil layer; Step 4: Determine the initial predicted strength based on the nozzle injection pressure, slurry flow rate, drill rod lifting speed, and drill rod rotation speed data in the construction parameter matrix; calculate the dynamic weight based on the construction depth and the static weight factor; combine the dynamic weight, slurry setting time, and strength correction factor to obtain the geological correction factor; simultaneously, introduce the influence of slurry temperature to determine the temperature influence factor; and determine the final predicted strength based on the geological correction factor and the temperature influence factor. Step 5: Set the three-level warning threshold and compare the final predicted intensity with the warning threshold to determine the warning level; if warnings appear in two consecutive time windows, calculate the optimal adjustment amount of the nozzle injection pressure and drill pipe lifting speed parameters.

[0007] Furthermore, obtaining the construction parameter matrix specifically includes: The multi-source sensor system includes a pressure sensor, an electromagnetic flowmeter, a high-precision encoder, a gyroscope, and a temperature sensor. The installation locations of the sensors are as follows: a pressure sensor is installed at the high-pressure pump outlet pipeline to monitor the nozzle injection pressure; an electromagnetic flowmeter is installed at the slurry delivery main pipeline, that is, between the cement slurry mixer outlet and the high-pressure pump inlet, to monitor the slurry flow rate; a high-precision encoder is installed at the end of the drilling rig winch drum shaft to measure the drill pipe lifting speed, and a gyroscope is installed on the housing of the drill pipe top drive device to monitor the drill pipe rotation speed; a temperature sensor is installed in the slurry mixing barrel outlet pipeline to monitor the slurry temperature; The sampling frequency is determined and the noise is eliminated by sliding average filtering. The time synchronization algorithm is used to align the data collected by the multi-source sensor system. The time-aligned construction parameters are stored as a matrix, which can be expressed as: ; Where X(t) represents the construction parameter matrix, P(t) is the nozzle injection pressure at time t, Q(t) is the slurry flow rate at time t, V(t) is the drill pipe lifting speed at time t, N(t) is the drill pipe rotation speed at time t, T(t) is the slurry temperature at time t, and t is the time variable; each row of the matrix represents a timestamp, and each column represents the construction parameter corresponding to the timestamp.

[0008] Furthermore, obtaining the static weight factor specifically includes: The static weight factor of each soil layer is calculated according to the boundary depth and dielectric constant of the soil layer. The calculation formula is: ; in, is the static weight factor of the i-th soil layer, is the dielectric constant of the i-th soil layer, is the minimum value of the dielectric constant of each soil layer, is the maximum value of the dielectric constant of each soil layer, is the boundary depth of the i+1th soil layer, that is, the top burial depth of the i+1th soil layer, is the boundary depth of the i-th soil layer, that is, the burial depth of the top of the i-th soil layer, i is the soil layer index, and the soil layers are indexed and numbered from top to bottom, and D is the designed pile length.

[0009] Furthermore, calculating the strength correction coefficient specifically includes: The geological parameters include permeability coefficient, plasticity index, water content, density, and cohesion; the geological parameters of each soil layer are integrated into a geological parameter matrix, where each row represents a geological parameter of a soil layer and each column represents a geological parameter value; In the middle of each soil layer, Cores were drilled at the location of the soil layer, and the thin-wall soil sampler static pressure method was used to take samples. Three groups of parallel samples were taken from each soil layer. The geological parameters of each parallel sample were measured through laboratory tests. The mean value of the geological parameters of each parallel sample in the same soil layer was calculated. The mean value was used as the geological parameter of the soil layer. The strength correction coefficient formula for each soil layer was calculated as follows: ; in, is the strength correction coefficient of the i-th soil layer, is the permeability coefficient of the i-th soil layer, is the plasticity index of the i-th soil layer, is the water content of the i-th soil layer, is the density of the i-th soil layer, is the cohesion of the i-th soil layer, are the weight coefficients of each item respectively.

[0010] Furthermore, determining the final prediction strength specifically includes: The formula for calculating the initial predicted strength is as follows: ; in, is the initial forecast intensity, is the slurry performance coefficient, is the benchmark strength value, P is the nozzle injection pressure, Q is the slurry flow rate, V is the drill rod lifting speed, N is the drill rod rotation speed, are the exponential coefficients of the corresponding terms; Get the construction depth, and determine the dynamic weight based on the construction depth and the static weight factor. The calculation formula is: ; in, Indicates the dynamic weight at the construction depth z meters, is the static weight factor of the soil layer at the construction depth of z meters, z is the construction depth, is the static weight influence coefficient, is the depth influence coefficient, is the boundary depth of the soil layer immediately adjacent to and below the j+1th soil layer at the construction depth of z meters, The boundary depth of the jth soil layer at the construction depth of z meters; The geological correction factor is obtained by combining the dynamic weight, slurry setting time and strength correction factor. The calculation formula is: when When , the calculation formula is as follows: ; when When , the calculation formula is as follows: ; when When , the calculation formula is as follows: ; when When , the calculation formula is as follows: ; in, Indicates the geological correction coefficient at the construction site z meters, is the strength correction coefficient of the jth soil layer, is the time attenuation coefficient, h represents the slurry setting time, and m represents the index of the lowest soil layer; Considering the effect of slurry temperature on strength, the temperature influence factor is calculated using the formula: ; in, is the temperature influence factor, is the linear influence coefficient of temperature on slurry, is the slurry temperature, is the rate coefficient of temperature effect decay over time; Introducing the influence of geological correction coefficient and temperature influence factor, the final predicted intensity is calculated using the following formula: ; in, It represents the final predicted strength of the soil layer at the construction depth of z meters.

[0011] Furthermore, calculating the optimal adjustment amount of relevant parameters specifically includes: The three-level warning mechanism is yellow warning, orange warning and red warning. A yellow warning is issued when , an orange warning is issued when A red alert is issued when is the strength design requirement value, It represents the final predicted strength of the soil layer at the construction depth of z meters; If two consecutive warning windows appear, the optimal adjustment of nozzle injection pressure and drill pipe lifting speed is calculated. The calculation formulas are: ; ; in, are the optimal adjustment amounts of nozzle injection pressure and drill pipe lifting speed respectively.

