Intelligent Induction-Based Building Foundation Pile Defect Detection Method and System

Abstract

The present invention discloses a method and system for detecting defects in building foundation piles based on intelligent sensing, which relates to the field of sensing technology. The present invention includes training a model for predicting foundation pile defects; clustering the environmental characteristic data of the foundation piles to obtain multiple environmental clustering clusters; calculating the defect response degree and defect influence factor for each environmental clustering cluster; collecting the sensor data of the foundation piles to be detected, the foundation pile environmental data sequence, and the foundation pile defect criterion table; inputting the sensor data of the actual foundation piles into the defect prediction model to obtain the foundation pile defect prediction value; calculating the environmental clustering cluster to which each foundation pile environmental data sequence belongs, and collecting the corresponding defect response degree and defect influence factor; calculating the influence range and severity of the foundation pile defects based on the sensor data of the foundation piles and the defect response degree and influence factor of the environmental clustering clusters; and judging the bearing capacity and integrity of the foundation piles based on the foundation pile defect criterion table, the foundation pile defect prediction value, and the defect influence range and severity.

Description

Technical Field

[0001] The present invention belongs to the technical field of sensing, and particularly relates to a method and system for detecting defects in building foundation piles based on intelligent sensing. Background Art

[0002] The method for detecting defects in building foundation piles based on intelligent sensing is a new type of detection technology that integrates sensing technology, signal processing technology, artificial intelligence, and Internet of Things technology. This technology aims to achieve precise and efficient detection of building foundation pile defects through intelligent means, providing a scientific basis for the safety assessment and maintenance of engineering structures.

[0003] Traditional detection methods include static load tests and core drilling methods. Traditional detection methods have limitations in terms of detection accuracy and efficiency, and it is difficult to meet the high requirements of modern engineering for the quality detection of foundation piles; although certain progress has been made in existing intelligent detection technologies, they are often limited to a single data processing or analysis link, lacking a systematic intelligent solution; some detection methods such as core drilling methods are destructive and costly; while static load tests have high requirements for the site and may have a certain impact on the environment; existing technologies still have deficiencies in the identification of defect types, and it is difficult to achieve precise classification and positioning of foundation pile defects. Summary of the Invention

[0004] In order to overcome the above-mentioned shortcomings and deficiencies of the existing technology, the first object of the present invention is to provide a method for detecting defects in building foundation piles based on intelligent sensing; the second object of the present invention is to provide a system for detecting defects in building foundation piles based on intelligent sensing.

[0005] The first object of the present invention adopts the following technical solution:

[0006] A method for detecting defects in building foundation piles based on intelligent sensing, comprising the following steps:

[0007] Step 1: Conduct X sample collection experiments. During each sample collection experiment, collect sensor data of the foundation pile, foundation pile defect label data, foundation pile environmental characteristic data, and foundation pile defect regression training data; where X is the number of times the selected sample collection experiment is carried out;

[0008] Step 2: Use the sensor data of the foundation pile as the input and the foundation pile defect label data as the output to train a defect prediction model for predicting foundation pile defects;

[0009] Step 3: Based on the foundation pile environmental characteristic data, use a clustering algorithm to cluster the foundation pile environments of the X sample collection experiments to obtain N environmental clustering clusters; N is the preset number of environmental clustering clusters;

[0010] Step 4: Based on the pile foundation defect regression training data, calculate the corresponding defect response degrees and defect influence factors for each environmental clustering cluster;

[0011] Step 5: Collect the sensor data of the pile foundation to be detected, the pile foundation environmental data sequence, and the pile foundation defect criterion table;

[0012] Step 6: Input the sensor data of the actual pile foundation into the defect prediction model to obtain the pile foundation defect prediction value;

[0013] Step 7: Calculate the environmental clustering cluster to which each pile foundation environmental data sequence belongs, and collect the corresponding defect response degrees and defect influence factors of the belonging environmental clustering cluster;

[0014] Step 8: Based on the sensor data of the pile foundation and the defect response degrees and defect influence factors of each pile foundation environmental data sequence, calculate the influence range and severity of the pile foundation defect;

[0015] Step 9: Based on the pile foundation defect criterion table, the pile foundation defect prediction value, and the influence range and severity of the pile foundation defect, judge the bearing capacity and integrity of the pile foundation.

