Intelligent monitoring system and method for construction quality of cement mixing pile

Abstract

The invention relates to the field of geotechnical engineering construction monitoring, and discloses an intelligent monitoring system and method for the construction quality of a cement mixing pile, and the system comprises a front-end sensing subsystem, an industrial control center and an execution control subsystem. The method comprises the following steps: fusing Beidou RTK elevation and encoder displacement data to generate an endogenous credible depth reference; constructing a stratum impedance fingerprint model by using the current and speed data of the drilling stage; and in the lifting grouting stage, the drill lifting speed is planned in a feedforward mode according to the model, the grouting flow is adjusted in a follow-up mode according to the actual drill lifting speed, meanwhile, the grouting effect is monitored by calculating the lubricating efficiency index, and multi-mode closed-loop correction is executed. According to the method, by establishing a stratum digital mapping and flow machine coupling control mechanism, the problems that the depth measurement error is large, construction parameters are not matched with the stratum, and quality evaluation lags behind are solved, and self-adaptive control and whole-process quality supervision of pile foundation construction are achieved.

Description

Technical Field

[0001] This invention relates to the field of geotechnical engineering construction monitoring, specifically to an intelligent monitoring system and method for the construction quality of cement mixing piles. Background Technology

[0002] Cement mixing piles, as a concealed engineering technique, are widely used in the reinforcement of soft soil foundations. Their core process involves using specialized deep mixing machinery to forcibly mix soft soil with cement grout deep within the foundation, solidifying it to form a pile body with integrity, water stability, and a certain strength. The control of pile length, matching of grout volume, and uniformity of mixing during construction directly determine the quality of the pile formation and the effectiveness of the foundation treatment.

[0003] Current construction monitoring technologies primarily rely on rotary encoders mounted on winches or guide rails for depth measurement. However, in complex construction site environments, winch wire ropes are prone to tangling or slippage, and the measuring wheel often slips between itself and the guide rail due to mud contamination, leading to irreversible cumulative errors in depth measurement. Although some equipment attempts to incorporate satellite positioning technology, single satellite signals are easily obstructed by the towering gantry crane of the piling machine or interfered with by multipath effects, resulting in elevation data jumps or loss. This drift in depth benchmark makes it difficult for the construction control system to accurately locate the bearing stratum depth, easily leading to insufficient pile length or ineffective drilling.

[0004] In terms of mixing control strategies, existing construction equipment generally adopts an open-loop control mode with constant rotation speed, constant lifting speed, and constant grouting flow rate. However, underground soil structures are complex, with alternating layers of soft and hard soil. When the drill bit is in a hard soil layer or a clay layer with high resistance, maintaining a fixed, rapid lifting speed will result in insufficient cutting passes, preventing the cement grout from fully mixing with the soil, leading to mud inclusions or uneven strength in the pile. Conversely, maintaining the same parameters in soft soil layers may result in excessive grouting or energy waste. This rigid control method, lacking real-time perception and response to geological characteristics, makes it difficult to guarantee uniform mixing throughout the entire pile length.

[0005] Furthermore, because the underground mixing process is invisible, existing quality monitoring methods are mostly limited to simple recording of grouting flow and drill rod depth on the surface, lacking effective evaluation indicators for the actual mixing effect underground. Construction personnel cannot determine in real time whether the grout has effectively coated soil particles or whether drill bit sticking or clogging has occurred. Verification of project quality usually relies on core sampling or excavation testing after the pile curing period. This delayed assessment method means that defects such as grouting interruptions and uneven mixing during construction cannot be detected and remedied in time. Once the test fails, high rework costs and project time losses are often incurred. Summary of the Invention

[0006] To address the technical shortcomings of existing cement mixing pile construction technology, such as the susceptibility of depth measurement to mechanical slippage or signal interference leading to benchmark drift, the inability of grouting volume and mixing speed to adaptively match changes in stratum hardness, and the lack of effective real-time quality verification methods during construction, this invention provides an intelligent monitoring system and method for cement mixing pile construction quality. This system solves the technical problems of discrete monitoring data, insufficient parameter control accuracy, and delayed quality assessment during construction.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] The first aspect of the present invention provides an intelligent monitoring system for the construction quality of cement mixing piles, the system comprising a front-end sensing subsystem, an industrial control center, and an execution control subsystem.

[0009] The front-end sensing subsystem is configured to collect the position information, motion status information and physical and mechanical response information of the cement mixing pile machine during construction, and aggregate and generate multi-dimensional sensing data to output to the industrial control center; the front-end sensing subsystem includes a reliable reference unit for establishing the spatial coordinate reference of the construction process, a process sensing unit for monitoring load changes and material consumption during the construction process, and an auxiliary verification unit for providing redundant position verification data and attitude correction data.

[0010] The industrial control center is electrically connected to the front-end sensing subsystem and the execution control subsystem, and is configured to receive the multi-dimensional sensing data, construct a formation impedance fingerprint model reflecting the soft and hard distribution characteristics of the formation based on the multi-dimensional sensing data, and generate flow-machine coupling control commands based on the formation impedance fingerprint model and send them to the execution control subsystem.

[0011] The execution control subsystem is configured to respond to the flow-machine coupling control command, adjust the drill rod movement state of the pile driver and the output flow rate of the grouting pump, and realize adaptive construction as the stratum impedance changes.

