Intelligent monitoring method and system for prestressed pipe pile quality

Abstract

The application provides a prestressed pipe pile quality intelligent monitoring method and system, and relates to the technical field of building engineering. The method comprises the following steps: arranging a laser displacement sensor array around a pile connecting platform of a pile press, emitting laser scanning of a pile head end face after welding pile connection is completed, judging the horizontal flatness of the pile head by calculating the height difference of each measuring point, and obtaining a flatness detection result; integrating the output signals of the inclination sensor and the laser displacement sensor through a data acquisition terminal, eliminating interference data generated by construction vibration after analog-digital conversion, obtaining accurate detection data after processing; real-time display of the pile body perpendicularity curve and the pile head flatness measuring point data, automatic triggering of an audible and light warning when the detection data exceeds a preset safety threshold, and synchronous pop-up of an adjustment scheme on the operation interface. The application realizes real-time acquisition, analysis and feedback of data in the whole pile pressing process, and improves the construction quality of the prefabricated pile.

Description

Technical Field

[0001] This invention relates to the field of building engineering technology, and in particular to a method and system for intelligent monitoring of the quality of prestressed concrete pipe piles. Background Technology

[0002] Prestressed concrete pipe piles are widely used in building construction, bridge engineering, and soft soil foundation treatment due to their advantages such as high bearing capacity, fast construction speed, and stable quality. As building projects become increasingly taller and larger, the requirements for pile foundation construction quality are becoming increasingly stringent. However, precast pile construction is a hidden project with a highly procedural nature, and traditional construction methods suffer from drawbacks such as difficulty in quality traceability and reliance on post-construction spot checks.

[0003] Numerous complex factors during construction can easily cause prestressed concrete pipe piles to tilt. These include uneven soil layers (such as soft and hard interlayers or the distribution of boulders) leading to uneven pressure and causing pile deviation, improper welding operations causing pile misalignment, and interference from environmental factors such as strong winds and temperature differences. Deviations in pile verticality not only reduce the bearing capacity of a single pile but may also lead to subsequent structural cracking, uneven settlement, and other quality hazards. In severe cases, rework is required, significantly increasing construction costs and time.

[0004] Relevant standards specify requirements for the verticality of prestressed concrete pipe piles. However, traditional testing methods mainly rely on manual observation using instruments such as total stations, theodolites, and levels, including methods like orthogonal observation with double theodolites and plumb line methods. These methods suffer from drawbacks such as low accuracy, poor real-time performance, and high manpower consumption. Furthermore, they are mostly random checks after construction is completed, making it impossible to detect and adjust deviations in a timely manner during construction. In the welding and splicing stage, there is a lack of effective flatness testing methods, making it difficult to avoid the risk of pile misalignment caused by uneven splicing from the source. When facing complex strata or obstacles, it is impossible to promptly detect changes in pile posture and adjust pile driving parameters.

[0005] In recent years, with the development of IoT and sensor technologies, intelligent monitoring of pile foundation construction has become a research hotspot in the industry. Some studies have attempted to use visual displacement analyzers and electronic levels to monitor pile displacement, or to use IoT sensors to monitor the verticality of cement mixing piles in real time. However, existing technical solutions mostly focus on monitoring a single parameter and lack a systematic solution that can simultaneously detect pile verticality and pile head flatness and achieve closed-loop control when the standard is exceeded. Summary of the Invention

[0006] This invention provides a method and system for intelligent monitoring of the quality of prestressed pipe piles, which can solve the problem of precast pile deviation, realize the real-time acquisition, analysis and feedback of data throughout the pile driving process, and improve the construction quality of precast piles.

[0007] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0008] Firstly, a method for intelligent monitoring of the quality of prestressed concrete pipe piles, the method comprising:

[0009] Step 1: Detect the geological conditions of the construction area through ground-penetrating radar and static cone penetration test, quantify the influence of soil heterogeneity on pile inclination, and establish a correlation model between geological conditions and verticality deviation to predict the risk level before construction.

[0010] Step 2: Fix dual-axis tilt sensors to key parts of the pile clamp and pile body of the pile driver to collect the tilt angle data of the pile body in real time, and use GPS positioning module to assist in correction to eliminate the influence of the pile driver's own displacement on the test results and obtain the verticality test results.

[0011] Step 3: Arrange a laser displacement sensor array around the pile splicing platform of the pile driver. After the pile splicing is completed, emit a laser to scan the end face of the pile head. Calculate the height difference of each measuring point to determine the horizontal flatness of the pile head and obtain the flatness test results.

[0012] Step 4: The output signals of the tilt sensor and the laser displacement sensor are integrated through the data acquisition terminal. After analog-to-digital conversion, the interference data caused by construction vibration is removed to obtain the processed and accurate detection data.

[0013] Step 5: Display the verticality curve of the pile body and the flatness measurement point data of the pile head in real time. When the detection data exceeds the preset safety threshold, an audible and visual warning will be automatically triggered, and an adjustment plan will pop up on the operation interface simultaneously.

[0014] Step 6: Through the linkage interface with the hydraulic system of the pile driver, when the pile body tilt is detected to exceed the threshold, a signal is automatically sent to adjust the pile driving speed or pile driving force distribution of the pile driver, so as to realize closed-loop management of monitoring and control.

[0015] Secondly, the intelligent monitoring system for the quality of prestressed concrete pipe piles includes:

[0016] A module is established to detect the geological conditions of the construction area through ground-penetrating radar and static cone penetration tests, quantify the influence of soil heterogeneity on pile inclination, and establish a correlation model between geological conditions and verticality deviation to predict the risk level before construction. Dual-axis tilt sensors are fixed at key parts of the pile driver's pile clamp and pile body to collect the inclination angle data of the pile body in real time, and a GPS positioning module is used to assist in correction to eliminate the influence of the pile driver's own displacement on the detection results and obtain the verticality detection results.

[0017] The acquisition module is used to arrange a laser displacement sensor array around the pile splicing platform of the pile driver. After the pile splicing is completed, the laser is emitted to scan the end face of the pile head. The horizontal flatness of the pile head is determined by calculating the height difference of each measuring point, and the flatness detection result is obtained. The output signals of the tilt sensor and the laser displacement sensor are integrated by the data acquisition terminal. After analog-to-digital conversion, the interference data caused by construction vibration is removed to obtain the processed and accurate detection data.

[0018] The display module is used to display the verticality curve of the pile body and the flatness measurement point data of the pile head in real time. When the detection data exceeds the preset safety threshold, it will automatically trigger an audible and visual warning and simultaneously pop up an adjustment plan on the operation interface.

[0019] The processing module is used to automatically send signals to adjust the pile driving speed or pile driving force distribution when the pile body tilt exceeds the threshold through the linkage interface with the hydraulic system of the pile driver, so as to realize closed-loop management of monitoring and control.

[0020] Thirdly, a computing device includes:

[0021] One or more processors;

[0022] A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

[0023] Fourthly, a computer-readable storage medium storing a program that, when executed by a processor, implements the method.

[0024] The above-described solution of the present invention has at least the following beneficial effects:

[0025] By using ground-penetrating radar detection and static cone penetration tests, a correlation model between geological conditions and verticality deviation is established. This allows for the prediction of risk levels in different areas before construction, thereby reducing the risk of pile deviation caused by geological inhomogeneity from the source.

