A multi-objective fuzzy comprehensive pile construction quality analysis method and device

Abstract

The present application discloses a multi-objective fuzzy comprehensive pile construction quality analysis method and device, the method includes: determining the key indicators of the pile construction process, and obtaining the monitoring values ​​of each key indicator of the pile construction process; constructing a pile construction process quality evaluation index system based on the key indicators; wherein the quality evaluation index system includes a project layer and an indicator layer; according to the monitoring values ​​of each key indicator and the corresponding construction specification value, the absolute value gray approximation correlation method is used to calculate the correlation of each key indicator; according to the correlation of the quality evaluation index system and each key indicator, the AHP-entropy weight combination weighting method is used to calculate the combined weight of each key indicator; according to the combined weight of each key indicator, the fuzzy comprehensive evaluation method is used to calculate the comprehensive evaluation vector, and the quality of the pile construction process is determined according to the comprehensive evaluation vector. The quality evaluation analysis of the pile construction process is realized, and the quality of pile construction is effectively improved.

Description

Technical Field

[0001] The present application belongs to the technical field of pile foundation construction quality, and specifically relates to a multi-objective fuzzy comprehensive pile foundation construction quality analysis method and device. Background Art

[0002] Pile foundation engineering is the foundation of construction engineering and the foundation of the entire construction project, playing a vital role. Common pile construction methods include cement soil mixing piles, bored piles, high-pressure rotary jet piles, etc. At present, the most widely used pile quality detection methods include drilling coring method, excavation test method, ground penetrating radar method, high-density electrical method and Rayleigh wave method. Most of the detection lags behind the construction. Once the quality is found to be unqualified during the detection, it is difficult to take economical and reasonable remedial measures, which seriously affects the construction progress and quality.

[0003] The patent with publication number CN 118390592 A discloses a construction method for dynamically monitoring the quality of high-pressure rotary jet piles. The rotary jet operation is performed according to the construction plan. When the rotary jet slurry is transported to the surface, a first high-precision peristaltic pump is used to quantitatively transport the return slurry at different depths during the pile construction process to a temperature-measurable reaction container. At the same time, a second high-precision peristaltic pump is used to transport the prepared mixed solution containing sodium hydroxide, ammonium chloride and calcium red indicator to the temperature-measurable reaction container according to a set ratio. After mixing for a set time, a third high-precision peristaltic pump is used to transport an ethylenediaminetetraacetate standard solution to the reaction container. At the same time, a color sensor automatically identifies the RGB value of the color in the container and transmits it to a data analysis system. Once the RGB value reaches the range of (0, 0, 250) to (0, 0, 255), the transportation of the ethylenediaminetetraacetate standard solution is stopped, and the temperature of the mixed solution in the reaction container is recorded. Finally, the soil layer depth, temperature and ethylenediaminetetraacetate standard solution consumption data are transmitted to the data analysis system. The data analysis system automatically calculates the cement content C based on the soil depth, temperature and standard solution value using the constructed machine learning network model. The samples of this patented indoor test deviate from the field environment and its application has limitations.

[0004] The patent with publication number CN 110427633 A discloses a cement mixing pile quality assessment method based on deep reinforcement learning, which includes the following steps: Step 1: Construct a cement mixing pile quality assessment deep reinforcement learning model based on DQN; Step 2: Use the deep reinforcement learning method to train the quality assessment deep network; Step 3: Perform verification tests regularly and calculate the positive and negative recall rates; Step 4: When the positive and negative recall rates tend to be balanced, the model training ends, otherwise go to step 2. Step 5: Use the trained deep reinforcement learning model to perform quality assessment on various cement mixing piles. The present invention takes into account the diversity of factors affecting the quality of cement mixing piles, and incorporates soft soil characteristics, curing agents and construction processes into the model for training, thereby improving the authenticity and reliability of the assessment results. This patent does not take into account construction monitoring data, and evaluates by setting parameters, which cannot reflect the actual construction quality.

