Anchorage load capacity intelligent calculation method based on multi-parameter self-adaptation

Abstract

The application discloses an intelligent calculation method for the bearing capacity of an anchor bolt based on multi-parameter self-adaption, and particularly relates to the technical field of anchor bolt connection design, which comprises the following steps: accessing multi-source data and establishing original credibility weight, and weightedly fusing output base material effective parameters and correction coefficients; introducing correction functions of concrete age, temperature and humidity coupling, carbonization depth and chemical glue type, and dynamically correcting the compressive strength of concrete and the chemical adhesive strength; performing three-dimensional finite element modeling on the anchor bolt group to solve the internal force distribution, selecting the regular group anchor or non-uniform group anchor checking branch according to the tension variation coefficient selection rule, calculating the bearing capacity and taking the most unfavorable envelope value, judging the failure mode, outputting the service life prediction and rechecking cycle, accessing the deviation comparison of the field sampling measured value, triggering the parameter self-learning reverse correction and updating the parameter library when the deviation is out of limit, dividing the early warning level through the utilization rate, generating the optimization suggestion for the high risk level, and outputting the scheme comparison table.

Description

Technical Field

[0001] This invention relates to the field of anchor bolt connection design technology, and more specifically, to an intelligent calculation method for anchor bolt bearing capacity based on multi-parameter adaptive design. Background Technology

[0002] Anchor bolts, as a primary technical means of post-anchoring connections, are widely used in engineering scenarios such as existing structure renovation, equipment foundation anchoring, curtain wall connections, reinforcement of embedded channels in rail transit, and nuclear power plant pipeline supports. The calculated bearing capacity of anchor bolts in the substrate directly affects structural safety; therefore, their bearing capacity verification is a crucial step in structural design.

[0003] Currently, the calculation software used in engineering for anchor bolt bearing capacity verification is mainly developed by European and American anchor bolt manufacturers and independent third-party software organizations. These software programs can perform bearing capacity verification for single anchors or groups of anchors according to various design specifications and generate calculation reports. They calculate the non-uniform internal force distribution of each anchor in a group using the finite element method; support internal force analysis for arbitrary anchor bolt locations and anchor plate shapes; allow user-selectable switching between multiple specifications; and perform specialized verification for dynamic load conditions such as earthquakes and fatigue.

[0004] However, in practical use, it still has some shortcomings. For example, the base material parameters used in the existing calculation methods are mainly derived from the standard design values ​​assumed in the design phase and the calibration values ​​given in the anchor product certification documents. The actual measured parameters from the construction site are not incorporated into the calculation process, resulting in significant deviations between the actual condition of the base material and the assumed design values, which cannot be dynamically corrected. The integration of BIM model parameters is limited. There is a lack of data closure between the load-bearing capacity calculation results generated in the design phase and the actual pull-out test data obtained in the construction phase. In the judgment result output stage, when a judgment fails, it fails to automatically generate targeted optimization suggestions based on the controlled failure mode, and fails to quantitatively compare the cost and schedule impact of optional optimization schemes, resulting in a lack of quantitative decision-making basis for designers in the scheme adjustment stage. The comprehensive output capability for long-term service performance evaluation of chemical anchor colloids, such as creep reduction under long-term conventional loads, colloid performance degradation due to temperature cycling, and the dynamic amplification factor value of the coupling between seismic fortification intensity and the structural location of the anchor, is insufficient, making it difficult to predict the remaining service life of the anchor and the recommended re-inspection cycle at a specific service life. In cross-border projects or projects with high safety requirements, the failure to provide parallel calculations and worst-case envelope outputs for two or more specifications, and the lack of normalization for the differences in reliability levels between different specifications, requires designers to repeatedly input and manually compare the results under different specifications. This is inefficient and prone to inaccurate judgments of design redundancy due to human error. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an intelligent calculation method for anchor bolt bearing capacity based on multi-parameter adaptive methods, which solves the problems mentioned in the background art through the following solutions.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an intelligent calculation method for anchor bolt bearing capacity based on multi-parameter adaptive calculation, comprising S1: accessing data source 1, establishing original confidence weights, and processing and outputting effective parameters of the base material, steel bar interference correction coefficient and historical pull-out statistical correction coefficient;

[0007] S2: The effective parameters of the substrate are introduced into a correction function to dynamically correct the concrete compressive strength and chemical adhesive bonding strength, thereby obtaining the effective strength and bonding strength of the substrate.