[0026] This invention enables the coordinated monitoring of pile verticality and pile head flatness, overcoming the limitations of traditional single-parameter monitoring. By using a dual-axis tilt sensor to monitor pile tilt in real time and a laser displacement sensor array to monitor pile joint flatness, the two work together to solve both the verticality control problem during pile driving and the flatness detection problem during the welding and splicing process, forming a complete quality control chain.

[0027] By employing a high-precision dual-axis tilt sensor and incorporating a GPS positioning module for auxiliary correction, the interference of factors such as the displacement of the pile driver itself and foundation settlement on the detection results can be effectively eliminated, and the true verticality deviation of the pile body can be obtained. The detection accuracy can reach ±0.1%, which is far higher than that of traditional manual observation methods.

[0028] To address the issue of strong construction vibrations interfering with sensor signals during pile driving, this invention integrates signals through a data acquisition terminal, performs analog-to-digital conversion, and employs a filtering algorithm to remove vibration noise, ensuring the accuracy and stability of the detection data. Furthermore, addressing the shortcomings of existing technologies that only provide simple alarms and lack sufficient on-site operational guidance, this invention not only displays monitoring data in real time but also automatically triggers audible and visual warnings when data exceeds limits, simultaneously displaying specific adjustment plans on the user interface. This allows operators to respond quickly and take effective measures, transforming traditional post-event rectification into in-process control. Attached Figure Description

[0029] Figure 1 This is a flowchart illustrating the intelligent monitoring method for prestressed pipe pile quality provided in an embodiment of the present invention.

[0030] Figure 2 This is a schematic diagram of the intelligent monitoring system for prestressed pipe pile quality provided in an embodiment of the present invention. Detailed Implementation

[0031] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0032] like Figure 1As shown, this embodiment was fully implemented in the supporting pile foundation project of a super high-rise office building in the core business district of a provincial capital city. The project has 45 floors above ground and 3 floors underground, with a seismic fortification intensity of 7 degrees. The foundation design adopts PHC-AB type prestressed concrete pipe piles with a pile diameter of 600mm, a wall thickness of 130mm, a single pile vertical compressive bearing capacity characteristic value of 3600kN, a designed effective pile length of 42m, and a total of 1280 piles. The construction area is located in a river alluvial plain with complex and variable geological conditions. The surface layer is distributed with 1.5-5m thick plain fill and miscellaneous fill, under which there is a 10-16m thick layer of fluid plastic to soft plastic silty clay. There are continuous dense silt interlayers and gravel lenses in some areas. At the same time, many granite boulders with a diameter of 150-400mm have been discovered. This is a high-risk area for pile tilting and splice displacement during the construction of prestressed pipe piles. During the initial trial pile construction of the project, traditional manual inspection methods such as orthogonal observation with double theodolites and plumb lines were used. This resulted in 8 piles exceeding the verticality standard and 3 piles cracking due to splice misalignment. The first-time acceptance rate was only 87%, leading to difficulties in quality traceability, high post-construction rectification costs, and project delays—perfectly aligning with the shortcomings of traditional construction techniques described in the background section. To address these issues, the project adopted the intelligent quality monitoring method for prestressed concrete pipe piles described in this invention throughout the entire construction cycle. The complete implementation process is as follows, with all steps strictly adhering to the scope of protection of the claims of this method, without any abbreviations or omissions in the technical details:

[0033] Step 1, Geological condition exploration and construction risk level prediction before construction:

[0034] Ten days before the official commencement of the project's pile foundation engineering, a full-area geological radar survey and static cone penetration test were completed in the construction area, fully implementing the geological-verticality correlation modeling and risk prediction requirements of this step. This survey employed a high-resolution geological radar with a center frequency of 400MHz, continuously scanning along the designed pile axis at 0.4m intervals in a grid pattern. The scanning range covered all designed pile locations and a 2.5m area surrounding each pile location, acquiring complete radar images of the geological distribution at full depth within the construction area. Image analysis was performed using professional geological interpretation software, accurately identifying four continuously distributed silty sand interlayers and 17 isolated boulder locations within the construction area. The burial depth, thickness, and spatial distribution range of each interlayer were determined and marked, as well as the planar location, burial depth, dimensions, and distribution range of each isolated boulder. Static cone penetration test points were simultaneously set up in the construction area in a grid of 18m×18m, and a total of 58 test points were tested in situ. The measured data of soil cone resistance and side friction resistance were collected at 0.1m intervals within a range from the ground surface to 6m below the design elevation of the pile bottom at each test point, so as to accurately quantify the physical and mechanical properties of soil layers at different depths.

[0035] After completing the ground-penetrating radar (GPR) scan and static cone penetration test, the spatial distribution image data of the strata acquired by the GPR and the soil mechanical parameter data acquired by the static cone penetration test were fused together using multi-source data processing. A refined 3D geological model, a 1:1 replica of the construction area, was then constructed using the convex hull algorithm. The model fully presents the vertical and horizontal distribution characteristics of the strata, the spatial occurrence of soft and hard interlayers and boulders, and the spatial distribution patterns of the mechanical parameters of each soil layer. The specific construction process is as follows:

[0036] The two types of core data acquired in the early stage were preprocessed in a targeted manner. For the ground-penetrating radar interpretation data, professional radar data processing software was used to first complete noise suppression (adaptive filtering algorithm to remove electromagnetic interference and clutter generated by equipment vibration), gain adjustment (depth gain control technology to balance the clarity of reflected signals from deep and shallow strata), and offset correction (correcting the positional offset of strata according to scanning parameters and terrain elevation). Then, discrete three-dimensional coordinate points of all key geological bodies were extracted from the processed radar images, including discrete points of the upper and lower interfaces of each soil layer (extracted according to a planar grid density of 0.4m×0.4m, each point containing the plane X and Y coordinates and the elevation Z value), discrete points of the outlines of 4 silt soft and hard interlayers (one outline point was extracted every 0.3m along the distribution range of the interlayer to determine the spatial boundary of the interlayer), and discrete points of the surface of 17 boulders (20-30 surface points were evenly extracted around the planar projection range of each boulder, covering the top, bottom and sides of the boulder to accurately reflect the spatial morphology of the boulder). For static cone penetration test data, abnormal data caused by equipment operation deviations and soil disturbance during the test (such as sudden changes in cone tip resistance or negative side friction resistance) are first removed. Linear interpolation is then used to supplement missing measurement point data, ensuring that complete cone tip resistance and side friction resistance data are available at 0.1m intervals within a 6m range from the ground surface to the design elevation below the pile bottom for each test point. Subsequently, the three-dimensional coordinates (plane X and Y coordinates are calibrated according to the grid layout, and the elevation Z value is calibrated according to the measured depth) of each static cone penetration test point are correlated with the corresponding mechanical parameters to form an integrated data point set of "three-dimensional coordinates - mechanical parameters". All extracted ground radar discrete coordinate points and static cone penetration integrated data points are uniformly converted to a plane rectangular coordinate system specific to the construction area (consistent with the project's designed pile position coordinate system) to eliminate coordinate deviations and ensure that all data points can be integrated within the same spatial coordinate system.

[0037] Based on the completion of the spatial outline construction of various geological bodies, the boundaries of geological bodies are determined, and the spatial outlines are constructed in sequence according to three types of geological bodies (main stratigraphic body, soft and hard interlayers, and isolated rocks) to achieve accurate reconstruction of geological bodies.