[0005] The patent with publication number CN 105155599 B discloses a remote information monitoring and analysis system for cement soil mixing piles, which consists of a field data acquisition module, a control module, a server module, a display module, a storage module, an alarm module and a signal transmission module. The control module is respectively linked to the field data acquisition module, the display module, the signal transmission module and the alarm module to control the function realization of each module. Through accurate monitoring during the construction process, a construction analysis report of the project site is generated in time, and the quality of the on-site construction is evaluated. This patent does not perform error and weight analysis on the monitoring value, and the reliability of the evaluation result is insufficient.

[0006] Therefore, none of the above existing technologies can make a relatively comprehensive and reliable assessment of the quality of pile foundation construction, either because the assessment method has limitations or because the monitoring data is not fully utilized. It is urgent to propose a method for assessing construction quality based on real-time monitoring data. Summary of the invention

[0007] Purpose: In view of at least one of the above technical problems, the present application provides a multi-objective fuzzy comprehensive pile foundation construction quality analysis method and device to achieve quality evaluation of the pile foundation construction process and effectively improve the pile foundation construction quality.

[0008] The technical solution adopted in this application is:

[0009] In a first aspect, the present application provides a method for analyzing the quality of pile foundation construction, comprising:

[0010] S1. Determine the key indicators of the pile foundation construction process and obtain the monitoring values ​​of each key indicator of the pile foundation construction process;

[0011] S2. Constructing a quality evaluation index system for the pile foundation construction process based on the key indicators; wherein the quality evaluation index system includes a project layer and an index layer, the pile foundation construction process project layer is divided into pile body structure B1, pile position deviation B2 and mud index B3, and the key indicators are correspondingly divided into several index layer indicators under the project layer;

[0012] S3. According to the monitoring values ​​of each key indicator and the corresponding construction specification values, the correlation degree of each key indicator is calculated by taking the absolute value grey approximate correlation method;

[0013] S4. According to the correlation between the quality assessment index system and each key indicator, the combined weight of each key indicator is calculated by using the AHP-entropy weight combination weighting method;

[0014] S5. According to the combined weights of the key indicators, a fuzzy comprehensive evaluation method is used to calculate a comprehensive evaluation vector, and the quality of the pile construction process is determined according to the comprehensive evaluation vector.

[0015] In some embodiments, the key indicators include drilling depth, verticality, pile position deviation, mud viscosity, and mud density in bored pile construction.

[0016] On this basis, the key indicators are further divided into several indicator-level indicators under the project level, including:

[0017] Pile body structure B1= (drilling depth C1, verticality C2), pile position deviation B2= (pile position deviation C3), mud index B3= (mud viscosity C4, mud specific gravity C5).

[0018] In some embodiments, S4, according to the correlation between the quality assessment index system and each key indicator, the combined weight of each key indicator is calculated using the AHP-entropy weight combination weighting method, including:

[0019] S41, constructing a project-level judgment matrix and an indicator-level judgment matrix according to the quality assessment indicator system, and calculating the subjective weight of each key indicator according to the project-level judgment matrix and the indicator-level judgment matrix;

[0020] S42. Use the entropy weight method to calculate the objective weight of each key indicator;

[0021] S43. Based on the subjective weight and objective weight of each key indicator, the combined weight is determined by using the "combined weighting" method.

[0022] In a second aspect, the present application provides a pile foundation construction quality analysis device, including a processor and a storage medium;

[0023] The storage medium is used to store instructions;

[0024] The processor is configured to operate according to the instructions to execute the method according to the first aspect.

[0025] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the method described in the first aspect when executed by a processor.

[0026] In a fourth aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the method of the first aspect when executing the computer program.

[0027] In a fifth aspect, the present application provides a computer program product, comprising a computer program, which implements the method described in the first aspect when executed by a processor.