[0012] The present invention further provides a high-pressure rotary jet grouting pile strength prediction device, which is used to implement the above-mentioned high-pressure rotary jet grouting pile strength prediction method, comprising: A multi-source data acquisition module is used to collect construction parameters in real time through a multi-source sensor system installed on the jet pile driver. The construction parameters include nozzle injection pressure, slurry flow rate, drill rod lifting speed, drill rod rotation speed and slurry temperature. The construction parameters are aligned through a time window and stored as a construction parameter matrix; The geological radar tomography analysis module is used to continuously scan along the pile axis using geological radar to obtain radar feedback data, analyze the radar feedback data to obtain the soil layer profile, boundary depth and dielectric constant of different soil layers, and assign a static weight factor to each soil layer; The core geological parameter calibration module is used to drill core samples in the middle of each soil layer, measure the geological parameters of each core sample, construct the geological parameter vector, and calculate the strength correction coefficient of each soil layer; The multi-factor coupled strength modeling module is used to determine the initial predicted strength based on the nozzle injection pressure, slurry flow rate, drill pipe hoist speed, and drill pipe rotation speed data in the construction parameter matrix; calculate the dynamic weight based on the construction depth and the static weight factor; combine the dynamic weight, slurry setting time, and strength correction factor to obtain the geological correction factor; and simultaneously introduce the influence of slurry temperature to determine the temperature influence factor. The final predicted strength is determined based on the geological correction factor and the temperature influence factor. The adaptive warning and parameter optimization module is used to set three-level warning thresholds and compare the final predicted intensity with the warning threshold to determine the warning level; if warnings appear in two consecutive time windows, the optimal adjustment amount of the nozzle injection pressure and drill pipe lifting speed parameters is calculated.

[0013] In the above technical solution, the technical effects and advantages provided by the present invention are: The present invention effectively solves the problems of insufficient strength prediction accuracy and poor geological adaptability in the construction of traditional high-pressure rotary jet piles through the deep integration of real-time monitoring of multi-source sensors and geological radar data. Compared with the existing technology, this solution can dynamically correct the strength influence of different soil layers, and combined with the real-time feedback of slurry temperature and setting time, it can significantly improve the prediction accuracy under complex stratum conditions and reduce the pile strength error. At the same time, based on the three-level early warning system, strength deviations can be detected in time during the construction process and the parameters can be automatically optimized and adjusted, reducing manual intervention and avoiding slurry waste or local strength defects caused by lagging control, thereby improving construction efficiency while ensuring the quality of piles. In addition, the present method provides reliable technical support for the intelligent construction of high-pressure rotary jet piles through the coordinated optimization of geological parameters and construction parameters, and is particularly suitable for engineering scenarios with variable geological conditions such as soft soil and sand layers. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 Schematic diagram of the overall method flow of the present invention; Figure 2 is the relationship diagram between geological correction coefficient and final predicted intensity; Figure 3 is the relationship diagram between the initial prediction intensity and the final prediction intensity; Figure 4 This is the relationship diagram between temperature influence factor and final predicted intensity; Figure 5 Schematic diagram of the structure of the device of the present invention. DETAILED DESCRIPTION

[0015] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0016] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0017] Example: See also Figure 1 , the present invention provides a technical solution: A method for predicting the strength of high-pressure jet grouting piles, comprising the following steps: Step 1: A multi-source sensor system installed on the jet grouting machine collects construction parameters in real time. The construction parameters include nozzle injection pressure, slurry flow, drill rod lifting speed, drill rod rotation speed, and slurry temperature. The construction parameters are aligned through a time window and stored as a construction parameter matrix.

[0018] In this embodiment, obtaining the construction parameter matrix specifically includes: The multi-source sensor system includes a pressure sensor, an electromagnetic flowmeter, a high-precision encoder, a gyroscope and a temperature sensor; The composition and installation configuration of the multi-source sensor system specifically include: the pressure sensor is preferably a piezoresistive pressure sensor, such as the Honeywell 26PC series, which is flange-mounted at the high-pressure pump outlet pipeline, with a pulse damper installed between the sensor and the pipeline, and a 4-20mA current loop output configured for electrical isolation during sampling to monitor the nozzle injection pressure; the electromagnetic flowmeter is preferably a slurry crystallization electromagnetic flowmeter, such as the Krohne Optiflux 4300, with a straight pipe section at least 5 times the pipe diameter at the cement slurry mixer outlet, the electrode surface is coated with a tungsten carbide wear-resistant layer, and the inner lining is polyurethane rubber to monitor the slurry flow; the high-precision encoder is preferably an absolute encoder, such as the Heidenhain ECN 413, resolution ≥18 bits, rigidly connected to the winch drum shaft via an elastic coupling, used to measure the drill pipe hoisting speed; the gyroscope is preferably an MSMS gyroscope, such as the ADIADXRS645, with a range of ±2000° / s. It should be installed on the top of the drill pipe using a vibration isolation bracket to monitor the drill pipe rotation speed; the temperature sensor is preferably a PT100 platinum resistance temperature sensor with Class A accuracy, using an insert-type mounting sleeve with heat conduction fins, installed in the outlet pipe of the slurry mixing tank to monitor the slurry temperature; The sampling frequency is determined to be 10 Hz, that is, data is collected every 0.1 seconds, and the noise is eliminated by sliding average filtering. The sliding average filtering adopts the weighted average algorithm, and the weight coefficient is set to [0.1, 0.15, 0.25, 0.15, 0.1]. The mean of each parameter is set to a reasonable range threshold. When the threshold is triggered, the triple standard deviation criterion is activated for elimination. The processed data is aligned using the time synchronization algorithm, and the time-aligned construction parameters are stored as a matrix, which can be expressed as: ; Where X(t) represents the construction parameter matrix, P(t) is the nozzle injection pressure at time t, Q(t) is the slurry flow rate at time t, V(t) is the drill pipe lifting speed at time t, N(t) is the drill pipe rotation speed at time t, T(t) is the slurry temperature at time t, and t is the time variable; each row of the matrix represents a timestamp, and each column represents the construction parameter corresponding to the timestamp.

[0019] The above-mentioned coordinated acquisition and real-time processing of a multi-source sensor system enables precise, synchronous monitoring and structured storage of high-pressure jet grouting pile construction parameters, laying a reliable data foundation for subsequent pile quality analysis and strength prediction. Specifically, the optimized layout and synchronous acquisition of pressure sensors, electromagnetic flowmeters, high-precision encoders, gyroscopes, and temperature sensors ensures the real-time and accurate acquisition of key parameters such as nozzle injection pressure, slurry flow, drill rod hoist speed, drill rod rotation speed, and slurry temperature. The application of sliding average filtering and time synchronization algorithms effectively suppresses noise interference and solves the time alignment problem of multi-source data, enabling the construction parameter matrix X(t) to fully, continuously, and highly fidelity reflect the dynamic changes during the construction process. This step not only improves the accuracy and reliability of data acquisition, but also provides high-quality input for subsequent machine learning modeling or physical model calculations through standardized and structured data organization, significantly improving the accuracy and engineering applicability of pile strength predictions.

[0020] Step 2: Use geological radar to continuously scan along the axis of the pile position to obtain radar feedback data. Analyze the radar feedback data to obtain the soil profile, boundary depth and dielectric constant of different soil layers, and assign a static weight factor to each soil layer.