[0016] Preferably, the experimental method of the sample collection experiment is as follows:

[0017] Select some pile foundations as experimental pile foundations and conduct loading tests with different loading parameters; the loading parameters include the loaded load weight and the distance between the loading point and the pile foundation center.

[0018] Preferably, the method for collecting the sensor data of the pile foundation, the pile foundation defect label data, the pile foundation environmental characteristic data, and the pile foundation defect regression training data is as follows:

[0019] In each sample collection experiment, collect the loading parameters used for loading, the pile foundation material, the pile foundation type, the pile foundation length, the pile foundation width, and the pile foundation slope to form the sensor data of the pile foundation;

[0020] In each sample collection experiment, collect the defect types and positions generated by the pile foundation when the pile foundation is loaded as the pile foundation defect label data;

[0021] In each sample collection experiment, within a range of M meters around the pile foundation, select p sets of monitoring point sets, each set of monitoring point sets includes q monitoring points, and the distances between the monitoring points in each set of monitoring point sets are different; where M, p, and q are preset coefficients respectively;

[0022] During the sample collection experiment, collect the vibration speed at the position of each monitoring point when the monitoring point receives vibration;

[0023] Take the vibration velocity of each monitoring point, the distance from the monitoring point to the loading position, and the load weight used for loading as a set of regression training samples, and all the regression training samples serve as the pile foundation defect regression training data for this sample collection experiment;

[0024] Take the pile foundation environmental characteristic data within M meters around the pile foundation as the pile foundation environmental characteristic data.

[0025] Preferably, the method for training a defect prediction model for predicting pile foundation defects is as follows:

[0026] Take the pile foundation sensor data of each sample collection experiment as the input of the defect prediction model. The defect prediction model takes the pile foundation defect prediction value corresponding to each sample collection experiment as the output, uses the pile foundation defect label data of each sample collection experiment as the prediction target, takes the difference between the pile foundation defect prediction value and the pile foundation defect label data as the prediction error, and takes minimizing the sum of prediction errors as the training target; train the defect prediction model until the sum of prediction errors reaches convergence and then stop training.

[0027] Preferably, the method for using a clustering algorithm to cluster the pile foundation environments of X sample collection experiments to obtain N environmental clustering clusters is as follows:

[0028] Take the pile foundation environmental characteristic data of each sample collection experiment as a data point, and each dimensional coordinate of the data point corresponds to an environmental parameter in the environmental data;

[0029] Use a clustering algorithm to divide the data points of all sample collection experiments into N environmental clustering clusters.

[0030] Preferably, the method for calculating the corresponding defect responsiveness and defect influence factor for each environmental clustering cluster is as follows:

[0031] Group the pile foundation defect regression training data of the sample collection experiment according to the clustering results of the environmental clustering clusters to obtain N groups of pile foundation defect regression training data groups;

[0032] For each environmental clustering cluster:

[0033] Randomly select a set of monitoring points from the p sets of monitoring point sets of each sample collection experiment. Combine all the regression training samples in the selected sets of monitoring points to form a set of regression training data, and collect p times to generate p sets of regression training data;

[0034] Perform regression analysis on each set of regression training data using the least squares method to obtain the corresponding defect responsiveness and defect influence factor;

[0035] Take the average value of the defect responsiveness corresponding to all sets of regression training data of this environmental clustering cluster as the defect responsiveness of this environmental clustering cluster;

[0036] Take the average value of the defect impact factors corresponding to all the group regression training data of the environmental clustering cluster as the defect impact factor of the environmental clustering cluster.

[0037] Preferably, the method for collecting the sensor data of the pile to be detected and the sequence of pile environment data is as follows:

[0038] Collect the loading parameters planned for the pile to be detected and the pile material, pile type, pile length, pile width, and pile slope of the pile to be detected to form the sensor data of the pile.

[0039] Connect the position where the planned load is applied and the position of the protected object, and collect the sequence of different pile environment types passed from the position where the planned load is applied to the position of the protected object. Combine the environmental characteristic data and the vibration transmission distance of each area where the pile environment type is located to form a set of pile environment data, and arrange the pile environment data in the order of the pile environment type to obtain the sequence of pile environment data; the vibration transmission distance is the length of the connection line between the position where the planned load is applied and the position of the protected object in the area where the pile environment type is located.