[0028] Beneficial effect: The multi-objective fuzzy comprehensive pile foundation construction quality analysis method and device provided in this application have the following advantages: the key indicators of the pile foundation construction process are selected to construct the quality evaluation index system of the pile foundation construction process. The pile foundation construction process indicators are divided into three categories, namely, pile body structure B1, pile position deviation B2 and mud index B3. The gray approximate correlation analysis method of taking absolute value is adopted to analyze the fluctuation state of the monitoring data during the construction process, calculate the correlation between the monitoring data of each indicator of the construction process and the numerical value of the construction specification, and evaluate the performance of each indicator during the construction process. The entropy weight method is introduced to calculate the objective weight value of each evaluation index, which is combined with the subjective weight value to determine the combined weight, and the weight coefficient of each indicator is determined by the AHP-entropy weight combination weighting method. The fuzzy comprehensive evaluation method is adopted to realize the comprehensive evaluation of the quality of the pile foundation construction process. The method is convenient, efficient, quantitative and reliable, and realizes the real-time evaluation of the quality of the pile foundation construction process, improves the prediction accuracy of the pile foundation construction risk, ensures the safety of the pile foundation construction risk control, and effectively improves the quality of the pile foundation construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a flowchart of a multi-objective fuzzy comprehensive pile construction quality analysis method according to an embodiment of the present application;

[0030] Figure 2 A schematic diagram of a bored pile construction process quality assessment index system according to an embodiment of the present application;

[0031] Figure 3 Schematic diagram of trapezoidal membership function distribution according to an embodiment of the present application. DETAILED DESCRIPTION

[0032] The present application is further described below in conjunction with the accompanying drawings and embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present application, and cannot be used to limit the protection scope of the present application.

[0033] In the description of this application, "several" means more than one, "more" means more than two, "greater than", "less than", "exceed", etc. are understood to exclude the number itself, and "above", "below", "within", etc. are understood to include the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0034] In the description of the present application, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0035] The term "and / or" is only a description of the association relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " generally indicates that the related objects are in an "or" relationship.

[0036] Embodiment 1: This embodiment provides a multi-objective fuzzy comprehensive pile construction quality analysis method, such as Figure 1 As shown, including:

[0037] S1. Determine the key indicators of the pile foundation construction process and obtain the monitoring values ​​of each key indicator of the pile foundation construction process.

[0038] In some embodiments, the key indicators include drilling depth, verticality, pile position deviation, mud viscosity, and mud density in bored pile construction.

[0039] In this embodiment, key indicators of the pile foundation construction process are selected, corresponding sensing equipment is installed, and an IoT monitoring system for the quality of the pile foundation construction process is established.

[0040] S2. Construct a quality evaluation index system for the pile foundation construction process based on the key indicators, wherein the quality evaluation index system includes a project layer and an indicator layer, the pile foundation construction process project layer is divided into pile body structure B1, pile position deviation B2 and mud index B3, and the key indicators are correspondingly divided into several indicator layer indicators under the project layer.

[0041] In some embodiments, the key indicators are divided into several indicator layer indicators under the project layer, including:

[0042] Pile body structure B1= (drilling depth C1, verticality C2), pile position deviation B2= (pile position deviation C3), mud index B3= (mud viscosity C4, mud specific gravity C5).

[0043] S3. According to the monitoring values ​​of each key indicator and the corresponding construction specification values, the absolute value grey approximate correlation method is used to calculate the correlation of each key indicator.