[0008] S3: Model the anchor bolt group in 3D and solve for the anchor bolt axial force, shear force, additional tensile force components, and tensile force distribution variation coefficient. Determine the distribution and enter the corresponding branch.

[0009] S4: Calculate the bearing capacity of multiple anchor bolts under failure mode, normalize the results of the standard, and take the most unfavorable envelope value as the design basis.

[0010] S5: Output the bearing capacity of various foundation failure modes for anchor bolts, output the dominance index of each failure mode according to the dominance discrimination algorithm, and determine the failure structural mode.

[0011] S6: Based on data source 2, establish calculation functions for creep reduction factor, fatigue life and vibration resistance amplification factor, and output the predicted service life of anchor bolts and the recommended re-inspection cycle according to the design service life.

[0012] S7: Access the measured bearing capacity of anchors randomly inspected at the construction site, calculate the deviation between the calculated value and the measured value, mark the test data and store it in the historical project database. When the deviation exceeds the threshold, trigger the parameter self-learning mechanism, reverse the correction of the base material parameters or correction coefficient and update the parameter library.

[0013] S8: The calculation results are divided into multiple warning levels based on the utilization rate. For the alert level and the failure level, targeted optimization suggestions are generated according to the dominant destruction mode. The modification cost and project time impact of each optimization scheme are estimated, and a scheme comparison table is output.

[0014] Preferably, the data source 1 includes design parameters, on-site measured parameters, BIM model parameters, and historical engineering database parameters; the original credibility weight is determined comprehensively based on the source level of the data source, the accuracy of the detection method, the timeliness of the data, and the sample size, and after normalization processing, the effective parameters of the base material, the reinforcement interference correction coefficient, and the historical pull-out statistical correction coefficient are output through weighted fusion.

[0015] Preferably, the correction functions include a concrete age correction function, a temperature and humidity coupling correction function, a carbonation depth correction function, and a chemical adhesive type correction function. The concrete age correction function determines the correction coefficient based on the actual age of the concrete from pouring to anchor bolt installation. The temperature and humidity coupling correction function is calculated based on temperature, relative humidity, and application time, and establishes separate function forms for the construction period and service period. The carbonation depth correction function determines the correction coefficient based on the ratio of the measured carbonation depth to the effective embedment depth of the anchor bolt. The chemical adhesive type correction function determines the bond strength correction coefficient based on the adhesive type. The combined effect of all correction functions outputs a corrected value for the concrete compressive strength and a corrected value for the chemical adhesive bond strength.

[0016] Preferably, the basic failure modes include at least steel failure, concrete cone failure, bond failure, splitting failure, edge failure, and prying failure; the dominance degree discrimination algorithm calculates the dominance degree index based on the relative magnitude of the bearing capacity of each failure mode, and uses a preset sharpness parameter to amplify the distinguishability of the most unfavorable failure mode; the judgment rule is as follows: if there is a dominance degree index greater than the preset dominance degree threshold, it is judged as a single dominant failure mode; otherwise, if the difference between the first two dominance degree indices is less than the preset difference threshold, it is judged as a composite failure mode and a composite failure correction coefficient is applied; otherwise, it is judged as a mixed failure mode and a complete dominance degree distribution is output.

[0017] Preferably, the structural failure mode includes a single dominant failure mode, a composite failure mode, or a mixed failure mode.

[0018] Preferably, the data source 2 includes load duration, service temperature, stress ratio, and seismic fortification intensity parameters; the creep reduction factor is calculated based on the load duration, service temperature, and stress ratio; the fatigue life is obtained by cyclically counting the variable amplitude fatigue load using the fatigue SN curve and rainflow counting method; and the seismic force amplification factor is determined by looking up a table or function based on the fortification intensity and the structural location of the anchor bolt.

