Quality tracing method and system for cast-in-place pile construction

Abstract

The invention relates to the technical field of quality tracing of cast-in-place piles, in particular to a quality tracing method and system for cast-in-place pile construction. The method comprises the following steps that geological information of an area where the cast-in-place pile is located is obtained by obtaining geographic position data of the cast-in-place pile and conducting geological condition detection; the terrain complexity of the area where the cast-in-place pile is located is estimated, the fracture probability is calculated according to the cast-in-place pile design data and the terrain complexity data, and the stability of the pile foundation is evaluated; the method comprises the following steps: analyzing fracture probability data, detecting attenuation of pile foundation bearing capacity, calculating the collapse probability of a cast-in-place pile, tracing the construction quality according to the collapse probability of the cast-in-place pile and terrain complexity data so as to track factors influencing the construction quality, and determining the construction quality of the cast-in-place pile by combining quality tracing data with design risk assessment data. And the design of the cast-in-place pile is optimized. According to the method, the design of the cast-in-place pile is optimized, so that the cast-in-place pile construction is safer and more perfect.

Description

Technical Field

[0001] The invention relates to the technical field of cast-in-place pile quality tracing, and in particular to a quality tracing method and system for cast-in-place pile construction. Background Art

[0002] In modern building foundation engineering, cast-in-place piles are a common deep foundation form and are widely used in large-scale engineering projects such as high-rise buildings, bridges, and tunnels. Cast-in-place piles have the advantages of strong bearing capacity, wide adaptability, and simple construction. Therefore, they are particularly important in soft foundations or complex geological conditions. The construction quality of cast-in-place piles directly affects the safety and stability of the entire project. Especially in the construction process of pile groups, the interaction between piles plays a vital role in the overall bearing capacity and stability of the pile group. The design and construction of cast-in-place piles mostly rely on traditional calculation methods and experience, but due to the complex interaction between pile groups, the existing design and calculation methods often find it difficult to fully consider the nonlinear characteristics of pile foundation stress and deformation. However, a traditional quality traceability method for cast-in-place pile construction has the problem of inaccurate analysis of the complexity of the cast-in-place pile stratum. There is also the problem of inaccurate analysis of the bearing capacity attenuation of cast-in-place piles. Summary of the invention

[0003] Based on this, it is necessary to provide a quality traceability method and system for bored pile construction to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a quality traceability method for cast-in-place pile construction includes the following steps:

[0005] Step S1: Acquire the geographical location data of the bored pile; perform a bored pile geological condition test according to the geographical location data of the bored pile to obtain the bored pile geological condition data; use the geographical location data of the bored pile and the geological condition data of the bored pile to estimate the terrain complexity of the bored pile to obtain the bored pile terrain complexity data;

[0006] Step S2: Obtaining cast-in-place pile design data; calculating cast-in-place pile rupture probability according to cast-in-place pile design data and cast-in-place pile terrain complexity data to obtain cast-in-place pile rupture probability data;

[0007] Step S3: Based on the bored pile rupture probability data, the bored pile bearing capacity attenuation detection is performed to obtain the bored pile bearing capacity attenuation data; the bored pile collapse probability is calculated using the bored pile bearing capacity attenuation data to obtain the bored pile collapse probability data;

[0008] Step S4: Tracing the construction quality of the bored piles according to the probability data of the bored pile collapse and the terrain complexity data of the bored piles, thereby obtaining the bored pile construction quality tracing data; optimizing the bored pile design data according to the bored pile construction quality tracing data, thereby obtaining the bored pile design optimization data.

[0009] The present invention can effectively trace the quality of cast-in-place pile construction by integrating multi-dimensional information such as geographical location, geological conditions, and design data, ensuring the long-term stability and reliability of the pile foundation. By obtaining geographical location data and detecting the geological conditions of the cast-in-place pile, the soil structure, groundwater conditions and key factors of the construction site can be accurately evaluated, and the complexity of the terrain can be estimated. This analysis provides a scientific basis for subsequent construction, helps identify potential construction difficulties, and avoids risks caused by geological factors during construction. By combining design data and terrain complexity to calculate the probability of rupture, it is helpful to predict the structural reliability of the pile foundation under different conditions, identify structural problems in advance, and reduce the risk of rupture and damage. By performing bearing capacity attenuation detection and landslide probability calculation based on rupture probability data, the stability of the pile foundation during use can be comprehensively evaluated, and the decline trend of the pile foundation can be revealed, thereby guiding maintenance and repair measures to prevent structural safety problems caused by insufficient bearing capacity. Further combining the probability of collapse with the complexity of the terrain, through quality traceability analysis, detailed traceability data is provided for construction quality. This process provides real-time feedback on construction quality issues, and optimizes the design based on the design risk data to ensure that the design of the bored piles is highly matched with the site environment and construction conditions, thereby improving the safety and reliability of the project. Therefore, the present invention optimizes a traditional method for tracing the quality of bored pile construction, and solves the problem of inaccurate analysis of the complexity of the bored pile formation in the traditional method for tracing the quality of bored pile construction. There is also the problem of inaccurate analysis of the bearing capacity attenuation of bored piles, which improves the accuracy of the analysis of the complexity of the bored pile formation and the accuracy of the analysis of the bearing capacity attenuation of bored piles.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Obtaining the geographical location data of the cast-in-place pile;

[0012] Step S12: Performing a cast-in-place pile geological condition detection according to the cast-in-place pile geographical location data to obtain cast-in-place pile geological condition data;

[0013] Step S13: performing groundwater condition detection of the bored piles according to the geographical location data of the bored piles to obtain groundwater condition data of the bored piles;

[0014] Step S14: using the bored pile groundwater status data and the bored pile geological condition data to evaluate the bored pile terrain complexity, and obtain the bored pile terrain complexity data.

[0015] The present invention provides all-round basic information for construction quality by systematically acquiring geographical location data, geological condition data and groundwater condition data of cast-in-place piles. In the first step, obtaining geographical location data of cast-in-place piles can provide accurate geographical coordinates for subsequent analysis, which is crucial for accurately locating the construction area and evaluating geological and groundwater conditions. Next, by performing geological condition detection based on geographical location data, the soil layer distribution, soil properties and underground structure conditions can be understood in detail, providing a key basis for judging the bearing capacity and stability of pile foundations. Groundwater condition detection further combines geographical and geological data to reveal the water level, flow direction and potential impact of groundwater on soil and pile foundations, helping to evaluate water level changes, permeability and other problems encountered during construction. On this basis, terrain complexity assessment is performed in combination with groundwater conditions and geological condition data, which can assess whether the terrain of the construction area has potential complexity, such as irregular strata, hidden faults and other problems, predict construction difficulty and guide the adjustment of construction plans. The comprehensive analysis of these data provides a scientific basis for accurately predicting the challenges encountered in cast-in-place pile construction, ensures sustainable stability and reliability during construction, effectively reduces risks and uncertainties during construction, and improves engineering quality and safety.

[0016] Preferably, step S12 comprises the following steps:

[0017] Step S121: drilling and sampling the soil layer at the geographical location of the cast-in-place pile according to the geographical location data of the cast-in-place pile to obtain the soil layer data at the geographical location of the cast-in-place pile;

[0018] Step S122: performing seismic wave reflection detection according to the geographical location data of the cast-in-place piles to obtain soil layer seismic wave reflection detection data;

[0019] Step S123: analyzing the stratum distribution law of the bored piles according to the soil layer data of the geographical location of the bored piles and the seismic wave reflection detection data of the soil layer to obtain the stratum distribution law data of the bored piles;

[0020] Step S124: collecting data on the sudden change of formation dip angle based on the data on the distribution law of the bored pile formation, and obtaining data on the sudden change of formation dip angle;

[0021] Step S125: using the data of the region where the formation dip angle suddenly changes to estimate the regional hidden interlayer, and obtaining the data of the regional hidden interlayer in the formation;

[0022] Step S126: collecting the depth of the stratum bearing layer based on the stratum distribution law data of the bored piles to obtain the depth data of the stratum bearing layer;

[0023] Step S127: Analyze the bearing capacity of the bored pile stratum using the stratum bearing layer depth data and the stratum area hidden interlayer data to obtain the bored pile stratum bearing capacity data;

[0024] Step S128: Use the bored pile stratum bearing capacity data and the bored pile stratum distribution law data to perform bored pile casting geological condition detection to obtain bored pile geological condition data.

[0025] The present invention provides a scientific basis for bored pile construction through a series of precise data collection and analysis, effectively guarantees the stability and bearing capacity of the pile foundation, and soil layer drilling sampling provides basic data for further understanding the soil structure of the construction area. By analyzing the depth, composition and type of the soil layer, the bearing capacity of the soil and its impact on the pile foundation construction are evaluated. The seismic wave reflection detection data provides dynamic real-time information for understanding the structure and distribution of underground soil layers, helping to identify potential soil layer differences and faults, thereby providing more accurate support for the design. The analysis of the stratum distribution law combines the soil layer data with the seismic wave detection results, reveals the distribution characteristics of the soil layer and its change trend, and provides a detailed stratum structure model for subsequent construction. The acquisition of the sudden change area of ​​the stratum inclination helps to identify the changes in the underground geological structure, such as rock layer inclination and faults, which is very important for evaluating the stability of bored piles. Through the estimation of the hidden interlayer in the region, the hidden weak layer or uneven soil layer can be discovered in advance, which has an adverse effect on the bearing capacity of the pile foundation. The collection of the depth of the bearing layer of the stratum ensures that the design can accurately select the most suitable bearing layer to improve the stability of the pile foundation. The comprehensive bearing capacity data of the stratum and the distribution law of the stratum can accurately predict the bearing capacity of the bored pile, provide protection for avoiding potential risks during the construction process, and provide detailed data support for the geological condition detection of the bored pile. Through this series of data collection and analysis, the safety and accuracy of bored pile construction are greatly improved, and the uncertainty and risk in the construction process are effectively reduced.

[0026] Preferably, step S14 comprises the following steps:

[0027] Step S141: collecting the groundwater level of the bored piles according to the groundwater status data of the bored piles to obtain the groundwater level data of the bored piles;

[0028] Step S142: measuring the groundwater terrain height difference according to the bored pile groundwater level data to obtain groundwater terrain height difference data;

[0029] Step S143: Calculate the groundwater flow velocity using the groundwater terrain height difference data to obtain groundwater flow velocity data;

[0030] Step S144: Predicting geological water flow erosion on the bored pile geological condition data based on the groundwater flow velocity data to obtain geological water flow erosion data;

[0031] Step S145: estimating the softening of the geological soil layer according to the geological water flow erosion data, thereby obtaining the softening data of the geological soil layer;

[0032] Step S146: performing geological soil layer sand loss analysis according to the geological water flow erosion data, thereby obtaining geological soil layer sand loss data;

[0033] Step S147: evaluating the terrain complexity of the bored pile according to the geological soil layer sand loss data and the geological soil layer softening data, and obtaining the terrain complexity data of the bored pile.