[0044] In some embodiments, step S3 may include:

[0045] S31. Determine the data sequence: extract the monitoring values ​​obtained at each set time interval (1 minute), assume that indicator i has n monitoring results during the construction of a single pile, and the complete set of monitoring results of indicator i during the construction process constitutes the monitoring sequence X i :

[0046] , (1)

[0047] Each monitoring result Corresponding to a standard value x0(n), the standard sequence X0 is composed of the standard value set:

[0048] , (2)

[0049] S32, according to the monitoring sequence and the standard sequence, the correlation degree is calculated by using the absolute value grey approximate correlation degree method;

[0050] Taking into account the defects that quality indicators should not compensate for each other, abandoning the traditional close correlation model that only considers the area between broken lines, this patent adopts the absolute value area and area difference calculation method. For the four key indicators of verticality, pile position deviation, mud viscosity, and mud specific gravity, the correlation calculation method is: , (3)

[0051] in, is the correlation degree of key indicator i, n is the number of monitoring results, is the number of monitoring times;

[0052] For drilling depth, a key indicator that only focuses on construction results and has nothing to do with the construction process and specifications, the correlation calculation method is: . (4)

[0053] S4. According to the correlation between the quality assessment index system and each key indicator, the combined weight of each key indicator is calculated by using the AHP-entropy weight combination weighting method;

[0054] In some embodiments, step S4 may include:

[0055] S41, constructing a project-level judgment matrix and an indicator-level judgment matrix according to the quality assessment indicator system, and calculating the subjective weight of each key indicator according to the project-level judgment matrix and the indicator-level judgment matrix;

[0056] S42. Use the entropy weight method to calculate the objective weight of each key indicator;

[0057] S43. Based on the subjective weight and objective weight of each key indicator, the combined weight is determined by using the "combined weighting" method.

[0058] Further, in some embodiments, S41, constructing a project-level judgment matrix and an indicator-level judgment matrix according to the quality assessment indicator system, and calculating the subjective weight of each key indicator according to the project-level judgment matrix and the indicator-level judgment matrix, including:

[0059] 1) Construct a judgment matrix: Compare each component pairwise and use relative standards to minimize errors caused by component differences, thereby improving measurement accuracy.

[0060] set up is the comparison result of the importance of index i and index j. For r indicators, the judgment matrix is ​​constructed by the values ​​of each indicator. for:

[0061] , (5)

[0062] The judgment matrix has the following properties:

[0063] 1. 2. ; 3. , where i, j=1,2,3,…,r;

[0064] According to the above principles and hierarchical indicator system, the importance of indicators at each level is compared in pairs, and finally the judgment matrix between elements at each level is obtained.

[0065] The scaling method of the judgment matrix elements is shown in Table 1 below:

[0066] Table 1 Scale division and its meaning of the scaling method

[0067]

[0068] 2) Hierarchical single sorting

[0069] ① Calculate the relative weight of each element in the judgment matrix. Calculate the project-level judgment matrix and weights

[0070] First, normalize each column vector in the item-level judgment matrix to obtain the normalized judgment matrix ; , for Normalized value;

[0071] (6)

[0072] Then, The intermediate parameters are obtained by adding rows :

[0073] (7)

[0074] Weight of project-level indicator e for:

[0075] (8)

[0076] Among them, e is the project level indicator code, ;

[0077] ② Similarly, calculate the indicator layer judgment matrix and indicator layer indicator weights under each project layer indicator

[0078] , (9)

[0079] in: is the weight of the indicator f at the indicator layer, is the weight of the indicator layer indicator f obtained by using the indicator layer judgment matrix, f is the indicator layer indicator code, g is the number of indicator layer indicators under the corresponding project layer indicator, It represents the sum of the weights of the indicator-level indicators under the project-level indicators;

[0080] 3) Total ranking of levels: The subjective weight of the ith indicator is The calculation method is:

[0081] . (10)

[0082] Entropy weight reflects the amount of useful information provided by each indicator for decision-making evaluation. Generally speaking, if the entropy corresponding to the indicator value is smaller, it means that the indicator value has a greater degree of variation, and the impact on the comprehensive evaluation is also greater, and its weight value is also greater; conversely, its weight value is smaller.

