A calculation method and system for detecting overfilling in cast-in-place pile construction

Abstract

The present invention provides a method and system for detecting over-pouring in cast-in-place pile construction, which belongs to the field of physical data detection. The compressive strength and density corresponding to each partition in the cast-in-place pile can be obtained by obtaining the ultrasonic wave velocity and attenuation coefficient corresponding to each partition in the cast-in-place pile; the derivatives between the ultrasonic wave velocity, attenuation coefficient and the concrete compressive strength and density are used for dimensional calculation to obtain the sound velocity compression difference vector and the attenuation density difference vector; the sound velocity compression difference vector and the attenuation density difference vector are compared to judge abnormalities. The method can effectively identify problems such as concrete non-uniformity and sediment accumulation, improve the quality control level in the cast-in-place pile construction process, especially in the pile bottom area with increasing depth, with high accuracy and sensitivity, ensuring the safety and reliability of the overall state of the pile body structure in over-pouring construction.

Description

Technical Field

[0001] The invention belongs to the field of physical data monitoring, and in particular relates to a calculation method and system for detecting over-filling in cast-in-place pile construction. Background Art

[0002] Cast-in-place piles are a common structural form in building foundation construction and are widely used in bridges, super high-rise buildings, large infrastructure and other projects. The construction quality of cast-in-place piles directly affects the stability and bearing capacity of the entire structure. Therefore, the construction quality inspection of cast-in-place piles has always been an important link in the engineering field. In order to ensure the safety and reliability of cast-in-place piles, a variety of inspection technologies are usually used during the construction process to evaluate the integrity and construction quality of the pile body, especially for the over-pouring phenomenon and sediment accumulation during the concrete pouring process. The industry has developed a variety of inspection methods.

[0003] At present, ultrasonic testing is widely used in the integrity detection of the pile body in the construction quality inspection of cast-in-place piles. The uniformity and density of concrete are evaluated through the propagation speed of ultrasonic waves, attenuation coefficient and other parameters. For example, the patent document with publication number CN109457691B provides an integrated monitoring device and control method for deep-water bored cast-in-place piles. This method has the advantages of non-destructive and real-time detection, and can quickly reflect the internal structural state of the pile body. However, ultrasonic testing has limited sensitivity to complex working conditions such as depth changes and sediment accumulation. Especially in the pile bottom area with increasing depth, the changes in ultrasonic wave velocity and attenuation coefficient are not easy to accurately capture, which affects the accuracy of the detection.

[0004] The coring method is a destructive detection method commonly used in bored pile quality inspection. For example, the bored pile coring auxiliary device provided in the patent document with publication number CN216615991U takes out the concrete core sample from the pile body and analyzes its physical parameters such as compressive strength, density, porosity, etc. to judge the quality of the concrete. The coring method has high detection accuracy, but the operation is cumbersome and time-consuming, and it cannot achieve full coverage of the full depth of the pile body, and can only detect local areas.

[0005] During cast-in-place pile construction, the accumulation of sediment at the bottom of the pile is an important factor affecting the bearing capacity of the pile foundation. Excessive sediment thickness will lead to a decrease in the bearing capacity of the pile bottom, which in turn affects the stability of the overall pile foundation. At present, the detection of sediment at the bottom of the pile is usually evaluated by combining coring and ultrasonic methods, but there are still certain technical difficulties in accurately detecting the thickness of the sediment and the sediment accumulation area.

[0006] In some projects, an overfilling warning system based on a fixed threshold is used. For example, the patent document with publication number CN113216193B provides an underwater bored pile concrete overfilling height control device and method. When it detects that a certain physical parameter (such as ultrasonic wave velocity or compressive strength) exceeds the set threshold, the system will issue an alarm. However, such systems often rely on manually set fixed thresholds, lack the ability to intelligently analyze data changes under complex construction conditions, and are prone to false alarms or missed alarms, resulting in unsatisfactory warning effects.

[0007] Although the above technical means have improved the monitoring ability of bored pile construction quality to a certain extent, there are still some obvious technical difficulties and limitations. The existing ultrasonic detection and coring methods cannot fully reflect the quality changes of the pile body at different depths, especially in the pile bottom area with increasing depth. The changes in physical parameters cannot be accurately captured, resulting in inaccurate judgments on the density and quality of the concrete. Existing detection methods are difficult to effectively deal with quality problems caused by sediment accumulation and depth changes at the bottom of the pile. Most of the current detection systems lack real-time performance, making it difficult to conduct real-time monitoring and judgment during the pouring process. Even when using ultrasonic real-time detection, due to the lack of intelligent data analysis methods, the system cannot make dynamic judgments based on the changing trends of physical quantities, making it difficult to promptly discover potential problems in the construction process.

[0008] The existing fixed threshold warning system is easily affected by external factors in complex construction environments, resulting in a high false alarm rate. When the physical parameters of concrete are disturbed by external factors (such as temperature and humidity changes), the system may falsely send out alarms, affecting construction efficiency. In addition, fixed thresholds are difficult to adapt to dynamic changes during the construction process, resulting in insufficient sensitivity and accuracy of the warning system.

[0009] Destructive testing methods such as coring can only test local areas of the pile body, and it is difficult to achieve full coverage of the entire depth of the pile body. This leads to insufficient representativeness of the test results and inability to fully reflect the overall quality of the pile body.

[0010] As the pouring depth increases, the density and quality of concrete often change. The existing detection system is not good at dealing with the nonlinear effects brought about by increasing depth, and it is difficult to accurately evaluate the changes in pile quality with depth.

[0011] In the structure of cast-in-place piles, the ultrasonic wave velocity is closely related to the compressive strength of concrete. This is because the propagation speed of ultrasound is affected by the density and elastic modulus of the medium, while the compressive strength of concrete is related to its internal density, porosity, and material uniformity. Therefore, when the strength of concrete is higher, its density is better, and the ultrasonic wave velocity is correspondingly faster. On the contrary, if the strength is lower, the ultrasonic wave velocity will be relatively slow.

[0012] As depth increases, the quality of concrete placement may change, especially at the bottom of the pile or near sediment, where material density and compressive strength may show a nonlinear relationship with depth. Therefore, direct derivatives that rely solely on wave velocity and compressive strength may not fully reflect these changes.

[0013] The attenuation coefficient of ultrasound in a material is related to the density of the material, the uniformity of the internal structure, and the energy dissipation characteristics. When the density of concrete is high and the structure is more uniform, the attenuation coefficient is low and the loss of ultrasound energy is small; when the density of concrete is low or there are more pores, the attenuation coefficient is large and the ultrasound attenuates faster.

[0014] Similar to wave velocity and compressive strength, the relationship between attenuation coefficient and density will also change with increasing depth. In the case of thick sediment at the bottom of the pile or uneven concrete, the relationship between attenuation coefficient and density will no longer be a simple linear relationship. Summary of the invention

[0015] The purpose of the present invention is to provide a method and system for calculating over-filling detection in bored pile construction, so as to solve one or more technical problems existing in the prior art and at least provide a beneficial choice or create conditions.