[0019] Preferably, the measured bearing capacity is uploaded to the server via the wireless module of the testing instrument; the deviation is the relative deviation between the calculated bearing capacity and the measured bearing capacity; when the absolute value of the deviation does not exceed the preset deviation threshold, the test data is marked as normal and stored in the historical engineering database for subsequent sample accumulation; when the absolute value of the deviation exceeds the preset deviation threshold, the test data is marked as abnormal and stored in the historical engineering database, and a parameter self-learning mechanism is triggered to call the reverse regression model to correct the base material parameters or correction coefficients, and the correction results are written into the parameter library.

[0020] Preferably, the utilization rate is the ratio of the actual design load effect borne by the anchor bolt to the design bearing capacity determined after multiple specifications are applied, and a preset threshold is used to classify the utilization rate into different warning levels.

[0021] Preferably, the alert level and the non-pass level automatically retrieve the corresponding optimization suggestion library based on the dominant damage mode, including optimization measures for different dominant damage modes, estimate the modification cost and project time impact for each optimization scheme, and output a scheme comparison table for selection.

[0022] The technical effects and advantages of this invention are as follows:

[0023] 1. This invention establishes a multi-source parameter acquisition and credibility normalization weighted fusion mechanism, incorporating design parameters, field measured parameters (concrete rebound strength, ultrasonic testing, core sampling strength, etc.), BIM model parameters (reinforcement distribution in the borehole area, component geometric boundaries, etc.), and parameters from historical engineering databases into a unified bearing capacity calculation process. Each data source is weighted according to four dimensions: source level, testing method accuracy, data timeliness, and sample size. After normalization, the weighted fusion outputs the effective parameters of the substrate. Based on this, a concrete age correction function, a temperature and humidity coupling correction function (distinguishing between construction and service periods), a carbonation depth correction function, and a chemical adhesive type correction function are introduced to perform secondary dynamic corrections on the concrete compressive strength and chemical adhesive bonding strength. This ensures that the bearing capacity calculation results accurately reflect the actual bearing state of the substrate, significantly reducing the systematic deviation caused by relying solely on design assumptions and product calibration values, and improving the precision of the design.

[0024] 2. This invention establishes a linkage mechanism for parallel reliability normalization envelope verification and multi-failure mode dominance determination across multiple standards. It supports the simultaneous invocation of two or more target design standards, independently calculating the bearing capacity of each anchor bolt under each failure mode. Through pre-established normalization coefficients, the results from each standard are unified to the same reliability level, and the most unfavorable envelope value is used as the design basis. Simultaneously, for six foundation failure modes—steel failure, concrete cone failure, bond failure, splitting failure, edge failure, and prying failure—the dominance index of each failure mode is output according to the dominance determination algorithm, automatically determining whether a single dominant failure mode, composite failure mode, or mixed failure mode is present. This frees designers from the work of repeatedly inputting and manually comparing data across different standards, improving the design reliability and work efficiency of cross-border projects and high-safety-level projects.

[0025] 3. This invention establishes a closed-loop data feedback mechanism across the entire chain, from design calculations to on-site testing and verification, and then to parameter self-learning. Portable pull-out testers are used at the construction site to sample and test anchor bolts. The measured bearing capacity is uploaded to the server via a wireless module, and the deviation between the calculated and measured values ​​is automatically calculated. Regardless of whether the deviation exceeds a preset threshold, the test data is tagged and stored in the historical project database for subsequent sample accumulation. When the deviation exceeds the threshold, the parameter self-learning mechanism is triggered, calling the back regression model to correct the base material parameters or correction coefficients, and the correction results are written into the parameter library for use in new projects. Based on parameters such as load duration, service life temperature, stress ratio, and seismic fortification intensity, calculation functions for creep reduction coefficient, fatigue life, and seismic force amplification coefficient are established. The predicted service life of the anchor bolts and recommended re-inspection cycles are output according to the design service life, continuously improving the calculation accuracy with the accumulation of historical data, while providing a quantitative basis for the safe operation and maintenance of existing structures. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the overall structure of the present invention;

[0027] Figure 2 This is a schematic diagram of the system architecture of the present invention;

[0028] Figure 3 This is a schematic diagram of the multi-source data credibility normalization weighted fusion of the present invention;

[0029] Figure 4 This is a schematic diagram of the finite element-standard linkage verification process of the present invention;