[0083] Furthermore, in some embodiments, S42, using an entropy weight method to calculate the objective weight of each key indicator includes:

[0084] According to the correlation of each key indicator, an initial matrix of m foundation piles and r indicators is constructed , and the initial matrix Normalize to get the normalized initial matrix ;

[0085] D pi = [ ρ pi ] p = 1 , 2 , ⋯ , m ; i = 1 , 2 , ⋯ , r (11)

[0086] In the formula is the correlation degree of the ith index of the pth pile foundation ;

[0087] Entropy of the i-th indicator for:

[0088] (12)

[0089] The objective weight of the i-th indicator for:

[0090] (13)

[0091] The calculation of objective weights takes into account the degree of variation of the indicator values ​​and determines their weights from an objective perspective, thereby reducing the impact of subjectivity.

[0092] The "combination weighting" method is used to determine the combination weights in order to avoid the defects of subjective weights and objective weights themselves.

[0093] Further, in some embodiments, S43, according to the subjective weight and objective weight of each key indicator, a "combination weighting" method is used to determine the combination weight, including:

[0094] The combined weight w of the i-th indicator i for:

[0095] (14)

[0096] Where: is the weighted linear combination index, 0≤ ≤1; , are the subjective weight and objective weight of the ith indicator respectively.

[0097] It should be noted that: =0 Only subjective judgment of personnel can be provided during the evaluation; =1 can only provide objective evaluation during evaluation; =0.5, the accuracy of the two is roughly the same; if there is a difference in the accuracy of the two, you can flexibly use .

[0098] S5. According to the combined weights of the key indicators, a fuzzy comprehensive evaluation method is used to calculate a comprehensive evaluation vector, and the quality of the pile construction process is determined according to the comprehensive evaluation vector.

[0099] In some embodiments, step S5 may include:

[0100] (1) Determine the factor set: The correlation between the monitoring value of each key indicator and the standard value is expressed in the form of a set, and the factor set U = (u1, u2, …, u i ), where u i is the correlation degree of the i-th indicator;

[0101] (2) Determine the solution set: Classify the quality of the construction process into excellent, qualified, and unqualified; the solution set V = (v1, v2, v3) = (excellent, qualified, unqualified);

[0102] (3) Establish membership function:

[0103] The relationship between the factor set and the solution set is reflected by the membership function; whether the membership function is selected correctly depends on whether it conforms to the objective law. In this embodiment, a trapezoidal distribution membership function is selected, and the membership function distribution curve is as follows: Figure 3 As shown in the figure, the membership functions of the ith indicator for the three comments in the solution set are:

[0104] excellent (15)

[0105] qualified (16)

[0106] Failure (17)

[0107] in: is the correlation degree of the corresponding indicator, a, b, c and d represent the cutoff values ​​of the membership function distribution, which are obtained from the construction quality control specifications and expert experience. The cutoff values ​​corresponding to different indicators are different;

[0108] According to the above three membership functions, the membership degree of the ith index of the mth pile body to the three elements in the scheme set is obtained, which is recorded as [r i1 ,r i2 ,r i3 ], so the fuzzy comprehensive matrix R of r indicators is expressed as:

[0109] R = [ r 11 r 12 r 13 r 21 r 22 r 23 ⋮ ⋮ ⋮ r r 1 r r 2 r r 3 ] (18)

[0110] (4) Calculate the comprehensive evaluation vector based on the combined weights of each key indicator and the fuzzy comprehensive matrix;

[0111] (19)

[0112] In the formula, is the comprehensive evaluation vector; is the combined weight matrix W of r key indicators = (w1, w2, ..., w r ) T ; " " represents the fuzzy synthesis operator; R is the fuzzy comprehensive matrix of r indicators, It is the degree of membership of the quality of the pile construction process relative to the first, second, and third comments. The comment corresponding to the pile has the largest degree of membership. If h1 is the largest among h1, h2, and h3, it is judged as excellent; if h2 is the largest, it is judged as qualified; if h3 is the largest, it is judged as unqualified.

[0113] In this embodiment, the fuzzy synthesis operator is the M (·, ⊕) operator, which can fully utilize the information of the fuzzy evaluation matrix while reflecting the weight of each indicator, and has a good comprehensive effect.