[0016] The present invention provides a method and system for detecting over-pouring in cast-in-place pile construction. The method can obtain the corresponding compressive strength and density of each partition in the cast-in-place pile by obtaining the ultrasonic wave velocity and attenuation coefficient corresponding to each partition in the cast-in-place pile; perform dimensional calculations through the derivatives between the ultrasonic wave velocity, attenuation coefficient and the concrete compressive strength and density to obtain the sound velocity compression difference vector and the attenuation density difference vector; compare the sound velocity compression difference vector and the attenuation density difference vector to determine abnormalities. The method can effectively identify problems such as concrete non-uniformity and sediment accumulation, improve the quality control level in the cast-in-place pile construction process, especially in the pile bottom area with increasing depth, with high accuracy and sensitivity, ensuring the safety and reliability of the overall state of the pile body structure in over-pouring construction.

[0017] In order to achieve the above object, according to one aspect of the present invention, a method for detecting over-filling in cast-in-place pile construction is provided, the method comprising the following steps:

[0018] For the bored piles that are overfilled, the bored piles are divided into zones and the depths of the zones are marked;

[0019] Performing ultrasonic testing on the cast-in-place pile to obtain ultrasonic wave velocity and attenuation coefficient corresponding to each partition in the cast-in-place pile;

[0020] Sampling concrete core samples of the bored piles to obtain the compressive strength, porosity and density corresponding to each partition of the bored piles;

[0021] The dimension calculation is performed through the derivatives between ultrasonic wave velocity, attenuation coefficient and concrete compressive strength and density to obtain the sound velocity compressive difference vector and the attenuation density difference vector;

[0022] Compare the sound velocity pressure difference vector and the attenuation density difference vector to determine the abnormality.

[0023] Among them, the sound velocity and compressive strength difference vector is calculated according to the derivative relationship between the ultrasonic wave velocity and the compressive strength of each partition, and is a numerical representation used to evaluate the trend of concrete quality changes in different partitions of the bored pile; the attenuation density difference vector is calculated according to the derivative relationship between the ultrasonic attenuation coefficient and the concrete density of each partition, and is a numerical representation used to detect the changes in concrete uniformity and density in different partitions of the bored pile.

[0024] Furthermore, the bored piles are marked and zoned: the coverage area of ​​ultrasonic testing of the bored piles should be consistent with the sampling coverage area of ​​concrete core samples of the bored piles, and the coverage area corresponds to the entire depth length of the bored piles.

[0025] Furthermore, ultrasonic testing is performed on the cast-in-place pile to obtain the ultrasonic wave velocity and attenuation coefficient corresponding to each partition in the cast-in-place pile.

[0026] Furthermore, concrete core samples are taken from the bored piles to obtain the corresponding compressive strength and density of each partition in the bored piles.

[0027] Furthermore, by performing dimensional calculations on the derivatives between ultrasonic wave velocity, attenuation coefficient, and concrete compressive strength and density, a method for obtaining a sound velocity compressive difference vector and an attenuation density difference vector can be as follows:

[0028] Calculate the derivative of the ultrasonic wave velocity of each partition with respect to its compressive strength, and use an array consisting of the derivatives of the ultrasonic wave velocity of each partition with respect to its compressive strength as the sound velocity-compressive difference vector;

[0029] The derivative of the attenuation coefficient of each partition with respect to its density is calculated, and an array consisting of the derivatives of the attenuation coefficient of each partition with respect to its density is used as the attenuation density difference vector.

[0030] Furthermore, the method of obtaining the sound velocity compressive difference vector and the attenuation density difference vector by performing dimensional calculation through the derivatives between the ultrasonic wave velocity, attenuation coefficient and the concrete compressive strength and density may be:

[0031] Calculate the derivative of the ultrasonic wave velocity of each partition with respect to its depth as the sound velocity depth derivative of each partition, and use the array composed of the sound velocity depth derivatives of each partition as the sound velocity depth derivative vector;

[0032] The derivative of the compressive strength of each partition with respect to its depth is calculated as the compressive depth derivative of each partition, and the array composed of the compressive depth derivatives of each partition is used as the compressive depth derivative vector.

[0033] Calculate the derivative of the attenuation coefficient of each partition with respect to its depth as the attenuation depth derivative of each partition, and use the array composed of the attenuation depth derivatives of each partition as the attenuation depth derivative vector;

[0034] The derivative of the density of each partition with respect to its depth is calculated as the density depth derivative of each partition, and the array composed of the compression depth derivatives of each partition is used as the density depth derivative vector.

[0035] The vector composed of the ratios of the values ​​of each dimension in the sound speed depth derivative vector and the values ​​of each dimension in the pressure resistance depth derivative vector are compared point by point, and the vector is the sound speed pressure resistance difference vector.

[0036] The vector composed of the ratios of the values ​​of each dimension in the attenuation depth derivative vector and the values ​​of each dimension in the density depth derivative vector is the attenuation density difference vector.

[0037] Furthermore, the method of performing dimensional calculation by using derivatives between ultrasonic wave velocity, attenuation coefficient and concrete compressive strength and density to obtain a sound velocity compressive difference vector and an attenuation density difference vector may also include:

[0038] Subtract the values ​​of each dimension in the depth derivative vector of the speed of sound from the values ​​of each dimension in the depth derivative vector of the resistance to compression point by point, and the vector composed of the differences obtained is the speed of sound and the resistance to compression difference vector;

[0039] The values ​​of each dimension in the attenuation depth derivative vector are subtracted point by point from the values ​​of each dimension in the density depth derivative vector, and the vector composed of the obtained differences is the attenuation density difference vector.

[0040] Furthermore, the sound velocity pressure difference vector and the attenuation density difference vector are compared to determine the abnormality, which can be specifically:

[0041] The similarity between the sound velocity pressure difference vector and the attenuation density difference vector is calculated, and when the similarity is less than a threshold value, it is determined that the cast-in-place pile is abnormal.

[0042] Further, the method of comparing the sound velocity pressure difference vector and the attenuation density difference vector to determine the abnormality is:

[0043] The cosine similarity between the sound velocity pressure difference vector and the attenuation density difference vector is calculated, and the correlation coefficient between the sound velocity pressure difference vector and the attenuation density difference vector is calculated, and the abnormality is judged by comparing the cosine similarity and the correlation coefficient. For example, if the cosine similarity between the sound velocity pressure difference vector and the attenuation density difference vector is less than the correlation coefficient between the sound velocity pressure difference vector and the attenuation density difference vector, it is judged that the cast-in-place pile has an abnormality.