[0040] Preferably, the method for calculating the belonging environmental clustering cluster of each sequence of pile environment data and collecting the defect response degree and defect impact factor corresponding to the belonging environmental clustering cluster is as follows:

[0041] Number the pile environment data in the sequence of the pile environment data sequence, and mark the number as i.

[0042] Calculate the distance between the environmental characteristic data of the i-th group of pile environment data and the center point of each environmental clustering cluster, and take the environmental clustering cluster with the closest distance as the belonging environmental clustering cluster of the pile environment data sequence. Mark the defect response degree and defect impact factor corresponding to the belonging environmental clustering cluster as Si and Fi respectively.

[0043] Preferably, the method for calculating the influence range and severity of the pile defect is as follows:

[0044] Mark the vibration transmission distance of the i-th group of pile environment data as Ri;

[0045] Mark the defect severity when the pile defect reaches the position where the i-th pile environment type is located as Di;

[0046] Mark the load weight of the planned load as Q;

[0047] For i = 1, that is, the first pile environment type, calculate the defect severity D1 of the first pile environment type according to the defect response degree and defect impact factor; the calculation formula of D1 is as follows:

[0048]

[0049] Among them, S1 is the defect responsiveness of the first pile foundation environmental type; Q is the load weight planned to be loaded; R1 is the vibration transmission distance of the first group of pile foundation environmental data; α is the defect influence factor;

[0050] Then for any i > 1, calculate the defect severity Di of the i-th pile foundation environmental type; the calculation formula of Di is as follows:

[0051]

[0052] Among them, Si is the defect responsiveness of the i-th pile foundation environmental type; D(i - 1) is the defect severity of the previous pile foundation environmental type; Ri is the vibration transmission distance of the i-th group of pile foundation environmental data; α is the defect influence factor;

[0053] Then the final severity DI of the pile foundation defect is the defect severity of the last pile foundation environmental type; DI = Di; where I is the number of elements in the pile foundation environmental data sequence.

[0054] The second object of the present invention adopts the following technical solution:

[0055] A building pile foundation defect detection system based on intelligent induction is used to implement a building pile foundation defect detection method based on intelligent induction, including

[0056] A sample collection module: used to conduct multiple sample collection experiments to collect various data of the pile foundation; a defect prediction model training module: based on the collected sample data, train a model for predicting pile foundation defects; an environmental clustering module: cluster the pile foundation environmental characteristic data to obtain multiple environmental clustering clusters; a defect responsiveness and influence factor calculation module: calculate the defect responsiveness and defect influence factor for each environmental clustering cluster; a data collection module: collect the sensor data of the pile foundation to be detected, the pile foundation environmental data sequence, and the pile foundation defect criterion table; a defect prediction module: input the sensor data of the actual pile foundation into the defect prediction model to obtain the pile foundation defect prediction value; an environmental clustering matching module: calculate the belonging environmental clustering cluster of each pile foundation environmental data sequence, and collect the corresponding defect responsiveness and defect influence factor; a defect influence range and severity calculation module: based on the sensor data of the pile foundation and the defect responsiveness and influence factor of the environmental clustering cluster, calculate the influence range and severity of the pile foundation defect; a pile foundation status evaluation module: based on the pile foundation defect criterion table, the pile foundation defect prediction value, and the defect influence range and severity, judge the bearing capacity and integrity of the pile foundation.

[0057] In summary, due to adopting the above technical solution, the beneficial effects of the present invention are:

[0058] 1. The present invention uses a clustering algorithm to cluster the pile foundation environmental characteristic data, obtaining multiple environmental clustering clusters, which helps to identify the pile foundation defect patterns under different environmental conditions and improves the adaptability and generalization ability of the model. Based on the pile foundation defect regression training data, the least squares method is used for regression analysis to calculate the defect response degree and defect influence factor of each environmental clustering cluster, quantifying the influence of pile foundation defects under different environmental conditions and providing a scientific basis for subsequent evaluation and prediction.