[0038] The convex hull construction of the main stratum involves classifying and grouping the discrete 3D coordinates of all upper and lower interfaces of the pre-processed soil layers according to soil type (plain fill, miscellaneous fill, silty clay, silty sand interlayer, and gravel lens). For each soil layer, all discrete points of the upper interface are extracted, and a 2D convex hull algorithm (Graham scan method) is used to construct the planar convex hull of the upper interface to determine the planar distribution boundary of the upper interface. Then, all discrete points of the lower interface are extracted, and the same 2D convex hull algorithm is used to construct the planar convex hull of the lower interface. The corresponding vertices of the upper and lower interface convex hulls are connected to form a closed 3D convex hull structure, which is the spatial contour of the soil layer. For discontinuous soil layers (such as locally distributed gravel lenses), spatial clustering analysis of discrete points is first used to divide the distribution area of ​​the soil layer. Then, the convex hull algorithm is applied to the discrete points within this area to construct an independent 3D convex hull, ensuring that the soil layer contour is consistent with the actual distribution. During the construction process, the accuracy parameters of the convex hull algorithm were adjusted (the point cloud fitting error was controlled within ±0.05m) to avoid jagged deviations in the convex hull profile and ensure the smoothness and realism of the formation profile.

[0039] The convex hull construction of the soft and hard interlayers was conducted for four consecutively distributed silty sand soft and hard interlayers. First, the discrete points of each interlayer's outline were sorted according to their spatial location, and discrete points abnormally deviating from the main body of the interlayer were removed, retaining the core outline points. Then, a three-dimensional convex hull algorithm (QuickHull algorithm) was used to perform a wrapping calculation on the discrete points of each interlayer, generating a closed three-dimensional convex hull. The spatial shape of this convex hull represents the actual spatial distribution outline of the soft and hard interlayers, clearly showing the burial depth, thickness, and horizontal distribution range of the interlayers. For the junction between the interlayer and the surrounding soil layers, boundary smoothing processing using the convex hull algorithm ensured a seamless connection between the interlayer convex hull and the surrounding soil convex hull, avoiding outline discontinuity and realistically reproducing the occurrence state of the interlayer in the strata.

[0040] For the construction of convex hulls for 17 granite boulders, convex hulls were constructed individually for each boulder. First, all discrete points on the surface of each boulder were imported into the modeling software. Then, a 3D convex hull algorithm was used to fully enclose these discrete points, generating a minimum 3D convex hull that completely covers all surface points of the boulder. The shape of this convex hull closely matches the actual shape of the boulder, accurately reflecting its planar position, burial depth, and dimensions. For smaller boulders with fewer discrete points (diameter ≤ 200mm), virtual discrete points (generated by interpolation based on surrounding ground-penetrating radar data) were added to ensure the integrity of the convex hull construction and avoid distortion of the convex hull outline due to sparse point clouds. For larger boulders (diameter > 200mm), the accuracy of the convex hull outline was improved by refining the calculation of convex hull vertices, ensuring that the spatial morphology reconstruction error of the boulder was controlled within ±0.1m.

[0041] After constructing the convex hull contours of all geological bodies, the mechanical parameters obtained from static cone penetration tests are assigned to the corresponding convex hull models according to the principle of three-dimensional coordinate matching. Specifically, the cone tip resistance and side friction data of each static cone penetration test point are mapped to the surrounding stratigraphic and interlayer convex hulls using a spatial interpolation algorithm (Kriging interpolation), so that each region of the convex hull model corresponds to a specific mechanical parameter value. For boulder convex hulls, the measured hardness parameters of the boulder (obtained through ground-penetrating radar interpretation) are uniformly assigned to determine the mechanical differences between the boulder and the surrounding soil layers. At the same time, the stratigraphic lithology information, interlayer and boulder distribution characteristics interpreted by ground-penetrating radar are simultaneously labeled onto the corresponding convex hull models, achieving a three-in-one model presentation of spatial morphology, mechanical parameters, and lithological characteristics.

[0042] After constructing the initial 3D geological convex hull model, multiple rounds of verification and optimization were conducted to ensure the model's accuracy. Verification was performed using field geological borehole data. Twelve geological boreholes within the construction area (evenly distributed across low, medium, and high-risk areas) were selected. Data such as stratigraphic layering, interlayer depth, and boulder locations measured in the boreholes were compared with the convex hull contours and parameters in the model. If the deviation exceeded ±0.15m, the distribution of discrete points for the corresponding geological bodies was adjusted, and the contour was reconstructed using the convex hull algorithm until the deviation met the requirements. Algorithm parameters were optimized by adjusting the point cloud fitting accuracy and boundary smoothing coefficient of the convex hull algorithm to eliminate problems such as contour distortion and poor connection in the model, ensuring that the spatial relationships between the geological convex hulls were consistent with the actual geological conditions. Data integrity verification was performed to check whether all geological survey data had been integrated into the model and whether the mechanical parameter assignments were uniform and accurate, ensuring that the model could fully represent the geological characteristics of the construction area.

[0043] All the verified and optimized convex hull models of strata, soft and hard interlayers, and boulders are spatially integrated to ensure that the positional relationships and connection states of each geological body are completely consistent with the actual geological conditions, forming a complete three-dimensional geological model of the construction area. The model marks the distribution range and mechanical parameters of each soil layer, the spatial distribution of each soft and hard interlayer, and the specific location and size of each boulder, achieving a 1:1 fine-grained restoration of the geological conditions of the construction area.

[0044] Based on the completed 1:1 detailed 3D geological model of the construction area, the stress and deformation patterns of the pile body during the entire pile driving process under different geological conditions are analyzed, as follows:

[0045] The constructed 3D geological model underwent preliminary verification to confirm the accuracy of the spatial location, lithological characteristics, and mechanical parameter assignments of various geological bodies (plain fill, miscellaneous fill, silty clay, silty sand interlayers, gravel lenses, and granite boulders) in the model. The model also ensured that the connections between different geological bodies were completely consistent with the actual geological conditions of the construction area, eliminating the influence of model errors on the analysis results. Simultaneously, based on the actual parameters of the PHC-AB type prestressed concrete pipe piles used, a pipe pile model was established that perfectly matched the actual pipe pile size and material. Core parameters such as the pipe pile's cross-sectional dimensions, length, concrete strength, and prestressing grade were determined to ensure consistency between the pipe pile model and the actual pipe piles used in construction. Furthermore, considering the pile driving parameters of the 1000-type fully hydraulic static pile driver, basic conditions such as pile driving method, pile driving speed, and pile driving force range were set during the simulation process to recreate the realistic scenario of actual pile driving construction.

[0046] Based on the geological characteristics of the construction area, three core simulation scenarios were specifically designed to simulate the stress and deformation of the pipe piles throughout the entire pile driving process under different geological conditions, ensuring comprehensive coverage and realism. The first scenario involves combinations of different soft and hard soil layers. According to the distribution characteristics of each stratum in the 3D geological model, typical soil layer combinations within the construction area were selected, including combinations of plain fill and silty clay, combinations of silty clay and silty sand interlayers, and combinations of silty sand interlayers and gravel lenses. The simulation of each soil layer combination was conducted, simulating the entire process of the pipe piles from pile driving initiation, soil entry, and penetration through various soil layers until reaching the designed pile length. The focus was on recording the stress changes and tilting deformation of the pipe piles at different soil layer interfaces. The second type of working condition involves different burial depths and thicknesses of soft and hard interlayers. For the four continuously distributed silty sand soft and hard interlayers identified in the model, different burial depth gradients (from 5m to 20m, divided into gradients at 2m intervals) and thickness gradients (from 0.5m to 3m, divided into gradients at 0.5m intervals) are set. The stress distribution, pile bending deformation, and tilting trend of the pipe pile when passing through the interlayer are simulated one by one under each combination of burial depth and thickness. The focus is on analyzing the degree of influence of the burial depth and thickness of the interlayer on the tilting of the pile. The third type of working condition involves different boulder sizes and burial depths. For the 17 granite boulders identified in the model, different size grades were defined based on boulder diameter (150-200mm, 200-300mm, 300-400mm), and different gradients were defined based on burial depth (from 8m to 30m, with gradients in 3m intervals). The stress conditions encountered by the pipe piles during pile driving with boulders of different sizes and burial depths were simulated one by one, including the local peak stress at the contact point between the pipe pile and the boulder, the overall tilt deformation of the pile, and areas of stress concentration in the pile, thus comprehensively understanding the influence of the boulders on the pile tilt. During each working condition simulation, real-time data of the entire pile driving process was continuously recorded to ensure the integrity and accuracy of the simulation results and avoid simulation deviations caused by human intervention.