[0030] Figure 5 This is a schematic diagram of the multi-destruction mode dominance determination logic of the present invention;

[0031] Figure 6 This is a schematic diagram of the closed-loop feedback process for on-site pull-out testing according to the present invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] refer to Figure 1 As shown, the method of this invention initiates eight steps sequentially from top to bottom during execution, and establishes a feedback loop pointing to S2 at S7, forming a closed-loop structure for parameter self-learning. The overall architecture of the system of this invention is as follows: Figure 2As shown, it consists of four layers: data acquisition layer, algorithm calculation layer, intelligent decision-making layer, and application output layer. Data is transferred between each layer through standardized interfaces.

[0034] A smart calculation method for anchor bearing capacity based on multi-parameter adaptive calculation includes S1: accessing data source 1, establishing original confidence weights, and processing and outputting effective parameters of the base material, reinforcement interference correction coefficient and historical pull-out statistical correction coefficient.

[0035] It should be specifically noted that the data source 1 includes design parameters, on-site measured parameters, BIM model parameters, and historical project database parameters; the original credibility weight is determined comprehensively based on the source level of the data source, the accuracy of the detection method, the timeliness of the data, and the sample size, and after normalization processing, the effective parameters of the base material, the steel bar interference correction coefficient, and the historical pull-out statistical correction coefficient are output through weighted fusion.

[0036] It should be further explained that the reference Figure 3 This invention integrates concrete design strength grade, anchor bolt type and specifications, and load conditions from design drawings through a design parameter input module; it integrates measured data such as rebound hammer strength, ultrasonic test values, and core sampling strength through a field testing data input module; it automatically reads the reinforcement distribution, component geometric boundaries, and nearby embedded parts positions in the drilling area through a BIM model parameter extraction module; and it queries the statistical values ​​of measured pull-out bearing capacity of similar projects through a historical engineering database input module.

[0037] Each data source is evaluated by the data credibility assessment module, which calculates the original credibility weight based on four dimensions: source level, detection method accuracy, data timeliness, and sample size. The specific steps are as follows:

[0038] In this embodiment, the weight of the design document source level is set to 0.7, the weight of on-site inspection data is set to 0.9, the weight of BIM model parameters is set to 0.8, and the weight of historical project database is set to 0.6. On-site inspection data directly reflects the actual condition of the base material, therefore it is given a higher weight. Then, the normalization formula is applied... The weights are normalized to a unit sum, where Let be the normalized confidence weight of the i-th data source. , The original credibility weights of the i-th and j-th data sources are given, where n is the total number of data sources. Finally, a weighted fusion algorithm is used. Output substrate actual compressive strength Reinforcing bar interference correction factor Historical pull statistical correction coefficient .in The parameter value provided for the i-th data source. This is the output value of the weighted fusion parameters.

[0039] in Enter S2 for secondary correction. Enter S3 to participate in finite element calculations. Enter S4 to modify the bearing capacity.

[0040] S2: The effective parameters of the substrate are introduced into a correction function to dynamically correct the concrete compressive strength and chemical adhesive bonding strength, thereby obtaining the effective strength and bonding strength of the substrate.

[0041] It should be specifically noted that the correction functions include a concrete age correction function, a temperature and humidity coupling correction function, a carbonation depth correction function, and a chemical adhesive type correction function. The concrete age correction function determines the correction coefficient based on the actual age of the concrete from pouring to anchor bolt installation. The temperature and humidity coupling correction function is calculated based on temperature, relative humidity, and application time, and establishes separate function forms for the construction period and the service period. The carbonation depth correction function determines the correction coefficient based on the ratio of the measured carbonation depth to the effective embedment depth of the anchor bolt. The chemical adhesive type correction function determines the bond strength correction coefficient based on the adhesive type. The combined effect of all correction functions outputs a corrected value for the concrete compressive strength and a corrected value for the chemical adhesive bond strength.