[0059] 2. The present invention arranges the pile foundation environmental data in sequence to form a pile foundation environmental data sequence. The orderly data processing can more accurately reflect the defect development of the pile foundation under different environmental conditions and improve the accuracy of evaluation. Based on the sensor data of the pile foundation and the defect response degree and influence factor of the environmental clustering cluster, the influence range and severity of the pile foundation defect are calculated. By quantifying the influence range and severity of the defect, the health status of the pile foundation can be evaluated more intuitively.

[0060] 3. The present invention combines the pile foundation defect criterion table, the pile foundation defect prediction value, and the defect influence range and severity to comprehensively evaluate the bearing capacity and integrity of the pile foundation. The comprehensive evaluation method provides comprehensive pile foundation status information and improves the safety of the project. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0062] Figure 1 Shows the flowchart of the method for detecting pile foundation defects of a building based on intelligent induction according to the present invention;

[0063] Figure 2 Shows the block diagram of the system for detecting pile foundation defects of a building based on intelligent induction according to the present invention;

[0064] Figure 3 Shows the flowchart of calculating the defect response degree and defect influence factor according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0065] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0066] In addition, the described features, structures, or characteristics may be combined in any suitable manner in one or more example embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the example embodiments of the present disclosure. However, those skilled in the art will realize that one or more of the specific details may be omitted to practice the technical solutions of the present disclosure, or other methods, components, steps, etc. may be adopted. In other cases, well-known structures, methods, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0067] Example 1:

[0068] Refer to Figure 1 As shown, the method for detecting defects in building foundation piles based on intelligent sensing in this embodiment is as follows:

[0069] Step 1: Conduct A sample collection experiments. During each sample collection experiment, collect the sensor data of the foundation pile, the defect label data of the foundation pile, the environmental characteristic data of the foundation pile, and the defect regression training data of the foundation pile; where A is the number of times of the selected sample collection experiments.

[0070] During each sample collection experiment, collect the loading parameters used for loading, the material of the foundation pile, the type of the foundation pile, the length of the foundation pile, the width of the foundation pile, and the slope of the foundation pile to form the sensor data of the foundation pile;

[0071] During each sample collection experiment, collect the defect type and location generated by the foundation pile when loading the foundation pile as the defect label data of the foundation pile;

[0072] During each sample collection experiment, within a range of X meters around the foundation pile, select p sets of monitoring point sets. Each set of monitoring point sets includes q monitoring points, and the distances between the monitoring points in each set of monitoring point sets and the foundation pile are different; where X, p, and q are preset coefficients respectively;

[0073] During the sample collection experiment, collect the vibration velocity at the location of each monitoring point when the monitoring point receives vibration;

[0074] Take the vibration velocity of each monitoring point, the distance between the monitoring point and the loading position, and the load weight used for loading as a set of regression training samples, and all the regression training samples as the defect regression training data of the foundation pile for this sample collection experiment;

[0075] Take the environmental characteristic data of the foundation pile within a range of X meters around the foundation pile as the environmental characteristic data of the foundation pile.

[0076] Select some foundation piles as experimental foundation piles and conduct loading tests with different loading parameters; the loading parameters include the load weight for loading and the distance between the loading point and the center of the foundation pile.

[0077] Step 2: Using the sensor data of the foundation pile as the input and the foundation pile defect label data as the output, train a defect prediction model for predicting foundation pile defects.

[0078] The method for training a defect prediction model for predicting foundation pile defects is as follows:

[0079] Use the foundation pile sensor data of each sample collection experiment as the input of the defect prediction model. The defect prediction model takes the foundation pile defect prediction value corresponding to each sample collection experiment as the output, uses the foundation pile defect label data of each sample collection experiment as the prediction target, uses the difference between the foundation pile defect prediction value and the foundation pile defect label data as the prediction error, and uses minimizing the sum of prediction errors as the training target; train the defect prediction model until the sum of prediction errors converges and then stop training.

[0080] Step 3: Based on the foundation pile environment feature data, use the clustering algorithm to cluster the foundation pile environments of A sample collection experiments to obtain N environmental clustering clusters; N is the preset number of environmental clustering clusters;

[0081] The method for using the clustering algorithm to cluster the foundation pile environments of A sample collection experiments to obtain N environmental clustering clusters is as follows:

[0082] Use the foundation pile environment feature data of each sample collection experiment as a data point, and each dimension coordinate of the data point corresponds to an environmental parameter in the environmental data;

[0083] Use the clustering algorithm to divide the data points of all sample collection experiments into N environmental clustering clusters.