[0047] For each simulated working condition, the results were analyzed and summarized to clarify the stress and deformation patterns of the pipe pile during the entire pile driving process under different geological conditions. Regarding stress patterns, the distribution characteristics of axial force, shear force, and bending moment of the pipe pile in different soil layers and under different working conditions were analyzed to determine the concentrated areas of stress on the pile body and to assess the impact of different geological conditions on the pile body stress. Regarding deformation patterns, the changes in the inclination angle and the degree of pile body bending deformation were observed to clarify the development trend of pile body inclination deformation and to identify the prone stages and key nodes of pile body inclination under different geological conditions. Through comparative analysis of the simulation results for all working conditions, the core principles were summarized as follows: When the pipe pile crosses the interface between soft and hard soil layers, the significant difference in the mechanical properties of the upper and lower soil layers leads to uneven stress on the pile body, resulting in inclination deformation; the shallower the burial depth and the greater the thickness of the soft-hard interlayer, the more significant the impact on pile body inclination; the larger the size and the shallower the burial depth of the boulder, the greater the resistance encountered during the pipe pile driving process, and the more likely the pile body is to experience inclination deformation. Furthermore, the direction and amplitude of pile body inclination will vary depending on the contact position between the boulder and the pipe pile. Among them, at the interface between the fluid silty soil layer and the dense silty sand interlayer, the difference in cone tip resistance and side friction between the two soil types is the most significant, and the uneven stress phenomenon when the pipe pile crosses the interface is the most prominent. Therefore, the amplitude of the pile body tilting deformation is the largest, and the corresponding pile body tilting influence coefficient is the highest. In the area where boulders are distributed, as the burial depth of the boulders increases, the force impact when the pipe pile crosses the boulders gradually decreases, and the amplitude of the pile body tilting deformation also decreases. Therefore, the pile body tilting influence coefficient shows a linear increasing trend with the increase of the burial depth of the boulders.

[0048] The influence coefficient of pile inclination was quantitatively calculated to provide a basis for risk level classification. Based on the simulation analysis results of all working conditions, a combination of qualitative and quantitative methods was used to quantitatively calculate the influence coefficient of pile inclination corresponding to different geological conditions. For each geological condition (different soil layer combinations, different interlayer depths and thicknesses, different boulder sizes and depths), combined with data such as pile inclination amplitude and uneven stress recorded during the simulation, the corresponding influence coefficient was calculated using a unified quantitative standard to determine the influence weight of different geological conditions on pile inclination. In particular, the influence coefficient of the interface between the fluid plastic silty soil layer and the dense silty sand interlayer was quantified. Combined with the measured simulation data of pile inclination deformation at this interface, it was determined to be the area with the highest influence coefficient among all geological conditions. At the same time, for the boulder distribution area, the linear relationship between the influence coefficient and the boulder burial depth was determined by comparing the influence coefficients of different burial depths.

[0049] The risk level of the construction area is classified according to the influence coefficient, and the risk standard for each area is determined. Based on the quantitatively calculated pile inclination influence coefficient and combined with the actual geological distribution characteristics of the construction area, the entire construction area is divided into three levels: low risk, medium risk, and high risk, and the classification criteria and coverage of each level are determined. The criteria for designating high-risk areas are as follows: the pile tilting influence coefficient reaches the highest level, and the geological conditions are complex. Specifically, this includes areas with concentrated distribution of boulders (i.e., areas with 3 or more of the 17 boulders concentrated in one area) and areas with continuous development of soft and hard interlayers (i.e., areas with a continuous length of more than 50m among the 4 silty sand soft and hard interlayers). In these areas, the probability of tilting deformation during pipe pile driving is extremely high, and the tilting amplitude is likely to exceed the allowable range of the specifications. The criteria for designating medium-risk areas are as follows: the pile tilting influence coefficient is at a medium level, and the geological conditions are relatively complex. Specifically, this includes areas with a soft soil (silty clay) thickness exceeding 12m. In these areas, during pipe pile driving, uneven settlement is likely to occur due to the low bearing capacity of the soft soil, which in turn can lead to pile tilting. The criteria for designating low-risk areas are as follows: the pile tilting influence coefficient is the lowest, and the geological conditions are relatively simple. Specifically, this includes areas with uniform soil layer distribution, no boulders, no soft and hard interlayers, and a soft soil thickness not exceeding 12m. In these areas, the pipe piles are subjected to uniform stress during pipe pile driving, and the probability of tilting deformation is extremely low.

[0050] A risk level prediction map is generated to implement proactive prevention and control measures. Based on the identified risk levels, combined with a 3D geological model and the distribution of designed pile locations, a risk level prediction map covering the entire construction area is generated. The map clearly marks the areas of low, medium, and high risk levels using different colors, and also identifies the specific location and corresponding risk level of each designed pile location, ensuring that construction personnel can intuitively and clearly understand the risk situation of each pile location. For high-risk pile locations identified in the prediction map, a specific pile driving construction plan and emergency response measures are prepared in advance. The plan specifies the pile driving parameters (such as pile driving speed and pile driving force control range), construction process, and monitoring focus for high-risk pile locations. Targeted preventative measures are developed to address potential pile tilting issues. The emergency response measures define the emergency response process, adjustment methods, and division of responsibilities in case of excessive pile tilt, ensuring rapid and standardized handling of any abnormal situations. Through these series of operations, proactive risk prediction and prevention are completed before construction, reducing the probability of pile tilting from the source.

[0051] The influence coefficients for the corresponding geological conditions are calculated in detail below:

[0052] Acquire all raw data recorded during the simulation process, categorize and archive them according to geological condition type, forming a complete computational dataset, specifically including:

[0053] Measured values ​​of cone tip resistance and side friction resistance for each soil layer (from static cone penetration tests and 3D geological model assignments), with the cone tip resistance of the soft soil layer (fluid-plastic silty clay) denoted as... Side friction resistance is denoted as The cone tip resistance of dense soil layers (silty sand interlayers, gravel lenses) is denoted as... Side friction resistance is denoted as The compressive strength of a boulder is denoted as . The burial depth of the soft and hard interlayer is denoted as... (Unit: m), thickness is denoted as (Unit: m), the diameter of the boulder is denoted as . (Unit: m), burial depth is denoted as (Unit: m); Under different geological conditions, the maximum inclination angle of the entire pipe pile driving process is recorded as: (Unit: °), the difference between the maximum stress and the average stress in the pile body is denoted as . (Unit: MPa)

[0054] Meanwhile, the original data after processing is verified to remove abnormal data caused by operational deviations or data recording errors during the simulation process, ensuring that all data used for calculation is true, complete, and valid, providing a solid foundation for subsequent formula calculations; in addition, the standards for the values ​​of all parameters in the calculation process are clarified to ensure that the units of the same type of parameters are consistent and the values ​​are standardized, avoiding calculation deviations caused by parameter confusion.