[0114] Specific application example: A 220kV substation reconstruction project in Nantong, the pile foundation of this project adopts bored piles, the pile diameter D is 600mm, and the effective pile length is about 27m. In this embodiment, pile positions Z0012, Z0103, and Z0232 are taken to evaluate the quality of bored pile construction process in real time.

[0115] (1) Evaluation indicator system

[0116] The multi-objective evaluation model of pile foundation construction process quality was used to evaluate the field test results of bored piles. The analysis showed that the bored pile index system is as follows: Figure 2 :

[0117] Based on the IoT monitoring results of the pile foundation construction process quality, the correlation between the actual data of the construction process and the standard value is calculated as shown in Table 2.

[0118] Table 2 Correlation between measured values ​​and standard values ​​of various indicators

[0119]

[0120] (2) Indicator weight

[0121] 1) Subjective weight

[0122] This project starts from the three project-level indicators of pile structure B1, pile position deviation B2 and mud index B3 and the five index-level indicators of drilling depth C1, verticality C2, pile position deviation C3, mud specific gravity C4 and mud viscosity C5. Experts compare the importance of each indicator in pairs according to the influence of various indicators on the quality of pile foundation construction process, and obtain the project-level judgment matrix as follows:

[0123]

[0124] Normalize the judgment matrix

[0125]

[0126] According to formula 7 and 8, W B =(0.500, 0.250, 0.250)

[0127] Similarly, the indicator layer judgment matrix and weights are shown in Tables 3 to 5.

[0128] Table 3: B1 indicator layer judgment matrix and weights

[0129]

[0130] Table 4: B2 indicator layer judgment matrix and weights

[0131]

[0132] Table 5: B3 indicator layer judgment matrix and weights

[0133]

[0134] The subjective weight of the quality evaluation index of bored pile construction process is:

[0135] Table 6 Subjective weights of bored pile construction process quality assessment indicators

[0136]

[0137] 2) Objective weight calculation

[0138] According to the previous calculation results, the initial matrix is:

[0139]

[0140] Normalized judgment matrix

[0141]

[0142] The objective weights of bored cast-in-place pile construction process quality assessment indicators are shown in Table 7:

[0143] Table 7 Objective weights of bored pile construction process quality assessment indicators

[0144]

[0145] 3) Combination weight

[0146] This project takes =0.5 to calculate the combined weights, and the results are shown in Table 8:

[0147] Table 8 Combination weights of bored pile construction process quality assessment indicators

[0148]

[0149] (3) Quality assessment of pile foundation construction process

[0150] According to the bored pile construction specifications and the pile foundation quality requirements of the construction site management personnel, the limit values ​​of the membership distribution function are determined as shown in Table 9:

[0151] Table 9 Membership distribution function limit values

[0152]

[0153] Calculate the fuzzy comprehensive matrix

[0154] Pile position Z0012

[0155] Pile position Z0103

[0156] Pile position Z0232

[0157] The comprehensive evaluation calculation results of the construction process quality of different foundation piles are as follows:

[0158]

[0159]

[0160]

[0161] According to the principle of taking the largest value, the results of the construction process quality assessment of the bored cast-in-place pile test foundation piles are as follows: the qualified value 0.506 of pile position Z0012 is the largest, and the construction process quality of pile position Z0012 is determined to be qualified; the excellent values ​​0.606 and 0.643 of pile positions Z0103 and Z0232 are the largest, and the construction process quality of pile positions Z0103 and Z0232 is determined to be excellent, and pile position Z0232 is better than Z0103.

[0162] Embodiment 2: Based on Embodiment 1, this embodiment provides a multi-objective fuzzy comprehensive pile construction quality analysis device, including a processor and a storage medium;

[0163] The storage medium is used to store instructions;

[0164] The processor is used to operate according to the instruction to execute the method according to embodiment 1.