[0012] Furthermore, to ensure the accuracy of the basic data, the trusted reference unit uses a combination of a BeiDou RTK fixed-altitude antenna and a positioning directional antenna to directly obtain absolute spatial coordinates; the process sensing unit monitors power and fluid parameters through a current sensor, an electromagnetic flowmeter, and a pressure transmitter; and the auxiliary verification unit obtains mechanical relative displacement and attitude through a depth encoder and an tilt sensor.

[0013] In terms of depth measurement, the industrial control center executes depth benchmark verification logic, configured to simultaneously extract absolute elevation data from the BeiDou RTK altitude-fixed antenna and relative displacement data from the depth encoder. The system calculates the correlation coefficient and cumulative deviation of the two sets of data within a preset time window. If and only if the correlation coefficient is greater than a preset threshold and the cumulative deviation is less than a preset threshold, the system locks the absolute elevation data as the intrinsically reliable depth benchmark, using it as the spatiotemporal coordinate basis for processing multidimensional sensing data, thus eliminating measurement errors caused by the failure of a single sensor.

[0014] In terms of ground sensing, the industrial control center executes model construction logic within the ground impedance modeling module. The system discretizes the preset pile length along the depth direction into several standard depth units, extracting the drilling current and drilling speed values ​​falling within each unit. The ground impedance index is calculated using the correspondence between the drilling current and drilling speed values, and the calculated indices are arranged in depth order to generate a ground impedance fingerprint model. This model quantifies the degree of resistance of the ground soil to cutting in the form of a numerical sequence.

[0015] In terms of process control, the flow-machine coupling control module of the industrial control center executes adaptive control logic during the lifting and mixing grouting stage. The system retrieves the corresponding formation impedance index from the formation impedance fingerprint model based on the current depth index, uses this index to plan the target lifting speed, reduces the lifting speed in areas with high impedance indices, and increases the lifting speed in areas with low impedance indices. Simultaneously, the system measures the actual lifting speed of the piling machine in real time, calculates the target grouting flow rate based on the actual lifting speed, and uses a PID algorithm to adjust the grouting pump speed to maintain a constant grouting volume and mixing energy per unit pile length.

[0016] In terms of quality closed-loop correction, the industrial control center also executes lubrication efficiency calculation logic. The system collects the working current of the pile driver during the lifting process in real time and retrieves the characteristic drilling current at the same depth position. Combining the lifting current and drilling current, the lubrication efficiency index is calculated. This calculation process includes a direction correction coefficient to compensate for the differences in soil conveying resistance and shear mode of the auger blades. The system compares the lubrication efficiency index with a preset effectiveness threshold: if it is in the first abnormal range, a command is generated to control the power head to pause lifting and increase the grouting flow; if it is in the second abnormal range, a command is generated to trigger a micro-reciprocating disturbance sequence, control the power head to drill downwards and execute pulse grouting until the lubrication efficiency index recovers to the preset range.

[0017] In terms of result evaluation, the industrial control center includes a quality assessment and data management module. This module divides the pile body data after pile formation into multiple assessment units, integrates the formation impedance index, lubrication efficiency index, and grouting volume deviation rate during the grouting process, calculates the comprehensive quality score for each assessment unit, and generates a digital construction quality report containing the quality rating results.

[0018] A second aspect of this invention provides an intelligent monitoring method for the construction quality of cement mixing piles, comprising the following steps:

[0019] S1. Generate an endogenous reliable depth benchmark: Simultaneously collect absolute elevation data and relative displacement data, calculate the correlation and deviation of the two sets of data within the time window, lock and output the endogenous reliable depth benchmark after data consistency verification, and establish the spatiotemporal coordinate system for construction.

[0020] S2. Constructing a formation impedance fingerprint model: During the drilling and soil cutting stage, the depth is located based on the endogenous reliable depth benchmark. The formation impedance index is calculated using the synchronously acquired drilling current and drilling speed values ​​to generate a formation impedance fingerprint model that reflects the soft and hard distribution characteristics of the soil layer, thus completing the digital mapping of underground formation properties.

[0021] S3. Perform flow-machine coupling adaptive control: During the grouting stage, the target drilling speed is planned based on the formation impedance fingerprint model corresponding to the current depth, and the grouting flow rate is adjusted according to the actual drilling speed. At the same time, the lubrication efficiency index is calculated, the mixing state of grout and soil is monitored, and the drilling speed and grouting method are corrected in a closed loop to generate construction process monitoring data containing process control parameters and response data.

[0022] S4. Generate digital construction quality reports: Based on the monitoring data of the construction process, the deviation of grouting volume and the uniformity of mixing along the entire pile length are evaluated in segments, and a digital construction quality report containing the quality rating results is generated.

[0023] This invention avoids the cumulative error problem of traditional encoder depth measurement by constructing an endogenous reliable depth benchmark; by establishing a formation impedance fingerprint model during the drilling stage, it realizes fluid-mechanical coupling adaptive control based on the real properties of the formation during the grouting stage, ensuring precise matching between mixing uniformity and grouting volume; and by real-time calculation and multi-modal correction of the lubrication efficiency index, it realizes real-time identification and compensation of quality defects during construction.

[0024] This invention provides an intelligent monitoring system and method for the construction quality of cement mixing piles. It has the following beneficial effects:

[0025] 1. This invention generates an intrinsically reliable depth benchmark by synchronously acquiring BeiDou RTK absolute elevation data and encoder relative displacement data, and performing correlation and deviation verification within a preset time window. This technical solution utilizes the characteristic of satellite positioning without cumulative error to correct the cumulative deviation caused by mechanical encoder slippage or wire rope entanglement. Simultaneously, it leverages the high-frequency continuous characteristics of the encoder to fill blind spots when satellite signals are blocked, ensuring the accuracy of the spatiotemporal coordinate benchmark during construction. This solves the problem of benchmark drift caused by mechanical failures or signal interference in traditional single-sensor depth measurement.