[0084] Step 4: Based on the foundation pile defect regression training data, calculate the corresponding defect response degree and defect influence factor for each environmental clustering cluster;

[0085] Refer to Figure 3 As shown, the method for calculating the corresponding defect response degree and defect influence factor for each environmental clustering cluster is as follows:

[0086] Group the foundation pile defect regression training data of the sample collection experiment according to the clustering results of the environmental clustering clusters to obtain N groups of foundation pile defect regression training data groups;

[0087] For each environmental clustering cluster:

[0088] Randomly select a set of monitoring point sets from the p sets of monitoring point sets of each sample collection experiment. Combine all the regression training samples in the selected monitoring point sets to form a set of regression training data, and collect p times in total to generate p sets of regression training data;

[0089] Perform regression analysis on each set of regression training data using the least squares method to obtain the corresponding defect responsiveness and defect impact factor;

[0090] Take the average value of the defect responsiveness corresponding to all sets of regression training data of this environmental clustering cluster as the defect responsiveness of this environmental clustering cluster;

[0091] Take the average value of the defect impact factors corresponding to all sets of regression training data of this environmental clustering cluster as the defect impact factor of this environmental clustering cluster.

[0092] Step Five: Collect the sensor data of the pile to be detected, the pile environment data sequence, and the pile defect criterion table;

[0093] The methods for collecting the sensor data of the pile to be detected and the pile environment data sequence are as follows:

[0094] Collect the loading parameters planned for the pile to be detected and the pile material, pile type, pile length, pile width, and pile slope of the pile to be detected to form the sensor data of the pile;

[0095] Connect the position of the planned load to the position of the protected object, collect the order of different pile environment types passed from the position of the planned load to the position of the protected object, form a set of pile environment data with the environmental characteristic data and vibration transmission distance of the area where each pile environment type is located, and arrange the pile environment data in the order of the pile environment type to obtain the pile environment data sequence; the vibration transmission distance is the length of the line connecting the position of the planned load to the position of the protected object in the area where this pile environment type is located.

[0096] Step Six: Input the sensor data of the actual pile into the defect prediction model to obtain the pile defect prediction value;

[0097] Step Seven: Calculate the environmental clustering cluster to which each pile environment data sequence belongs, and collect the defect responsiveness and defect impact factor corresponding to the environmental clustering cluster;

[0098] The methods for calculating the environmental clustering cluster to which each pile environment data sequence belongs and collecting the defect responsiveness and defect impact factor corresponding to the environmental clustering cluster are as follows:

[0099] Number the pile environment data in the pile environment data sequence in the sequence order, and mark the number as i, i = 1, 2, 3,... 3, i.

[0100] Calculate the distance between the environmental characteristic data of the i-th group of pile environment data and the center point of each environmental clustering cluster, take the environmental clustering cluster with the closest distance as the environmental clustering cluster to which this pile environment data sequence belongs, and mark the defect responsiveness and defect impact factor corresponding to this environmental clustering cluster as Si and Fi respectively.

[0101] Step Eight: Calculate the influence range and severity of the pile foundation defect based on the sensor data of the pile foundation and the defect response degrees and defect influence factors of each pile foundation environment data sequence.

[0102] The method for calculating the influence range and severity of the pile foundation defect is as follows:

[0103] Mark the vibration transmission distance of the i-th group of pile foundation environment data as Ri.

[0104] Mark the defect severity when the pile foundation defect reaches the position of the i-th pile foundation environment type as Di.

[0105] Mark the planned load weight as Q.

[0106] For i = 1, that is, the first pile foundation environment type, calculate the defect severity D1 of the first pile foundation environment type according to the defect response degree and defect influence factor. The calculation formula of D1 is as follows:

[0107]

[0108] Where, S1 is the defect response degree of the first pile foundation environment type; Q is the planned load weight; R1 is the vibration transmission distance of the first group of pile foundation environment data; α is the defect influence factor.