[0034] The present invention improves the safety and scientificity of bored pile construction by comprehensively collecting data on groundwater conditions and their association with geological conditions. Groundwater level collection can accurately reflect the actual conditions of groundwater in the construction area, help predict the trend of water level changes, and provide an important basis for pile foundation design. The measurement of groundwater terrain height difference further reveals the flow trend of groundwater and its scouring effect, which helps to analyze the potential impact of groundwater flow on the stability of bored piles. Combined with the calculation of groundwater flow velocity, the speed of groundwater flow and its erosion effect are deeply analyzed to evaluate the degree of influence of water flow on geological layers. By estimating water flow erosion, the changes in soil layers caused by groundwater erosion, especially the softening of soil layers and sand loss, are identified in advance, so as to make corresponding adjustments to the geological conditions of bored piles. The analysis of softening and sand loss effectively predicts the changes in geological soil layers, especially under the long-term action of water flow, the strength of the soil layer will be affected, which provides a basis for further optimization of bored pile design. The evaluation of the terrain complexity of bored piles combined with these data can accurately reflect the geological complexity and potential construction risks of the construction area.

[0035] Preferably, step S2 comprises the following steps:

[0036] Step S21: Obtaining cast-in-place pile design data;

[0037] Step S22: Calculate the bored pile construction cavity according to the bored pile design data and the bored pile terrain complexity data to obtain the bored pile construction cavity data;

[0038] Step S23: Calculate the pile end defects of the cast-in-place pile according to the cast-in-place pile design data and the cast-in-place pile terrain complexity data to obtain cast-in-place pile foundation defect data;

[0039] Step S24: using the cast-in-place pile foundation defect data to estimate the cast-in-place pile position offset, and obtaining cast-in-place pile position offset data;

[0040] Step S25: Calculate the probability of rupture of the bored pile based on the bored pile position offset data and the bored pile construction cavity data to obtain the bored pile rupture probability data.

[0041] The present invention analyzes the design data of cast-in-place piles and the complexity of terrain, and the method effectively improves the risk warning and quality assurance during the construction of cast-in-place piles. Under the combination of cast-in-place pile design data and terrain complexity, the voids generated during the construction process can be accurately calculated, which helps to identify potential defect areas in the construction in advance and avoid construction failures or later safety hazards caused by the voids affecting the stability of the pile body. Further, the calculation of pile end defects in combination with the defect data of the cast-in-place pile foundation helps to identify structural defects existing in the design or construction process, ensure that the pile foundation can fully withstand the design load, and reduce the risk of damage caused by defects. The pile position offset is further estimated through the pile end defect data, and the pile position error occurring during the construction process is effectively captured, so that timely adjustments can be made to avoid the adverse effects of the pile position offset on the overall structure. The calculation of the probability of rupture using the pile position offset data and the construction void data can accurately evaluate the risk of rupture of the cast-in-place pile, provide a scientific basis for subsequent construction and design optimization, and ensure the safety and long-term stability of the project.

[0042] Preferably, step S22 comprises the following steps:

[0043] Step S221: Calculating the pile end depth of the cast-in-place pile according to the cast-in-place pile design data, thereby obtaining the cast-in-place pile end depth data;

[0044] Step S222: estimating the stratum position of the pile end of the cast-in-place pile based on the cast-in-place pile terrain complexity data and the pile end depth data of the cast-in-place pile to obtain the cast-in-place pile stratum position data;

[0045] Step S223: performing underground soil fine particle loss detection on the bored pile stratum position data based on the bored pile terrain complexity data to obtain underground soil fine particle loss data;

[0046] Step S224: Calculate the probability of pore formation in the underground soil layer at the pile end according to the underground soil fine particle loss data to obtain the probability data of pore formation in the underground soil layer at the pile end;

[0047] Step S225: Predicting the collapse of the hole wall soil layer according to the terrain complexity data of the bored pile and the pore formation probability data of the underground soil layer at the pile end, and obtaining the hole wall soil layer collapse data;

[0048] Step S226: Calculate the voids in the bored pile construction by using the hole wall soil layer collapse data and the probability data of pore formation in the underground soil layer at the pile end, and obtain the bored pile construction void data.

[0049] The present invention provides a scientific basis for improving the stability and accuracy of pile foundation construction through a detailed analysis of the bored pile design data and the complexity of the terrain. By calculating the depth of the pile end, the depth of the pile end can be accurately grasped, which helps to optimize the positioning and construction depth of the pile foundation, thereby ensuring that the pile foundation can achieve the expected bearing capacity and stability. Based on the data analysis of the complexity of the terrain, the stratum position of the pile end depth can be further estimated, providing accurate stratum information for construction, ensuring that the pile foundation passes through the correct soil layer and avoids potential obstacles. Then, through the detection of fine particle loss in the underground soil layer, the loss of fine particles in the soil layer can be effectively evaluated, which is crucial to avoid loose soil layers and affect the stability of the pile foundation. Through further analysis of the fine particle loss data, the probability of formation of pores in the underground soil layer at the pile end can be evaluated, providing a prediction basis for the subsequent soil layer stability. Based on the calculation of the probability of pore formation, the collapse of the hole wall soil layer is estimated, the collapse risk is discovered in time, and serious soil collapse during the construction process is avoided. By combining the collapse data and the probability of pore formation to calculate the construction voids, the void problems that occur during the construction process can be effectively identified, the integrity of the pile foundation and the construction quality can be ensured, and the safety and reliability of the project can be further guaranteed.

[0050] Preferably, step S25 comprises the following steps:

[0051] Step S251: Predicting the interaction between the bored piles according to the bored pile position offset data to obtain the interaction data between the bored piles;

[0052] Step S252: using the interaction data between the bored piles to analyze the deformation of the soil around the bored piles, and obtaining the deformation data of the soil around the bored piles;

[0053] Step S253: using the deformation data of the soil around the bored pile to perform local stress concentration detection on the bored pile, and obtaining local stress concentration data on the bored pile;

[0054] Step S254: performing a characteristic analysis of the position of the void in the bored pile construction according to the void data in the bored pile construction to obtain the characteristic data of the position of the void in the bored pile construction;

[0055] Step S255: performing coupling effect analysis using the characteristic data of the position of the void during the construction of the cast-in-place pile and the local stress concentration data of the cast-in-place pile to obtain coupling effect data of the cast-in-place pile construction;

[0056] Step S256: Calculate the probability of rupture of the bored pile according to the coupled effect data of bored pile construction to obtain the probability data of rupture of the bored pile.

[0057] The present invention can deeply understand the mutual influence between different pile foundations through the analysis of the displacement and interaction of the cast-in-place pile position, and then provide a scientific basis for the prediction of the interaction between pile foundations during the construction process. This helps to optimize the pile foundation layout in the design stage and avoid uneven settlement or displacement of the pile foundation caused by too strong interaction. By analyzing the deformation data of the soil body around the cast-in-place pile, the deformation trend of the soil body during the pile foundation construction process can be clearly identified, and the stability of the soil body and the bearing capacity around the pile foundation can be predicted. By further analyzing the local stress concentration data, the stress concentration area of ​​the pile foundation can be identified, and protective measures can be provided for subsequent construction to avoid damage to the pile foundation caused by excessive stress concentration. Combined with the analysis of the construction void position characteristics, potential void problems in the construction process can be effectively identified to prevent the void problem from causing a decline in construction quality or pile foundation damage. Through the coupling effect analysis, the construction void position characteristics are combined with the local stress concentration situation, and the influence of the interaction between the two on the stability of the pile foundation can be evaluated, so as to more accurately predict the probability of pile foundation rupture, and provide a strong guarantee for engineering quality and construction safety. These steps can comprehensively improve the risk control and quality monitoring in the pile foundation construction process, and ensure the efficiency and reliability of pile foundation construction.

[0058] Preferably, step S3 comprises the following steps:

[0059] Step S31: performing an aging trend analysis of the cast-in-place piles based on the cast-in-place pile rupture probability data to obtain cast-in-place pile aging trend data;

[0060] Step S32: performing stability decay assessment according to the cast-in-place pile aging trend data and cast-in-place pile rupture probability data to obtain cast-in-place pile stability decay data;

[0061] Step S33: performing bearing capacity attenuation detection of the cast-in-place pile according to the cast-in-place pile aging trend data and the cast-in-place pile stability attenuation data to obtain the bearing capacity attenuation data of the cast-in-place pile;

[0062] Step S34: Calculate the probability of the pile collapse by using the bearing capacity decay data of the pile and the aging trend data of the pile to obtain the probability data of the pile collapse.

[0063] The present invention can predict the aging phenomenon of pile foundation during use by analyzing the aging trend of the probability data of cast-in-place pile rupture, and identify the factors that cause the structural performance to decline in advance. This provides a scientific basis for the planning of later maintenance and reinforcement measures, and helps to extend the service life of the pile foundation. Stability decay assessment based on aging trend and rupture probability data helps to accurately assess the stability changes of the pile foundation under long-term loads and timely identify potential risk points in the pile foundation. This assessment provides data support for the implementation of preventive maintenance and reinforcement measures, and effectively prevents major safety accidents caused by pile foundation stability problems. The bearing capacity decay is detected in combination with the aging trend and stability decay data, and the bearing capacity changes of the pile foundation during actual use are accurately judged. This provides detailed reference data for engineering design, construction management and later evaluation, ensures the reliability of pile foundation performance and bearing capacity, calculates the probability of collapse in combination with the aging trend and bearing capacity decay data, and can accurately predict the risk of pile foundation collapse under different service life, thereby effectively preventing and controlling pile foundation safety hazards and ensuring the overall stability and safety of the building.