[0055] Based on the core influencing factors of pile tilt under various geological conditions, specific quantitative calculation formulas were designed for each. All formulas are derived from simulation data and practical engineering experience, and the influence coefficients are obtained directly by substituting parameters. The specific formulas are as follows:

[0056] Formulas for calculating the pile inclination influence coefficient for different soil layer combinations:

[0057] The differences in mechanical parameters between soil layers (differences in cone tip resistance and side skin resistance) and the maximum inclination angle of the pile are calculated using the following formulas:

[0058] ;

[0059] in, This represents the pile inclination influence coefficient corresponding to different soil layer combinations. It has no unit; the larger the value, the greater the influence of the soil layer combination on the pile inclination. This represents the maximum inclination angle of the pipe pile under the simulated working condition of this soil layer combination (unit: °), with a value range of 0.01°~0.5°; and These represent the measured values ​​of cone tip resistance and side friction resistance in soft soil layers (such as fluid-plastic silty clay), respectively. The value range is 500~1500 kPa. The values ​​range from 10 to 30 kPa and are derived from static cone penetration test data. and These represent the measured values ​​of cone tip resistance and side friction resistance of dense soil layers (such as silt interlayers and gravel lenses) combined with soft soil layers, respectively. The value range is 2000~5000 kPa. The values ​​range from 50 to 120 kPa and are derived from static cone penetration test data. The numerator is the sum of the absolute values ​​of the differences in mechanical parameters between the two types of soil layers, reflecting the degree of difference in mechanical properties between the soil layers. The larger the difference, the stronger the heterogeneity of the soil layer and the greater the impact on the pile inclination. The denominator is the sum of the mechanical parameters of the soft soil layer, used to normalize the difference and avoid distortion of the calculation results due to the difference in mechanical strength of the soil layers themselves. Finally, the influence coefficient corresponding to the soil layer combination is obtained by multiplying it with the maximum inclination angle of the pile.

[0060] The formulas for calculating the pile inclination influence coefficients corresponding to different embedment depths and thicknesses of soft and hard interlayers are as follows:

[0061] The calculation formulas for interlayer thickness, embedment depth, and uneven stress distribution in the pile body are as follows:

[0062] ;

[0063] in, The value represents the pile inclination influence coefficient corresponding to different combinations of soft and hard interlayer burial depth and thickness. The larger the value, the greater the influence of the interlayer condition on the pile inclination. The actual thickness of the soft and hard interlayer (unit: m) is represented, with a value ranging from 0.5 to 3 m, determined by ground-penetrating radar interpretation and three-dimensional geological model; This indicates the actual burial depth of the soft and hard interlayer (unit: m), with a range of 5~20m. This represents the difference between the maximum stress and the average stress of the pile body under the simulated working condition (unit: MPa), with a value range of 0.5~5MPa; This ratio reflects the relative influence of the interlayer. The greater the interlayer thickness and the shallower the embedment depth, the larger the ratio, indicating a more significant hindering effect of the interlayer on the pile driving process; it is also related to the degree of uneven stress on the pile body. Multiply by 0.01 and then normalize to obtain the final impact coefficient, ensuring that the coefficient value is within a reasonable range to facilitate subsequent risk level classification.

[0064] The formulas for calculating the pile inclination influence coefficient for different boulder sizes and burial depths are as follows:

[0065] The formulas for calculating the size of the boulder, its embedment depth, and the maximum inclination angle of the pile are as follows:

[0066] ;

[0067] in, This represents the pile inclination influence coefficient corresponding to different combinations of boulder size and burial depth. The larger the value, the greater the influence of the boulder condition on the pile inclination. This indicates the maximum inclination angle of the pipe pile recorded in the simulation under the isolated rock condition (unit: °), with a value range of 0.01°~0.4°; The actual diameter of the boulder (unit: m) ranges from 0.15 to 0.4 m and is determined by ground-penetrating radar interpretation and a three-dimensional geological model. This indicates the actual burial depth of the boulder (unit: m), with a range of 8~30m. This represents the compressive strength of a boulder (unit: MPa), with a value range of 80~150 MPa. The ratio reflects the relative obstruction capacity of the boulder. The larger the boulder and the shallower its burial depth, the larger the ratio, indicating that the boulder has a stronger obstruction effect on the driving of the pipe pile. This is used to quantify the impact of boulder hardness on the pile body. The higher the hardness, the greater the impact on the pile body, which in turn exacerbates the pile body tilt. Multiplying the three factors together yields the final influence coefficient, which clearly reflects the comprehensive influence of boulder size, burial depth, and hardness on pile body tilt.

[0068] Substitute the data by type to calculate the influence coefficient for different geological conditions. Combining the simulated working conditions from the previous section, substitute the data into the formula one by one to calculate the pile inclination influence coefficient for each type of geological condition. A specific example is shown below:

[0069] Calculation of influence coefficients for soil layer combinations (taking a combination of fluid silty clay and silty sand interlayers as an example):

[0070] Known parameters: , , , , ;

[0071] Substitute into the formula The calculation shows that: ;

[0072] The results indicate that the pile inclination influence coefficient at the interface between the fluid silty soil layer and the dense silty sand interlayer is approximately 1.53, which is the highest among all soil layer combinations.

[0073] Calculation of influence coefficients for soft and hard interlayers (taking a silty sand interlayer with a burial depth of 5m and a thickness of 3m as an example):

[0074] Known parameters: , , Substitute into the formula The calculation shows that: ;

[0075] If it is a silty sand interlayer with a depth of 20m and a thickness of 0.5m, it is known that... , , Substituting into the formula, we can calculate the result. It can be seen that the shallower the burial depth and the greater the thickness of the interlayer, the greater the influence coefficient.

[0076] Calculation of the influence coefficient for a boulder (taking a boulder with a diameter of 0.4m and a burial depth of 8m as an example):

[0077] Known parameters: , , , Substitute into the formula The calculation shows that: If it is a solitary rock with a diameter of 0.4m and a burial depth of 30m, it is known that... With other parameters remaining unchanged, the result is obtained by substituting them into the formula. It can be seen that the greater the burial depth of the isolated rock, the smaller the influence coefficient, showing a linear decreasing trend.

[0078] Coefficient verification and unified calibration ensure accurate and reliable calculation results. After the influence coefficients corresponding to all geological conditions are calculated, multiple rounds of verification and calibration are performed to avoid calculation deviations. Influence coefficients of the same type but different parameters are compared to check whether they conform to the preset influence rules (e.g., the larger the interlayer thickness, the higher the coefficient; the larger the burial depth of the boulder, the lower the coefficient; the interface coefficient of the fluid silty soil layer and the silt interlayer is the highest). If abnormal data is found, the parameter values ​​and calculation process are rechecked to correct the deviations. Secondly, combined with the actual geological borehole data of the construction area, the geological conditions corresponding to the borehole locations are compared with the calculated influence coefficients to confirm that the coefficients can truly reflect the degree of geological influence in the area. All influence coefficients are uniformly calibrated to ensure that the influence coefficients of different types of geological conditions are comparable.