[0026] 2. This invention utilizes current and velocity data from the drilling phase to construct a formation impedance fingerprint model reflecting the distribution of soil hardness and softness. During the grouting phase, this model is used to feedforward plan the drilling speed and dynamically adjust the grouting flow rate. This control mechanism enables the piling machine to automatically adjust construction parameters based on the geological impedance at different depths. In hard soil layers, the speed is reduced to increase the number of cuts; in soft soil layers, the speed is increased to optimize efficiency. This ensures that the grouting volume and mixing energy density per unit pile length are precisely matched with the actual formation properties, solving the problem of uneven mixing or material waste caused by formation changes in the traditional constant-speed, constant-volume construction mode.

[0027] 3. This invention generates a lubrication efficiency index by calculating the ratio of the lifting working current to the characteristic drilling current in real time, and performs multi-modal closed-loop corrections, including enhanced mixing and micro-reciprocating disturbances, based on this index. This method utilizes the rheological lubrication drag reduction effect generated after cement grout is injected into the soil to quantitatively evaluate the grouting and mixing effects. It can identify abnormal areas of insufficient grouting or inadequate mixing in real time during construction and automatically trigger remedial actions, achieving online verification and closed-loop control of the quality of concealed works, avoiding the risk of pile breakage and rework costs caused by delayed post-construction inspections. Attached Figure Description

[0028] Figure 1 This is a schematic diagram of the structure of an intelligent monitoring system for cement mixing pile construction quality according to an embodiment of the present invention;

[0029] Figure 2 This is a flowchart of an intelligent monitoring method for the construction quality of cement mixing piles according to an embodiment of the present invention.

[0030] Among them, 100 is the front-end sensing subsystem; 110 is the trusted reference unit; 111 is the Beidou RTK altitude-fixing antenna; 112 is the Beidou RTK positioning and directional antenna; 131 is the depth encoder; 120 is the process sensing unit; 121 is the dual-channel current sensor; 122 is the dual-channel electromagnetic flowmeter; 123 is the grouting pressure transmitter; 200 is the industrial control center; 210 is the data acquisition module; 220 is the formation impedance modeling module; 230 is the flow-machine coupling control module; 240 is the communication and storage module; 300 is the execution control subsystem; 310 is the power head frequency converter; and 320 is the slurry pump frequency converter. Detailed Implementation

[0031] The technical solutions in 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] See attached document Figure 1 The present invention provides an intelligent monitoring system and method for the construction quality of cement mixing piles. The system includes a front-end sensing subsystem 100, an industrial control center 200, and an execution control subsystem 300.

[0033] The front-end sensing subsystem 100 is used to collect the location information, motion status information, and physical and mechanical response information of the cement mixing pile machine during construction. The front-end sensing subsystem 100 includes a reliable reference unit 110, a process sensing unit 120, and an auxiliary verification unit 130.

[0034] The reliable reference unit 110 is used to establish the spatial coordinate reference for the construction process. The reliable reference unit 110 includes a BeiDou RTK elevation-fixing antenna 111 and a BeiDou RTK positioning and directional antenna 112. The BeiDou RTK elevation-fixing antenna 111 is rigidly fixed to the top of the pile driver's power head. This antenna moves synchronously with the lifting and lowering movement of the power head, and is used to receive satellite differential signals in real time and calculate the absolute elevation data of the power head. The BeiDou RTK positioning and directional antennas 112 are installed in pairs at the top of the pile driver's gantry or at both ends of the crossbeam, and are used to calculate the pile driver's planar coordinate data and heading angle data.

[0035] The process sensing unit 120 is used to monitor load changes and material consumption during construction. The process sensing unit 120 includes a dual-channel current sensor 121, a dual-channel electromagnetic flowmeter 122, and a grouting pressure transmitter 123. The dual-channel current sensor 121 is connected in series in the three-phase input lines of the left and right mixing motors to collect the real-time operating current values ​​of the motors. The dual-channel electromagnetic flowmeter 122 is installed at the outlet side of the high-pressure cement slurry delivery pipeline to measure the instantaneous flow rate of the slurry. The grouting pressure transmitter 123 is installed at the rotary joint or the end of the grouting pipe to monitor the fluid pressure value within the pipeline.

[0036] The auxiliary verification unit 130 provides redundant position verification data and attitude correction data. The auxiliary verification unit 130 includes a depth encoder 131 and a tilt sensor 132. The depth encoder 131 is mechanically coupled to the drum shaft or guide pulley shaft of the pile driver winch, and is used to collect the number of rotational pulses of the winch and convert them into relative displacement. The tilt sensor 132 is installed at the bottom or middle of the pile driver mast, and is used to measure the tilt angle of the mast in two mutually perpendicular directions in real time.

[0037] The industrial control hub 200 is the core processing unit of the system, which is electrically connected to the front-end sensing subsystem 100 and the execution control subsystem 300 through an industrial fieldbus or analog interface. The industrial control hub 200 includes a data acquisition module 210, a formation impedance modeling module 220, a flow-mechanism coupling control module 230, and a communication and storage module 240.