[0064] Preferably, step S4 comprises the following steps:

[0065] Step S41: performing a cast-in-place pile construction difficulty analysis based on cast-in-place pile collapse probability data and cast-in-place pile terrain complexity data to obtain cast-in-place pile construction difficulty data;

[0066] Step S42: performing a risk assessment on the design of cast-in-place piles according to the cast-in-place pile construction difficulty data and the cast-in-place pile collapse probability data to obtain cast-in-place pile design risk data;

[0067] Step S43: tracing the construction quality of the bored piles according to the bored pile design risk data, thereby obtaining the bored pile construction quality tracing data;

[0068] Step S44: optimizing the bored pile design data according to the bored pile construction quality traceability data and the bored pile design risk data to obtain bored pile design optimization data.

[0069] The present invention combines the collapse probability data of cast-in-place piles with the terrain complexity data to perform construction difficulty analysis, which can accurately evaluate the challenges and risks faced in the pile foundation construction process, and provide the project management team with a more targeted decision-making basis, thereby optimizing the construction plan and reducing potential difficulties and costs in the construction process. Design risk assessment is performed based on construction difficulty and collapse probability data to identify in advance the hidden dangers of the design under specific geological conditions. This risk warning mechanism can provide a scientific reference for the risk management and countermeasures of the project. Through the combination of construction quality traceability and design risk data, the source of quality problems in the construction process can be traced back, and deficiencies in quality control can be discovered and resolved in a timely manner to ensure that problems in the construction process will not affect the performance of the pile foundation in the later stage. Through optimization analysis based on quality traceability and design risk data, the cast-in-place pile design plan can be accurately optimized to improve the scientificity and practicality of the design.

[0070] The present invention also provides a quality traceability system for bored pile construction, which is used to execute the quality traceability method for bored pile construction as described above. The quality traceability system for bored pile construction comprises:

[0071] The terrain complexity estimation module is used to obtain the geographical location data of the bored piles; perform the bored pile geological condition detection according to the geographical location data of the bored piles to obtain the bored pile geological condition data; use the geographical location data of the bored piles and the bored pile geological condition data to estimate the terrain complexity of the bored piles to obtain the bored pile terrain complexity data;

[0072] The cast-in-place pile rupture probability calculation module is used to obtain cast-in-place pile design data; the cast-in-place pile rupture probability is calculated according to the cast-in-place pile design data and cast-in-place pile terrain complexity data to obtain cast-in-place pile rupture probability data;

[0073] The collapse probability calculation module is used to detect the attenuation of the bearing capacity of the cast-in-place pile based on the probability data of the cast-in-place pile rupture, and obtain the bearing capacity attenuation data of the cast-in-place pile; and calculate the probability of the collapse of the cast-in-place pile using the bearing capacity attenuation data of the cast-in-place pile, and obtain the probability data of the collapse of the cast-in-place pile;

[0074] The bored pile design optimization module is used to trace the bored pile construction quality based on the bored pile collapse probability data and the bored pile terrain complexity data, so as to obtain the bored pile construction quality traceability data; and to optimize the bored pile design data based on the bored pile construction quality traceability data, so as to obtain the bored pile design optimization data.

[0075] The present invention is that, by integrating multi-dimensional information such as geographical location, geological conditions, and design data, the quality traceability of cast-in-place pile construction can be effectively carried out to ensure the long-term stability and reliability of the pile foundation. By obtaining geographical location data and detecting the geological conditions of the cast-in-place pile, the soil structure, groundwater conditions and key factors of the construction site can be accurately evaluated, and the complexity of the terrain can be estimated. This analysis provides a scientific basis for subsequent construction, helps identify potential construction difficulties, and avoids risks caused by geological factors during construction. By combining design data and terrain complexity to calculate the probability of rupture, it is helpful to predict the structural reliability of the pile foundation under different conditions, identify structural problems in advance, and reduce the risk of rupture and damage. By performing bearing capacity attenuation detection and landslide probability calculation based on rupture probability data, the stability of the pile foundation during use can be comprehensively evaluated, and the decline trend of the pile foundation can be revealed, thereby guiding maintenance and repair measures to prevent structural safety problems caused by insufficient bearing capacity. Further combining the probability of landslide with the complexity of the terrain, through quality traceability analysis, detailed traceability data is provided for construction quality. This process provides real-time feedback on construction quality issues, and optimizes the design based on the design risk data to ensure that the design of the bored piles is highly matched with the site environment and construction conditions, thereby improving the safety and reliability of the project. Therefore, the present invention optimizes a traditional method for tracing the quality of bored pile construction, and solves the problem of inaccurate analysis of the complexity of the bored pile formation in the traditional method for tracing the quality of bored pile construction. There is also the problem of inaccurate analysis of the bearing capacity attenuation of bored piles, which improves the accuracy of the analysis of the complexity of the bored pile formation and the accuracy of the analysis of the bearing capacity attenuation of bored piles. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 A schematic diagram of the steps of a quality traceability method for cast-in-place pile construction;

[0077] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0078] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0079] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0080] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0081] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0082] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0083] To achieve this, please refer to Figures 1 to 3 , a quality traceability method for cast-in-place pile construction, comprising the following steps:

[0084] Step S1: Acquire the geographical location data of the bored pile; perform a bored pile geological condition test according to the geographical location data of the bored pile to obtain the bored pile geological condition data; use the geographical location data of the bored pile and the geological condition data of the bored pile to estimate the terrain complexity of the bored pile to obtain the bored pile terrain complexity data;

[0085] Step S2: Obtaining cast-in-place pile design data; calculating cast-in-place pile rupture probability according to cast-in-place pile design data and cast-in-place pile terrain complexity data to obtain cast-in-place pile rupture probability data;

[0086] Step S3: Based on the bored pile rupture probability data, the bored pile bearing capacity attenuation detection is performed to obtain the bored pile bearing capacity attenuation data; the bored pile collapse probability is calculated using the bored pile bearing capacity attenuation data to obtain the bored pile collapse probability data;

[0087] Step S4: Tracing the construction quality of the bored piles according to the probability data of the bored pile collapse and the terrain complexity data of the bored piles, thereby obtaining the bored pile construction quality tracing data; optimizing the bored pile design data according to the bored pile construction quality tracing data, thereby obtaining the bored pile design optimization data.

[0088] In the embodiment of the present invention, reference Figure 1 As shown, in this example, the quality traceability method of cast-in-place pile construction includes the following steps:

[0089] Step S1: Acquire the geographical location data of the bored pile; perform a bored pile geological condition test according to the geographical location data of the bored pile to obtain the bored pile geological condition data; use the geographical location data of the bored pile and the geological condition data of the bored pile to estimate the terrain complexity of the bored pile to obtain the bored pile terrain complexity data;

[0090] In an embodiment of the present invention, the specific geographical location data of the bored pile is obtained by a positioning system. These data are obtained by satellite positioning, total station or drone ground scanning, and the geological background of the area is determined according to the precise location of the bored pile. Next, according to the geographical location data, geological detection tools such as geological radar or drilling sampling equipment are used to detect the geological conditions of the area where the bored pile is located. These tests measure parameters such as the structure, type, and hardness of the soil layer to form the geological condition data of the bored pile. Combined with the geographical location and geological condition data, the complexity of the terrain around the bored pile is evaluated by the terrain complexity analysis method, and the complexity index is generated using GIS software. These indicators include terrain undulation, soil distribution, and groundwater flow state, etc., so as to obtain the terrain complexity data of the bored pile. The main tools used in this step include geological exploration instruments, drilling equipment, and GIS software. These tools are used in combination to ensure the accuracy of geographical and geological data and can fully reflect the actual conditions of the construction site.

[0091] Step S2: Obtaining cast-in-place pile design data; calculating cast-in-place pile rupture probability according to cast-in-place pile design data and cast-in-place pile terrain complexity data to obtain cast-in-place pile rupture probability data;

[0092] In an embodiment of the present invention, design data of the bored piles are collected, including the specifications, depth, expected bearing capacity, etc. of the designed pile foundation. These design data usually come from engineering design drawings, technical documents, and design calculations. Based on the bored pile design data and terrain complexity data, a mathematical model (such as finite element analysis, structural mechanics analysis) is used to calculate the probability of rupture of the bored piles in complex terrain. Specifically, the pile foundation depth and pile end bearing capacity in the design data work together with factors such as soil resistance and terrain undulations in the terrain complexity to affect the stability of the pile body. By analyzing these factors, the probability of rupture of the bored piles is calculated. This calculation process uses strength theory and compression test results, combined with terrain complexity data, and automatically processes and generates rupture probability data through a computer program.

[0093] Step S3: Based on the bored pile rupture probability data, the bored pile bearing capacity attenuation detection is performed to obtain the bored pile bearing capacity attenuation data; the bored pile collapse probability is calculated using the bored pile bearing capacity attenuation data to obtain the bored pile collapse probability data;

[0094] In an embodiment of the present invention, based on the data of the probability of rupture of the bored piles calculated in the first step, the bearing capacity attenuation detection of the bored piles is performed. This process uses sensors and stress-strain testing equipment to detect the bearing capacity of the bored piles at different stages, monitor the information such as the crack extension, displacement and deformation of the pile body, and compare and analyze it with the data of the probability of rupture to obtain the attenuation data of the bearing capacity. In addition, based on the bearing capacity attenuation data, the probability of collapse of the bored piles is calculated. To this end, by long-term monitoring of data such as pile foundation settlement, lateral pressure, soil compression, etc., and combining the existing probability of rupture and soil quality data, the probability of collapse is calculated using a probability statistics method. These operations are achieved through an integrated sensor system and on-site monitoring equipment, and the specific equipment used includes stress sensors, displacement sensors and settlement sensors.

[0095] Step S4: Tracing the construction quality of the bored piles according to the probability data of the bored pile collapse and the terrain complexity data of the bored piles, thereby obtaining the bored pile construction quality tracing data; optimizing the bored pile design data according to the bored pile construction quality tracing data, thereby obtaining the bored pile design optimization data.

[0096] In the embodiment of the present invention, the probability data of cast-in-place pile collapse and the terrain complexity data obtained in the early stage are used to trace the quality of cast-in-place pile construction. The purpose of construction quality traceability is to determine the links that cause quality problems by tracing back each data and operation in the construction process. Through the digital management platform, various data of the construction site (such as construction progress, material use, environmental changes, etc.) are compared with the cast-in-place pile design data to track the root cause of the construction quality problems. Combined with the quality data generated during the construction process, such as concrete mix ratio, pile position offset and other information, quality traceability analysis is performed to generate construction quality traceability data. These data are automatically recorded and uploaded through the Internet of Things device, and the quality information in the construction is fed back in real time. Next, the cast-in-place pile design is optimized by combining the quality traceability data with the design risk data. Specifically, by analyzing the potential risk points in the design process, such as the pile end is too deep, the concrete strength does not meet the standard and other factors, the design scheme is optimized, and the depth, material and structure of the pile foundation are adjusted. The design optimization process is simulated by the design software to ensure that the optimized design meets the safety and stability requirements.