[0044] The present invention also provides a calculation system for detecting over-filling of cast-in-place pile construction, the calculation system for detecting over-filling of cast-in-place pile construction comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor, the processor implementing the steps in the calculation method for detecting over-filling of cast-in-place pile construction when executing the computer program, the calculation system for detecting over-filling of cast-in-place pile construction can be run in computing devices such as desktop computers, notebook computers, mobile phones, PDAs, and cloud data centers, the executable systems may include, but are not limited to, processors, memories, and server clusters, the processor executing the computer program runs in the following system units:

[0045] A data acquisition unit is used to divide the super-cast pile into zones, obtain the data of ultrasonic wave velocity and attenuation coefficient corresponding to each zone in the cast-in-place pile, and obtain the data of compressive strength and density corresponding to each zone in the cast-in-place pile;

[0046] A dimension calculation unit, used to perform dimension calculation through derivatives between ultrasonic wave velocity, attenuation coefficient and concrete compressive strength and density, and obtain a sound velocity compressive difference vector and an attenuation density difference vector;

[0047] The numerical detection unit is used to compare the sound velocity pressure difference vector and the attenuation density difference vector to determine the abnormality.

[0048] The beneficial effects of the present invention are as follows: the present invention provides a method and system for detecting over-pouring in cast-in-place pile construction, which can obtain the compressive strength and density corresponding to each partition in the cast-in-place pile by obtaining the ultrasonic wave velocity and attenuation coefficient corresponding to each partition in the cast-in-place pile; perform dimensional calculations through the derivatives between the ultrasonic wave velocity, attenuation coefficient and the concrete compressive strength and density to obtain the sound velocity compression difference vector and the attenuation density difference vector; compare the sound velocity compression difference vector and the attenuation density difference vector to determine abnormalities. This method can effectively identify problems such as concrete non-uniformity and sediment accumulation, improve the quality control level in the cast-in-place pile construction process, especially in the pile bottom area with increasing depth, with high accuracy and sensitivity, ensuring the safety and reliability of the overall state of the pile body structure in over-pouring construction.

[0049] The detection methods used in traditional bored pile construction (such as ultrasonic detection and coring) have low detection accuracy in complex situations such as uneven pile concrete quality and sediment accumulation, and are particularly unable to accurately reflect the impact of increasing depth on the detection results. The technical solution of the present invention not only improves the sensitivity to changes in physical quantities by introducing depth derivatives, difference calculations, and the combination of cosine similarity and correlation coefficients, but also can more accurately identify quality problems and abnormal areas of the pile body through the calculation of depth changes. This improvement improves the accuracy of detection, especially in the pile bottom area where the depth changes significantly, and can accurately capture problems such as sediment accumulation and uneven concrete.

[0050] Traditional detection methods often have difficulty accurately identifying local material degradation and sediment accumulation problems, especially in local areas at different depths of the pile bottom and pile body, where anomalies are not easy to be discovered in time. The present invention can accurately detect abnormal changes in local areas by comprehensively analyzing physical quantities such as sound velocity, attenuation coefficient, compressive strength and density, combined with depth derivative and difference calculation methods, especially in terms of sediment accumulation and material degradation at the pile bottom. This refined detection method can more accurately identify local hidden dangers, thereby ensuring the overall stability of the pile body structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The above and other features of the present invention will become more obvious by describing in detail the embodiments shown in the accompanying drawings. The same reference numerals in the accompanying drawings of the present invention represent the same or similar elements. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other accompanying drawings can be obtained based on these accompanying drawings without creative work. In the accompanying drawings:

[0052] Figure 1 Shown is a flow chart of a calculation method for overfilling detection in cast-in-place pile construction;

[0053] Figure 2 The figure shows the system structure diagram of a calculation system for detecting over-filling in bored pile construction. DETAILED DESCRIPTION

[0054] The following will be combined with the embodiments and drawings to clearly and completely describe the concept, specific structure and technical effects of the present invention, so as to fully understand the purpose, scheme and effect of the present invention. It should be noted that the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0055] In the description of the present invention, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0056] like Figure 1 The flowchart of the calculation method for detecting overfilling of cast-in-place pile construction according to the present invention is shown below. Figure 1 A method and system for detecting over-filling in bored pile construction according to an embodiment of the present invention will be described.

[0057] The present invention provides a calculation method for detecting over-filling in cast-in-place pile construction, and the method specifically comprises the following steps:

[0058] Divide the cast-in-place piles to be super-cast into different zones, obtain the data of ultrasonic wave velocity and attenuation coefficient corresponding to each zone in the cast-in-place pile, and obtain the data of compressive strength and density corresponding to each zone in the cast-in-place pile;

[0059] The dimension calculation is performed through the derivatives between ultrasonic wave velocity, attenuation coefficient and concrete compressive strength and density to obtain the sound velocity compressive difference vector and the attenuation density difference vector;

[0060] Compare the sound velocity pressure difference vector and the attenuation density difference vector to determine the abnormality.

[0061] Furthermore, the bored piles may be marked and zoned: wherein the coverage area of ​​ultrasonic testing of the bored piles should be consistent with the sampling coverage area of ​​concrete core samples of the bored piles and correspond to the entire depth length of the bored piles.

[0062] In some embodiments, the length of the cast-in-place pile is evenly divided into a plurality of different partitions, and each partition is marked and stored in the computer simulation software. The depth of each partition is the depth of the center point of each partition, and the depth can represent the distance from the top of the cast-in-place pile to the geometric center of each partition. The column of the cast-in-place pile can be horizontally cut in the three-dimensional modeling to cut into a plurality of small columns as partitions.

[0063] Furthermore, ultrasonic testing is performed on the cast-in-place pile to obtain the ultrasonic wave velocity and attenuation coefficient corresponding to each partition in the cast-in-place pile:

[0064] In some embodiments, the values ​​of ultrasonic wave velocity and attenuation coefficient corresponding to each partition are obtained. For ultrasonic detection to obtain ultrasonic wave velocity and attenuation coefficient, an ultrasonic detector can be selected, and a probe suitable for the concrete environment can be selected to ensure that it is suitable for the complex structure of the cast-in-place pile. According to the design plan, the cast-in-place pile is divided into multiple partitions along the depth direction, and each partition represents a specific depth range. The division of the partitions can be achieved through three-dimensional modeling software, or manually measured and marked on site. The ultrasonic probe is placed on the top or side of the cast-in-place pile. By emitting an ultrasonic signal, the propagation speed and attenuation of the signal in the cast-in-place pile are measured. After the signal enters the concrete from the transmitting end, it will penetrate each partition and receive the echo signal through the receiving end. The ultrasonic wave velocity is calculated by measuring the propagation time of the ultrasonic wave in the concrete and the distance of the propagation path. The faster the wave velocity, the higher the density and compressive strength of the concrete. The attenuation coefficient is obtained by measuring the degree of energy loss of the ultrasonic signal during the propagation process. A high attenuation coefficient usually indicates that there are pores or cracks inside the concrete, resulting in the attenuation of the signal energy. The ultrasonic wave velocity and attenuation coefficient of each partition are automatically recorded. After the detection data of each partition is exported, it is processed using data analysis software, and the physical data of each partition is stored in the construction database for subsequent use.