[0021] In this embodiment, obtaining the static weight factor specifically includes: Geological radar is used to scan along the axis of the pile position to obtain the stratigraphic profile within a depth of 20 meters. A shielded antenna array with a center frequency of 100 MHz is used to ensure a balance between the detection depth (20 meters) and the resolution (vertical ≤ 0.2 meters). The antenna spacing is set to 0.5 meters to optimize the profile continuity. The scanning mode is continuous scanning with a common offset distance, the survey line spacing is 0.2 meters, the scanning speed is controlled within 0.5 m / s, and sampling is triggered by an encoder (one measuring point every 5 cm).

[0022] The radar feedback data includes the following core information: two-way travel time profile, amplitude signal, waveform characteristics and spatial coordinates. Bandpass filtering is used to remove high-frequency noise and low-frequency drift. The bandpass filtering uses a Chebyshev II filter with a passband frequency of 80-120MHz and a stopband attenuation of ≥40dB to eliminate high-frequency electromagnetic interference (such as equipment noise) and low-frequency baseline drift (such as antenna swing effect). An exponential gain function is used to compensate for the attenuation of depth signals, such as a time-varying gain function, to compensate for the energy attenuation of electromagnetic waves with depth. The average signal of the first 10ns of each survey line is extracted as the antenna coupling response, which is subtracted from the original data one by one to retain the formation reflection signal. Reflection layer identification includes amplitude threshold monitoring, phase axis tracking and time-to-depth conversion. Amplitude threshold monitoring includes setting dynamic thresholds. , is the dynamic threshold, is the local window amplitude mean, The standard deviation is the value of the standard deviation. If the amplitude of three consecutive measurement points exceeds the threshold and the phase is consistent, it is marked as a valid reflection interface. The normalized cross-correlation algorithm (window length of 5 measurement points, step length of 1 measurement point) is used to calculate the waveform similarity of the event axis tracking, and the same soil layer interface is tracked across the survey line. The broken event axis is repaired by cubic spline interpolation. In the time-to-depth conversion, the depth is calculated based on the calibrated dielectric constant and light speed. The initial dielectric constant is calibrated by the borehole data. When it is not calibrated, it is estimated using the Wyllie time average formula. The dielectric constant difference is calculated using the reflection coefficient, and the dielectric constant model is iteratively optimized with the goal of minimizing the root mean square error. The Levenberg-Marquardt algorithm is used to adjust the dielectric constant of each layer to maximize the matching degree between the positive edge synthetic record (based on the ray tracing model) and the measured waveform. The output data are transmitted, including the soil layer boundary depth and dielectric constant. The static weight factor of each soil layer is calculated based on the soil layer boundary depth and dielectric constant. The calculation formula is: ; in, is the static weight factor of the i-th soil layer, is the dielectric constant of the i-th soil layer, is the minimum value of the dielectric constant of each soil layer, is the maximum value of the dielectric constant of each soil layer, is the boundary depth of the i+1th soil layer, that is, the top burial depth of the i+1th soil layer, is the boundary depth of the i-th soil layer, that is, the burial depth of the top of the i-th soil layer, i is the soil layer index, and the soil layers are indexed and numbered from top to bottom, and D is the designed pile length.

[0023] The static weight factor essentially reflects the relative importance of different soil layers on the final pile quality. The dielectric constant, as a characterization of the propagation characteristics of electromagnetic waves, indirectly reflects key physical properties of the soil, such as moisture content, density, and mineral composition. The dielectric constant normalization process realizes the quantitative characterization of the electrical differences of the soil layers. The dielectric constant directly reflects key characteristics such as moisture content and density of the soil. These parameters are closely related to the penetration and diffusion effect of cement slurry in rotary jet piles. For example, the significant difference between saturated soft clay and dry sand layer will affect the slurry consolidation effect. To eliminate the dimensional effect, the normalization process maps dielectric constants of different orders of magnitude to the [0,1] interval to avoid interference with weight distribution under absolute numerical values. In addition, key soil layers are highlighted. When the dielectric constant of a soil layer is close to the maximum value (such as a high-water-content soft layer), the normalization term approaches 1, automatically amplifying its influence on the pile quality. Thickness weight term This embodies the principle of spatial proportion. Soil thickness is proportional to the contact area of the pile. Thicker soil layers (such as thick sand layers) naturally have a greater impact on the overall pile strength. For example, a 5-meter-thick sand layer receives 10 times the foundation weight as a 0.5-meter-thin clay interlayer. The pile length is also normalized to ensure that the sum of the weights of each soil layer does not exceed 1, avoiding over-range issues while preserving relative proportional relationships. When the pile tip is located in the middle of a soil layer, the actual penetration depth is automatically calculated to avoid weight distortion. The static weight factor product structure reflects the coupling effect requirement. The influence of soil layer properties (dielectric constant) and spatial proportion (thickness) on pile strength is multiplicative rather than simply additive. For example, even a thin, highly water-rich weak layer can significantly reduce local strength, necessitating a product to amplify its impact. Furthermore, nonlinear compensation is considered. In actual projects, the impact of unfavorable strata on strength often increases exponentially (for example, a silt layer causes pile diameter reduction). The product form better reflects this nonlinear characteristic than a linear combination.

[0024] Through the refined detection and intelligent data processing of geological radar, high-precision analysis and quantitative evaluation of the stratum characteristics for high-pressure jet jet pile construction have been achieved, providing key geological parameter support for pile strength prediction. Specifically, the continuous scanning and multi-dimensional signal processing (including bandpass filtering, gain compensation, and coupling interference elimination) of the geological radar effectively extract key indicators such as soil boundary depth and dielectric constant, overcoming the shortcomings of traditional drilling methods such as low efficiency and discrete data. The soil layer division method based on amplitude threshold monitoring, phase axis tracking, and dielectric constant inversion significantly improves the accuracy and resolution of complex stratum interface identification, and is particularly suitable for the detection of heterogeneous strata such as interlayers and weak zones. The dynamic calculation of static weight factors (integrating dielectric constant differences and soil layer thickness weights) scientifically quantifies the differentiated effects of different soil layers on pile strength, enabling subsequent construction parameter optimization and strength prediction models to be adaptively adjusted according to stratum characteristics, ultimately achieving controllable pile quality and improved prediction accuracy.

[0025] Step 3: Drill core samples in the middle of each soil layer and measure the geological parameters of each core sample to construct a geological parameter vector and calculate the strength correction coefficient of each soil layer.