[0042] It should be further explained that in the output of S1 Based on this, the present invention calls the following correction function to perform secondary dynamic correction on the substrate parameters:

[0043] Concrete age correction function Based on the actual number of days from concrete pouring to anchor bolt installation, values ​​are taken for three stages: 0.85 for the first 28 days, reflecting insufficient early strength development; 1.0 for 28 to 90 days, indicating that the strength has reached the design value under standard curing conditions; and 0.95 to 1.05 for the period after 90 days, adjusted according to carbonation depth. In particular, the surface strength of long-aged concrete may be slightly improved due to carbonation, but it needs to be reduced if the carbonation depth is too large.

[0044] Temperature and humidity coupling correction function Function curves were established separately for the construction and service periods. During the construction period, temperature and humidity affected the curing quality and bond strength development of the chemical adhesive; during the service period, temperature and humidity affected the long-term performance degradation of the adhesive. The function form is as follows: Where T is temperature, RH is relative humidity, and t is the exposure time. Typical values ​​in this embodiment are 0.85 to 1.0 within the temperature range of 5 to 35°C; values ​​outside this range are extrapolated from experimental data and reduced accordingly.

[0045] Carbonization depth correction function , the correction factor is determined according to the ratio of the on-site measured carbonization depth d to the effective embedded depth h of the anchor bolt. When d / h≤0.3, the correction factor is 1.0; when 0.3<d / h≤0.6, it is 0.90 to 0.95; when d / h>0.6, it is 0.80 to 0.90, and anti-carbonization measures are recommended.

[0046] Chemical adhesive type correction function , three types of adhesives, namely epoxy resin adhesive, vinyl ester adhesive and polyester adhesive, are distinguished, and different bond strength correction factors are taken respectively. Epoxy resin adhesive has low curing shrinkage and stable bonding performance, the correction factor is 1.0; that of vinyl ester adhesive is 0.85 to 0.95; that of polyester adhesive is 0.75 to 0.85.

[0047] After the combined action of the above correction functions, the corrected value of concrete compressive strength is output and the corrected value of chemical adhesive bond strength , which are used as input parameters for subsequent steps.

[0048] S3: Perform three-dimensional modeling on the anchor group, solve for the axial force, shear force, additional tension component, and tension distribution variation coefficient of the anchor bolts, determine the distribution situation and enter the corresponding branch.

[0049] It should be further noted that, with reference to Figure 4 , the present invention performs three-dimensional finite element modeling on the anchor group. During modeling, the geometric and material properties of the anchor plate, the contact support stiffness of the base material, and the axial and shear stiffness parameters of each anchor bolt are input. Through finite element solution, the axial force and shear force borne by each anchor bolt under the action of external load, as well as the additional tension caused by the bending deformation of the anchor plate, are directly obtained.

[0050] After the solution is completed, the variation coefficient of the tension of all anchor bolts is calculated. When the variation coefficient is less than 10%, it indicates that the forces on each anchor bolt are relatively uniform. At this time, the regular group anchor checking branch is adopted to calculate the bearing capacity; when the variation coefficient reaches or exceeds 10%, it indicates that the force is severely uneven due to load eccentricity, anchor plate stiffness change or irregular anchor bolt arrangement. At this time, the non-uniform group anchor distribution algorithm is enabled, which takes the anchor bolt stiffness and the center of gravity of the anchor group as the distribution basis, and distributes the total tension, shear force and bending moment according to the actual stiffness proportion of each anchor bolt.

[0051] Both branches finally output the design axial force and design shear force of each anchor bolt as the load input for subsequent code checking. The threshold of 10% for the variation coefficient is only used as the criterion for automatically selecting an economical calculation path, and users can also force the non-uniform distribution algorithm to be enabled according to engineering needs to obtain more conservative results.

[0052] S4: Calculate multiple sets of bearing capacities of the anchor bolts under the failure mode, normalize the code results, and take the most unfavorable envelope value as the design basis.

[0053] It should be further explained that, based on two or more target standards selected by the user in the system configuration, the bearing capacity formula of each standard is invoked separately, and the bearing capacity result is calculated independently for each anchor bolt under each failure mode, denoted as... In this context, the subscript i represents the i-th failure mode, and the subscript "specification" represents the design specification adopted.