[0065] Furthermore, concrete core samples are collected from the cast-in-place piles to obtain the compressive strength and density corresponding to each partition in the cast-in-place piles:

[0066] In some embodiments, for concrete core sampling to obtain compressive strength, porosity and density, a selected drilling coring machine is used, and the coring machine should be able to extract core samples of sufficient quality in partitions at different depths. Industrial drilling tools including but not limited to diamond drill bits are used to ensure that complete concrete core samples can be drilled smoothly from the cast-in-place piles. According to the pre-demarcated cast-in-place pile partitions, concrete core samples are drilled from the center of each partition. To ensure that the core samples in each partition are representative enough, multiple core samples can be taken in each partition. When drilling core samples, it is necessary to pay attention to maintaining the verticality of the borehole to avoid damage or distortion of the sample that affects the experimental data. Each core sample should correspond to a specific depth to ensure that the sampled partition data is consistent.

[0067] The core samples of each partition are sent to the laboratory for testing. The core samples are cut into the size specified in the standard and placed in the compressive strength testing machine to measure their compressive strength. The testing equipment applies uniform pressure to the core sample until the core sample breaks. The maximum bearing capacity at the time of breaking is recorded and the compressive strength of the core sample is calculated. The compressive strength can reflect the bearing capacity of concrete at a specific depth. The higher the value, the better the quality of the concrete.

[0068] The porosity test adopts the saturated water method or vacuum water absorption method. After the core sample is saturated to its maximum water absorption, the mass difference before and after drying is measured to calculate its internal porosity. The higher the porosity, the more holes there are inside the concrete and the worse the quality. Alternatively, the difference obtained by subtracting the porosity from 1 can be used instead of the density value. The density of the concrete core sample is calculated by measuring the volume and mass of the core sample. The volume of the sample is usually measured by the Archimedes method or the nuclear magnetic resonance method, and then the density is obtained by dividing the dry weight of the sample by the volume. The higher the density, the better the compactness of the concrete, which usually corresponds to a higher compressive strength. The compressive strength, porosity and density data of each partition are recorded in the database for subsequent retrieval.

[0069] Furthermore, in Scheme 5-1, according to the theory of the prior art, there can be:

[0070] Calculate the derivative of the ultrasonic wave velocity of each partition with respect to its compressive strength, and use an array consisting of the derivatives of the ultrasonic wave velocity of each partition with respect to its compressive strength as the sound velocity-compressive difference vector;

[0071] The derivative of the attenuation coefficient of each partition with respect to its density is calculated, and an array consisting of the derivatives of the attenuation coefficient of each partition with respect to its density is used as the attenuation density difference vector. In this system, because the traditional ultrasonic wave velocity detection is only used to directly reflect the compressive strength, the method of the present invention determines the quality change trend by calculating the derivative of the ultrasonic wave velocity with respect to the compressive strength. This calculation method is different from the traditional method of directly using the wave velocity to derive the compressive strength. Specifically, we divide the cast-in-place piles into multiple depth partitions, measure the ultrasonic wave velocity and compressive strength of each partition respectively, and obtain the local derivative by taking the wave velocity difference of adjacent partitions and dividing it by the compressive strength difference to reflect the rate of change of the physical quantity. This derivative provides a more dynamic quantification method that can capture the gradual change of quality in the depth direction, and is particularly suitable for detecting the nonlinear effect of increasing depth on physical properties.

[0072] At the same time, the calculation method of the attenuation density difference vector is based on the local derivative of the attenuation coefficient and density in the partition, reflecting the density change trend of concrete at different depths. In this way, we can detect subtle changes in material uniformity and density, and then identify potential structural inhomogeneities or sediment accumulation inside the pile body.

[0073] In engineering, some commonly used Python libraries can be used to handle these calculation requirements. If you want to use NumPy for numerical derivative calculations, NumPy's np.gradient() or np.diff() functions are used to calculate the numerical derivatives of discrete data. The ultrasonic wave speed and compressive strength data arrays are called, wave_speeds is the array of ultrasonic wave speeds, and compressive_strengths is the array of compressive strengths. Then the ultrasonic wave speed and compressive strength data arrays are input into the np.gradient() function, and the derivative of the wave speed relative to the compressive strength is output as wave_speed_derivative=np.gradient(wave_speeds,compressive_strengths). If you want to perform interpolation derivative calculations, you can use SciPy's interp1d and derivative functions for data interpolation and derivative calculations. SciPy's functions can interpolate partitioned data into continuous and differentiable ones, and then calculate its derivatives, which are suitable for processing non-uniformly distributed data.

[0074] Furthermore, Scheme 5-2 is an optimization of Scheme 5-1, which can be specifically:

[0075] Calculate the derivative of the ultrasonic wave velocity of each partition with respect to its depth as the sound velocity depth derivative of each partition, and use the array composed of the sound velocity depth derivatives of each partition as the sound velocity depth derivative vector;

[0076] The derivative of the compressive strength of each partition with respect to its depth is calculated as the compressive depth derivative of each partition, and the array composed of the compressive depth derivatives of each partition is used as the compressive depth derivative vector.

[0077] Calculate the derivative of the attenuation coefficient of each partition with respect to its depth as the attenuation depth derivative of each partition, and use the array composed of the attenuation depth derivatives of each partition as the attenuation depth derivative vector;

[0078] The derivative of the density of each partition with respect to its depth is calculated as the density depth derivative of each partition, and the array composed of the compression depth derivatives of each partition is used as the density depth derivative vector.

[0079] The vector composed of the ratios of the values ​​of each dimension in the sound speed depth derivative vector and the values ​​of each dimension in the pressure resistance depth derivative vector are compared point by point, and the vector is the sound speed pressure resistance difference vector.

[0080] The vector composed of the ratios of the values ​​of each dimension in the attenuation depth derivative vector and the values ​​of each dimension in the density depth derivative vector is the attenuation density difference vector.

[0081] Furthermore, Scheme 5-3 can be further improved on the basis of Scheme 5-2 as follows:

[0082] Subtract the values ​​of each dimension in the depth derivative vector of the speed of sound from the values ​​of each dimension in the depth derivative vector of the resistance to compression point by point, and the vector composed of the differences obtained is the speed of sound and the resistance to compression difference vector;

[0083] The values ​​of each dimension in the attenuation depth derivative vector are subtracted point by point from the values ​​of each dimension in the density depth derivative vector, and the vector composed of the obtained differences is the attenuation density difference vector.

[0084] Specifically, for Scheme 5-1, the reason why it is necessary to calculate the derivatives of the sound velocity and compressive strength, the attenuation coefficient and density is that according to the prior art, there is a physical relationship between the sound velocity and compressive strength, the attenuation coefficient and density, which together reflect the mechanical properties and propagation characteristics of the material. However, at different depths, these relationships may change due to factors such as concrete density and sediment thickness. By calculating the derivatives, the rate of change between these parameters can be quantified, and anomalies or deviations can be detected by comparing these rates of change.

[0085] The derivative can capture the changing trend of each physical parameter in each partition. For example, if the derivative of the speed of sound relative to the compressive strength is large, it means that the concrete strength in this partition is relatively low and the wave speed is greatly affected by the density. Similarly, the derivative of the attenuation coefficient relative to the density can reflect the change of the energy dissipation of the wave at different depths, helping to judge the uniformity and quality changes of the concrete.