[0026] In this embodiment, calculating the strength correction coefficient specifically includes: The geological parameters include permeability coefficient, plasticity index, water content, density, and cohesion. The geological parameters of each soil layer are integrated into a geological parameter matrix, where each row represents a geological parameter of a soil layer and each column represents a geological parameter value, in the following format: ; Where G is the geological parameter matrix, n is the total number of parallel samples; In the middle of each soil layer, Drill cores at the location, use a thin-walled fixed piston soil sampler, and static pressure method to sample to avoid damage to the soil sample structure caused by impact vibration. Take 3 groups of parallel samples from each soil layer, and determine the geological parameters of each parallel sample through laboratory tests, such as the permeameter uses the variable head method to determine the permeability coefficient, the liquid plastic limit instrument determines the plasticity index, the constant temperature oven uses the drying method to determine the water content, the electronic balance uses the ring knife method to determine the density, and the triaxial instrument uses the unconfined compression test to determine the cohesion. Calculate the mean value of the geological parameters of each parallel sample in the same soil layer, and use the mean value as the geological parameter of the soil layer. The strength correction coefficient formula for each soil layer is calculated as follows: ; in, is the strength correction coefficient of the i-th soil layer, is the permeability coefficient of the i-th soil layer, is the plasticity index of the i-th soil layer, is the water content of the i-th soil layer, is the density of the i-th soil layer, is the cohesion of the i-th soil layer, are the weight coefficients of each item, .

[0027] The strength correction coefficient is a parameter that quantifies the influence of different soil layers on the strength of jet grouting piles. Its value directly reflects the strengthening or weakening effect of the soil layer on the final pile strength. A high value may correspond to a dense sand layer (favorable for slurry consolidation), while a low The value may indicate a weak layer with high water content (which may lead to uneven strength). By integrating multi-dimensional geological parameters, complex soil engineering properties are converted into a single comparable index. The permeability coefficient affects the slurry diffusion range. The smaller the value, the more obstructed the slurry penetration, and the resistance effect needs to be amplified. The plasticity index reflects that high-IP soil is prone to shrinkage and cracking, and a positive correction is required to compensate for strength loss. High water content dilutes the cement slurry and weakens the hydration reaction, so it is reflected as a negative term. Gravity and cohesion directly represent the strength of the soil skeleton and are positively correlated with the bearing capacity of the pile. In the formula, the permeability coefficient, plasticity index, gravity, and cohesion are all positively correlated with the strength correction factor. As these parameters increase, the strength correction factor also increases. Water content is negatively correlated with the strength correction factor. As the water content increases, the strength correction factor decreases.

[0028] Here is set 、 、 、 、 The dominant type of slurry diffusion is to set the permeability coefficient to the maximum weight. The permeability coefficient directly determines the penetration radius and uniformity of cement slurry in the soil layer, which is the primary control factor for the quality of jet grouting piles. If the low permeability soil layer will cause the slurry to remain near the nozzle, forming a "candied haws"-shaped inhomogeneous pile body, it is necessary to significantly increase the permeability coefficient. To strengthen its correction effect. Maximum, ensuring that the effect of permeability differences on strength is exponentially amplified. The sensitivity of soil deformation makes the weight of the plasticity index second only to the permeability coefficient. The plasticity index reflects the tendency of soil to shrink and crack. For example, high Ip soil is prone to cracks after consolidation, which weakens the integrity of the pile. Because the effect of soil plastic deformation on the long-term strength of the pile is more lasting than the instantaneous mechanical parameters, the weight of the plasticity index is higher than that of gravity and cohesion. The skeleton support effect makes the gravity weight in the middle. The gravity represents the density of the soil and the compaction effect of its own weight, which affects the lateral constraint force of the slurry consolidation body. High-gravity soil provides good confining pressure, but it has no direct chemical effect on the slurry solidification process, so Lower than But higher than The hydration inhibition effect results in a lower weight for water content. Excessive water content dilutes the cement paste, delaying the hydration reaction and reducing early strength. Cohesion reflects the in-situ strength of the soil, but jet-jet piles primarily rely on cement paste solidification rather than undisturbed soil strength, so cohesion has the lowest weight.

[0029] Step 4: Determine the initial predicted strength based on the nozzle injection pressure, slurry flow rate, drill rod lifting speed and drill rod rotation speed data in the construction parameter matrix; calculate the dynamic weight according to the construction depth and the static weight factor; combine the dynamic weight, slurry setting time and strength correction factor to obtain the geological correction factor; at the same time, introduce the influence of slurry temperature and determine the temperature influence factor. Determine the final predicted strength based on the geological correction factor and the temperature influence factor.

[0030] In this embodiment, determining the final prediction strength specifically includes: The formula for calculating the initial predicted strength is as follows: ; in, is the initial forecast intensity, is the slurry performance coefficient, which can be determined by the construction parameters and slurry ratio record data in the historical engineering data using the laboratory calibration method. As the benchmark strength value, the strength of pure cement soil under the same geological conditions can be taken, and it can be measured by indoor static pressure molding test blocks without stirring, or it can be set according to the minimum strength requirements of jet grouting piles in the specification. P is the nozzle injection pressure, Q is the slurry flow rate, V is the drill rod lifting speed, and N is the drill rod rotation speed. are the exponential coefficients of the corresponding terms, set 、 、 、 .

[0031] The initial predicted strength reflects the theoretical strength of high-pressure jet grouting piles under ideal homogeneous formation conditions, considering only construction process parameters. This parameter establishes a direct correlation between process parameters and pile strength by quantifying the effects of key construction variables such as nozzle injection pressure, slurry flow rate, drill rod lifting speed, and rotational speed on slurry diffusion and consolidation. Its technical effect is to remove interfering formation factors, providing a baseline strength reference for subsequent geological corrections. This allows the prediction model to clearly distinguish between the contributions of construction technology and geological conditions, avoiding error amplification caused by the coupling of multiple factors. For example, a low calculated value can directly indicate an unreasonable construction parameter setting (such as insufficient pressure or excessively rapid lifting), rather than a misjudgment of a formation defect.

[0032] The independent variables in the formula (P, Q, V, and N) directly control the interaction between slurry and soil during jet grouting pile construction: nozzle pressure P determines the slurry jet's fragmentation capacity and influences the range of soil disturbance; slurry flow rate Q determines the cement content and dominates the density of the solidified mass; lifting speed V influences slurry penetration time and is negatively correlated with pile uniformity; and drill rod speed N influences slurry-soil mixing efficiency through stirring. These parameters are weighted by exponential terms, the physical significance of which is to match the nonlinear characteristics of the different parameters' effects on strength.

[0033] The formula clearly expresses the synergistic and antagonistic relationship between parameters through the ratio structure of the numerator and the denominator: the injection pressure P and the slurry flow Q are strength enhancement factors, and their increase will increase ; The lifting speed V and the rotation speed N are strength suppression factors. Their increase will reduce the slurry action time or mixing uniformity, thereby reducing This design is consistent with engineering experience - for example, high-pressure slow-increase process (high P and low V) usually achieves higher strength, while fast-increase and low-consumption process (low P and high V) easily leads to insufficient strength.