[0097] Preferably, step S1 comprises the following steps:

[0098] Step S11: Obtaining the geographical location data of the cast-in-place pile;

[0099] Step S12: Performing a cast-in-place pile geological condition detection according to the cast-in-place pile geographical location data to obtain cast-in-place pile geological condition data;

[0100] Step S13: performing groundwater condition detection of the bored piles according to the geographical location data of the bored piles to obtain groundwater condition data of the bored piles;

[0101] Step S14: using the bored pile groundwater status data and the bored pile geological condition data to evaluate the bored pile terrain complexity, and obtain the bored pile terrain complexity data.

[0102] In the embodiment of the present invention, the geographical location data of the cast-in-place pile is obtained by high-precision positioning equipment, and the position of the cast-in-place pile is accurately obtained by using a satellite positioning system (GNSS). In this process, the target position is accurately measured by a total station or a laser rangefinder to ensure the accuracy of the specific coordinate data of the pile foundation. The use of these devices can provide accurate geographical coordinate information in a two-dimensional plane or a three-dimensional space, and the acquisition of geographical data will also be calibrated in real time in combination with the on-site environment to eliminate errors. This geographical location data provides accurate basic data for subsequent geological conditions and groundwater conditions detection, which can ensure the reliability of subsequent analyses. When performing the geological condition detection of the cast-in-place pile, the specific detection location of the cast-in-place pile location is selected according to the geographical location data obtained in step S11. Geological exploration equipment such as a drilling machine, underground radar (GPR), soil sampling tools, etc. are used for geological detection. The drilling machine enters the ground by vertical piling, collects soil layer samples and performs physical and chemical analysis, analyzes the type, particle size, density and other parameters of the soil, and generates a geological data report. The underground radar detects the underground structure through radio waves, obtains data such as the thickness, density and hardness of the soil layer, and can judge the stability of the underground rock layer. These detection data provide detailed geological information for the design, construction and subsequent assessment of terrain complexity of cast-in-place piles. Based on the geographical location information obtained in step S11, the groundwater condition of cast-in-place piles is detected. The detection of groundwater conditions is achieved by arranging groundwater level monitoring holes and water quality sensors. The groundwater level is determined by drilling in the selected cast-in-place pile location area, and the depth and flow of groundwater are measured in real time using water level sensors. Secondly, water quality analysis instruments are used to detect the water quality components of groundwater, such as salt content, pH value, conductivity, etc. These indicators help analyze the impact of changes in hydrogeological conditions on pile foundation construction. Through these real-time monitoring equipment, real-time groundwater condition data can be obtained to ensure that the hydrological conditions in cast-in-place pile construction are fully understood. Changes in groundwater conditions will directly affect the bearing capacity of the pile foundation. Combined with the cast-in-place pile geological condition data obtained in step S12 and the groundwater condition data obtained in step S13, the terrain complexity is evaluated. The terrain complexity assessment depends on a comprehensive analysis of multiple factors, including different types of soil layers, dynamic changes in groundwater, and the compressibility of soil. These data are integrated and calculated to produce a comprehensive terrain complexity index. Specifically, numerical analysis methods are used to combine multiple parameters such as soil hardness, density, groundwater level, etc. in a weighted manner to evaluate the complexity of the terrain. During the evaluation process, computer-aided design software (such as AutoCAD or GIS software) is used to perform three-dimensional modeling of geographic data, and all geological and hydrological data are input into the system to generate a complexity assessment map. This process is processed by algorithms, and the impact of different parameters is considered proportionally to form a specific and visual terrain complexity result.

[0103] Preferably, step S12 comprises the following steps:

[0104] Step S121: drilling and sampling the soil layer at the geographical location of the cast-in-place pile according to the geographical location data of the cast-in-place pile to obtain the soil layer data at the geographical location of the cast-in-place pile;

[0105] Step S122: performing seismic wave reflection detection according to the geographical location data of the cast-in-place piles to obtain soil layer seismic wave reflection detection data;

[0106] Step S123: analyzing the stratum distribution law of the bored piles according to the soil layer data of the geographical location of the bored piles and the seismic wave reflection detection data of the soil layer to obtain the stratum distribution law data of the bored piles;

[0107] Step S124: collecting data on the sudden change of formation dip angle based on the data on the distribution law of the bored pile formation, and obtaining data on the sudden change of formation dip angle;

[0108] Step S125: using the data of the region where the formation dip angle suddenly changes to estimate the regional hidden interlayer, and obtaining the data of the regional hidden interlayer in the formation;

[0109] Step S126: collecting the depth of the stratum bearing layer based on the stratum distribution law data of the bored piles to obtain the depth data of the stratum bearing layer;

[0110] Step S127: Analyze the bearing capacity of the bored pile stratum using the stratum bearing layer depth data and the stratum area hidden interlayer data to obtain the bored pile stratum bearing capacity data;

[0111] Step S128: Use the bored pile stratum bearing capacity data and the bored pile stratum distribution law data to perform bored pile casting geological condition detection to obtain bored pile geological condition data.

[0112] In the embodiment of the present invention, the soil layer of the geographical location of the cast-in-place pile is drilled and sampled. The drilling location is selected by calibrating the geographical location data of the area where the cast-in-place pile is located. In this process, a geological drilling device (such as a rotary drill) is used to drill the predetermined soil layer. During the drilling process, a high-quality sampling tube is used to collect and preserve the soil sample for subsequent experimental analysis. The depth and position of the drilling are determined by the actual situation of the soil layer and the pile foundation design requirements. After the soil sample is marked and preserved, it is sent to the laboratory for further physical and chemical analysis to obtain parameters such as the clay content, particle size distribution, porosity, humidity, and density of the soil. The seismic wave reflection detection is performed using the geographical location data of the cast-in-place pile, and a suitable detection area is selected according to the geographical location data of the cast-in-place pile. A seismic wave source and a receiving device are arranged in the area, and a seismic wave is generated on the ground using an artificial source (such as a vibrator, a hammer, etc.). The seismic wave propagates through the soil layer, reflects between different underground media (such as rock layers and soil layers), reflects back to the ground and is captured by a receiving device (such as a seismic detector). The information such as the propagation time and amplitude of these reflected waves is recorded and transmitted to the computer system for analysis. By processing the seismic wave reflection data, the reflection characteristics of different soil layers are obtained, thereby inferring the distribution, hardness, thickness, etc. of the underground soil layer. Based on the soil layer data of the geographical location of the cast-in-place pile and the seismic wave reflection detection data of the soil layer, the cast-in-place pile stratum distribution law analysis is carried out, and the data of the soil layer drilling sampling is combined with the data of the seismic wave reflection detection, and the geological exploration analysis software (such as GeoPlot or GEO5, etc.) is used for processing. In the data integration process, the depth, type, density, water content, etc. of the soil layer are marked, and the seismic wave reflection data is used to calculate the change of the underground structure. By comprehensively analyzing the physical properties of the soil layer and the propagation characteristics of the seismic wave, the distribution law of different underground soil layers is revealed, such as the transition area between the soft soil layer and the hard soil layer, the tilt direction of the soil layer, etc. The data of the cast-in-place pile stratum distribution law are collected, and the soil layer distribution law obtained according to step S123 is used to identify the areas with large changes in the stratum, and the characteristics of these areas are that the soil layer dip angle is suddenly changed. The tilt angles of these areas are accurately measured using geological survey equipment, such as total stations, electronic levels or tilt information in seismic reflection wave data. Usually, by multi-point acquisition of tilt angle data of multiple measuring points, the specific tilt characteristics of the strata in the area can be obtained. The collected data include tilt angle, direction, rate of change, etc. The regional hidden interlayer is estimated using the regional data of the sudden change in stratum tilt angle. The regional data of the sudden change in stratum tilt angle obtained in step S124 are compared with the existing soil type data, and the existing hidden interlayer is identified through multi-point sampling and geological survey. Interlayers are usually manifested as interlayers between soil layers, and their physical properties, strength and stability are greatly different from those of the surrounding soil layers. In order to accurately estimate the hidden interlayer, electromagnetic wave detection technology or underground radar detection equipment (such as GPR) is used to perform detailed scanning of the suspected interlayer area.These devices can reveal potential interlayer areas and infer their thickness, range and composition through the differences in electromagnetic wave propagation characteristics between different soil layers, and combine the estimated results of hidden interlayers with stratum data to form a more complete underground structure map. Based on the data of the distribution law of the stratum distribution of cast-in-place piles, the depth of the stratum bearing layer is collected, and the soil layer data analyzed in step S123 are comprehensively analyzed to determine the bearing layer with higher bearing capacity in the stratum. The bearing layer is usually located in a deeper soil layer, has higher strength and stability, and can provide better support for cast-in-place piles. Drilling equipment (such as a borehole sampler) is used to perform dense drilling at the location of the suspected bearing layer, obtain the physical property data of the layer of soil, and determine the bearing capacity of the layer through static or dynamic tests. The bearing capacity of the cast-in-place pile stratum is analyzed using the depth data of the stratum bearing layer and the hidden interlayer data in the stratum area, and the bearing capacity of different strata is analyzed by combining the depth of the bearing layer and the hidden interlayer data. Based on the results of standard bearing capacity tests (such as static penetration test, standard penetration test, etc.), combined with parameters such as soil compaction and particle size distribution, the bearing capacity of different layers is calculated. In addition, the presence of hidden interlayers leads to a decrease in local bearing capacity, so the influence of interlayer position on the bearing capacity of pile foundation must be specially considered. The bearing capacity of each layer is evaluated by numerical simulation methods (such as finite element analysis) to obtain the data of the bearing capacity required for cast-in-place piles. The geological conditions for cast-in-place piles are tested using the bearing capacity data of cast-in-place pile strata and the data of stratum distribution law. The obtained bearing capacity data is combined with the stratum distribution law, and the geological conditions are tested using soil mechanics analysis methods. The geological condition data for cast-in-place piles are obtained by detailed analysis of factors such as the characteristics of different soil layers, the depth of the bearing layer, and the influence of hidden interlayers on the bearing capacity.