[0109] Then for any i > 1, calculate the defect severity Di of the i-th pile foundation environment type. The calculation formula of Di is as follows:

[0110]

[0111] Where, Si is the defect response degree of the i-th pile foundation environment type; D(i - 1) is the defect severity of the previous pile foundation environment type; Ri is the vibration transmission distance of the i-th group of pile foundation environment data; α is the defect influence factor.

[0112] Then the final severity DI of the pile foundation defect is the defect severity of the last pile foundation environment type; DI = Di; where, I is the number of elements in the pile foundation environment data sequence.

[0113] Step Nine: Judge the bearing capacity and integrity of the pile foundation based on the pile foundation defect criterion table, the pile foundation defect prediction value, and the influence range and severity of the pile foundation defect.

[0114] Generate a detailed pile foundation defect report according to the detection results, including information such as defect type, location, severity, etc., provide visual detection results, analyze the defect causes, provide maintenance suggestions, record and track the maintenance history of the pile foundation.

[0115] The beneficial effects of this embodiment are as follows: improving the detection accuracy and efficiency, realizing the intelligent prediction and evaluation of pile foundation defects; adapting to the detection requirements under different environmental conditions through cluster analysis; providing detailed defect reports and maintenance suggestions, and providing a scientific basis for the safe use and maintenance of pile foundations.

[0116] Embodiment 2:

[0117] Refer to Figure 2 As shown, the intelligent induction-based building pile foundation defect detection system of this embodiment includes a sample collection module, a defect prediction model training module, an environmental clustering module, a defect response degree and influence factor calculation module, a data collection module, a defect prediction module, an environmental clustering matching module, a defect influence range and severity calculation module, and a pile foundation status evaluation module.

[0118] Sample collection module: used to conduct multiple sample collection experiments and collect various data of the pile foundation.

[0119] Defect prediction model training module: based on the collected sample data, train a model for predicting pile foundation defects.

[0120] Environmental clustering module: cluster the pile foundation environmental characteristic data to obtain multiple environmental clustering clusters.

[0121] Defect response degree and influence factor calculation module: calculate the defect response degree and defect influence factor for each environmental clustering cluster.

[0122] Data collection module: collect the sensor data of the pile foundation to be detected, the pile foundation environmental data sequence, and the pile foundation defect criterion table.

[0123] Defect prediction module: input the sensor data of the actual pile foundation into the defect prediction model to obtain the pile foundation defect prediction value.

[0124] Environmental clustering matching module: calculate the environmental clustering cluster to which each pile foundation environmental data sequence belongs, and collect the corresponding defect response degree and defect influence factor.

[0125] Defect influence range and severity calculation module: based on the sensor data of the pile foundation, the defect response degree and influence factor of the environmental clustering cluster, calculate the influence range and severity of the pile foundation defect.

[0126] Pile foundation status evaluation module: based on the pile foundation defect criterion table, the pile foundation defect prediction value, and the defect influence range and severity, judge the bearing capacity and integrity of the pile foundation.

[0127] The beneficial effects of this embodiment are as follows: realizing the intelligent detection and evaluation of pile foundation defects, improving the detection accuracy and efficiency; facilitating system maintenance and upgrade through modular design; providing a scientific basis for the safe use and maintenance of pile foundations, reducing safety risks, and extending the service life of pile foundations.

[0128] The impact factor of the present invention is used to measure the degree of influence of different factors or variables on a certain result or decision. The definition of the impact factor refers to the value assigned to each factor when comparing and evaluating multiple factors, in order to reflect its importance or priority. These impact factors can be determined according to specific circumstances and requirements, and are usually jointly formulated and confirmed by professionals or relevant stakeholders. By reasonably setting the impact factors, it can help the program or system make more accurate decisions or predictions.

[0129] As described above, it is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent substitutions or changes, shall be covered by the protection scope of the present invention.