[0034] In the initial predicted strength calculation formula, exponential coefficients were set based on the deep coupling between the high-pressure jet grouting pile formation mechanism and engineering practice. The nozzle jet pressure has the largest exponent, as it directly determines the slurry's ability to cut and penetrate the soil. Increasing pressure significantly increases the pile diameter and enhances soil fragmentation, and its nonlinear effect is reflected by an exponent greater than 1. The exponential coefficient for slurry flow rate is set at 0.8, reflecting that while increasing flow rate improves slurry coverage, excessive flow dilutes the cement content, so its contribution needs to be moderately weakened. The exponential coefficient for drill rod lifting speed is set at 0.5, indicating that increasing speed linearly reduces the slurry injection volume per unit depth, but this effect is partially offset by pressure and flow rate. The exponential coefficient for drill rod rotation speed is the smallest, as rotation speed primarily affects slurry distribution uniformity rather than core strength parameters, resulting in diminishing marginal effects. This coefficient combination not only conforms to the process principle of "pressure-dominant, flow-coordinated, and motion parameter-assisted," but also quantifies the asymmetric influence of each parameter on strength formation through exponential differences, ensuring consistency between the model's predictions and engineering experience.

[0035] Get the construction depth, and determine the dynamic weight based on the construction depth and the static weight factor. The calculation formula is: ; in, Indicates the dynamic weight at the construction depth z meters, is the static weight factor of the soil layer at the construction depth of z meters, z is the construction depth, is the static weight influence coefficient, is the depth influence coefficient, is the boundary depth of the soil layer immediately adjacent to and below the j+1th soil layer at the construction depth of z meters, The boundary depth of the jth soil layer at the construction depth of z meters; The dynamic weight reflects the real-time impact of the soil layer at construction depth z on pile strength. It is essentially the result of the dynamic adjustment of the static weight factor at a specific construction location. This parameter quantifies the spatial variation in the impact of geological conditions on pile strength by integrating the inherent characteristics of the soil layer (static weight) with the relative depth of the construction location relative to the soil layer interface. Its technical effect is to solve the problem of traditional methods where geological weights are fixed and cannot reflect strength variations within the same soil layer or in interfacial transition zones. This allows pile strength predictions to more precisely capture the strength mutation characteristics at key locations such as soil layer interfaces and thin interlayers, providing a more accurate geological basis for the dynamic adjustment of construction parameters.

[0036] The static weight factor in the formula characterizes the fundamental influence of the inherent properties of the soil layer (such as dielectric constant and thickness) on strength, while the fractional term quantifies the relative position of the construction point z within the current soil layer. The combination of the two reflects the core logic of dynamic weighting: even within the same soil layer, different depths may have different effects on strength (such as increased constraint when close to the underlying hard soil), while the static weight only reflects the overall characteristics of the soil layer. and Control the weight ratio of static and position effects respectively, usually set To give priority to ensuring the dominance of soil characteristics.

[0037] The dynamic weight is positively correlated with the static weight, that is, the worse the inherent geological conditions of the soil layer (such as the high water content soft layer, The larger the value is), the greater its dynamic weight is. The fractional term for relative position is also positively correlated, indicating that the closer to the bottom of the soil layer (closer to the subsoil), the more significantly the strength is affected by the subsoil's constraint effect. For example, when construction point z moves from the top of the soil layer (relative position = 0) to the bottom (relative position = 1), if the underlying layer is a dense sand layer, the dynamic weight will gradually increase to reflect the boundary reinforcement effect. This design allows the dynamic weight to inherit the macroscopic geological evaluation of the static weight while adaptively capturing local position effects.

[0038] The geological correction factor is obtained by combining the dynamic weight, slurry setting time and strength correction factor. The calculation formula is: when When , the calculation formula is as follows: ; when When , the calculation formula is as follows: ; when When , the calculation formula is as follows: ; when When , the calculation formula is as follows: ; in, Indicates the geological correction coefficient at the construction site z meters, is the strength correction coefficient of the jth soil layer, is the time decay coefficient, which is set to 0.2, h represents the slurry setting time, and m represents the index of the lowest soil layer; The geological correction factor reflects the comprehensive correction effect of soil characteristics and slurry solidification process on the pile strength at a specific construction depth z. Using the slurry as a benchmark, the geological influence of different soil layers is quantified through dynamic weights and strength correction coefficients, and the time-varying contribution of the degree of solidification to strength is characterized by combining the attenuation effect of the slurry setting time h. The technical effect is that it achieves a coupled correction of geological conditions and time factors, enabling the prediction model to dynamically reflect the heterogeneity of strength development during slurry consolidation. This is particularly suitable for working with multiple intersecting strata or conditions with large differences in setting times. For example, it can automatically reduce the influence weight of weak soil layers in the early setting stage to avoid over-correction.

[0039] The dynamic weight combines the static characteristics of the soil layer with the construction location information to locate the key influencing layer; the strength correction coefficient quantifies the inherent influence tendency of each soil layer on the strength through soil parameters (such as permeability and plasticity); and the slurry setting time adjusts the timeliness of geological influence through the exponential decay term - the initial stage of setting ( ) The geological correction is weak (because the slurry has not yet fully solidified). As the solidification progresses (h increases), the geological influence gradually becomes apparent until it stabilizes ( The setting of the effective soil layer set S(z) (usually including the current layer and adjacent layers) avoids the redundancy of the calculation of the entire soil layer and focuses on the local influence range of the construction point z.

[0040] K(z) and W(z), There is a positive correlation between the two, that is, the worse the geological conditions (such as high The weak layer) or the larger the dynamic weight W(z) (such as at the soil interface), the more significant the negative correction of strength (K(z) increases to reduce the predicted value); and the solidification time h positively adjusts the modified strength through the increasing exponential term, reflecting the "slow release" effect of the solidification process on the geological impact. For example, for high In the initial stage (h is small), the correction is weak (K(z) is close to 1) because the slurry fluidity is still good; as the solidification progresses (h increases), its negative impact gradually becomes more prominent (K(z) increases significantly), which is consistent with the engineering observation that "soft soil strength development lags behind in the later stage".