[0038] The data acquisition module 210 is used to receive the raw analog or digital signals output by the front-end sensing subsystem 100, and perform filtering, analog-to-digital conversion, and timestamp alignment processing. The data acquisition module 210 is internally configured with depth benchmark verification logic, which is configured to perform the generation and verification operation of the endogenous reliable depth benchmark in the subsequent step S1, so as to realize the verification and locking of the data source.

[0039] The formation impedance modeling module 220 is configured to calculate the formation impedance index based on the working current value and the drilling speed value during the drilling phase, and generate a formation impedance distribution sequence.

[0040] The flow-machine coupling control module 230 is configured to plan the drilling speed according to the formation impedance distribution sequence during the drilling phase, and adjust the grouting flow rate according to the actual drilling speed. The flow-machine coupling control module 230 is also configured to calculate the lubrication efficiency index and adjust the control strategy according to the index.

[0041] The communication storage module 240 is used to store construction process data and result reports, and uploads the encrypted data to a remote server via a wireless network.

[0042] The execution control subsystem 300 is used to respond to control commands issued by the industrial control center 200. The execution control subsystem 300 includes a power head frequency converter 310 and a grout pump frequency converter 320. The power head frequency converter 310 is electrically connected to the mixing motor and the winch motor, and is used to adjust the rotational speed and lifting speed of the drill rod. The grout pump frequency converter 320 is electrically connected to the grout pump motor, and is used to adjust the speed of the grout pump to control the output flow rate.

[0043] See attached document Figure 2 This invention provides an intelligent monitoring method for the construction quality of cement mixing piles, comprising the following steps:

[0044] S1, Generate an intrinsically reliable depth benchmark; During the initialization phase, the system synchronously collects the absolute elevation data output by the Beidou RTK fixed-elevation antenna 111 and the relative displacement data output by the depth encoder 131, calculates the correlation coefficient and cumulative deviation of the two sets of data within a preset time window; If the verification passes, the intrinsically reliable depth benchmark is locked and output as the spatiotemporal coordinate basis for subsequent construction processes.

[0045] S2, Construct a formation impedance fingerprint model; During the drilling and soil cutting stage, the system records the drilling current and drilling speed values ​​at each depth based on the endogenous reliable depth benchmark at a preset sampling frequency; Calculate the formation impedance index using the drilling current and drilling speed values, and generate a formation impedance fingerprint model that reflects the soft and hard distribution characteristics of the soil layer using depth as an index.

[0046] S3 executes fluid-machine coupling adaptive control; during the upward grouting stage, the system plans the target drilling speed according to the formation impedance fingerprint model corresponding to the current depth, and adjusts the grouting flow rate according to the actual drilling speed; at the same time, the system monitors the upward current value in real time, and calculates the lubrication efficiency index by combining the drilling current value in the formation impedance fingerprint model, and performs closed-loop correction of the drilling speed and grouting method according to the lubrication efficiency index, generating construction process monitoring data;

[0047] S4 generates a digital construction quality report. After construction is completed, the system evaluates the grouting volume deviation and mixing uniformity of the entire pile length in segments based on the monitoring data of the construction process, and finally generates a digital construction quality report containing the quality rating results and archives it.

[0048] To more clearly illustrate the specific implementation principles and algorithm logic of each step in this invention, the steps S1 to S4 will be described in detail below.

[0049] The method for generating and verifying an endogenous trusted depth benchmark includes the following steps:

[0050] In step S101, the data acquisition module 210 synchronously acquires data from the front-end sensing subsystem 100 via a communication interface at a preset sampling frequency. Specifically, the data acquired includes: positioning data frames containing UTC timestamps output by the BeiDou RTK altitude-fixed antenna 111, and pulse count values ​​output by the depth encoder 131. The data acquisition module uses linear interpolation to map satellite positioning data and encoder pulse data onto the same discrete time series t, thus achieving time alignment of the data.

[0051] Step S102, Independent calculation of depth data:

[0052] The industrial control center 200 calculates the first depth value D based on satellite positioning according to the sensor calibration parameters. gnss (t) and the second depth value D based on mechanical encoding enc (t).

[0053] For the first depth value, the industrial control center 200 calculates it using the following formula:

[0054] D gnss (t)=(H cal -H rtk (t))+D init ;

[0055] In the formula, H cal This indicates the calibration elevation of the BeiDou RTK elevation-fixing antenna 111 when the drill bit tip is located at the depth reference plane (usually the ground at the borehole opening); H rtk (t) represents the absolute elevation measurement value output by the Beidou RTK fixed-elevation antenna 111 at time t; D init This represents the initial depth of penetration during calibration (0 if calibrated at the borehole opening). This formula eliminates the geometric offset error ΔH caused by the antenna installation position. off The direct impact of this is that physical calibration is used to ensure the accuracy of the zero depth point.

[0056] For the second depth value, the industrial control center 200 calculates it using the following formula:

[0057] D enc (t)=D enc (t-1)+[N p (t)-N p (t-1)]×K pulse ;

[0058] In the formula, D enc (t-1) represents the encoder depth at the previous sampling time; N p (t) and N p (t-1) represent the cumulative encoder pulse counts at the current time and the previous time, respectively; K pulseThis represents the equivalent coefficient of vertical displacement of the drill bit corresponding to a unit pulse.

[0059] Step S103, calculate the consistency verification index:

[0060] To verify the reliability of the data, the industrial control center 200 establishes a sliding time window of length W, which contains W sampling points that trace back from the current time t.