[0086] However, for the proposal of Scheme 5-2, Scheme 5-1 captures the linear relationship between these parameters by calculating the direct derivatives of wave velocity and compressive strength, attenuation coefficient and density. However, in practice, the nonlinear effect brought about by increasing depth often has a greater impact, especially at the bottom of the pile or in the sediment area, where these linear relationships may no longer hold. Therefore, Scheme 5-2 introduces the depth derivative to further consider the impact of depth on the relationship between various parameters.

[0087] Scheme 5-2 introduces the depth dimension for optimization because as the depth of the bored pile increases, especially at the bottom of the pile or near the sediment area, the density, porosity, fluidity, etc. of the material will change. This causes the ultrasonic wave velocity, attenuation coefficient and other parameters to no longer show a simple linear relationship with the compressive strength and density, and may show nonlinear or complex gradual patterns. Therefore, the depth increment is a factor that must be considered.

[0088] In Scheme 5-2, by calculating the derivatives of sound velocity, compressive strength, attenuation coefficient and density with respect to depth, the effect of depth on these parameters can be captured, thereby obtaining a more accurate assessment of the quality of the partition. The role of the depth derivative is to decompose the nonlinear effect of depth into a measurable rate of change, thereby improving the accuracy of the overall calculation.

[0089] Therefore, in the improvement of Scheme 5-2, the change in sound velocity, compressive strength, attenuation coefficient and density can be linked to the depth through the depth derivative, capturing the impact of depth changes. This method can eliminate the linear assumption in simple derivative calculations, making the analysis results more consistent with the actual engineering situation.

[0090] At different depths, various physical parameters may change non-uniformly with depth. By introducing depth derivatives, the system can capture the nonlinear changes within each partition and combine them with the corresponding physical parameter derivatives to ultimately optimize risk identification.

[0091] In the further improved point-to-point comparison calculation method of Scheme 5-3, in response to the problems existing in the point-to-point ratio calculation in Scheme 5-2, especially the limitations of the point-to-point ratio in Scheme 5-2, the point-to-point ratios of the sound velocity depth derivative and the pressure resistance depth derivative, and the attenuation depth derivative and the density depth derivative are used to evaluate the parameter consistency of each partition. However, the calculation of the point-to-point ratio assumes that there is a consistent change trend and direction between the two vectors, but in practice, especially when there is a complex nonlinear relationship, this assumption may not hold. The ratio calculation may not effectively reflect the relative changes of the two, which may lead to misjudgment or inaccurate risk assessment.

[0092] Therefore, the improvement of Scheme 5-3 introduces the physical meaning of difference calculation. Compared with ratio calculation, difference calculation can better reflect the absolute change of two vectors. By calculating the difference between the depth derivative of sound velocity and the depth derivative of pressure resistance, and the depth derivative of attenuation and the depth derivative of density, the deviation between the two can be clearly determined, rather than simply relying on the relative size of the ratio. This method can better capture the differences in actual physical quantities, especially in nonlinear situations, where the difference can more accurately describe the relationship between them.

[0093] By introducing the difference calculation, Scheme 5-3 not only avoids the possible errors in the ratio calculation, but also can more accurately identify the abnormalities of the physical parameters in each partition. For example, when the difference is large, it may mean that the concrete quality of the partition is poor or there are sediment problems; when the difference is close to zero, it indicates that the structure is uniform and the risk is low.

[0094] The core of Solution 5-1 is the basic derivative calculation, which provides preliminary risk identification by capturing the changing trend of physical parameters. Solution 5-2 considers the nonlinear effect of increasing depth on the relationship between physical parameters, further improving the accuracy of detection. Solution 5-3 better captures the absolute changes between physical parameters and eliminates the inaccuracy in ratio calculation.

[0095] Furthermore, in Scheme 6-1:

[0096] The similarity between the sound velocity anti-pressure difference vector and the attenuation density difference vector is calculated, and when the similarity is less than a threshold, it is determined to be abnormal.

[0097] In cast-in-place pile construction, the correlation between physical quantities, such as the speed of sound and compressive strength, attenuation coefficient and density, is usually close. Under normal construction conditions, the trend of these parameters should remain consistent. Therefore, the cosine similarity should be close to 1 under normal construction conditions, indicating a consistent trend. In good quality cast-in-place pile construction, the cosine similarity is expected to remain between 0.9 and 1, reflecting the strong correlation and consistency of the changes in physical quantities. When problems such as uneven concrete quality, sediment accumulation, or material deterioration occur, the correlation between physical quantities may become weaker and the cosine similarity will drop rapidly. The threshold for anomalies is usually set between 0.7 and 0.85. Within this range, although the physical quantities are partially correlated, there may be local inconsistencies and anomalies.

[0098] According to the statistical analysis of the collected data, the similarity threshold is set roughly between 0.75 and 0.85: greater than 0.85, it means that the physical quantities are highly consistent, reflecting good construction quality; less than 0.75, it means that there are large changes and inconsistent trends between the physical quantities, which may indicate that there are abnormalities in the pile body. Preferably, in practical applications, the similarity threshold between the acoustic velocity compression difference vector and the attenuation density difference vector in Scheme 6-1 can be set to 0.8.

[0099] Furthermore, Scheme 6-2 improves the above-mentioned Scheme 6-1, and specifically can be:

[0100] Calculate the cosine similarity between the sound velocity pressure resistance difference vector and the attenuation density difference vector, calculate the correlation coefficient between the sound velocity pressure resistance difference vector and the attenuation density difference vector, compare the cosine similarity and the correlation coefficient, and judge the abnormality. For example, if the cosine similarity is less than the correlation coefficient, an abnormality occurs.

[0101] In the construction of cast-in-place piles, as the concrete is poured and solidified layer by layer, some key abnormal phenomena may occur in the pile body. For example, the accumulation of sediment will cause the strength and density of the pile bottom to decrease, and at the same time affect the propagation of sound waves, resulting in a slower sound velocity and an increased attenuation coefficient. Due to the non-uniformity of concrete, the concrete at different depths of the pile body may have problems such as uneven density and changes in compressive strength, especially at the bottom of the pile or due to improper pouring operations. Therefore, the relationship between physical quantities such as sound velocity, compressive strength, attenuation coefficient, and density should reflect these changes. For example, when the density of the pile body decreases or the sediment increases, the attenuation coefficient increases, the sound velocity slows down, the compressive strength decreases, and the density decreases. By calculating these changes, we can accurately determine whether there is an over-pouring abnormality in a certain area.

[0102] In practical applications, the cosine similarity between the sound velocity compression difference vector and the attenuation density difference vector is calculated, and the correlation coefficient between the sound velocity compression difference vector and the attenuation density difference vector is calculated because during the construction process, if the sound velocity and the compressive strength change in the same direction, or the attenuation coefficient and the density change trend are consistent, it means that the structure is relatively uniform and there is no significant abnormality in the concrete quality. At this time, the cosine similarity is close to 1, indicating that the internal materials of the pile body change in the same trend.