[0041] Through a hierarchical and progressive selection mechanism for adjacent soil layers, the calculation of the geological correction coefficient considers both the direct influence of the soil layer at the current construction depth and the boundary effects of the adjacent upper and lower soil layers. This rationale is demonstrated by the following: for non-boundary soil layers (j>1), the influence of the upper layer (j-1), the current layer (j), and the lower layer (j+1) are simultaneously incorporated, enabling the capture of strength gradients at soil interface points caused by sudden changes in material properties (e.g., stress concentration at the junction of soft and hard soil layers). For the top soil layer (j=1) or the bottom soil layer (j=m), only the adjacent single-sided soil layer is considered, avoiding ineffective virtual layer interference. This design not only conforms to the physical laws of stress diffusion in geotechnical materials (the impact range is typically 2 to 3 times the pile diameter), but also achieves a balance between computational efficiency and accuracy through conditional judgment. This design is particularly applicable to complex strata containing thin soil layers or lenses, ensuring that the correction coefficient K(z) transitions smoothly rather than abruptly at soil interface points, thereby more realistically reflecting the spatial continuity of pile strength.

[0042] Considering the effect of slurry temperature on strength, the temperature influence factor is calculated using the formula: ; in, is the temperature influence factor, is the linear influence coefficient of temperature on slurry, set to 0.02, is the slurry temperature, is the rate coefficient of temperature effect decay over time, set to 0.005; The temperature impact factor reflects the time-varying effect of slurry temperature on the strength of high-pressure jet-jet piles. Its physical significance lies in quantifying the contribution of changes in the cement hydration reaction rate caused by temperature differences to the ultimate strength. This parameter dynamically characterizes the nonlinear attenuation of the temperature effect during the slurry solidification process by taking the deviation of the slurry temperature from the standard curing temperature (20°C) as the input variable and combining it with an exponential term that decays over time. Its technical effect is to address the problem of traditional prediction models ignoring seasonal temperature fluctuations or differences in hydration reactions caused by underground temperature differences. This allows the expected strength to be automatically reduced during construction in low-temperature environments, while the predicted strength is appropriately increased under high-temperature conditions, significantly improving the accuracy of strength predictions for piles in cold regions or deep burials.

[0043] The independent variables slurry temperature and slurry setting time in the formula correspond to the spatial and temporal dimensions of the temperature effect, respectively: temperature directly determines the initial rate of cement hydration reaction. Generally, every 10°C increase in temperature can double the early strength growth rate; while setting time reflects the gradual weakening of the temperature effect as the curing process progresses through an exponential decay term, which is consistent with the phenomenon that the influence of temperature on strength in actual engineering is mainly concentrated in the early stage (24-72 hours). and To control the temperature sensitivity and decay rate respectively, solidification tests at different temperatures must be performed for calibration.

[0044] Temperature impact factor It is positively correlated with the slurry temperature T, indicating that the higher the temperature, the greater the strength enhancement effect; and negatively correlated with the slurry setting time, that is, the temperature effect gradually decreases with the extension of curing time. This relationship accurately simulates the curing mechanism of cement-based materials: "temperature accelerates early hydration but has limited effect on the final strength." For example, when T = 5 ° C (low temperature), It may be a negative value, reflecting the slow development of strength; when T=35℃ (high temperature), It is significantly positive in the initial stage, but converges rapidly with the increase of h, which is consistent with the actual observation that "high temperature promotes early strength but may reduce long-term strength".

[0045] Introducing the influence of geological correction coefficient and temperature influence factor, the final predicted intensity is calculated using the following formula: ; in, It represents the final predicted strength of the soil layer at the construction depth of z meters.

[0046] The final predicted strength is the core quantitative indicator of the quality of high-pressure jet grouting piles. Its physical meaning is that it comprehensively reflects the coupling effects of three factors on the strength of the pile: construction process parameters, geological condition corrections, and temperature time-varying effects. This parameter is obtained by converting the initial predicted strength and geological correction factor The product of is taken as the basis, and then the temperature influence factor is superimposed , realizing a complete strength mapping from ideal working conditions to actual engineering environments. Its technical effect is that it breaks through the limitations of traditional methods in which construction parameters, geological conditions and environmental factors are separated from each other. It can simultaneously respond to complex working conditions such as process adjustments (such as pressure changes), sudden changes in formations (such as weak interlayers) and seasonal temperature differences (such as winter construction), providing an evaluation benchmark for project acceptance and parameter optimization that is both theoretically rigorous and engineering applicability.

[0047] Independent variables in formulas and Each represents a correction mechanism in different dimensions: the geological correction factor, a spatially distributed function, quantifies the non-uniform influence of stratum characteristics on strength at different depths along the pile shaft through the synergistic effect of dynamic weighting and the strength correction factor; while the temperature influence factor, a time-varying function, captures the time-dependent regulation of slurry temperature on strength development by altering hydration reaction kinetics. These two variables are coupled with the initial predicted strength through product and summation relationships, aligning with the engineering understanding that "geological conditions amplify / minimize process effects" while also reflecting the material characteristic that "temperature effects independently affect the curing process."

[0048] The final predicted strength is strictly positively correlated with the geological correction coefficient, indicating that in areas with more unfavorable geological conditions, higher initial strength is required for compensation; it is also positively correlated with the temperature influence factor, but is constrained by the time decay term. This relationship has obvious time-sensitive characteristics - temperature fluctuations in the early stage of construction have a significant impact, while as the solidification time increases, the temperature contribution gradually converges. In particular, when When it is a negative value (low temperature condition), the formula automatically realizes strength reduction, which is completely consistent with the experience in actual engineering that low temperature maintenance requires extending the age or taking insulation measures.

[0049] By dynamically integrating construction process parameters, geological characteristics, and the time-varying effects of the slurry solidification process, a multi-dimensional and accurate prediction of the strength of high-pressure jet grouting piles is achieved. The initial predicted strength is based on the power-law relationship between nozzle pressure, slurry flow rate, lifting speed, and rotational speed, quantifying the direct control effect of the construction process on strength formation. The dynamic weight reflects the differential influence of different soil layers along the pile body through the adaptive coupling of the static weight factor and the construction depth. The geological correction coefficient further introduces a decay function of the strength correction coefficient and the slurry setting time to capture the interactive effect of soil properties and the solidification process. The temperature influence factor corrects the time-varying effect of the slurry temperature on the hydration reaction. By integrating the above factors, the final predicted strength not only overcomes the defect of the traditional method of separating geological conditions and construction parameters, but also realizes the full chain modeling from macro-process control to micro-solidification mechanism, significantly improving the reliability and engineering applicability of pile strength prediction in complex strata, and providing a scientific basis for the real-time optimization of construction parameters.

[0050] In this embodiment, relevant parameters during the high-pressure jet grouting pile construction process are collected, and the corresponding initial predicted strength, geological correction coefficient, and temperature influence factor are calculated for analysis. The specific calculation data are shown in Table 1 below: Table 1: Final prediction intensity related parameter values

[0051] Reference Figure 2-Figure 4 , and the data in the above table show that as the values of the geological correction coefficient, initial prediction intensity and temperature influence factor increase, the corresponding final prediction intensity value will increase, and there is a positive correlation between each variable and the dependent variable.