[0079] Step 2, Real-time acquisition and precise correction detection of pile verticality:

[0080] Before driving a single pipe pile, the installation and initial calibration of the dual-axis tilt sensor and GPS positioning module were completed for the 1000-type fully hydraulic static pile driver used in this project, fully implementing the requirements for real-time verticality monitoring and error correction in this step. First, a high-precision dual-axis tilt sensor was fixed to the pile clamp of the pile driver using a high-strength rigid clamp. This sensor was installed close to the clamping contact surface between the pile clamp and the pipe pile, used to monitor the tilt attitude of the upper part of the pipe pile in real time. Simultaneously, another dual-axis tilt sensor of the same model was fixed to the middle of the pile body using a clamp-type special clamp, used to monitor the overall tilt deformation of the pipe pile in real time. After both sensors were installed, multiple rounds of initial calibration were performed using a high-precision industrial level to ensure that the sensor's installation reference surface was absolutely horizontal, eliminating the influence of installation errors on the detection results. The sampling frequency of both sensors was set to 10Hz to meet the real-time continuous acquisition requirements throughout the pile driving process.

[0081] A GPS receiver is installed at the center of the top of the pile driver. At the same time, a differential GPS reference station is set up in a location with a wide field of vision, no buildings obstructing the view, and stable foundation in the surrounding construction area. The straight-line distance between the reference station and the GPS receiver is controlled within 4km. A differential GPS positioning system with centimeter-level accuracy is built to collect real-time data on the position changes, displacement, and attitude of the pile driver during the pile driving process.

[0082] Throughout the entire process of pipe pile driving, as the pipe pile is continuously and uniformly driven down, two dual-axis tilt sensors installed at the pile clamp and the middle of the pile simultaneously collect raw data on the tilt angles of the pipe pile in the two orthogonal directions of the X and Y axes. Simultaneously, a differential GPS positioning system collects the position and displacement data of the pile driver at the same time. This data is used to calculate the tilt and pitch angles of the pile driver caused by foundation settlement and machine movement. The data acquisition terminal adds a unified timestamp to all collected data, achieving time synchronization between the tilt angle data and the GPS positioning data. Through the data processing unit, the attitude change of the pile driver itself is first calculated. Then, the raw tilt angle data collected by the two dual-axis tilt sensors are subtracted from the corresponding attitude angles of the pile driver at the same time, completely eliminating the interference of the pile driver's displacement, tilt, and pitch on the pile tilt detection results, thus obtaining the true verticality deviation data of the pipe pile relative to the gravity direction. Finally, the upper tilt deviation data obtained by the sensor at the pile clamp head and the overall tilt deviation data obtained by the sensor in the middle of the pile body are weighted and averaged according to a weight ratio of 6:4 to obtain the final real-time verticality detection result of the pipe pile, realizing uninterrupted, high-precision, and interference-free real-time monitoring of the pile verticality throughout the entire process.

[0083] Step 3, Precise inspection of the flatness of the pile head end face during pile splicing:

[0084] To address the construction requirements of this project, where each pipe pile section is 12m long and requires splicing of three sections, a high-precision laser displacement sensor is fixedly installed at each of the four corners of the splicing platform of the pile driver. The four sensors form a rectangular laser displacement sensor array. The laser emitting ends of all sensors are vertically aligned with the end face of the pile head to be spliced ​​below. The four sensors correspond to the center measuring points of the four quadrants of the pile head end face. After installation, the four laser displacement sensors are uniformly calibrated to ensure that the measuring reference plane of all sensors is completely consistent with the horizontal plane of the splicing platform, thus eliminating the influence of installation reference error on the flatness test results.

[0085] When the first section of the pipe pile is driven to a position where the top surface of the pile head is 1.2m above the splicing platform, the driving operation is stopped, and the splicing of the upper and lower sections of the pipe pile begins. First, the second section of the pipe pile is smoothly hoisted directly above the first section, and the pile heads of the upper and lower sections are aligned and adjusted. Then, the pipe pile interface is welded. The welding operation strictly follows the specifications, completing multi-layer, multi-pass welding. After welding is completed, the weld is allowed to cool naturally to room temperature before the pile head flatness inspection process is initiated. Four laser displacement sensors at the four corners of the splicing platform simultaneously emit laser beams, which are perpendicularly irradiated to the corresponding measuring points on the pile head end face. The sensors synchronously receive the reflected echo signals, accurately measuring the vertical distance between the four measuring points and the sensor reference plane. The data acquisition terminal simultaneously acquires the distance measurements of the four measuring points. Based on the distance measurements of four measuring points, the height difference between the pile head end face and the horizontal plane is calculated to obtain the flatness deviation value of the pile head end face. The preset allowable deviation threshold for pile head flatness in this project is 2mm. If the distance deviation of any of the four measuring points exceeds 2mm, the system will determine that the flatness of the pile head in this splicing is unqualified and prohibits it from proceeding to the subsequent pile driving process. If the distance deviation of all four measuring points is within the preset threshold range, the pile head flatness is determined to be qualified, and the subsequent pile driving operation can continue, thus avoiding the risk of pile tilting caused by uneven pile head from the source of splicing.

[0086] Step 4, Multi-source detection data integration and interference removal:

[0087] Throughout the entire process of pipe pile construction, a dedicated data acquisition terminal, wall-mounted in the pile driver's operating room, integrates real-time detection signals from all dual-axis tilt sensors and laser displacement sensors, fully implementing the data conversion and interference removal requirements of this step. The data acquisition terminal is equipped with a multi-channel synchronous sampling circuit, capable of simultaneously receiving eight analog signals, fully meeting the synchronous acquisition needs of all sensors. First, the multi-channel synchronous sampling circuit synchronously converts the analog electrical signals output from the dual-axis tilt sensors and laser displacement sensors into digital signals suitable for processing. Simultaneously, a unique timestamp is added to each acquired digital signal to ensure time synchronization of pile verticality data, pile head flatness data, and GPS positioning data, avoiding processing errors caused by data timing misalignment.

[0088] To address the issue of abnormal fluctuations in raw data caused by vibrations from the pile driver, environmental noise from surrounding construction machinery, and electromagnetic interference during pile welding, the data acquisition terminal's built-in adaptive filtering unit performs end-to-end filtering on the acquired raw digital signals. This effectively eliminates abrupt interference data caused by construction vibrations, environmental noise, and electromagnetic interference, while fully preserving accurate and valid data on changes in pile posture and pile head flatness. The result is high-precision, high-reliability detection data, providing accurate data support for subsequent real-time display, out-of-range warnings, and coordinated control. In this embodiment, the fluctuation range of the filtered detection data is controlled within 0.05°, fully meeting the accuracy requirements for prestressed concrete pipe pile construction monitoring.

[0089] Step 5: Real-time display of test data and automatic warning of exceeding standards:

[0090] The accurate detection data, after filtering, is transmitted in real time to the industrial control touchscreen in the pile driver's control room via wired communication, and simultaneously transmitted to the project management department's remote monitoring platform via 5G wireless communication. Both the operating interface and the remote monitoring platform use graphical displays to fully implement the real-time display and early warning requirements of this step. Specifically, the left side of the pile driver's operating interface displays a dynamic curve showing the change in pile verticality with pile driving depth, simultaneously displaying the specific value of the current verticality deviation, the allowable deviation value according to specifications, and the percentage of deviation. The right side of the operating interface displays the flatness deviation values ​​of the four laser displacement measuring points in the pile splicing stage in real time in the form of a bar chart, while also marking the preset flatness safety threshold, allowing operators to intuitively and clearly grasp the real-time construction quality.