[0103] As for the problem in actual working conditions, when the cosine similarity decreases significantly (close to 0 or negative), it may reflect that the concrete quality in a certain area has changed suddenly and the correlation between physical parameters has been destroyed. For example, the concrete strength decreases but the attenuation coefficient increases, or the concrete density decreases but the sound speed does not slow down synchronously. These abnormal phenomena may be reflected in the sediment accumulation area or the uneven pouring area.

[0104] In its optimization direction, the threshold can be adjusted dynamically, and different cosine similarity thresholds can be considered to be set at different depths of the pile body. Especially in the pile bottom area, due to the influence of sediment, the change of physical quantity may be more drastic, so a lower threshold can be set to capture potential anomalies.

[0105] In pile integrity assessment, the correlation coefficient reflects the numerical relationship between different physical quantities. A high correlation coefficient usually means that these quantities keep changing synchronously within a certain range. For example, in a homogeneous material area, the relationship between the speed of sound and the compressive strength should show a strong linear correlation.

[0106] In actual working conditions, when the correlation coefficient is high but the cosine similarity is low, it means that the numerical correlation between these physical quantities is strong, but the trends are inconsistent. A typical scenario is that although the concrete density and attenuation coefficient remain strongly correlated, due to depth changes or sediment accumulation, the decrease in density and the increase in attenuation coefficient are not synchronized, which often means that the quality of the pile bottom or the sediment thickness exceeds the standard.

[0107] Therefore, during the construction process, if the correlation coefficients between the velocity of sound and the compressive strength, attenuation coefficient, and density are significantly reduced, it may reflect that there are local quality problems in the pile body or pile bottom. Therefore, different correlation coefficient thresholds can be set according to different construction environments. Especially in the area close to the pile bottom, the changes in the correlation coefficient should be analyzed to detect sediment accumulation or uneven concrete.

[0108] Therefore, in actual over-pouring detection, the combined use of cosine similarity and correlation coefficient can effectively identify different types of anomalies. For example, when the cosine similarity is lower than the correlation coefficient, it means that although the correlation between the physical quantities is strong, the trends are inconsistent, which may mean that the concrete quality in a local area of ​​the pile body has changed suddenly, such as a decrease in density or a decrease in sound speed; when the correlation coefficient is also low, it may further indicate that there are more serious structural incomplete problems at the bottom or middle of the pile. According to different construction stages, especially in areas with more sediment at the bottom of the pile, the system can dynamically adjust the thresholds of the similarity and correlation coefficient to ensure that anomalies caused by changes in concrete quality or over-pouring can be accurately captured.

[0109] In overfilling detection, the joint analysis of multiple parameters such as sound velocity, compressive strength, attenuation coefficient, density, etc. can form a more comprehensive assessment. By combining the similarity and correlation coefficient of these physical quantities, the potential risks of the pile body can be further predicted. In particular, when these physical quantities show inconsistent change trends in a certain area at the same time (such as density decrease, attenuation coefficient increase), the system can issue an early warning to remind that there may be problems with local sediment accumulation or uneven pouring. Through the accumulation of historical data and comprehensive analysis of real-time detection results, a time series-based prediction model can be introduced to further optimize the prediction of abnormal conditions at the bottom of the pile. This model can identify potential overfilling risks caused by increasing depth, uneven materials, or sediment accumulation in advance.

[0110] It can be seen that based on optimization schemes 6-1 and 6-2, the present invention can optimize the application of cosine similarity and correlation coefficient to be more in line with the actual construction site by calculating the physical data variables that appear frequently in the construction of cast-in-place piles, such as sediment accumulation, concrete inhomogeneity, etc. In this way, cosine similarity is used to detect the consistency of the changing trend of physical quantities, and the correlation coefficient is used to judge the linear correlation of numerical values. Combining these two, potential anomalies in the pile body can be identified more accurately, and the accuracy and flexibility of the detection system can be improved through dynamic threshold adjustment and data fusion.

[0111] In some embodiments, in the over-pouring detection of bored pile construction, Scheme 6-1 and Scheme 6-2 can detect a variety of abnormal phenomena through judgment, including concrete non-uniformity, sediment accumulation, over-pouring, quality problems caused by depth changes, local material degradation, and stability problems of the soil layer under the pile end. Specifically, it is manifested in the similarity and correlation changes between physical quantities such as sound velocity, compressive strength, attenuation coefficient, density, etc., and these anomalies can be effectively identified by combining cosine similarity and correlation coefficient, indicating potential risks in construction.

[0112] Concrete may have uneven density, high porosity, or varying compressive strength at different depths in the pile. If the quality of the concrete changes significantly at certain depths, this may cause changes in the relationship between ultrasonic wave velocity and compressive strength, and between attenuation coefficient and density. Cosine similarity and correlation coefficient can detect such inconsistencies, indicating that the quality of the concrete is not uniform in a certain partition. Discontinuing trends between physical quantities, such as a significant slowing of the speed of sound without a corresponding decrease in compressive strength, may indicate the presence of large local pores or structural inhomogeneity in the concrete.

[0113] The accumulation of sediment at the bottom of the pile will cause the quality of the pile bottom to deteriorate, which will in turn affect the stability of the entire pile foundation. Sediment accumulation will cause the ultrasonic attenuation coefficient to increase sharply, the sound speed to slow down, and the compressive strength of the pile bottom to decrease significantly. Schemes 6-1 and 6-2 can find abnormal conditions at the bottom of the pile by comparing and analyzing these physical quantities. The relationship between the attenuation coefficient and the density becomes abnormal, the correlation coefficient decreases, and the cosine similarity may show a significant directional change, indicating the presence of sediment at the bottom of the pile.

[0114] Over-pouring refers to the concrete exceeding the designed height during pouring, which may result in substandard quality at the top of the pile and affect the overall structure. Over-pouring can cause abnormal changes in the physical parameters at the top of the pile, especially the velocity of sound and compressive strength, which may show abnormal trends. Schemes 6-1 and 6-2 can detect the over-pouring phenomenon by comparing the similarity and correlation between physical quantities. The correlation coefficient between the velocity of sound and the compressive strength in the top area decreases, and the cosine similarity decreases, indicating an over-pouring problem at the top of the pile.

[0115] As the pouring depth increases, the quality and density of concrete may gradually decrease, especially in the area close to the bottom of the pile. The change in concrete quality caused by the change in depth will affect the linear relationship between ultrasonic wave velocity, attenuation coefficient and compressive strength and density. Schemes 6-1 and 6-2 can identify pile quality anomalies caused by depth by dynamically monitoring the changing trends of these physical quantities. As the depth increases, the correlation of physical quantities decreases significantly, and the correlation coefficient and cosine similarity show inconsistent changes, indicating quality problems caused by increasing depth.