[0054] Because different standards employ different safety factor systems, the reliability levels corresponding to the bearing capacity results output by each standard vary, making direct horizontal comparisons impossible. Therefore, this invention pre-establishes normalization coefficients for the results of each standard to a unified reliability level. The reliability index β was determined by calibrating various specifications under typical operating conditions, with a default value range of 0.92 to 1.08. Specifically, This means that by multiplying the bearing capacity result calculated under a certain standard by this coefficient, the corresponding reliability level is unified to the benchmark reliability level, thereby achieving comparability of results from multiple standards.

[0055] Based on this, the system follows the envelope formula. The system calculates the most unfavorable envelope value after normalization adjustment for each failure mode, taking the minimum value among all standard calculation results as the basis for the design bearing capacity of the failure mode. Simultaneously, it automatically outputs a comparison table of results from multiple standards and a detailed table of the normalization coefficients used, for design review and compliance review of cross-border projects.

[0056] S5: Output the bearing capacity of various foundation failure modes for anchor bolts, output the dominance index of each failure mode according to the dominance discrimination algorithm, and determine the failure structural mode.

[0057] It should be specifically noted that the basic failure modes include at least steel failure, concrete cone failure, bond failure, splitting failure, edge failure, and prying failure; the dominance degree discrimination algorithm calculates the dominance degree index based on the relative magnitude of the bearing capacity of each failure mode, and uses a preset sharpness parameter to amplify the distinguishability of the most unfavorable failure mode; the judgment rule is as follows: if there is a dominance degree index greater than the preset dominance degree threshold, it is judged as a single dominant failure mode; otherwise, if the difference between the first two dominance degree indices is less than the preset difference threshold, it is judged as a composite failure mode and a composite failure correction coefficient is applied; otherwise, it is judged as a mixed failure mode and a complete dominance degree distribution is output.

[0058] The structural failure modes include single dominant failure modes, composite failure modes, or mixed failure modes.

[0059] It should be further clarified that the bearing capacity values ​​of the six foundation failure modes (R1, R2, R3, R4, R5, and R6) are determined according to the dominance degree algorithm. The dominance index of each failure mode is calculated. Here, k is the sharpness parameter; in this invention, k=4 is used by default to amplify the discriminative power of the most unfavorable failure mode. It should be noted that the output of this algorithm... It is a dominance index calculated based on the relative magnitudes of the bearing capacity of each failure mode, used to help identify the most unfavorable failure path. It is not the failure probability defined in structural reliability theory based on the joint distribution of load and resistance. , denoted as the bearing capacity value under the i-th and j-th failure modes.

[0060] The present invention determines the following in order: First, it determines whether or not the presence of the substance exists. If it exists, it is determined to be a single dominant destruction mode; if it does not exist, the first two are judged. If the difference is less than 10%, it is determined to be a composite failure mode and a composite failure correction coefficient γ is applied. In this invention, it is taken as 0.85 to 0.95. Otherwise, it is determined to be a mixed failure mode, and the complete dominance distribution is output for designers to refer to.

[0061] S6: Based on data source 2, establish calculation functions for creep reduction factor, fatigue life and vibration resistance amplification factor, and output the predicted service life of anchor bolts and the recommended re-inspection cycle according to the design service life.

[0062] It should be specifically noted that the data source 2 includes load duration, service temperature, stress ratio, and seismic fortification intensity parameters; the creep reduction factor is calculated based on the load duration, service temperature, and stress ratio; the fatigue life is obtained by cyclically counting the variable amplitude fatigue load using the fatigue SN curve and rainflow counting method; and the seismic force amplification factor is determined by looking up a table or function based on the fortification intensity and the structural location of the anchor bolt.

[0063] It should be further noted that step S6 is optional. The creep, fatigue, and seismic assessments can be run independently or in combination. In this embodiment, they are calculated separately as follows:

[0064] Creep reduction factor Where t is the load duration and T is the service life temperature. Stress relative to the ultimate adhesive strength The ratio. For chemical anchors subjected to long-term loads, the bearing capacity needs to be multiplied by the creep reduction factor. This invention is based on the load duration t and stress ratio. Combined with temperature correction, the creep reduction factor is output, with a typical value range of 0.55 to 0.85.

[0065] The fatigue life Nf is calculated by cyclically counting the variable amplitude fatigue load using the fatigue SN curve and the rainflow counting method. The fatigue performance varies for different base materials and different anchoring depths.