[0061] The Industrial Control Center 200 calculates the Pearson correlation coefficient, denoted as ρ(t), between the first and second depth value sequences within a sliding window. This coefficient reflects the similarity in the changing trends of the two sets of depth data.

[0062] Meanwhile, the industrial control center 200 calculates the average absolute error between the first depth value and the second depth value within the sliding window, denoted as ε(t). This index reflects the average degree of deviation between the two sets of data in terms of value.

[0063] Step S104, Determination of the intrinsically reliable depth benchmark:

[0064] The industrial control center 200 compares the calculated Pearson correlation coefficient ρ(t) with the preset correlation threshold, and compares the mean absolute error ε(t) with the preset deviation threshold.

[0065] If ρ(t) is greater than the preset correlation threshold and ε(t) is less than the preset deviation threshold, the industrial control center 200 determines that the current depth data source is reliable and that no encoder slippage or satellite signal abnormality has occurred. At this time, the industrial control center 200 selects the first depth value D without cumulative error characteristics. gnss (t) serves as an endogenous reliable depth benchmark, used to drive subsequent formation modeling and grouting control.

[0066] If any of the above conditions are not met, the industrial control center 200 determines that the depth reference is invalid and controls the pile driver to suspend operation or issue an alarm signal.

[0067] The formation impedance fingerprinting and drilling sensing method includes the following steps:

[0068] Step S201, Status identification and data sampling during drilling:

[0069] The formation impedance modeling module 220 monitors the operating status of the piling machine in real time. The formation impedance modeling module 220 determines that the system is in an effective drilling and soil cutting state when the following two conditions are met simultaneously: first, the value of the endogenous reliable depth benchmark shows an increasing trend; second, the vertical downward speed of the drill bit is greater than the preset minimum cutting speed threshold.

[0070] Under effective drilling and soil cutting conditions, the formation impedance modeling module 220 synchronously acquires the current drilling current value and drilling speed value at a preset sampling frequency. The formation impedance modeling module 220 performs moving average filtering on the original current signal to obtain a smoothed drilling current value.

[0071] Step S202, Spatial discretization mapping of time-domain data:

[0072] To eliminate the impact of drilling speed fluctuations on data density, the formation impedance modeling module 220 maps the time-domain sampled data to the spatial depth domain.

[0073] The formation impedance modeling module 220 divides the designed pile length along the depth direction into several standard depth units, each with a fixed length step Δz. The module 220 reads the drilling current and drilling speed values ​​of all sampling points falling within each depth unit and calculates their arithmetic mean, which is then used as the characteristic drilling current I corresponding to that depth unit. down (z) and characteristic drilling speed v down (z). Where z represents the center depth index of the depth cell.

[0074] Step S203, Calculation of formation impedance index:

[0075] For each depth unit, the formation impedance modeling module 220 calculates the formation impedance index R at each depth using the formation impedance index calculation formula. d (z), this index is used to quantify the degree to which the formation soil hinders drill bit cutting. The formula for calculating the formation resistance index is:

[0076]

[0077] In the formula, R d (z) represents the formation impedance index at depth z; I d own(z) represents the characteristic downhole current at depth z; v d own(z) represents the characteristic down-drilling speed at depth z; η represents the electromechanical conversion efficiency coefficient, which is used to map the current value to the equivalent cutting torque. Its value is positively correlated with the rated power of the mixing motor and the transmission ratio of the reducer; ξ represents the numerical stability damping constant, which is a non-zero positive number used to prevent numerical overflow of the calculation result when the characteristic down-drilling speed approaches zero.

[0078] Step S204, construct the formation impedance fingerprint model:

[0079] The formation impedance modeling module 220 will calculate the R of all depth cells. dThe (z) values ​​are arranged in depth order to generate a formation impedance sequence. The formation impedance modeling module 220 performs envelope processing on this sequence to remove abrupt noise points, ultimately generating a formation impedance fingerprint model for the pile location. This model serves as the basis for velocity planning and quality verification during the subsequent grouting stage and is stored in the system database.

[0080] The adaptive closed-loop control method based on flow-mechanical coupling and lubrication verification includes the following steps:

[0081] Step S301, Feedforward planning of drilling speed based on formation impedance fingerprint:

[0082] During the upward mixing and grouting stage, the flow-machine coupling control module 230 retrieves the corresponding formation impedance index R from the pre-built formation impedance fingerprint model based on the current depth z of the drill bit. d (z). The flow-machine coupling control module 230 dynamically adjusts the drilling speed according to the soil hardness to ensure that the mixing energy density per unit length of pile body matches the stratum impedance. That is, the speed is reduced in hard soil layers to increase the number of mixing and cutting operations, and the speed is appropriately increased in soft soil layers to improve construction efficiency, thereby optimizing the construction period while ensuring the uniformity of mixing.

[0083] The flow-machine coupling control module 230 uses an adaptive speed planning formula to calculate the target drilling speed v at the current depth. target (z), the adaptive velocity programming formula is:

[0084]

[0085] In the formula, v target (z) represents the target hoisting speed at depth z; R d (z) represents the formation impedance index at that depth; Ω represents the stirring efficiency constant, which is determined by the geometric parameters of the stirring blades and the required uniformity of stirring; V min and V max These represent the minimum and maximum allowable drilling speeds for the piling machine, respectively; Clip(·) represents the clipping function, used to limit the calculation results to [V]. min V max Within the interval, that is, when the calculated value is less than V min Take V at time min , greater than V max Take V at time max .