[0052] Step 5: Set the three-level warning threshold and compare the final predicted intensity with the warning threshold to determine the warning level; if warnings appear in two consecutive time windows, calculate the optimal adjustment amount of the nozzle injection pressure and drill pipe lifting speed parameters.

[0053] In this embodiment, calculating the optimal adjustment amount of the relevant parameters specifically includes: The three-level warning mechanism is yellow warning, orange warning and red warning. A yellow warning is issued when , an orange warning is issued when A red alert is issued when is the strength design requirement value, It represents the final predicted strength of the soil layer at the construction depth of z meters; the strength design requirement value can be determined according to the design parameters in the project book.

[0054] A yellow warning indicates a minor deviation, possibly due to local formation variation or parameter fluctuations. It is recommended to observe subsequent trends. An orange warning indicates a significant deviation, requiring inspection of the construction equipment (such as nozzle blockage) or slurry mix. A red warning warns of a serious quality risk and requires immediate shutdown to review geological survey data or process plans.

[0055] A dual-window confirmation mechanism is used. If the first window triggers an early warning, the section is marked as a verification area. If the second window triggers an early warning again, it is determined to be a persistent abnormality, and parameter optimization is started to calculate the optimal adjustment amount of nozzle injection pressure and drill pipe lifting speed. The calculation formulas are: ; ; in, are the optimal adjustment values of nozzle injection pressure and drill pipe lifting speed respectively. If the upper limit of the equipment is exceeded, it should be adjusted according to the maximum allowable value. The lower limit of the lifting speed should be set, not less than 5cm / min, to prevent slurry from accumulating.

[0056] A three-level early warning and parameter optimization mechanism achieves closed-loop intelligent control of the high-pressure jet grouting pile construction process. The technical effectiveness of this system is demonstrated by the three-level early warning threshold system (yellow / orange / red), which is triggered by the percentage deviation between the predicted strength value and the designed value. This not only avoids the oversensitivity or insensitivity caused by a single threshold, but also intuitively distinguishes risk levels. The parameter optimization module is automatically triggered when two consecutive time windows are issued, ensuring that abnormal operating conditions are continuously confirmed rather than fluctuating randomly. Finally, the optimal adjustment amount is calculated by inversely solving the partial derivatives of the intensity prediction with respect to the injection pressure and lifting speed, achieving a quantitative mapping from "intensity deviation identification" to "process parameter correction." Overall, this step establishes an intelligent closed loop of "real-time monitoring-dynamic prediction-threshold warning-parameter self-adjustment" throughout the entire jet grouting pile construction process, marking a paradigm shift from traditional experience-driven to data-driven decision-making.

[0057] See also Figure 2The present invention further provides a high-pressure rotary jet grouting pile strength prediction device, which is used to implement the above-mentioned high-pressure rotary jet grouting pile strength prediction method, comprising: A multi-source data acquisition module is used to collect construction parameters in real time through a multi-source sensor system installed on the jet pile driver. The construction parameters include nozzle injection pressure, slurry flow rate, drill rod lifting speed, drill rod rotation speed and slurry temperature. The construction parameters are aligned through a time window and stored as a construction parameter matrix; The geological radar tomography analysis module is used to continuously scan along the pile axis using geological radar to obtain radar feedback data, analyze the radar feedback data to obtain the soil layer profile, boundary depth and dielectric constant of different soil layers, and assign a static weight factor to each soil layer; The core geological parameter calibration module is used to drill core samples in the middle of each soil layer, measure the geological parameters of each core sample, construct the geological parameter vector, and calculate the strength correction coefficient of each soil layer; The multi-factor coupled strength modeling module is used to determine the initial predicted strength based on the nozzle injection pressure, slurry flow rate, drill pipe hoist speed, and drill pipe rotation speed data in the construction parameter matrix; calculate the dynamic weight based on the construction depth and the static weight factor; combine the dynamic weight, slurry setting time, and strength correction factor to obtain the geological correction factor; and simultaneously introduce the influence of slurry temperature to determine the temperature influence factor. The final predicted strength is determined based on the geological correction factor and the temperature influence factor. The adaptive warning and parameter optimization module is used to set three-level warning thresholds and compare the final predicted intensity with the warning threshold to determine the warning level; if warnings appear in two consecutive time windows, the optimal adjustment amount of the nozzle injection pressure and drill pipe lifting speed parameters is calculated.

[0058] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0059] 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. Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0060] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0061] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A method for predicting the strength of high-pressure jet grouting piles, characterized in that: The specific steps include: Step 1: A multi-source sensor system installed on the jet grouting machine collects construction parameters in real time. The construction parameters include nozzle injection pressure, slurry flow rate, drill rod lifting speed, drill rod rotation speed, and slurry temperature. The construction parameters are aligned using a time window and stored as a construction parameter matrix. Step 2: Use geological radar to continuously scan along the pile axis to obtain radar feedback data. Analyze the radar feedback data to obtain the soil profile, boundary depth and dielectric constant of different soil layers, and assign a static weight factor to each soil layer. Step 3: Drill core samples in the middle of each soil layer and measure the geological parameters of each core sample to construct a geological parameter vector and calculate the strength correction coefficient of each soil layer; Step 4: Determine the initial predicted strength based on the nozzle injection pressure, slurry flow rate, drill rod lifting speed, and drill rod rotation speed data in the construction parameter matrix; calculate the dynamic weight based on the construction depth and the static weight factor; combine the dynamic weight, slurry setting time, and strength correction factor to obtain the geological correction factor; simultaneously, introduce the influence of slurry temperature to determine the temperature influence factor; and determine the final predicted strength based on the geological correction factor and the temperature influence factor. Step 5: Set the three-level warning threshold and compare the final predicted intensity with the warning threshold to determine the warning level; if warnings appear in two consecutive time windows, calculate the optimal adjustment amount of the nozzle injection pressure and drill pipe lifting speed parameters.

2. A method for predicting the strength of high-pressure jet grouting piles according to claim 1, characterized in that: Obtaining the construction parameter matrix specifically includes: The multi-source sensor system includes a pressure sensor, an electromagnetic flowmeter, a high-precision encoder, a gyroscope, and a temperature sensor. The installation locations of the sensors are as follows: a pressure sensor is installed at the high-pressure pump outlet pipeline to monitor the nozzle injection pressure; an electromagnetic flowmeter is installed at the slurry delivery main pipeline, that is, between the cement slurry mixer outlet and the high-pressure pump inlet, to monitor the slurry flow rate; a high-precision encoder is installed at the end of the drilling rig winch drum shaft to measure the drill pipe lifting speed, and a gyroscope is installed on the housing of the drill pipe top drive device to monitor the drill pipe rotation speed; a temperature sensor is installed in the slurry mixing barrel outlet pipeline to monitor the slurry temperature; The sampling frequency is determined and the noise is eliminated by sliding average filtering. The time synchronization algorithm is used to align the data collected by the multi-source sensor system. The time-aligned construction parameters are stored as a matrix, which can be expressed as: ； Where X(t) represents the construction parameter matrix, P(t) is the nozzle injection pressure at time t, Q(t) is the slurry flow rate at time t, V(t) is the drill pipe lifting speed at time t, N(t) is the drill pipe rotation speed at time t, T(t) is the slurry temperature at time t, and t is the time variable; each row of the matrix represents a timestamp, and each column represents the construction parameter corresponding to the timestamp.