[0113] Preferably, step S14 comprises the following steps:

[0114] Step S141: collecting the groundwater level of the bored piles according to the groundwater status data of the bored piles to obtain the groundwater level data of the bored piles;

[0115] Step S142: measuring the groundwater terrain height difference according to the bored pile groundwater level data to obtain groundwater terrain height difference data;

[0116] Step S143: Calculate the groundwater flow velocity using the groundwater terrain height difference data to obtain groundwater flow velocity data;

[0117] Step S144: Predicting geological water flow erosion on the bored pile geological condition data based on the groundwater flow velocity data to obtain geological water flow erosion data;

[0118] Step S145: estimating the softening of the geological soil layer according to the geological water flow erosion data, thereby obtaining the softening data of the geological soil layer;

[0119] Step S146: performing geological soil layer sand loss analysis according to the geological water flow erosion data, thereby obtaining geological soil layer sand loss data;

[0120] Step S147: evaluating the terrain complexity of the bored pile according to the geological soil layer sand loss data and the geological soil layer softening data, and obtaining the terrain complexity data of the bored pile.

[0121] In the embodiment of the present invention, the groundwater level is collected according to the groundwater status data of the bored pile, and the geographical location data of the bored pile is used to select representative groundwater monitoring points around the bored pile. A water level meter is installed at the monitoring point, and the device is usually a float-type water level meter, a pressure sensor or a capacitive water level meter. The groundwater level change is monitored in real time by the water level meter, and the static water level of the groundwater is recorded. At this time, the data acquisition device can transmit the water level information to the monitoring system in real time and store it in the database for subsequent analysis. The water level acquisition frequency is set to hourly or daily data, which is adjusted according to the groundwater fluctuation and construction needs. Through the water level data collected for a long time, the water level change trend of the groundwater can be obtained, and the groundwater terrain height difference is measured according to the groundwater level data of the bored pile. By comparing the water levels of multiple groundwater monitoring points, the water level data of groundwater in different areas are obtained, and the relative elevation difference is measured between the selected monitoring points. A high-precision level or GPS device is used for elevation calibration to ensure measurement accuracy. Then, these water level data are combined with the surface elevation data, and the groundwater terrain height difference at different monitoring points is obtained by difference calculation. The height difference data reflects the potential direction and trend of groundwater flow. According to the distribution of groundwater height difference in different regions, the basic characteristics of groundwater flow can be inferred, thereby providing accurate basic data for subsequent groundwater flow velocity calculation and geological erosion analysis. The groundwater flow velocity is calculated using the groundwater terrain height difference data. According to the groundwater terrain height difference data obtained in step S142, the groundwater flow velocity is calculated using Darcy's law or water flow dynamics formula in combination with parameters such as soil permeability and porosity. The groundwater flow velocity is obtained by a comprehensive analysis of the height difference, permeability and water flow resistance of groundwater flow in different regions. In order to improve accuracy, multiple monitoring points are set and flow velocity measurements are performed regularly. If the flow velocity changes greatly, it is necessary to use equipment for calibrating the flow velocity, such as a flow meter or a velocity sensor to assist in detection. Through long-term monitoring, groundwater flow velocity data is obtained, and geological water flow erosion is estimated for the geological condition data of the cast-in-place pile based on the groundwater flow velocity data. By combining data such as groundwater flow velocity, groundwater flow path, soil layer characteristics (such as soil permeability and stability), the soil layer around the cast-in-place pile is estimated by using a water flow erosion model, and the groundwater flow velocity and flow direction are combined with the soil type to analyze the erosion effect of water flow on different soil layers. Secondly, the water flow erosion assessment method in geology, such as flow velocity-soil erosion coefficient, is used to calculate the erosion intensity of water flow on different soil layers. By simulating the long-term impact of water flow on the soil layer, it is possible to infer which soil layers will be eroded and damaged, thereby affecting the construction quality and stability of the cast-in-place pile. This step forms an effective water flow erosion estimation result through the combination of accurate physical models and field data. The softening of the geological soil layer is estimated according to the geological water flow erosion data, and the soil layer area most seriously affected by erosion is identified based on the water flow erosion intensity data obtained in step S144.By conducting laboratory tests (such as particle size analysis, strength test, soil consolidation test, etc.) on soil samples in these areas, the softening degree of these soil layers is further estimated. The softening assessment process takes into account factors such as decreased soil cohesion, increased soil porosity, and loose soil caused by water erosion. By estimating the softening of the soil layer, it is determined which soil layers cannot provide sufficient bearing capacity, thereby affecting the stability of the cast-in-place pile. In this process, standard soil mechanics testing methods, such as triaxial shear test and direct shear test, are used to measure the mechanical properties of the softened soil layer. The sand loss analysis of the geological soil layer is performed based on the geological water erosion data. Combined with the water erosion data obtained in step S144, the erosion effect of groundwater flow on the soil layer, especially the loss of the sand layer, is analyzed, and the loss rate of sand in the water flow is calculated based on the speed, direction and erosion intensity of the water flow. Through field measurements or simulation experiments, the loss amount and distribution law of sand at different flow rates are obtained. In order to improve the accuracy, representative sand samples are selected to conduct sand loss tests. The actual situation is further inferred by measuring the loss mass and loss rate of sand under the action of water flow. The complexity of the cast-in-place pile terrain is evaluated based on the sand loss data of the geological soil layer and the softening data of the geological soil layer. The terrain complexity is evaluated by combining the data obtained in steps S145 and S146, combined with the geographical location and design requirements of the cast-in-place piles, and the areas with more drastic terrain changes are identified according to the softening degree of the soil layer and the sand loss. The geological conditions in these areas lead to instability or unpredictable risks during the pile foundation construction process. Then, geological modeling software (such as GEO5 or PLAXIS) is used to simulate the deformation, settlement of the soil layer and the impact of water flow on the terrain, so as to further evaluate the complexity of cast-in-place pile construction.

[0122] Preferably, step S2 comprises the following steps:

[0123] Step S21: Obtaining cast-in-place pile design data;

[0124] Step S22: Calculate the bored pile construction cavity according to the bored pile design data and the bored pile terrain complexity data to obtain the bored pile construction cavity data;

[0125] Step S23: Calculate the pile end defects of the cast-in-place pile according to the cast-in-place pile design data and the cast-in-place pile terrain complexity data to obtain cast-in-place pile foundation defect data;

[0126] Step S24: using the cast-in-place pile foundation defect data to estimate the cast-in-place pile position offset, and obtaining cast-in-place pile position offset data;

[0127] Step S25: Calculate the probability of rupture of the bored pile based on the bored pile position offset data and the bored pile construction cavity data to obtain the bored pile rupture probability data.

[0128] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0129] Step S21: Obtaining cast-in-place pile design data;

[0130] In an embodiment of the present invention, the design data of the cast-in-place pile is obtained, which includes various design parameters of the cast-in-place pile, such as the type of pile foundation, the diameter of the pile, the length of the pile, the construction process, the type of concrete used, the expected bearing capacity, etc. These data are derived from the design drawings and related technical documents in the engineering design stage, and are usually entered into the management system by engineers manually or automatically. In order to ensure the integrity and accuracy of the design data, it is necessary to verify it multiple times and compare it with the geological survey data and the actual construction situation. Use a dedicated building information management system (such as a BIM or CAD system) to store and organize these design data.

[0131] Step S22: Calculate the bored pile construction cavity according to the bored pile design data and the bored pile terrain complexity data to obtain the bored pile construction cavity data;

[0132] In an embodiment of the present invention, the calculation of the bored pile construction voids is performed based on the bored pile design data and the bored pile terrain complexity data, and the terrain complexity data is obtained by conducting a terrain survey of the location of the bored pile. The terrain complexity data involves information such as soil layer type, soil moisture, groundwater level, and geological faults. Using these data, combined with the bored pile design data, the void area formed during the construction process is calculated through numerical simulation or on-site survey methods, and the soil density of the construction area is detected using geological radar detection, drilling sampling and other technologies, and whether the soil forms voids due to water flow, air flow or soil loosening during the construction process is analyzed. The location, size and impact of these voids on the pile foundation are used to obtain void data in this step.

[0133] Step S23: Calculate the pile end defects of the cast-in-place pile according to the cast-in-place pile design data and the cast-in-place pile terrain complexity data to obtain cast-in-place pile foundation defect data;

[0134] In an embodiment of the present invention, the pile end defects of the bored pile are calculated based on the bored pile design data and the bored pile terrain complexity data. Based on the bored pile design data, the pile end design requirements and soil layer characteristics of the bored pile are understood. Then, combined with the terrain complexity data, the factors affecting the pile end quality during the construction process are analyzed. During the construction process, the pile end will produce defects such as pores, cracks or irregular sections due to factors such as uneven compaction and uneven concrete pouring. Through on-site detection means, such as ultrasonic detection, echo reflection method, drilling sampling analysis, etc., the construction progress and pile end conditions of the bored piles are monitored in real time. Using these data, the probability of the existence of pile end defects is calculated, and its impact on the bearing capacity of the pile foundation is further analyzed to obtain the pile end defect data.

[0135] Step S24: using the cast-in-place pile foundation defect data to estimate the cast-in-place pile position offset, and obtaining cast-in-place pile position offset data;

[0136] In an embodiment of the present invention, the displacement of the bored pile position is estimated using the defect data of the bored pile foundation. The displacement of the pile position is usually caused by factors such as uneven load, uneven settlement of the stratum or groundwater flow during construction. Through on-site pile foundation displacement measurement, a total station or a GNSS positioning system is used to track the displacement of each bored pile in real time. The displacement of the pile position is estimated by measuring and analyzing the deviation between the actual position and the designed position of each pile foundation, combined with the defect data of the bored pile foundation. This displacement estimation method takes into account various factors that cause the displacement, such as the unevenness of the geological layer or the construction error. The pile position displacement data is obtained through a certain mathematical model and displacement prediction formula.

[0137] Step S25: Calculate the probability of rupture of the bored pile based on the bored pile position offset data and the bored pile construction cavity data to obtain the bored pile rupture probability data.

[0138] In an embodiment of the present invention, the probability of rupture of the cast-in-place pile is calculated based on the pile position offset data and the construction void data of the cast-in-place pile. The risk of cast-in-place pile rupture is usually caused by the combined effect of pile position offset and construction void. In this step, a rupture probability model is established by combining the pile position offset data in step S24 with the construction void data in step S22, and the rupture risk assessment is performed by statistically analyzing the pile position offset and void distribution of the cast-in-place piles, combining soil layer characteristics, groundwater level and other factors, and using probability theory and numerical analysis methods. This process uses Monte Carlo simulation, finite element analysis and other calculation methods to simulate the probability of rupture, and calculates the rupture probability data of the cast-in-place pile under the current construction conditions.