[0086] Using this formula, the flow-machine coupling control module 230 reduces the drilling speed when encountering high-resistivity hard soil layers and increases the drilling speed when encountering low-resistivity soft soil layers. The flow-machine coupling control module 230 calculates v... target (z) is converted into a control command and sent to the power head inverter 310 to drive the hoist motor.

[0087] Step S302, Grouting flow rate follow-up control based on actual drilling speed:

[0088] The flow coupling control module 230 measures the actual drilling speed v of the drill bit in real time by using the elevation data from the differential Beidou RTK altitude-fixing antenna 111 or reading the pulse frequency from the depth encoder 131. act (t). Using the actual drilling speed instead of the target planned speed for calculation can eliminate errors caused by mechanical execution lag.

[0089] The flow-machine coupling control module 230 uses the grouting flow follow-up formula to calculate the target grouting flow rate Q at the current moment. target (t), the formula for grouting flow rate is:

[0090] Q target (t)=M design ·v act (t)·(1+δ);

[0091] In the formula, Q target (t) represents the target grouting flow rate; M design This indicates the grouting volume per linear meter required by design specifications; v act (t) represents the actual drilling speed measured in real time; δ represents the pipeline loss compensation coefficient, which is used to compensate for pressure loss and measurement error during slurry transportation.

[0092] Step S303, Closed-loop regulation of the grouting pump:

[0093] The flow-machine coupling control module 230 will calculate Q target (t) is used as the set value, and the instantaneous flow rate Q fed back in real time by the dual-channel electromagnetic flowmeter 122 is used as the set value. meas (t) is used as a feedback value and input to the PID controller. The PID controller calculates the deviation and outputs an adjustment signal to the grout pump frequency converter 320 to adjust the speed of the grouting pump, so that the actual flow rate follows the change of the target flow rate, thereby achieving real-time synchronous matching between the grouting flow rate and the drilling speed.

[0094] Following the aforementioned steps, the method further includes the following steps:

[0095] Step S304, Real-time calculation of rheological lubrication difference:

[0096] During the upward grouting process, the flow-mechanical coupling control module 230 collects the upward working current I of the stirring motor in real time. up (z). Since the cement grout will change the rheological properties of the soil after being injected into the soil and reduce the shear resistance of the mixing blades, the flow-machine coupling control module 230 quantifies the effectiveness of grouting by comparing the lifting current and the drilling current at the same depth.

[0097] The flow-machine coupling control module 230 uses the lubrication efficiency index calculation formula to calculate the lubrication efficiency index λ(z) at the current depth. The lubrication efficiency index calculation formula is as follows:

[0098]

[0099] In the formula, λ(z) represents the lubrication efficiency index, which characterizes the degree to which the grout improves soil resistance; I up (z) represents the real-time stirring current at the current depth z; I down (z) represents the characteristic down-drilling current at the same depth recorded in the formation impedance fingerprint model in step S2; θ represents the direction correction coefficient, used to correct for load reference differences caused by different vertical movement directions of the drill rod. Since the auger bit mainly overcomes the soil penetration resistance during down-drilling cutting, and mainly overcomes the viscous resistance and self-weight of the grout mixture during up-drilling mixing, this coefficient is used to map the current data under the up-drilling condition to the same mechanical reference surface as the down-drilling condition, thereby eliminating mechanical factors and simply reflecting the grouting lubrication effect. Its value range is usually set between 0.9 and 1.1.

[0100] Step S305, Threshold determination of grouting effectiveness:

[0101] The flow-machine coupling control module 230 compares the calculated λ(z) with the preset effectiveness threshold λ. th Compare them.

[0102] If λ(z) is greater than or equal to λ th The flow-machine coupling control module 230 determines that the current grouting is normal, and the system continues to operate by maintaining the current adaptive speed planning strategy.

[0103] If λ(z) is less than λ th The flow coupling control module 230 determined that there was an abnormality in grouting, which indicated that the grout was not effectively injected or was not fully mixed with the soil.

[0104] Step S306, Multimodal closed-loop correction under abnormal operating conditions:

[0105] When an abnormality in grouting is detected, the flow-mechanical coupling control module 230 automatically triggers a graded correction control strategy based on the numerical range of λ(z):

[0106] Level 1 Correction Mode: Enhanced Mixing

[0107] When λ(z) is in the first abnormal interval (i.e., λ... th >λ(z)≥λ minWhen the pumping mode is activated, the flow-machine coupling control module 230 controls the power head to pause lifting and maintain rotation in place via the power head frequency converter 310. Simultaneously, it instructs the grout pump frequency converter 320 to temporarily increase the grouting flow rate to a preset multiple (e.g., 1.2 times) of the target flow rate. After a preset time, the flow-machine coupling control module 230 recalculates λ(z). If the indicators return to normal, the standard lifting mode is restored.

[0108] Secondary correction mode: micro-reciprocating disturbance and pulse grouting

[0109] When λ(z) is in the second outlier interval (i.e., λ(z) < λ) min At this time, the flow-mechanical coupling control module 230 triggers a micro-reciprocating disturbance sequence. This sequence includes:

[0110] The flow machine coupling control module 230 controls the power head to stop lifting and drill downwards a preset distance Δh via the power head frequency converter 310;

[0111] During the back-drilling process, the flow machine coupling control module 230 controls the grouting pump to perform pulse grouting through the grout pump frequency converter 320, that is, controls the grouting pump speed to alternate between low speed and high speed at a preset frequency, and uses pressure fluctuations to clear the grouting channel and disturb the surrounding soil.