3. A method for predicting the strength of high-pressure jet grouting piles according to claim 1, characterized in that: Obtaining the static weight factor specifically includes: The static weight factor of each soil layer is calculated according to the boundary depth and dielectric constant of the soil layer. The calculation formula is: ； in, is the static weight factor of the i-th soil layer, is the dielectric constant of the i-th soil layer, is the minimum value of the dielectric constant of each soil layer, is the maximum value of the dielectric constant of each soil layer, is the boundary depth of the i+1th soil layer, that is, the top burial depth of the i+1th soil layer, is the boundary depth of the i-th soil layer, that is, the burial depth of the top of the i-th soil layer, i is the soil layer index, and the soil layers are indexed and numbered from top to bottom, and D is the designed pile length.

4. A method for predicting the strength of high-pressure jet grouting piles according to claim 3, characterized in that: Calculating the strength correction coefficient specifically includes: The geological parameters include permeability coefficient, plasticity index, water content, density, and cohesion; the geological parameters of each soil layer are integrated into a geological parameter matrix, where each row represents a geological parameter of a soil layer and each column represents a geological parameter value; In the middle of each soil layer, Cores were drilled at the location of the soil layer, and the thin-wall soil sampler static pressure method was used to take samples. Three groups of parallel samples were taken from each soil layer. The geological parameters of each parallel sample were measured through laboratory tests. The mean value of the geological parameters of each parallel sample in the same soil layer was calculated. The mean value was used as the geological parameter of the soil layer. The strength correction coefficient formula for each soil layer was calculated as follows: ； in, is the strength correction coefficient of the i-th soil layer, is the permeability coefficient of the i-th soil layer, is the plasticity index of the i-th soil layer, is the water content of the i-th soil layer, is the density of the i-th soil layer, is the cohesion of the i-th soil layer, are the weight coefficients of each item respectively.

5. A method for predicting the strength of high-pressure jet grouting piles according to claim 1, characterized in that: Determining the final prediction strength specifically includes: The formula for calculating the initial predicted strength is as follows: ； in, is the initial forecast intensity, is the slurry performance coefficient, is the benchmark strength value, P is the nozzle injection pressure, Q is the slurry flow rate, V is the drill rod lifting speed, N is the drill rod rotation speed, are the exponential coefficients of the corresponding terms; Get the construction depth, and determine the dynamic weight based on the construction depth and the static weight factor. The calculation formula is: ； in, Indicates the dynamic weight at the construction depth z meters, is the static weight factor of the soil layer at the construction depth of z meters, z is the construction depth, is the static weight influence coefficient, is the depth influence coefficient, is the boundary depth of the soil layer immediately adjacent to and below the j+1th soil layer at the construction depth of z meters, The boundary depth of the jth soil layer at the construction depth of z meters; The geological correction factor is obtained by combining the dynamic weight, slurry setting time and strength correction factor. The calculation formula is: when When , the calculation formula is as follows: ； when When , the calculation formula is as follows: ； when When , the calculation formula is as follows: ； when When , the calculation formula is as follows: ； in, represents the geological correction coefficient at the construction site z meters away, j represents the soil layer at the construction site z meters away, is the strength correction coefficient of the jth soil layer, is the time attenuation coefficient, h represents the slurry setting time, and m represents the index of the lowest soil layer; Considering the effect of slurry temperature on strength, the temperature influence factor is calculated using the formula: ； in, is the temperature influence factor, is the linear influence coefficient of temperature on slurry, is the slurry temperature, is the rate coefficient of temperature effect decay over time; Introducing the influence of geological correction coefficient and temperature influence factor, the final predicted intensity is calculated using the following formula: ； in, It represents the final predicted strength of the soil layer at the construction depth of z meters.

6. A method for predicting the strength of high-pressure jet grouting piles according to claim 1, characterized in that: Calculating the optimal adjustment amount of relevant parameters specifically includes: The three-level warning mechanism is yellow warning, orange warning and red warning. A yellow warning is issued when , an orange warning is issued when A red alert is issued when is the strength design requirement value, It represents the final predicted strength of the soil layer at the construction depth of z meters; If two consecutive warning windows appear, the optimal adjustment of nozzle injection pressure and drill pipe lifting speed is calculated. The calculation formulas are: ； ； in, are the optimal adjustment amounts of nozzle injection pressure and drill pipe lifting speed respectively.

7. A high-pressure jet grouting pile strength prediction device, characterized in that: The high-pressure rotary jet grouting pile strength prediction device is used to implement the high-pressure rotary jet grouting pile strength prediction method according to any one of claims 1 to 6, comprising: A multi-source data acquisition module is used to collect construction parameters in real time through a multi-source sensor system installed on the jet pile driver. The construction parameters include nozzle injection pressure, slurry flow rate, drill rod lifting speed, drill rod rotation speed and slurry temperature. The construction parameters are aligned through a time window and stored as a construction parameter matrix; The geological radar tomography analysis module is used to continuously scan along the pile axis using geological radar to obtain radar feedback data, analyze the radar feedback data to obtain the soil layer profile, boundary depth and dielectric constant of different soil layers, and assign a static weight factor to each soil layer; The core geological parameter calibration module is used to drill core samples in the middle of each soil layer, measure the geological parameters of each core sample, construct the geological parameter vector, and calculate the strength correction coefficient of each soil layer; The multi-factor coupled strength modeling module is used to determine the initial predicted strength based on the nozzle injection pressure, slurry flow rate, drill pipe hoist speed, and drill pipe rotation speed data in the construction parameter matrix; calculate the dynamic weight based on the construction depth and the static weight factor; combine the dynamic weight, slurry setting time, and strength correction factor to obtain the geological correction factor; and simultaneously introduce the influence of slurry temperature to determine the temperature influence factor. The final predicted strength is determined based on the geological correction factor and the temperature influence factor. The adaptive warning and parameter optimization module is used to set three-level warning thresholds and compare the final predicted intensity with the warning threshold to determine the warning level; if warnings appear in two consecutive time windows, the optimal adjustment amount of the nozzle injection pressure and drill pipe lifting speed parameters is calculated.

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