[0091] Throughout the construction process, the system compares the detected data with preset safety thresholds in real time. This project strictly adheres to the relevant requirements of the "Technical Specification for Building Pile Foundations" JGJ94, with preset allowable deviation thresholds for pile verticality of 0.3% of pile length and pile head flatness of 2mm. When the real-time detected deviations in pile verticality or pile head flatness exceed the preset safety thresholds, the system immediately and automatically triggers the audible and visual warning device. The audible and visual alarm installed on the top of the pile driver's operating room emits a high-frequency warning sound and a red flash, while a red warning prompt box pops up in a prominent position on the operating interface, simultaneously pushing targeted adjustment plans and standardized operating guidelines. For example, when a pile is detected to be tilted excessively in the positive X-axis direction, the operation interface will simultaneously display a prompt to immediately pause the automatic pile driving process, adjust the pressure of the corresponding pile driving cylinder in the positive X-axis direction, reduce the pile driving speed to 50% of the original set speed, and simultaneously slowly correct the pile posture, guiding operators to quickly and properly complete the deviation adjustment. When the pile splice flatness test exceeds the standard, the operation interface will simultaneously display a prompt to pause subsequent pile driving construction, check whether there is any welding slag residue on the pile head end face, whether the upper and lower piles are misaligned, clean the pile head end face, and recalibrate the pile head levelness before resuming the flatness test. At the same time, all warning information, exceeding data, and adjustment operation records will be simultaneously uploaded to the remote monitoring platform for permanent archiving, enabling project management to achieve full traceability and supervision of pile foundation construction quality.

[0092] Step 6: Closed-loop linkage control between monitoring and pile driving construction:

[0093] In this embodiment, the system establishes a linkage control interface with the multi-channel solenoid valves of the pile driver's hydraulic system through the PLC controller built into the pile driver, realizing closed-loop linkage management between the monitoring system and the pile driving construction system, and fully implementing the closed-loop control requirements of this step. During the entire pile driving process, when the system detects that the verticality deviation of the pile exceeds the preset safety threshold, it immediately pauses the automatic pile driving process. At the same time, based on the direction and value of the pile deviation, the PLC controller accurately calculates the pile driving force difference that needs to be adjusted for the corresponding side pile driving cylinder, and automatically sends pulse control signals to the corresponding solenoid valves of the hydraulic system to adjust the oil supply pressure of the side pile driving cylinder corresponding to the deviation direction, thereby achieving dynamic balance adjustment of different side pile driving forces and gradually correcting the tilt posture of the pile. Simultaneously, the system will automatically adjust the pile driving speed of the pile driver according to the magnitude of the deviation value to avoid further aggravation of pile tilt caused by rapid pile driving.

[0094] For example, when the system detects that the pile is tilted excessively in the negative Y-axis direction, it automatically increases the oil supply pressure of the hydraulic cylinder on the side corresponding to the positive Y-axis direction and simultaneously decreases the oil supply pressure of the hydraulic cylinder on the side corresponding to the negative Y-axis direction. The tilt of the pile is slowly corrected by the difference in pile driving force between the two sides. At the same time, the pile driving speed is automatically reduced to 30% of the original set speed, and the changes in pile verticality are monitored in real time throughout the process. While the system automatically adjusts the pile driving force distribution and speed, the pile driver's control panel will simultaneously prompt the operator to manually fine-tune the position of the pile driver and the clamping posture of the pile clamp until the pile verticality deviation returns to the allowable range. The system then automatically cancels the warning and resumes the normal pile driving process, while simultaneously archiving all data and operation records of this adjustment process. For construction at high-risk pile locations, the system will also pre-set the upper limit of pile driving speed and graded control parameters of pile driving force based on the risk level prediction map generated in the early stage. When the pile is driven to the elevation corresponding to the soft and hard interlayer or the depth of the boulder, the system will automatically reduce the pile driving speed to avoid the risk of pile tilting in advance. This truly realizes intelligent management of the entire process, including pre-risk prediction, real-time monitoring during the process, automatic early warning of exceeding the standard, dynamic closed-loop control, and full data traceability.

[0095] This embodiment, through the aforementioned complete intelligent monitoring method for prestressed concrete pipe pile quality, completed the construction of all 1280 prestressed concrete pipe piles in the project. Acceptance by a third-party testing agency showed a 99.8% first-time pass rate for pile verticality, with no instances of pile displacement or cracking caused by substandard pile joint flatness. Compared to traditional construction methods, the first-time pass rate for pile foundation construction increased by 12.8%, the rework rate for excessive pile tilt decreased to zero, and the cumulative construction period was shortened by 18 days. This significantly reduced rework and rectification costs and the risk of project delays. Simultaneously, it achieved complete traceability of quality data throughout the entire pile foundation construction process, thoroughly resolving the industry pain points mentioned in the background technology, such as difficulty in tracing the quality of traditional pipe pile construction, reliance on post-construction sampling, low accuracy, poor real-time performance, and inability to achieve closed-loop control. This fully meets the high-quality management requirements for pile foundation construction of super high-rise buildings.

[0096] like Figure 2 As shown, embodiments of the present invention also provide an intelligent monitoring system for the quality of prestressed concrete pipe piles, including:

[0097] A module is established to detect the geological conditions of the construction area through ground-penetrating radar and static cone penetration tests, quantify the influence of soil heterogeneity on pile inclination, and establish a correlation model between geological conditions and verticality deviation to predict the risk level before construction. Dual-axis tilt sensors are fixed at key parts of the pile driver's pile clamp and pile body to collect the inclination angle data of the pile body in real time, and a GPS positioning module is used to assist in correction to eliminate the influence of the pile driver's own displacement on the detection results and obtain the verticality detection results.

[0098] The acquisition module is used to arrange a laser displacement sensor array around the pile splicing platform of the pile driver. After the pile splicing is completed, the laser is emitted to scan the end face of the pile head. The horizontal flatness of the pile head is determined by calculating the height difference of each measuring point, and the flatness detection result is obtained. The output signals of the tilt sensor and the laser displacement sensor are integrated by the data acquisition terminal. After analog-to-digital conversion, the interference data caused by construction vibration is removed to obtain the processed and accurate detection data.

[0099] The display module is used to display the verticality curve of the pile body and the flatness measurement point data of the pile head in real time. When the detection data exceeds the preset safety threshold, it will automatically trigger an audible and visual warning and simultaneously pop up an adjustment plan on the operation interface.

[0100] The processing module is used to automatically send signals to adjust the pile driving speed or pile driving force distribution when the pile body tilt exceeds the threshold through the linkage interface with the hydraulic system of the pile driver, so as to realize closed-loop management of monitoring and control.

[0101] It should be noted that this system is a system corresponding to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0102] Embodiments of the present invention also provide a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0103] Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0104] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for intelligent monitoring of the quality of prestressed concrete pipe piles, characterized in that, The method includes: Step 1: Detect the geological conditions of the construction area through ground-penetrating radar and static cone penetration test, quantify the influence of soil heterogeneity on pile inclination, and establish a correlation model between geological conditions and verticality deviation to predict the risk level before construction. Step 2: Fix dual-axis tilt sensors to key parts of the pile clamp and pile body of the pile driver to collect the tilt angle data of the pile body in real time, and use GPS positioning module to assist in correction to eliminate the influence of the pile driver's own displacement on the test results and obtain the verticality test results. Step 3: Arrange a laser displacement sensor array around the pile splicing platform of the pile driver. After the pile splicing is completed, emit a laser to scan the end face of the pile head. Calculate the height difference of each measuring point to determine the horizontal flatness of the pile head and obtain the flatness test results. Step 4: The output signals of the tilt sensor and the laser displacement sensor are integrated through the data acquisition terminal. After analog-to-digital conversion, the interference data caused by construction vibration is removed to obtain the processed and accurate detection data. Step 5: Display the verticality curve of the pile body and the flatness measurement point data of the pile head in real time. When the detection data exceeds the preset safety threshold, an audible and visual warning will be automatically triggered, and an adjustment plan will pop up on the operation interface simultaneously. Step 6: Through the linkage interface with the hydraulic system of the pile driver, when the pile body tilt is detected to exceed the threshold, a signal is automatically sent to adjust the pile driving speed or pile driving force distribution of the pile driver, so as to realize closed-loop management of monitoring and control.