[0112] After the back-drilling is completed, the flow machine coupling control module 230 controls the power head to re-lift through the abnormal region at a corrected speed lower than the standard speed through the power head frequency converter 310 until λ(z) returns to the effective range.

[0113] Through the above mechanism, the system ensures that all parts of the pile have undergone quality verification based on impedance changes, reducing the risk of pile breakage or uneven mixing.

[0114] The multidimensional assessment and data archiving method for construction quality includes the following steps:

[0115] Step S401, Pile body element division and multi-source process data fusion:

[0116] After the construction of a single mixing pile is completed, the quality assessment and data management module performs a data fusion operation. The quality assessment and data management module divides the pile depth range into N discrete pile body assessment units with a preset depth step size.

[0117] Next, the quality assessment and data management module uses depth z as an index to map various types of data generated during construction to each assessment unit. Specifically, the integrated data includes:

[0118] Formation resistance index R generated during the drilling phase d 9z);

[0119] The lubrication efficiency index λ(z) calculated during the lifting stage;

[0120] Grouting volume deviation rate E during grouting process V (z).

[0121] The quality assessment and data management module uses the following formula to calculate the grouting volume deviation rate E. V (z):

[0122]

[0123] In the formula, V meas (z) represents the cumulative measured grouting volume within this depth unit, obtained by integrating the instantaneous flow rate within the time window; V target (z) represents the theoretical grout volume required for this depth unit calculated according to the design specifications (i.e., the design grouting volume per linear meter, M). design Multiply by the depth unit step size Δz). Using the theoretical design value as the evaluation benchmark, it can effectively identify the problem of insufficient grouting volume caused by abnormal control system algorithm or human intervention.

[0124] Step S402, calculate the quality score based on the weighted bias model:

[0125] For each pile assessment unit, the quality assessment and data management module uses a comprehensive quality scoring formula to calculate its quality score S. qual (z). This formula quantifies and evaluates construction quality by calculating the degree to which each indicator deviates from the preset standard. The comprehensive quality scoring formula is:

[0126]

[0127] In the formula, S qual (z) represents the overall quality score of the evaluation unit at depth z, with a maximum score of 100; ω1 represents the grouting volume accuracy weighting coefficient, used to set the weight of the impact of grouting volume deviation on the score; ω2 represents the mixing quality weighting coefficient, used to set the weight of the impact of insufficient lubrication performance on the score; λ th This represents the preset acceptable threshold for lubrication performance; max(0,·) represents the limiting function, which calculates the deduction when the actual lubrication performance λ(z) is less than the acceptable threshold, otherwise the deduction value is zero.

[0128] Step S403, the quality level determination and data storage quality assessment and data management module determines the quality level based on the calculated S... qual (z) Assign a quality grade label to each pile unit according to the preset rating criteria:

[0129] When S qual When (z)≥90, it is judged as Grade A;

[0130] When 80≤S qualWhen (z) < 90, it is classified as Grade B;

[0131] When 60≤S qual When (z) < 80, it is judged as Grade C;

[0132] When S qual When (z) < 60, it is classified as Grade D.

[0133] The quality assessment and data management module automatically generates construction reports containing grouting volume curves, lubrication efficiency curves, and quality score distribution charts. For units rated as Grade D, the system marks them on the visual interface and prompts operators to verify them. All raw data and assessment results are stored in the system database for subsequent project acceptance and quality traceability.

Claims

1. An intelligent monitoring system for the construction quality of cement mixing piles, characterized in that, It includes a front-end sensing subsystem, an industrial control center, and an execution control subsystem; The front-end sensing subsystem is configured to collect the position information, motion status information and physical and mechanical response information of the cement mixing pile machine during construction, and aggregate and generate multi-dimensional sensing data to output to the industrial control center; the front-end sensing subsystem includes a reliable reference unit for establishing the spatial coordinate reference of the construction process, a process sensing unit for monitoring load changes and material consumption during the construction process, and an auxiliary verification unit for providing redundant position verification data and attitude correction data. The industrial control center is electrically connected to the front-end sensing subsystem and the execution control subsystem, and is configured to receive the multi-dimensional sensing data, construct a formation impedance fingerprint model reflecting the soft and hard distribution characteristics of the formation based on the multi-dimensional sensing data, and generate flow-machine coupling control commands based on the formation impedance fingerprint model and send them to the execution control subsystem. The execution control subsystem is configured to respond to the flow-machine coupling control command, adjust the drill rod movement state of the pile driver and the output flow rate of the grouting pump, and realize adaptive construction as the stratum impedance changes.

2. The intelligent monitoring system for cement mixing pile construction quality according to claim 1, characterized in that, The front-end sensing subsystem collects the basic information that constitutes the multidimensional sensing data through the following units: The reliable reference unit includes a Beidou RTK fixed-altitude antenna rigidly fixed to the top of the pile driver's power head and a Beidou RTK positioning and directional antenna installed on the top of the pile driver's gantry or at both ends of the crossbeam. The process sensing unit includes a dual-channel current sensor connected in series in the input line of the mixing motor, a dual-channel electromagnetic flowmeter installed on the outlet side of the high-pressure cement slurry conveying pipeline, and a grouting pressure transmitter installed at the end of the pipeline. The auxiliary verification unit includes a depth encoder coupled to the pile driver winch and an inclination sensor installed on the pile driver mast.