[0139] Preferably, step S22 comprises the following steps:

[0140] Step S221: Calculating the pile end depth of the cast-in-place pile according to the cast-in-place pile design data, thereby obtaining the cast-in-place pile end depth data;

[0141] Step S222: estimating the stratum position of the pile end of the cast-in-place pile based on the cast-in-place pile terrain complexity data and the pile end depth data of the cast-in-place pile to obtain the cast-in-place pile stratum position data;

[0142] Step S223: performing underground soil fine particle loss detection on the bored pile stratum position data based on the bored pile terrain complexity data to obtain underground soil fine particle loss data;

[0143] Step S224: Calculate the probability of pore formation in the underground soil layer at the pile end according to the underground soil fine particle loss data to obtain the probability data of pore formation in the underground soil layer at the pile end;

[0144] Step S225: Predicting the collapse of the hole wall soil layer according to the terrain complexity data of the bored pile and the pore formation probability data of the underground soil layer at the pile end, and obtaining the hole wall soil layer collapse data;

[0145] Step S226: Calculate the voids in the bored pile construction by using the hole wall soil layer collapse data and the probability data of pore formation in the underground soil layer at the pile end, and obtain the bored pile construction void data.

[0146] In the embodiment of the present invention, the pile end depth calculation of the cast-in-place pile is performed based on the cast-in-place pile design data, and a preliminary depth estimation is performed based on the data such as the pile foundation type, pile length, and pile end design depth indicated in the design drawings. If the specific pile end depth is not provided in the design data, it is necessary to determine the depth of the underground rock and soil layer by means of drilling sampling and other means based on the on-site geological exploration data, and then calculate the actual design depth of the pile end. The calculation of the pile end depth should take into account the ground elevation, the preset length of the pile body, and the depth changes that occur during the construction process. Through this calculation, the pile end depth data of each cast-in-place pile is obtained. The pile end stratum position is estimated based on the pile end depth data based on the cast-in-place pile terrain complexity data. The terrain complexity data contains information such as the structural characteristics of the soil, the groundwater level, and the stratum inclination. These factors directly affect the actual position of the pile end, so they must be comprehensively considered, using existing geological survey data, such as stratum profiles and soil types, combined with the depth data of the cast-in-place pile design, to calculate the actual stratum position of the pile end. In order to improve the accuracy, electromagnetic wave detection technology (such as resistivity method) or geological radar is used to detect the underground strata in real time. These technologies help determine the soil layer actually penetrated by the pile end and the distribution of different strata, and calculate the position data of the pile end strata. Based on the terrain complexity data of the bored pile, the pile end strata position data is used to detect the loss of fine particles in the underground soil layer. Fine particle loss refers to the loss of fine soil particles (such as clay, sand, etc.) in the underground soil layer due to water flow or mechanical action. In order to detect the loss of fine particles, it is necessary to analyze the hydraulic characteristics of the soil layer. According to the groundwater flow in the terrain complexity data and the hydrological characteristics of the soil layer, a soil layer water flow model is established, and the erosion of the soil layer by water flow is evaluated in real time by measuring and monitoring the groundwater level. The permeability test (such as soil permeability coefficient test) and gravel distribution experiment are used to further detect and evaluate the loss of fine particles and obtain the data of fine particle loss in the underground soil layer. The probability of pore formation in the underground soil layer at the pile end is calculated based on the data of fine particle loss in the underground soil layer. The loss of fine particles often leads to changes in the pore structure of the soil layer, especially in the soil layer around the pile end, where the pores expand or change. By locating the affected area of ​​fine particle loss and combining the compaction and permeability of the soil layer, the hydraulic model is used to calculate the probability of pore formation. This process analyzes factors such as the particle size and flow rate of the lost particles, simulates the conditions for pore formation through experimental data and simulation methods, and obtains the probability data of pore formation in the underground soil layer at the pile end based on this calculation. The collapse of the hole wall soil layer is estimated based on the terrain complexity data of the cast-in-place pile and the probability data of pore formation in the underground soil layer at the pile end. Hole wall collapse refers to the collapse of the pile hole wall due to the instability of the underground soil layer during the construction process. The stability of the soil layer is evaluated by combining the density and water content of the underground soil layer in the terrain complexity data and the pore situation at the pile end.Then, based on the probability data of pore formation in the soil layer at the pile end, the compressive strength and shear strength of the soil layer are analyzed using a soil mechanics model (such as Kalman filtering or finite element analysis), and the risk of collapse is then evaluated. Through multi-point sampling and field monitoring data, the estimated data for the collapse of the hole wall soil layer is obtained. The calculation of the voids in the construction of the cast-in-place pile is performed using the collapse data of the hole wall soil layer and the probability data for the pore formation in the underground soil layer at the pile end. Construction voids are usually caused by factors such as the loss of the underground soil layer and the collapse of the hole wall. In order to calculate the voids generated during the construction process, the collapse estimation data of step S225 is combined with the particle loss of the soil layer, and the formation process of the void is simulated using fluid mechanics and soil mechanics models. The specific method is to simulate the position and size of the voids by analyzing factors such as the compaction, porosity and fluidity of the soil layer, combined with conditions such as the groundwater flow rate, and through this calculation, the void data for the construction of the cast-in-place pile is obtained.

[0147] Preferably, step S25 comprises the following steps:

[0148] Step S251: Predicting the interaction between the bored piles according to the bored pile position offset data to obtain the interaction data between the bored piles;

[0149] Step S252: using the interaction data between the bored piles to analyze the deformation of the soil around the bored piles, and obtaining the deformation data of the soil around the bored piles;

[0150] Step S253: using the deformation data of the soil around the bored pile to perform local stress concentration detection on the bored pile, and obtaining local stress concentration data on the bored pile;

[0151] Step S254: performing a characteristic analysis of the position of the void in the bored pile construction according to the void data in the bored pile construction to obtain the characteristic data of the position of the void in the bored pile construction;

[0152] Step S255: performing coupling effect analysis using the characteristic data of the position of the void during the construction of the cast-in-place pile and the local stress concentration data of the cast-in-place pile to obtain coupling effect data of the cast-in-place pile construction;

[0153] Step S256: Calculate the probability of rupture of the bored pile according to the coupled effect data of bored pile construction to obtain the probability data of rupture of the bored pile.

[0154] In an embodiment of the present invention, the interaction between the cast-in-place piles is estimated based on the displacement data of the cast-in-place piles. Pile displacement refers to the displacement phenomenon caused by external forces (such as uneven strata, vibration of construction equipment, etc.) during the construction of the cast-in-place piles. This step obtains the displacement data by monitoring the changes in the pile positions, and uses GPS equipment and total stations for precise measurement. In addition, the position information of the cast-in-place piles is obtained by three-dimensional laser scanning technology, and the dynamic changes of the pile positions are analyzed by data processing software. After obtaining the pile displacement data, the interaction between the piles is estimated in combination with the number and arrangement of the cast-in-place piles and the mechanical properties of the surrounding strata. This process involves a soil-structure interaction model, which calculates the mutual influence between the pile foundation and the surrounding soil, including the influence of the pile displacement on the adjacent piles, and obtains the interaction data between the cast-in-place piles. The deformation analysis of the soil around the cast-in-place piles is performed using the interaction data between the cast-in-place piles, and the influence area of ​​the adjacent pile foundations on the surrounding soil is determined by analyzing the interaction data between the cast-in-place piles. In order to more accurately evaluate soil deformation, a foundation settlement monitoring system is used, such as installing laser levels, settlement scales and other equipment to monitor soil deformation in real time. The data collected by these devices, combined with the changes in the interaction between piles and foundations, are used to simulate the deformation process of the soil using the finite element analysis method. This analysis can reveal the settlement, displacement and strain changes caused by the interaction of cast-in-place piles on the surrounding soil, thereby obtaining the deformation data of the soil around the cast-in-place piles. The deformation data of the soil around the cast-in-place piles are used to detect the local stress concentration of cast-in-place piles. Local stress concentration refers to the phenomenon that a large stress concentration occurs in certain parts of the cast-in-place piles due to the deformation of the surrounding soil and the interaction of the pile foundation. In order to perform stress concentration detection, the soil deformation data obtained in step S252 is combined with the stress-strain characteristics of the soil, and the stress analysis software is used for simulation calculation. The software uses a finite element model to model the pile body and the surrounding soil, and predicts the stress changes in various parts of the pile foundation by analyzing the mechanical response of each layer of soil. Special attention is paid to the turning point, contact surface and edge of the pile foundation, because these positions are often areas of stress concentration. Through this analysis process, the local stress concentration data of bored piles are obtained. The location characteristics of bored pile construction voids are analyzed based on the bored pile construction void data. Construction voids are usually caused by factors such as the loss of underground soil layers, soil compression or pile instability. After obtaining the bored pile construction void data, the precise location and scale information of the voids are obtained through drilling, geophysical exploration (such as geological radar) and other methods. Subsequently, based on the void distribution characteristics, the spatial distribution and morphology of the construction voids are analyzed using three-dimensional modeling technology and geomechanical models. This analysis can not only reveal the depth of the void, but also show the relationship between the void and the soil layer around the bored pile, and obtain the spatial location and related characteristic data of the construction void. The coupling effect analysis is carried out using the location characteristic data of the bored pile construction voids and the local stress concentration data of the bored piles.The coupling effect analysis refers to the influence of the existence of voids and local stress concentration on the stability and bearing capacity of cast-in-place piles. According to the spatial position and morphological data of the construction voids obtained in step S254, combined with the local stress concentration data obtained in step S253, the coupling effect of voids and stress concentration is analyzed. In this process, numerical simulation methods, such as finite element method, are used to consider the interaction between soil and pile foundation to simulate the behavior of pile foundation under the joint influence of voids and stress concentration. This analysis helps to evaluate the load concentration effect caused by voids on pile foundations, as well as the structural deformation or damage caused. Through the coupling effect analysis, the coupling effect data of cast-in-place pile construction is obtained. The probability of cast-in-place pile rupture is calculated based on the coupling effect data of cast-in-place pile construction. The calculation of the probability of rupture needs to comprehensively consider the load of the pile foundation, the resistance of the soil layer, the external factors in the construction process, etc. In this step, the probability of pile foundation rupture is calculated by using the coupling effect data of step S255, combined with the design bearing capacity of the pile foundation, the compressive strength of the soil layer and the changes in the construction environment, and the probability theory and reliability analysis methods are used. The Monte Carlo simulation method is used to perform random simulations on various construction scenarios to obtain the probability of rupture under different scenarios. The calculation results are helpful to evaluate the risk of cast-in-place pile rupture during construction and obtain the probability data of cast-in-place pile rupture.