[0066] Vibration resistance amplification factor The dynamic amplification factor is determined by establishing a two-dimensional lookup table function based on "fortification intensity × structural location of anchor bolt (roof / floor / equipment foundation)". Typical values ​​are: 1.8 to 2.5 for roof, 1.4 to 1.8 for floor, and 1.2 to 1.4 for equipment foundation.

[0067] After considering the above three reductions, the predicted service life of the anchor bolts and the recommended re-inspection cycle are output based on the design service life of 50 years, 75 years and 100 years.

[0068] S7: Access the measured bearing capacity of anchor bolts randomly inspected at the construction site, calculate the deviation between the calculated value and the measured value, mark the test data and store it in the historical project database. When the deviation exceeds the threshold, trigger the parameter self-learning mechanism, reverse the correction of the base material parameters or correction coefficient and update the parameter library.

[0069] It should be specifically noted that the measured bearing capacity is uploaded to the server via the wireless module of the testing instrument; the deviation is the relative deviation between the calculated bearing capacity and the measured bearing capacity; when the absolute value of the deviation does not exceed the preset deviation threshold, the test data is marked as normal and stored in the historical engineering database for subsequent sample accumulation; when the absolute value of the deviation exceeds the preset deviation threshold, the test data is marked as abnormal and stored in the historical engineering database, and a parameter self-learning mechanism is triggered to call the back regression model to correct the base material parameters or correction coefficients, and the correction results are written into the parameter library.

[0070] It should be further noted that a portable pull-out tester was used at the construction site to sample and test the anchor bolts, and the actual bearing capacity was measured. The data is uploaded to the server via the instrument's built-in wireless module (4G / Wi-Fi). The system processes the data according to the deviation formula. Calculate the deviation between the calculated value and the measured value, where This represents the calculated bearing capacity of the anchor bolts. Regardless of whether |Δ| exceeds the 15% threshold, all test data are marked as "normal" or "abnormal" and stored in the historical project database. When |Δ| ≤ 15%, the data is used for confidence-weighted sample accumulation for subsequent projects; when |Δ| > 15%, in addition to being stored in the database, the data triggers a parameter self-learning mechanism, calling the back regression model to correct the base material parameters or correction coefficients, and the correction results are written into the parameter database for use by new projects. The retention of abnormal data provides the parameter self-learning mechanism with a complete training sample foundation, and the calculation accuracy continues to improve with the continuous accumulation of historical data.

[0071] S8: The calculation results are divided into multiple warning levels based on the utilization rate. For the alert level and the failure level, targeted optimization suggestions are generated according to the dominant destruction mode. The modification cost and project time impact of each optimization scheme are estimated, and a scheme comparison table is output.

[0072] It should be specifically noted that the utilization rate is the ratio of the actual design load effect borne by the anchor bolt to the design bearing capacity determined after multiple specifications are applied. A preset threshold is used to classify the utilization rate into different warning levels.

[0073] The warning level and non-pass level automatically retrieve the corresponding optimization suggestion library based on the dominant damage mode, including optimization measures for different dominant damage modes, estimate the modification cost and project time impact for each optimization scheme, and output a scheme comparison table for selection.

[0074] It should be further explained that, for each anchor bolt, the utilization rate η is defined as the ratio of the actual design load effect S borne by the anchor bolt to the design bearing capacity Re determined after multi-specification envelope, i.e., η = S / Re, where S is the design axial force calculated for the anchor bolt. With design shear force The combined effect value, the specific combination method is determined according to the requirements of the adopted design specifications; Re is the design bearing capacity of the anchor bolt obtained by taking the normalized most unfavorable envelope value after parallel verification by multiple specifications.

[0075] Based on this, the warning levels are divided according to the value of η, using four threshold levels (0.5, 0.8, 0.95), and are respectively labeled as safe level (η≤0.5), attention level (0.5<η≤0.8), alert level (0.8<η≤0.95) and non-pass level (η>0.95).