3. The intelligent monitoring system for cement mixing pile construction quality according to claim 1, characterized in that, The industrial control center includes: The data acquisition module is configured to receive the multidimensional sensing data and perform timestamp alignment processing. The formation impedance modeling module is configured to calculate the formation impedance index based on the working current value and drilling speed value in the multidimensional sensing data during the drilling phase, and generate the formation impedance fingerprint model. The flow-machine coupling control module is configured to plan the drilling speed according to the formation impedance fingerprint model during the drilling stage, adjust the grouting flow rate according to the actual drilling speed, and calculate the lubrication efficiency index to generate the flow-machine coupling control command containing specific control parameters. The communication storage module is used to store construction process data and generate result reports.

4. The intelligent monitoring system for cement mixing pile construction quality according to claim 2, characterized in that, The data acquisition module is configured with depth benchmark verification logic, which is configured to execute: Simultaneously extract the absolute elevation data from the Beidou RTK fixed-elevation antenna and the relative displacement data from the depth encoder from the multi-dimensional sensing data; Calculate the correlation coefficient and cumulative deviation between the absolute elevation data and the relative displacement data within a preset time window; When the correlation coefficient is greater than a preset correlation threshold and the cumulative deviation is less than a preset deviation threshold, the absolute elevation data is locked as an endogenous reliable depth benchmark. The endogenous reliable depth benchmark is output as the spatiotemporal coordinate basis for processing the multidimensional sensing data.

5. The intelligent monitoring system for cement mixing pile construction quality according to claim 3, characterized in that, The formation impedance modeling module is configured to perform the model building steps: The designed pile length is divided into several standard depth units along the depth direction; For each standard depth unit, the drilling current value and drilling speed value falling within the range of the standard depth unit are extracted from the multi-dimensional sensing data. Using the drilling current and drilling speed values, the formation impedance index is calculated using the formation impedance index calculation formula. All the calculated formation impedance indices are arranged in depth order to generate the formation impedance fingerprint model.

6. The intelligent monitoring system for cement mixing pile construction quality according to claim 3, characterized in that, The flow-machine coupling control module is configured to execute control logic during the upward stirring and grouting stage: Based on the current depth index, retrieve the corresponding formation impedance index from the formation impedance fingerprint model; Using the formation impedance index, the target drilling speed is calculated using an adaptive velocity planning formula; The actual drilling speed of the pile driver is measured in real time, and the target grouting flow rate is calculated based on the actual drilling speed using the grouting flow rate follow-up formula. The adjustment amount for the grouting pump speed is calculated using a PID control algorithm, and the adjustment amount and the target drilling speed are encapsulated into the flow machine coupled control command.

7. The intelligent monitoring system for cement mixing pile construction quality according to claim 3, characterized in that, The fluid-mechanical coupling control module is also configured to perform lubrication efficiency calculation logic: Real-time acquisition of the operating current of the pile driver during the lifting process; Extract the characteristic drilling current at the same depth location from the formation impedance fingerprint model; Combining the working current during the lifting process with the characteristic drilling current, the lubrication efficiency index is calculated using the lubrication efficiency index calculation formula. The lubrication efficiency index calculation formula includes a direction correction coefficient used to compensate for the differences in soil conveying resistance and shearing mode of the spiral blade.

8. The intelligent monitoring system for cement mixing pile construction quality according to claim 7, characterized in that, The fluid-mechanical coupling control module is configured with anomaly correction logic based on the lubrication efficiency index: The lubrication performance index is compared with a preset effectiveness threshold. If the lubrication efficiency index is in the first abnormal range, the flow machine coupling control command is generated to indicate that the power head should stop lifting and increase the grouting flow rate. If the lubrication efficiency index is in the second abnormal range, the flow machine coupling control command is generated to indicate the triggering of a micro reciprocating disturbance sequence, control the power head to drill back down a preset distance, and execute pulse grouting.

9. The intelligent monitoring system for cement mixing pile construction quality according to claim 3, characterized in that, The industrial control center also includes a quality assessment and data management module, configured to perform: The pile body data after pile completion is divided into multiple evaluation units; For each evaluation unit, the formation impedance index from the formation impedance fingerprint model, the lubrication efficiency index, and the grouting volume deviation rate of the grouting process are integrated. The quality score for each evaluation unit is calculated by using a comprehensive quality scoring formula on the fused data. A digital construction quality report is generated based on the quality score.

10. A method for intelligent monitoring of the construction quality of cement mixing piles, characterized in that, Includes the following steps: S1. Generate an endogenous reliable depth benchmark: Simultaneously collect absolute elevation data and relative displacement data, calculate the correlation and deviation of the absolute elevation data and the relative displacement data within the time window, and lock and output the endogenous reliable depth benchmark after verification. S2. Constructing a formation impedance fingerprint model: During the drilling and soil cutting stage, based on the endogenous reliable depth benchmark, the depth is located, and the formation impedance index is calculated using the synchronously acquired drilling current value and drilling speed value to generate the formation impedance fingerprint model that reflects the soft and hard distribution characteristics of the soil layer. S3. Perform flow-machine coupling adaptive control: During the grouting stage, the target drilling speed is planned according to the formation impedance fingerprint model corresponding to the current depth, and the grouting flow rate is adjusted according to the actual drilling speed. At the same time, the lubrication efficiency index is calculated, and the drilling speed and grouting method are corrected in a closed loop to generate construction process monitoring data containing process control parameters and response data. S4. Generate a digital construction quality report: Based on the monitoring data of the construction process, the deviation of grouting volume and the uniformity of mixing along the entire pile length are evaluated in segments, and a digital construction quality report containing the quality rating results is generated.

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