[0116] For testing and analysis, the experimental control group used Scheme 5-1, Scheme 5-2, and Scheme 5-3 in combination with Scheme 6-1 or 6-2, respectively, to form a total of 6 technical schemes in the actual application scenario of over-pouring detection of bored pile construction. 50 bored piles with over-pouring were obtained for data detection, and the results of each scheme on five key test items were analyzed, including accuracy, real-time response capability, anomaly detection capability, reliability and false alarm rate. The specific records are as follows:

[0117] In terms of accuracy testing, the accuracy of Scheme 5-1 combined with Scheme 6-1 was 78%, and the accuracy was increased to 85% when combined with Scheme 6-2. In contrast, the accuracy of Scheme 5-2 combined with Scheme 6-1 was 82%, and increased to 88% when combined with Scheme 6-2. Scheme 5-3 performed best in this regard, with an accuracy of 86% when combined with Scheme 6-1, and the highest accuracy when combined with Scheme 6-2, reaching 92%. These data show that Scheme 5-2 has a significantly higher accuracy than Scheme 5-1 due to the introduction of depth derivative optimization, and Scheme 5-3 has the highest accuracy due to further optimization based on difference calculation.

[0118] In the test of real-time response capability, the average response time of Solution 5-1 combined with Solution 6-1 was 0.45 seconds, and it was slightly optimized when combined with Solution 6-2, reducing it to 0.42 seconds. In contrast, the average response time of Solution 5-2 combined with Solution 6-1 was 0.41 seconds, and it was further reduced to 0.38 seconds when combined with Solution 6-2. The response time of Solution 5-3 combined with Solution 6-1 was 0.39 seconds, while the response speed was the fastest when combined with Solution 6-2, at only 0.35 seconds. It can be seen that Solution 5-3 has the best real-time performance due to the optimization in calculation, and the response time is further reduced when combined with Solution 6-2, showing stronger real-time processing capabilities.

[0119] In the evaluation of anomaly detection capability, the score of Scheme 5-1 combined with Scheme 6-1 was 70 points, and it was improved to 80 points when combined with Scheme 6-2. Scheme 5-2 performed slightly better in this test, scoring 75 points when combined with Scheme 6-1, and 85 points when combined with Scheme 6-2. Scheme 5-3 performed best, scoring 82 points when combined with Scheme 6-1, and further improved to 90 points when combined with Scheme 6-2. This shows that Scheme 5-3 performed best in terms of anomaly detection sensitivity when combined with Scheme 6-2, and was able to more accurately capture subtle anomalies in construction.

[0120] In the reliability test, the score of Scheme 5-1 combined with Scheme 6-1 was 88%, and it increased to 90% when combined with Scheme 6-2. Scheme 5-2 performed more stably, scoring 90% when combined with Scheme 6-1, and increased to 93% when combined with Scheme 6-2. Scheme 5-3 scored 92% when combined with Scheme 6-1, and reached the highest score of 95% when combined with Scheme 6-2. This shows that Scheme 5-3 combined with Scheme 6-2 performed most stably under complex construction conditions and could maintain high detection consistency and reliability.

[0121] In the test of false alarm rate, the false alarm rate of scheme 5-1 combined with scheme 6-1 was high, reaching 15%, and the false alarm rate was reduced to 10% when combined with scheme 6-2. The false alarm rate of scheme 5-2 combined with scheme 6-1 was 12%, and it was reduced to 8% when combined with scheme 6-2. The false alarm rate of scheme 5-3 was the lowest, 10% when combined with scheme 6-1, and the lowest false alarm rate when combined with scheme 6-2 was only 5%. It can be seen that the effect of combining scheme 6-2 is very obvious in reducing false alarms, especially when scheme 5-3 is combined with scheme 6-2, the false alarm rate is the lowest, showing better detection accuracy.

[0122] By introducing the depth derivative, Scheme 5-2 can capture the impact of increasing depth on the relationship between physical quantities, and is therefore superior to Scheme 5-1 in terms of accuracy, real-time response capability, anomaly detection capability, and false alarm rate. Scheme 5-3 further optimizes the processing of nonlinear changes by using difference calculations, improves accuracy, anomaly detection capability, and reliability, while reducing false alarm rates, and performs better than Scheme 5-2 overall. When combined with Scheme 6-2, by using a combined analysis of cosine similarity and correlation coefficient, anomalies can be detected more accurately and the false alarm rate can be effectively reduced. Whether combined with Scheme 5-1, Scheme 5-2, or Scheme 5-3, Scheme 6-2 always performs better than Scheme 6-1, especially in terms of accuracy and false alarm rate.

[0123] In summary, Scheme 5-3 combined with Scheme 6-2 has the best overall performance. In the actual application of over-pouring detection in bored pile construction, it has achieved the best level in these test items in terms of accuracy, anomaly detection capability, real-time performance and false alarm control.

[0124] The above-described over-filling detection calculation system for bored pile construction runs on any computing device such as a desktop computer, a laptop computer, a mobile phone, a PDA or a cloud data center. The above-described computing device includes: a processor, a memory and a computer program stored in the memory and running on the above-described processor. When the above-described processor executes the above-described computer program, the above-described steps in the above-described over-filling detection calculation method for bored pile construction are implemented. The executable system may include, but is not limited to, a processor, a memory and a server cluster.

[0125] The embodiment of the present invention provides a calculation system for detecting over-filling of cast-in-place pile construction, such as Figure 2 As shown, a calculation system for detecting over-filling of cast-in-place pile construction in this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned calculation method for detecting over-filling of cast-in-place pile construction are implemented. The processor executes the computer program and runs it in the following system units:

[0126] A data acquisition unit is used to divide the super-cast pile into zones, obtain the data of ultrasonic wave velocity and attenuation coefficient corresponding to each zone in the cast-in-place pile, and obtain the data of compressive strength and density corresponding to each zone in the cast-in-place pile;

[0127] A dimension calculation unit, used to perform dimension calculation through derivatives between ultrasonic wave velocity, attenuation coefficient and concrete compressive strength and density, and obtain a sound velocity compressive difference vector and an attenuation density difference vector;

[0128] The numerical detection unit is used to compare the sound velocity pressure difference vector and the attenuation density difference vector to determine the abnormality.

[0129] Among them, preferably, all undefined variables in the present invention, if not clearly defined, can be manually set thresholds.

[0130] Among them, dimensionless numerical calculations are used between physical quantities of different units.

[0131] The above-mentioned calculation system for detecting overfilling of bored pile construction can be run in computing devices such as desktop computers, laptop computers, mobile phones, PDAs, and cloud data centers. The above-mentioned calculation system for detecting overfilling of bored pile construction includes, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above-mentioned example is only an example of a calculation method and system for detecting overfilling of bored pile construction, and does not constitute a limitation on a calculation method and system for detecting overfilling of bored pile construction. It can include more or fewer components than the above example, or a combination of certain components, or different components. For example, the above-mentioned calculation system for detecting overfilling of bored pile construction can also include input and output devices, network access devices, buses, etc.

[0132] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete component gate circuits or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the calculation system for detecting overfilling of cast-in-place pile construction, and various sub-areas of the entire calculation system for detecting overfilling of cast-in-place pile construction are connected by various interfaces and lines.