2. The intelligent monitoring method for prestressed pipe pile quality according to claim 1, characterized in that, Step 1: Geological conditions of the construction area are investigated using ground-penetrating radar and static cone penetration tests. The influence of soil heterogeneity on pile inclination is quantified, and a correlation model between geological conditions and verticality deviation is established to predict the risk level before construction, including: The construction area was scanned by ground-penetrating radar to obtain images of the geological strata and identify the distribution of soft and hard interlayers and boulders; at the same time, static cone penetration tests were conducted to obtain data on the cone resistance and side friction of each soil layer. By fusing ground-penetrating radar images with static cone penetration data, a three-dimensional geological model reflecting the heterogeneity of the strata is established. Based on the three-dimensional geological model, the stress and deformation patterns of the pile body under different geological conditions are analyzed through numerical simulation, and the influence coefficient of soil heterogeneity on pile body inclination is quantified. According to the influence coefficient, the construction area is divided into different risk levels, and a risk level prediction map is generated.

3. The intelligent monitoring method for prestressed pipe pile quality according to claim 2, characterized in that, Step 2: Fix dual-axis tilt sensors to key parts of the pile clamp and pile body of the pile driver to collect the tilt angle data of the pile body in real time. Use a GPS positioning module to assist in correction to eliminate the influence of the pile driver's own displacement on the test results, and obtain the verticality test results, including: Two dual-axis tilt sensors are fixed to both sides of the pile clamp and the middle of the pile body using clamps. One sensor is installed near the pile clamp to monitor the upper tilt, and the other is installed in the middle of the pile body to monitor the overall tilt. Both sensors are initially calibrated using a level to ensure they are installed horizontally. A GPS receiver is installed on top of the pile driver, and a differential GPS reference station is set up at the ground benchmark station. During the pile driving process, the X-axis and Y-axis tilt angle data of the two tilt sensors are collected in real time, and the position and displacement data of the pile driver output by the GPS receiver are collected simultaneously. By calculating the attitude changes of the pile driver itself, including the tilt and pitch angles of the machine body caused by foundation settlement or movement; by subtracting the tilt angle data collected by the two tilt angle sensors from the attitude angle of the pile driver itself at the corresponding moment, the true verticality deviation of the pile body relative to the direction of gravity is obtained. The deviation data from the two sensors are weighted and averaged to obtain the final verticality detection result.

4. The intelligent monitoring method for prestressed pipe pile quality according to claim 3, characterized in that, Step 3: Arrange a laser displacement sensor array around the pile splicing platform of the pile driver. After the pile splicing is completed, emit a laser to scan the end face of the pile head. Calculate the height difference at each measuring point to determine the horizontal flatness of the pile head and obtain the flatness test results, including: Laser displacement sensors are installed at the four corners of the pile splicing platform, with each sensor aligned with the corresponding measuring point on the end face of the pile head. After welding is completed, control all sensors to simultaneously emit lasers and receive reflected signals to measure the distance between each measuring point and the sensor; The height difference between the pile head end face and the horizontal plane is calculated based on the distance values ​​of the four measuring points to obtain the flatness deviation; if the distance deviation of any measuring point exceeds the preset threshold, it is judged that the flatness is unqualified.

5. The intelligent monitoring method for prestressed pipe pile quality according to claim 4, characterized in that, Step 4: Integrate the output signals of the tilt sensor and the laser displacement sensor through the data acquisition terminal, and after analog-to-digital conversion, remove the interference data caused by construction vibration to obtain the processed and accurate detection data, including: The data acquisition terminal uses a multi-channel synchronous sampling circuit to synchronously convert the analog signals from the tilt sensor and the laser displacement sensor into digital signals; The collected raw data is filtered to eliminate interference from pile driver vibration and environmental noise, resulting in accurate processed test data.

6. The intelligent monitoring method for the quality of prestressed pipe piles according to claim 5, characterized in that, Step 5: Real-time display of pile verticality curve and pile head flatness measurement data; when the detected data exceeds the preset safety threshold, an audible and visual warning is automatically triggered, and an adjustment plan pops up simultaneously on the operation interface, including: The system displays the verticality variation curve of the pile body and the bar chart of the flatness of each measuring point in real time using a graphical interface. When real-time data exceeds the threshold, an audible and visual alarm is automatically triggered, and an operation guide suggesting parameter adjustments pops up on the interface.

7. The intelligent monitoring method for prestressed pipe pile quality according to claim 6, characterized in that, Step 6: Through the linkage interface with the hydraulic system of the pile driver, when the pile tilt is detected to exceed the threshold, a signal is automatically sent to adjust the pile driving speed or pile driving force distribution of the pile driver, realizing closed-loop management of monitoring and control, including: The PLC controller is connected to the solenoid valve of the hydraulic system of the pile driver. When the verticality deviation exceeds the threshold, the difference in pile driving force that needs to be adjusted is calculated according to the direction of the deviation, and a pulse signal is sent to the hydraulic system to adjust the pressure of the corresponding side pile driving cylinder to achieve dynamic balance of pile driving force. The operator is prompted on the operation screen to manually adjust the position of the pile driver until the verticality is restored to the allowable range.

8. A prestressed concrete pipe pile quality intelligent monitoring system, wherein the system implements the method as described in any one of claims 1 to 7, characterized in that, include: A module is established to detect the geological conditions of the construction area through ground-penetrating radar and static cone penetration tests, quantify the influence of soil heterogeneity on pile inclination, and establish a correlation model between geological conditions and verticality deviation to predict the risk level before construction. Dual-axis tilt sensors are fixed at key parts of the pile driver's pile clamp and pile body to collect the inclination angle data of the pile body in real time, and a GPS positioning module is used to assist in correction to eliminate the influence of the pile driver's own displacement on the detection results and obtain the verticality detection results. The acquisition module is used to arrange a laser displacement sensor array around the pile splicing platform of the pile driver. After the pile splicing is completed, the laser is emitted to scan the end face of the pile head. The horizontal flatness of the pile head is determined by calculating the height difference of each measuring point, and the flatness detection result is obtained. The output signals of the tilt sensor and the laser displacement sensor are integrated by the data acquisition terminal. After analog-to-digital conversion, the interference data caused by construction vibration is removed to obtain the processed and accurate detection data. The display module is used to display the verticality curve of the pile body and the flatness measurement point data of the pile head in real time. When the detection data exceeds the preset safety threshold, it will automatically trigger an audible and visual warning and simultaneously pop up an adjustment plan on the operation interface. The processing module is used to automatically send signals to adjust the pile driving speed or pile driving force distribution when the pile body tilt exceeds the threshold through the linkage interface with the hydraulic system of the pile driver, so as to realize closed-loop management of monitoring and control.

9. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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