[0130] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only the specific embodiments. Obviously, according to the content of this specification, many modifications and changes can be made. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A building foundation pile defect detection method based on intelligent sensing, characterized in that: The following steps are involved: Step 1: Conduct X sample collection experiments. During each sample collection experiment, collect sensor data of pile foundations, pile foundation defect label data, pile foundation environmental feature data, and pile foundation defect regression training data; where X is the number of sample collection experiments selected; Step 2: Using the sensor data of the pile as input and the label data of the pile defects as output, a defect prediction model for predicting pile defects is trained; Step 3: Based on the pile environment characteristic data, use a clustering algorithm to cluster the pile environments of the X sample collection experiments to obtain N environmental clusters; N is the number of preset environmental clusters; Step 4: Based on the pile defect regression training data, the corresponding defect response and defect impact factor are calculated for each environmental cluster; Step 5: Collect sensor data of the pile to be tested, pile environment data sequence and pile defect criterion table; Step 6: Input the sensor data of the actual pile foundation into the defect prediction model to obtain the pile foundation defect prediction value; Step 7: Calculate and obtain the environmental cluster to which each pile environmental data sequence belongs, and collect the defect response and defect influencing factor corresponding to the environmental cluster; Step 8: Based on the sensor data of the piles and the defect responsiveness and defect impact factor of each pile environment data sequence, calculate the impact range and severity of the pile defects; Step 9: Based on the pile defect criterion table, the pile defect prediction value, and the impact range and severity of the pile defect, determine the bearing capacity and integrity of the pile; The method for calculating the corresponding defect responsiveness and defect impact factor for each environmental cluster is as follows: The pile defect regression training data of the sample collection experiment are grouped according to the clustering results of the environmental clustering clusters to obtain N groups of pile defect regression training data groups; For each environment cluster: A set of monitoring points is randomly selected from the p sets of monitoring points in each sample collection experiment, and all regression training samples in all the selected monitoring point sets are combined to form a set of regression training data, which is collected p times in total to generate p sets of regression training data; each set of regression training data is subjected to regression analysis using the least squares method to obtain the corresponding defect responsiveness and defect impact factor; the average value of the defect responsiveness corresponding to all the groups of regression training data of the environmental cluster cluster is taken as the defect responsiveness of the environmental cluster cluster; the average value of the defect impact factor corresponding to all the groups of regression training data of the environmental cluster cluster is taken as the defect impact factor of the environmental cluster cluster.

2. A building foundation pile defect detection method based on intelligent sensing according to claim 1, characterized in that: The experimental method of the sample collection experiment is: Some foundation piles are selected as experimental foundation piles, and loading tests are performed using different loading parameters; the loading parameters include the loaded load weight and the distance between the loading point and the center of the foundation pile.

3. A building foundation pile defect detection method based on intelligent sensing according to claim 1, characterized in that: The method of collecting the sensor data of the pile foundation, the label data of the pile foundation defect, the characteristic data of the pile foundation environment and the regression training data of the pile foundation defect is as follows: In each sample collection experiment, the sensor data of the piles, which consist of the loading parameters, pile material, pile type, pile length, pile width, and pile slope, are collected; In each sample collection experiment, the defect types and locations of the piles generated when the piles are loaded are collected as pile defect label data; In each sample collection experiment, p groups of monitoring points are selected within the range of M meters around the foundation pile. Each group of monitoring points includes q monitoring points, and the distances between the monitoring points in each group of monitoring points and the foundation pile are different. During the sample collection experiment, the vibration speed at the location of each monitoring point when the monitoring point receives vibration is collected; The vibration velocity of each monitoring point, the distance between the monitoring point and the loading position, and the load weight used for loading are taken as a set of regression training samples, and all regression training samples are used as the regression training data of the pile defects in this sample collection experiment; The pile foundation environment characteristic data within a range of M meters around the pile foundation is taken as the pile foundation environment characteristic data.

4. The method for detecting building foundation pile defects based on intelligent sensing according to claim 1, characterized in that: The method of training the defect prediction model for predicting pile defects is as follows: The pile sensor data of each sample collection experiment is used as the input of the defect prediction model, the defect prediction model uses the pile defect prediction value corresponding to each sample collection experiment as the output, the pile defect label data of each sample collection experiment as the prediction target, the difference between the pile defect prediction value and the pile defect label data as the prediction error, and minimizing the sum of the prediction errors as the training target; The defect prediction model is trained until the sum of the prediction errors reaches convergence and then the training is stopped.