[0133] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the method and system for detecting over-casting in cast-in-place pile construction by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0134] The present invention provides a method and system for detecting over-pouring in cast-in-place pile construction. The method can obtain the corresponding compressive strength and density of each partition in the cast-in-place pile by obtaining the ultrasonic wave velocity and attenuation coefficient corresponding to each partition in the cast-in-place pile; perform dimensional calculations through the derivatives between the ultrasonic wave velocity, attenuation coefficient and the concrete compressive strength and density to obtain the sound velocity compression difference vector and the attenuation density difference vector; compare the sound velocity compression difference vector and the attenuation density difference vector to determine abnormalities. The method can effectively identify problems such as concrete non-uniformity and sediment accumulation, improve the quality control level in the cast-in-place pile construction process, especially in the pile bottom area with increasing depth, with high accuracy and sensitivity, ensuring the safety and reliability of the overall state of the pile body structure in over-pouring construction.

[0135] Although the description of the present invention has been quite detailed and has been described in particular with respect to several described embodiments, it is not intended to be limited to any of these details or embodiments or any particular embodiment, so as to effectively cover the intended scope of the present invention. In addition, the present invention is described above with the embodiments foreseeable by the inventors, and its purpose is to provide a useful description, and those non-substantial changes to the present invention that are not currently foreseen may still represent equivalent changes of the present invention.

Claims

1. A method for calculating overfill detection in cast-in-place pile construction, characterized in that: The method comprises the following steps: Divide the cast-in-place piles to be overcasted into different zones, obtain the data of ultrasonic wave velocity and attenuation coefficient corresponding to each zone in the cast-in-place pile, and obtain the data of compressive strength and density corresponding to each zone in the cast-in-place pile; perform dimensional calculation by derivatives between ultrasonic wave velocity, attenuation coefficient and concrete compressive strength and density, and obtain a sound velocity-compression difference vector and an attenuation density difference vector; compare the sound velocity-compression difference vector and the attenuation density difference vector to determine abnormality; Wherein, the sound velocity and compression difference vector is calculated based on the derivative relationship between the ultrasonic wave velocity and the compressive strength of each partition, and is used to evaluate the trend of concrete quality changes in different partitions of the cast-in-place pile as a numerical representation; the attenuation density difference vector is calculated based on the derivative relationship between the ultrasonic attenuation coefficient and the concrete density of each partition, and is used to detect the change of concrete uniformity and density in different partitions of the cast-in-place pile as a numerical representation; the derivative of the ultrasonic wave velocity of each partition relative to its compressive strength is calculated, and an array composed of the derivatives of the ultrasonic wave velocity of each partition relative to its compressive strength is used as the sound velocity and compression difference vector; the derivative of the attenuation coefficient of each partition relative to its density is calculated, and an array composed of the derivatives of the attenuation coefficient of each partition relative to its density is used as the attenuation density difference vector; Among them, the method of obtaining the sound velocity compressive difference vector and the attenuation density difference vector by performing dimensional calculation through the derivatives between the ultrasonic wave velocity, attenuation coefficient and the concrete compressive strength and density is either: Calculate the derivative of the ultrasonic wave velocity of each partition with respect to its depth as the sound velocity depth derivative of each partition, and use the array composed of the sound velocity depth derivatives of each partition as the sound velocity depth derivative vector; Calculate the derivative of the compressive strength of each partition with respect to its depth as the compressive depth derivative of each partition, and use the array composed of the compressive depth derivatives of each partition as the compressive depth derivative vector; Calculate the derivative of the attenuation coefficient of each partition with respect to its depth as the attenuation depth derivative of each partition, and use the array composed of the attenuation depth derivatives of each partition as the attenuation depth derivative vector; Calculate the derivative of the density of each partition with respect to its depth as the density depth derivative of each partition, and use the array composed of the compression depth derivatives of each partition as the density depth derivative vector; The vector composed of the ratios of the values ​​of each dimension in the sound velocity depth derivative vector and the values ​​of each dimension in the compression resistance depth derivative vector is the sound velocity compression resistance difference vector. The vector composed of the ratios of the values ​​of each dimension in the attenuation depth derivative vector and the values ​​of each dimension in the density depth derivative vector is the attenuation density difference vector. Among them, the method of obtaining the sound velocity compressive difference vector and the attenuation density difference vector by performing dimensional calculation through the derivatives between the ultrasonic wave velocity, attenuation coefficient and the concrete compressive strength and density is either: Subtract the values ​​of each dimension in the depth derivative vector of the speed of sound from the values ​​of each dimension in the depth derivative vector of the resistance to compression point by point, and the vector composed of the differences is the speed of sound and the resistance to compression difference vector; The values ​​of each dimension in the attenuation depth derivative vector are subtracted point by point from the values ​​of each dimension in the density depth derivative vector, and the vector composed of the obtained differences is the attenuation density difference vector.

2. A method for detecting overfilling in cast-in-place pile construction according to claim 1, characterized in that: in, The bored piles are marked and divided into zones: the coverage area of ​​ultrasonic testing of the bored piles should be consistent with the coverage area of ​​sampling of concrete core samples of the bored piles, and the coverage area corresponds to the entire depth of the bored piles.

3. A method for detecting overfilling in cast-in-place pile construction according to claim 1, characterized in that: in, By performing ultrasonic testing on the cast-in-place pile, the numerical values ​​of the ultrasonic wave velocity and the attenuation coefficient corresponding to each partition in the cast-in-place pile are obtained.

4. A method for calculating over-filling detection in cast-in-place pile construction according to claim 1, characterized in that: in, By sampling the concrete core sample of the cast-in-place pile, the value of the compressive strength and the value of the density corresponding to each partition in the cast-in-place pile are obtained.

5. A method for detecting overfilling in bored pile construction according to claim 1, characterized in that: Compare the sound velocity pressure difference vector and the attenuation density difference vector to determine the abnormality, specifically: The similarity between the sound velocity pressure difference vector and the attenuation density difference vector is calculated. When the similarity is less than a threshold value, the cast-in-place pile is abnormal.

6. A method for calculating overfill detection in cast-in-place pile construction according to claim 1, characterized in that: The method of comparing the sound velocity pressure difference vector and the attenuation density difference vector to determine the abnormality is: Calculate the cosine similarity between the sound velocity pressure resistance difference vector and the attenuation density difference vector, calculate the correlation coefficient between the sound velocity pressure resistance difference vector and the attenuation density difference vector, and judge the abnormality by comparing the cosine similarity and the correlation coefficient; if the cosine similarity between the sound velocity pressure resistance difference vector and the attenuation density difference vector is less than the correlation coefficient between the sound velocity pressure resistance difference vector and the attenuation density difference vector, then the cast-in-place pile has an abnormality.

7. A calculation system for detecting overfilling in cast-in-place pile construction, characterized in that: The over-filling detection calculation system for bored pile construction runs in any computing device such as a desktop computer, a laptop computer or a cloud data center. The computing device includes: a processor, a memory and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps in the over-filling detection calculation method for bored pile construction as described in any one of claims 1 to 6 are implemented.

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