[0155] Preferably, step S3 comprises the following steps:

[0156] Step S31: performing an aging trend analysis of the cast-in-place piles based on the cast-in-place pile rupture probability data to obtain cast-in-place pile aging trend data;

[0157] Step S32: performing stability decay assessment according to the cast-in-place pile aging trend data and cast-in-place pile rupture probability data to obtain cast-in-place pile stability decay data;

[0158] Step S33: performing bearing capacity attenuation detection of the cast-in-place pile according to the cast-in-place pile aging trend data and the cast-in-place pile stability attenuation data to obtain the bearing capacity attenuation data of the cast-in-place pile;

[0159] Step S34: Calculate the probability of the pile collapse by using the bearing capacity decay data of the pile and the aging trend data of the pile to obtain the probability data of the pile collapse.

[0160] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0161] Step S31: performing an aging trend analysis of the cast-in-place piles based on the cast-in-place pile rupture probability data to obtain cast-in-place pile aging trend data;

[0162] In an embodiment of the present invention, an aging trend analysis of a cast-in-place pile is performed based on the probability data of cast-in-place pile rupture. The aging trend of a cast-in-place pile reflects the deterioration of the pile foundation during use, especially as time goes by, due to factors such as changes in the geological environment and aging of materials, the strength and performance of the cast-in-place pile will gradually decrease. In order to analyze the aging trend, it is necessary to collect relevant information based on historical data and field monitoring data, including factors such as pile foundation rupture events, soil layer changes, groundwater level changes, and climate change. By performing regression analysis on these data, using a time series model, and combining the probability data of cast-in-place pile rupture, the aging rate of the pile foundation is calculated. In this process, special attention is paid to the deterioration process of pile foundation materials (such as concrete, steel bars, etc.) and the influence of the groundwater environment on the pile foundation. The aging trend data of the cast-in-place pile is obtained by analysis, and the data is used to describe the aging changes of the pile foundation over time.

[0163] Step S32: Perform stability decay assessment based on the aging trend data of the cast-in-place pile and the probability data of cast-in-place pile rupture to obtain the stability decay data of the cast-in-place pile;

[0164] In an embodiment of the present invention, a stability decay assessment is performed based on the aging trend data of the cast-in-place piles and the probability data of cast-in-place pile rupture. The stability decay assessment of the pile foundation is mainly to assess the decay of its stability over time by analyzing the long-term use status and aging process of the pile foundation. The aging trend data obtained in step S31 is used, combined with the influence of the surrounding environment of the pile foundation (such as soil type, groundwater level changes, etc.), and the stability change of the pile foundation in long-term use is calculated using a structural mechanics model. Combined with the probability data of rupture, the rate at which the stability of the pile foundation decreases over time is assessed. This process uses numerical simulation and finite element analysis techniques to simulate the effects of pile foundation aging and soil reaction on pile foundation stability, and is corrected in combination with actual monitoring data (such as foundation settlement, displacement, etc.). Through the stability decay assessment, the stability decay data of the cast-in-place pile is obtained.

[0165] Step S33: performing bearing capacity attenuation detection of the cast-in-place pile according to the cast-in-place pile aging trend data and the cast-in-place pile stability attenuation data to obtain the bearing capacity attenuation data of the cast-in-place pile;

[0166] In an embodiment of the present invention, a bearing capacity decay detection of cast-in-place piles is performed based on the aging trend data of cast-in-place piles and the stability decay data of cast-in-place piles. The bearing capacity decay detection of cast-in-place piles is mainly used to evaluate the reduction in bearing capacity of pile foundations during use due to factors such as material aging and environmental changes. The performance changes of cast-in-place pile materials (such as concrete, steel bars, etc.) over time are judged through aging trend data, and the risk of bearing capacity reduction is predicted through rupture probability data. On this basis, combined with the stability decay data, mechanical models and numerical simulation methods are used to calculate the decay of the bearing capacity of the pile foundation over time. This process also needs to be verified using on-site loading test data or based on existing standard data to obtain the bearing capacity decay data of cast-in-place piles, which reflects the trend of the bearing capacity of the pile foundation over time.

[0167] Step S34: Calculate the probability of the pile collapse by using the bearing capacity decay data of the pile and the aging trend data of the pile to obtain the probability data of the pile collapse.

[0168] In an embodiment of the present invention, the probability of collapse of the cast-in-place pile is calculated using the bearing capacity decay data of the cast-in-place pile and the aging trend data of the cast-in-place pile. The collapse probability calculation of the cast-in-place pile is to predict the risk of instability and collapse of the pile foundation during long-term use based on the bearing capacity decay and aging trend of the pile foundation. The bearing capacity decay data obtained in step S33 is combined with the aging trend data to evaluate whether the cast-in-place pile will reach the critical point of instability or collapse when the bearing capacity is reduced. Using the bearing capacity of the soil, the material properties of the pile foundation and the environmental conditions, combined with probability theory and reliability analysis methods, multiple simulation calculations are performed to evaluate the probability of collapse under different conditions. Using the Monte Carlo simulation method or probability model, through a large number of simulation experiments, the probability data of the collapse of the cast-in-place pile under specific conditions are obtained. This calculation will reflect the safety risks faced by the cast-in-place pile during actual construction and use, and obtain the probability data of the collapse of the cast-in-place pile.

[0169] Preferably, step S4 comprises the following steps:

[0170] Step S41: performing a cast-in-place pile construction difficulty analysis based on cast-in-place pile collapse probability data and cast-in-place pile terrain complexity data to obtain cast-in-place pile construction difficulty data;

[0171] Step S42: performing a risk assessment on the design of cast-in-place piles according to the cast-in-place pile construction difficulty data and the cast-in-place pile collapse probability data to obtain cast-in-place pile design risk data;

[0172] Step S43: tracing the construction quality of the bored piles according to the bored pile design risk data, thereby obtaining the bored pile construction quality tracing data;

[0173] Step S44: optimizing the bored pile design data according to the bored pile construction quality traceability data and the bored pile design risk data to obtain bored pile design optimization data.

[0174] In an embodiment of the present invention, the difficulty of bored pile construction is analyzed based on the probability data of bored pile collapse and the terrain complexity data of bored pile. The difficulty analysis of bored pile construction is to evaluate the difficulties faced in the construction process of bored piles by combining the probability of collapse of bored piles with the terrain complexity of the construction environment, and to judge the risk of instability of pile foundations in a specific environment by the probability data of bored pile collapse. Next, the terrain complexity data of the area where the bored piles are located is collected, including factors such as slope, soil layer structure, and groundwater level changes. These data can be obtained through geological exploration, soil layer analysis, and other methods. Then, based on these data, combined with the characteristics of the pile foundation and the soil reaction, the soil disturbance, construction difficulty, and potential safety risks encountered during the construction process are calculated through a numerical model. For example, in areas with more complex terrain, the construction difficulty is higher, and additional support or reinforcement measures are required. The bored pile design risk assessment is performed based on the difficulty data of bored pile construction and the probability data of bored pile collapse. The design risk assessment aims to evaluate the potential risks of bored piles in the design stage by combining the construction difficulty data with the probability of collapse. The construction difficulty data obtained in step S41 is combined with the actual construction conditions on site to analyze the challenges faced during the construction process, such as uneven soil layers, abnormal groundwater and other factors. Then, the probability of collapse of bored piles is integrated with the construction difficulty data, and the potential risks in the construction process are quantified by taking into account the influence of different soil types and different construction equipment. These factors are converted into design risk assessment data by using risk matrix or fuzzy logic reasoning method. These evaluation results will provide a basis for the design of bored piles, help adjust design parameters, reduce unexpected problems during construction, obtain bored pile design risk data, and trace the quality of bored pile construction according to the bored pile design risk data. Construction quality traceability is to trace the quality problems that occur during the construction process according to the design risk data of bored piles. The design risk data obtained in step S42 is used to identify the factors that affect the quality of pile foundations during construction, such as pile position deviation, uneven concrete pouring, and pile body cracking. Use on-site quality inspection tools, such as concrete strength testers, pile foundation quality monitoring instruments, etc., to monitor various quality indicators during the construction process in real time. By associating the design risk data with the real-time monitoring data during the construction process, a quality traceability model is formed. During the construction process, the quality of the pile foundation construction is checked regularly, and any abnormal conditions during the construction are recorded. Through the traceability analysis of quality problems during the construction process, the quality traceability data of the bored pile construction is obtained, and the bored pile design is optimized based on the bored pile construction quality traceability data and the bored pile design risk data. The design optimization process is based on the construction quality traceability, and optimizes the bored pile design by analyzing the design risks. Through the quality traceability data obtained in step S43, the problems and their causes in the construction process are understood, such as pile body cracks, insufficient concrete density, etc.Next, combined with the design risk assessment data in step S42, the potential risks in the design stage are analyzed, such as insufficient depth of pile foundation design, mismatched bearing capacity, etc. Based on these data, an optimization algorithm (such as genetic algorithm, simulated annealing method, etc.) is used to adjust the design parameters and optimize the pile foundation design. For example, when there is a problem with the construction quality, it is necessary to increase the steel bar ratio of the pile foundation or deepen the pile foundation depth. Through this design optimization process, the optimized data of cast-in-place pile design is obtained.

[0175] The present invention also provides a quality traceability system for bored pile construction, which is used to execute the quality traceability method for bored pile construction as described above. The quality traceability system for bored pile construction comprises:

[0176] The terrain complexity estimation module is used to obtain the geographical location data of the bored piles; perform the bored pile geological condition detection according to the geographical location data of the bored piles to obtain the bored pile geological condition data; use the geographical location data of the bored piles and the bored pile geological condition data to estimate the terrain complexity of the bored piles to obtain the bored pile terrain complexity data;

[0177] The cast-in-place pile rupture probability calculation module is used to obtain cast-in-place pile design data; the cast-in-place pile rupture probability is calculated according to the cast-in-place pile design data and cast-in-place pile terrain complexity data to obtain cast-in-place pile rupture probability data;

[0178] The collapse probability calculation module is used to detect the attenuation of the bearing capacity of the cast-in-place pile based on the probability data of the cast-in-place pile rupture, and obtain the bearing capacity attenuation data of the cast-in-place pile; and calculate the probability of the collapse of the cast-in-place pile using the bearing capacity attenuation data of the cast-in-place pile, and obtain the probability data of the collapse of the cast-in-place pile;

[0179] The bored pile design optimization module is used to trace the bored pile construction quality based on the bored pile collapse probability data and the bored pile terrain complexity data, so as to obtain the bored pile construction quality traceability data; and to optimize the bored pile design data based on the bored pile construction quality traceability data, so as to obtain the bored pile design optimization data.