5. The method for detecting building foundation pile defects based on intelligent sensing according to claim 1, characterized in that: The method of clustering the pile environment of the X-time sample collection experiment using the clustering algorithm to obtain N environment clusters is as follows: The environmental characteristic data of the foundation pile in each sample collection experiment is taken as a data point, and each dimensional coordinate of the data point corresponds to an environmental parameter in the environmental data; the data points of all sample collection experiments are divided into N environmental clusters using a clustering algorithm.

6. The method for detecting building foundation pile defects based on intelligent sensing according to claim 1, characterized in that: The method of collecting the sensor data of the pile to be detected and the pile environment data sequence is: Collecting the sensor data of the piles including the loading parameters planned for the piles to be tested and the pile material, pile type, pile length, pile width and pile slope of the piles to be tested; Connect the planned loading position and the protection object position, collect the order of different pile environment types from the planned loading position to the protection object position, combine the environmental characteristic data and vibration transmission distance of the area where each pile environment type is located to form a group of pile environment data, arrange the pile environment data in the order of pile environment types, and obtain a pile environment data sequence; the vibration transmission distance is the length of the line connecting the planned loading position and the protection object position in the area where the pile environment type is located.

7. A building foundation pile defect detection method based on intelligent sensing according to claim 6, characterized in that: The method of calculating and obtaining the environment cluster to which each pile environment data sequence belongs and collecting the defect responsiveness and defect impact factor corresponding to the environment cluster is as follows: The pile environment data in the pile environment data sequence are numbered in sequence order, and the number is marked as i; The distance between the environmental characteristic data of the i-th group of foundation pile environmental data and the center point of each environmental cluster is calculated, and the environmental cluster with the closest distance is taken as the environmental cluster to which the foundation pile environmental data sequence belongs, and the defect responsiveness and defect influencing factor corresponding to the environmental cluster are marked as Si and Fi respectively.

8. The method for detecting building foundation pile defects based on intelligent sensing according to claim 7, characterized in that: The method for calculating the scope and severity of the pile foundation defects is as follows: The vibration transmission distance of the i-th group of pile environment data is marked as Ri; The severity of the pile defect when it reaches the location of the i-th pile environment type is marked as Di; Mark the planned load weight as Q; For i=1, i.e. the first pile environment type, the defect severity D1 of the first pile environment type is calculated based on the defect responsiveness and defect impact factor; the calculation formula of D1 is as follows: Among them, S1 is the defect responsiveness of the first pile environment type; Q is the planned load weight; R1 is the vibration transmission distance of the first group of pile environment data; α is the defect influence factor; For any i>1, calculate the defect severity Di of the i-th pile environment type; the calculation formula of Di is as follows: Wherein, Si is the defect response of the i-th pile environment type; D(i-1) is the defect severity of the previous pile environment type; Ri is the vibration transmission distance of the i-th group of pile environment data; α is the defect influence factor; Then the final severity DI of the pile defect is the defect severity of the last pile environment type; DI=Di; wherein I is the number of elements in the pile environment data sequence.

9. A building foundation pile defect detection system based on intelligent sensing, used to implement a building foundation pile defect detection method based on intelligent sensing as claimed in claim 1, characterized in that: System includes Sample collection module: used to conduct multiple sample collection experiments and collect various data of pile foundations; defect prediction model training module: based on the collected sample data, a model for predicting pile foundation defects is trained; environmental clustering module: the environmental characteristic data of pile foundations are clustered to obtain multiple environmental clustering clusters; defect responsiveness and influencing factor calculation module: the defect responsiveness and defect influencing factor are calculated for each environmental clustering cluster; data collection module: the sensor data of the pile foundation to be detected, the pile foundation environmental data sequence and the pile foundation defect criterion table are collected; defect prediction module: the sensor data of the actual pile foundation is input into the defect prediction model to obtain the pile foundation defect prediction value; environmental cluster matching module: the environmental clustering cluster to which each pile foundation environmental data sequence belongs is calculated, and the corresponding defect responsiveness and defect influencing factor are collected; defect impact range and severity calculation module: the impact range and severity of pile foundation defects are calculated based on the sensor data of the pile foundation and the defect responsiveness and influencing factor of the environmental clustering cluster; pile foundation state assessment module: the bearing capacity and integrity of the pile foundation are judged based on the pile foundation defect criterion table, the pile foundation defect prediction value and the defect impact range and severity.

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