[0165] Embodiment 3: Based on Embodiment 1, this embodiment provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described in Embodiment 1 is implemented.

[0166] Embodiment 4: Based on Embodiment 1, this embodiment provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the method described in Embodiment 1 when executing the computer program.

[0167] Embodiment 5: Based on Embodiment 1, this embodiment provides a computer program product, including a computer program, which implements the method described in Embodiment 1 when executed by a processor.

[0168] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0169] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0170] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0171] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0172] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for analyzing pile foundation construction quality, characterized in that: include: S1. Determine the key indicators of the pile foundation construction process and obtain the monitoring values ​​of each key indicator of the pile foundation construction process; S2. Constructing a quality evaluation index system for the pile foundation construction process based on the key indicators; wherein the quality evaluation index system includes a project layer and an index layer, the pile foundation construction process project layer is divided into pile body structure B1, pile position deviation B2 and mud index B3, and the key indicators are correspondingly divided into several index layer indicators under the project layer; S3. According to the monitoring values ​​of each key indicator and the corresponding construction specification values, the correlation degree of each key indicator is calculated by taking the absolute value grey approximate correlation method; S4. According to the correlation between the quality assessment index system and each key indicator, the combined weight of each key indicator is calculated by using the AHP-entropy weight combination weighting method; S5. According to the combined weights of the key indicators, a fuzzy comprehensive evaluation method is used to calculate a comprehensive evaluation vector, and the quality of the pile construction process is determined according to the comprehensive evaluation vector.

2. The method according to claim 1, characterized in that: The key indicators include drilling depth, verticality, pile position deviation, mud viscosity and mud density in bored cast-in-place pile construction.

3. The method according to claim 2, characterized in that The key indicators are divided into several indicator-level indicators under the project level, including: Pile body structure B1= (drilling depth C1, verticality C2), pile position deviation B2= (pile position deviation C3), mud index B3= (mud viscosity C4, mud specific gravity C5).

4. The method according to claim 2, characterized in that: S3. According to the monitoring values ​​of each key indicator and the corresponding construction specification values, the absolute value grey approximate correlation method is used to calculate the correlation of each key indicator, including: S31, determine the data sequence: extract the monitoring value obtained at each set time interval, and assume that indicator i has n monitoring results during the construction of a single pile The complete monitoring result set of indicator i during the construction process constitutes the monitoring sequence X i : ， Each monitoring result Corresponding to a standard value x0(n), the standard sequence X0 is composed of the standard value set: ， S32, according to the monitoring sequence and the standard sequence, the correlation degree is calculated by using the absolute value grey approximate correlation degree method; For the four key indicators of verticality, pile position deviation, mud viscosity and mud density, the correlation calculation method is: , For drilling depth, a key indicator that only focuses on construction results and has nothing to do with the construction process and specifications, the correlation calculation method is: , in, is the correlation degree of key indicator i, n is the number of monitoring results, For the The monitoring results obtained were: for The corresponding standard value.

5. The method according to claim 1, characterized in that S4. According to the correlation between the quality assessment index system and each key indicator, the combined weight of each key indicator is calculated using the AHP-entropy weight combination weighting method, including: S41, constructing a project-level judgment matrix and an indicator-level judgment matrix according to the quality assessment indicator system, and calculating the subjective weight of each key indicator according to the project-level judgment matrix and the indicator-level judgment matrix; S42. Use the entropy weight method to calculate the objective weight of each key indicator; S43. Based on the subjective weight and objective weight of each key indicator, the combined weight is determined by using the "combined weighting" method.

6. The method according to claim 5, characterized in that S41, constructing a project-level judgment matrix and an indicator-level judgment matrix according to the quality assessment indicator system, and calculating the subjective weight of each key indicator according to the project-level judgment matrix and the indicator-level judgment matrix, including: 1) Construct a judgment matrix set up is the comparison result of the importance of index i and index j. For r indicators, the judgment matrix is ​​constructed by the values ​​of each indicator. for: ， The judgment matrix has the following properties:

1.