[0180] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A quality traceability method for cast-in-place pile construction, characterized in that: The following steps are involved: Step S1: Acquire the geographical location data of the bored pile; perform a bored pile geological condition test according to the geographical location data of the bored pile to obtain the bored pile geological condition data; use the geographical location data of the bored pile and the geological condition data of the bored pile to estimate the terrain complexity of the bored pile to obtain the bored pile terrain complexity data; Step S2: Obtaining cast-in-place pile design data; The probability of pile rupture is calculated based on the pile design data and the pile terrain complexity data, and the pile rupture probability data is obtained; Step S3: performing a bearing capacity attenuation test on the bored pile based on the bored pile rupture probability data to obtain bearing capacity attenuation data on the bored pile; The probability of pile collapse is calculated by using the bearing capacity decay data of piles, and the probability data of pile collapse is obtained. Step S4: Tracing the construction quality of the bored piles according to the probability data of the bored pile collapse and the terrain complexity data of the bored piles, thereby obtaining the bored pile construction quality tracing data; optimizing the bored pile design data according to the bored pile construction quality tracing data, thereby obtaining the bored pile design optimization data.

2. The quality tracing method for cast-in-place pile construction according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Obtaining the geographical location data of the cast-in-place pile; Step S12: Performing a cast-in-place pile geological condition detection according to the cast-in-place pile geographical location data to obtain cast-in-place pile geological condition data; Step S13: performing groundwater condition detection of the bored piles according to the geographical location data of the bored piles to obtain groundwater condition data of the bored piles; Step S14: using the bored pile groundwater status data and the bored pile geological condition data to evaluate the bored pile terrain complexity, and obtain the bored pile terrain complexity data.

3. The quality tracing method for cast-in-place pile construction according to claim 2 is characterized in that: Step S12 includes the following steps: Step S121: drilling and sampling the soil layer at the geographical location of the cast-in-place pile according to the geographical location data of the cast-in-place pile to obtain the soil layer data at the geographical location of the cast-in-place pile; Step S122: performing seismic wave reflection detection according to the geographical location data of the cast-in-place piles to obtain soil layer seismic wave reflection detection data; Step S123: analyzing the stratum distribution law of the bored piles according to the soil layer data of the geographical location of the bored piles and the seismic wave reflection detection data of the soil layer to obtain the stratum distribution law data of the bored piles; Step S124: collecting data on the sudden change of formation dip angle based on the data on the distribution law of the bored pile formation, and obtaining data on the sudden change of formation dip angle; Step S125: using the data of the region where the formation dip angle suddenly changes to estimate the regional hidden interlayer, and obtaining the data of the regional hidden interlayer in the formation; Step S126: collecting the depth of the stratum bearing layer based on the stratum distribution law data of the bored piles to obtain the depth data of the stratum bearing layer; Step S127: Analyze the bearing capacity of the bored pile stratum using the stratum bearing layer depth data and the stratum area hidden interlayer data to obtain the bored pile stratum bearing capacity data; Step S128: Use the bored pile stratum bearing capacity data and the bored pile stratum distribution law data to perform bored pile casting geological condition detection to obtain bored pile geological condition data.

4. The quality tracing method for cast-in-place pile construction according to claim 2 is characterized in that: Step S14 includes the following steps: Step S141: collecting the groundwater level of the bored piles according to the groundwater status data of the bored piles to obtain the groundwater level data of the bored piles; Step S142: measuring the groundwater terrain height difference according to the bored pile groundwater level data to obtain groundwater terrain height difference data; Step S143: Calculate the groundwater flow velocity using the groundwater terrain height difference data to obtain groundwater flow velocity data; Step S144: Predicting geological water flow erosion on the bored pile geological condition data based on the groundwater flow velocity data to obtain geological water flow erosion data; Step S145: estimating the softening of the geological soil layer according to the geological water flow erosion data, thereby obtaining the softening data of the geological soil layer; Step S146: performing geological soil layer sand loss analysis according to the geological water flow erosion data, thereby obtaining geological soil layer sand loss data; Step S147: evaluating the terrain complexity of the bored pile according to the geological soil layer sand loss data and the geological soil layer softening data, and obtaining the terrain complexity data of the bored pile.

5. The quality tracing method for cast-in-place pile construction according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: Obtaining cast-in-place pile design data; Step S22: Calculate the bored pile construction cavity according to the bored pile design data and the bored pile terrain complexity data to obtain the bored pile construction cavity data; Step S23: Calculate the pile end defects of the cast-in-place pile according to the cast-in-place pile design data and the cast-in-place pile terrain complexity data to obtain cast-in-place pile foundation defect data; Step S24: using the cast-in-place pile foundation defect data to estimate the cast-in-place pile position offset, and obtaining cast-in-place pile position offset data; Step S25: Calculate the probability of rupture of the bored pile based on the bored pile position offset data and the bored pile construction cavity data to obtain the bored pile rupture probability data.

6. The quality tracing method for bored pile construction according to claim 5 is characterized in that: Step S22 includes the following steps: Step S221: Calculating the pile end depth of the cast-in-place pile according to the cast-in-place pile design data, thereby obtaining the cast-in-place pile end depth data; Step S222: estimating the stratum position of the pile end of the cast-in-place pile based on the cast-in-place pile terrain complexity data and the pile end depth data of the cast-in-place pile to obtain the cast-in-place pile stratum position data; Step S223: performing underground soil fine particle loss detection on the bored pile stratum position data based on the bored pile terrain complexity data to obtain underground soil fine particle loss data; Step S224: Calculate the probability of pore formation in the underground soil layer at the pile end according to the underground soil fine particle loss data to obtain the probability data of pore formation in the underground soil layer at the pile end; Step S225: Predicting the collapse of the hole wall soil layer according to the terrain complexity data of the bored pile and the pore formation probability data of the underground soil layer at the pile end, and obtaining the hole wall soil layer collapse data; Step S226: Calculate the voids in the bored pile construction by using the hole wall soil layer collapse data and the probability data of pore formation in the underground soil layer at the pile end, and obtain the bored pile construction void data.

7. The quality tracing method for bored pile construction according to claim 5 is characterized in that: Step S24 includes the following steps: Step S251: Predicting the interaction between the bored piles according to the bored pile position offset data to obtain the interaction data between the bored piles; Step S252: using the interaction data between the bored piles to analyze the deformation of the soil around the bored piles, and obtaining the deformation data of the soil around the bored piles; Step S253: using the deformation data of the soil around the bored pile to perform local stress concentration detection on the bored pile, and obtaining local stress concentration data on the bored pile; Step S254: performing a characteristic analysis of the position of the void in the bored pile construction according to the void data in the bored pile construction to obtain the characteristic data of the position of the void in the bored pile construction; Step S255: performing coupling effect analysis using the void position characteristic data of the cast-in-place pile construction and the local stress concentration data of the cast-in-place pile to obtain the coupling effect data of the cast-in-place pile construction; Step S256: Calculate the probability of rupture of the bored pile according to the coupled effect data of bored pile construction to obtain the probability data of rupture of the bored pile.

8. The quality tracing method for bored pile construction according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: performing an aging trend analysis of the cast-in-place pile based on the cast-in-place pile rupture probability data to obtain cast-in-place pile aging trend data; Step S32: performing stability decay assessment according to the cast-in-place pile aging trend data and cast-in-place pile rupture probability data to obtain cast-in-place pile stability decay data; Step S33: performing bearing capacity attenuation detection of the cast-in-place pile according to the cast-in-place pile aging trend data and the cast-in-place pile stability attenuation data to obtain the bearing capacity attenuation data of the cast-in-place pile; Step S34: Calculate the probability of the pile collapse by using the bearing capacity decay data of the pile and the aging trend data of the pile to obtain the probability data of the pile collapse.

9. The quality tracing method for cast-in-place pile construction according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing a cast-in-place pile construction difficulty analysis based on cast-in-place pile collapse probability data and cast-in-place pile terrain complexity data to obtain cast-in-place pile construction difficulty data; Step S42: performing a risk assessment on the design of cast-in-place piles according to the cast-in-place pile construction difficulty data and the cast-in-place pile collapse probability data to obtain cast-in-place pile design risk data; Step S43: tracing the construction quality of the bored piles according to the bored pile design risk data, thereby obtaining the bored pile construction quality tracing data; Step S44: optimizing the bored pile design data according to the bored pile construction quality traceability data and the bored pile design risk data to obtain bored pile design optimization data.

10. A quality traceability system for cast-in-place pile construction, characterized in that: Used to implement the quality traceability method for bored pile construction as claimed in claim 1, the quality traceability system for bored pile construction comprises: The terrain complexity estimation module is used to obtain the geographical location data of the bored piles; perform the bored pile geological condition detection according to the geographical location data of the bored piles to obtain the bored pile geological condition data; use the geographical location data of the bored piles and the bored pile geological condition data to estimate the terrain complexity of the bored piles to obtain the bored pile terrain complexity data; The cast-in-place pile rupture probability calculation module is used to obtain cast-in-place pile design data; the cast-in-place pile rupture probability is calculated according to the cast-in-place pile design data and cast-in-place pile terrain complexity data to obtain cast-in-place pile rupture probability data; The collapse probability calculation module is used to detect the attenuation of the bearing capacity of the cast-in-place pile based on the probability data of the cast-in-place pile rupture, and obtain the bearing capacity attenuation data of the cast-in-place pile; and calculate the probability of the collapse of the cast-in-place pile using the bearing capacity attenuation data of the cast-in-place pile, and obtain the probability data of the collapse of the cast-in-place pile; The bored pile design optimization module is used to trace the bored pile construction quality based on the bored pile collapse probability data and the bored pile terrain complexity data, so as to obtain the bored pile construction quality traceability data; and to optimize the bored pile design data based on the bored pile construction quality traceability data, so as to obtain the bored pile design optimization data.

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