2. ; 3. , where i, j=1,2,3,…,r; According to the above principles and hierarchical index system, the importance of indicators at each level is compared in pairs, and finally the judgment matrix between elements at each level is obtained; 2) Hierarchical single sorting Calculate the relative weight of each element in the judgment matrix; 21) Calculate the project-level judgment matrix and the weights of project-level indicators First, normalize each column vector in the item-level judgment matrix to obtain the normalized judgment matrix ; , for Normalized value; ， Then, The intermediate parameters are obtained by adding rows : ， Weight of project-level indicator e for: ， Among them, e is the project level indicator code, ; 22) Similarly, calculate the indicator layer judgment matrix and indicator layer indicator weights under each project layer indicator ， in: is the weight of the indicator f at the indicator layer, is the weight of the indicator layer indicator f obtained by using the indicator layer judgment matrix, f is the indicator layer indicator code, g is the number of indicator layer indicators under the corresponding project layer indicator, It represents the sum of the weights of the indicator-level indicators under the project-level indicators; 3) Total ranking of levels: The subjective weight of the ith indicator is .

7. The method according to claim 5, characterized in that S42. The entropy weight method is used to calculate the objective weight of each key indicator, including: According to the correlation of each key indicator, an initial matrix of m foundation piles and r indicators is constructed , and the initial matrix Normalize to get the normalized initial matrix ; ， In the formula is the correlation degree of the ith indicator of the pth pile foundation; Entropy of the i-th indicator for: ， The objective weight of the i-th indicator for: 。 8. The method according to claim 5, characterized in that S43. Based on the subjective weight and objective weight of each key indicator, the combined weight is determined by the "combined weighting" method, including: The combined weight w of the i-th indicator i for: ， Where: is the weighted linear combination index, 0≤ ≤1, , are the subjective weight and objective weight of the ith indicator respectively.

9. The method according to claim 1, characterized in that: S5. Calculate the comprehensive evaluation vector using the fuzzy comprehensive evaluation method according to the combined weights of the key indicators, and determine the quality of the pile construction process according to the comprehensive evaluation vector, including: (1) Determine the factor set: The correlation between the monitoring value of each key indicator and the standard value is expressed in the form of a set, and the factor set U = (u1, u2, …, u i ), where u i is the correlation degree of the i-th indicator; (2) Determine the solution set: Classify the quality of the construction process into excellent, qualified, and unqualified. The solution set V = (v1, v2, v3) = (excellent, qualified, unqualified); (3) Establishing the membership function: The relationship between the factor set and the solution set is reflected by the membership function; the membership functions of the i-th indicator for the three comments in the solution set are: excellent , Passed , Failure , a, b, c and d represent the cutoff values ​​of the membership function distribution. is the correlation degree of the corresponding indicator; According to the above three membership functions, the membership degree of the ith index of the mth pile body to the three elements in the scheme set is obtained, which is recorded as [r i1 ,r i2 ,r i3 ], so the fuzzy comprehensive matrix R of r indicators is expressed as: ， (4) Calculate the comprehensive evaluation vector based on the combined weights of each key indicator and the fuzzy comprehensive matrix; ， In the formula, is the comprehensive evaluation vector; The combined weight matrix W of r key indicators is (w1, w2, ... , w r ) T , where w i is the combined weight of the i-th indicator;" " represents the fuzzy synthesis operator; R is the fuzzy comprehensive matrix of r indicators, It is the degree of membership of the quality of the pile construction process relative to the first, second, and third comments. The comment corresponding to the pile has the largest degree of membership. If h1 is the largest among h1, h2, and h3, it is judged as excellent; if h2 is the largest, it is judged as qualified; if h3 is the largest, it is judged as unqualified.

10. A pile construction quality analysis device, characterized in that: including processor and storage medium; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the method according to any one of claims 1 to 9.

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