[0076] For warning and non-passing levels, based on the dominant failure mode output by S5, the system automatically retrieves optimization suggestion libraries: When cone-shaped failure is dominant, suggestions such as "increase burial depth / increase edge distance" are output; when adhesive failure is dominant, suggestions such as "change adhesive type / extend curing time" are output; when steel failure is dominant, suggestions such as "increase anchor bolt diameter / upgrade material grade" are output; and when combined failure is dominant, suggestions such as "increase the number of anchor bolts / adjust the arrangement" are output. For each optimization scheme, the modification cost (materials + labor) and construction period impact (hours) are estimated, and a scheme comparison table is output for designers to select the optimal solution.

[0077] The method of this invention can be implemented on a general-purpose computer platform or deployed on a cloud computing server, and can be used collaboratively by designers, construction personnel, and maintenance personnel via a web or mobile terminal. The method can be used as a standalone intelligent calculation system for anchor bolt bearing capacity, or it can be embedded as a module into BIM design platforms, construction management platforms, and operation and maintenance monitoring platforms, achieving deep integration with existing information systems.

[0078] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0079] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart calculation method for anchor bolt bearing capacity based on multi-parameter adaptive method, characterized in that, include: S1: Connect to data source 1, establish the original credibility weight, and process and output the effective parameters of the base material, the steel bar interference correction coefficient and the historical pull-out statistical correction coefficient. S2: The effective parameters of the substrate are introduced into a correction function to dynamically correct the concrete compressive strength and chemical adhesive bonding strength, thereby obtaining the effective strength and bonding strength of the substrate. S3: Model the anchor bolt group in 3D and solve for the anchor bolt axial force, shear force, additional tensile force components, and tensile force distribution variation coefficient. Determine the distribution and enter the corresponding branch. S4: Calculate the bearing capacity of multiple anchor bolts under failure mode, normalize the results of the standard, and take the most unfavorable envelope value as the design basis. S5: Output the bearing capacity of various foundation failure modes for anchor bolts, output the dominance index of each failure mode according to the dominance discrimination algorithm, and determine the failure structural mode. S6: Based on data source 2, establish calculation functions for creep reduction factor, fatigue life and vibration resistance amplification factor, and output the predicted service life of anchor bolts and the recommended re-inspection cycle according to the design service life. S7: Access the measured bearing capacity of anchors randomly inspected at the construction site, calculate the deviation between the calculated value and the measured value, mark the test data and store it in the historical project database. When the deviation exceeds the threshold, trigger the parameter self-learning mechanism, reverse the correction of the base material parameters or correction coefficient and update the parameter library. S8: The calculation results are divided into multiple warning levels based on the utilization rate. For the alert level and the failure level, targeted optimization suggestions are generated according to the dominant destruction mode. The modification cost and project time impact of each optimization scheme are estimated, and a scheme comparison table is output.

2. The intelligent calculation method for anchor bearing capacity based on multi-parameter adaptive method according to claim 1, characterized in that: The data source 1 includes design parameters, on-site measured parameters, BIM model parameters, and historical project database parameters. The original credibility weight is determined comprehensively based on the source level of the data source, the accuracy of the detection method, the timeliness of the data, and the sample size. After normalization, the effective parameters of the base material, the steel bar interference correction coefficient, and the historical pull-out statistical correction coefficient are output through weighted fusion.

3. The intelligent calculation method for anchor bearing capacity based on multi-parameter adaptive method according to claim 1, characterized in that: The correction functions include a concrete age correction function, a temperature and humidity coupling correction function, a carbonation depth correction function, and a chemical adhesive type correction function, wherein the concrete age correction function determines the correction coefficient based on the actual age of the concrete from pouring to anchor bolt installation. The temperature and humidity coupling correction function is calculated based on temperature, relative humidity, and application time, and the function forms are established separately for the construction period and the service period; the carbonation depth correction function determines the correction coefficient based on the ratio of the measured carbonation depth to the effective embedment depth of the anchor bolt; the chemical adhesive type correction function determines the bond strength correction coefficient based on the type of adhesive. The combined effect of the various correction functions outputs the corrected values ​​for concrete compressive strength and chemical adhesive bond strength.

4. The intelligent calculation method for anchor bearing capacity based on multi-parameter adaptive method according to claim 1, characterized in that: The basic failure modes include at least steel failure, concrete cone failure, bond failure, splitting failure, edge failure, and prying failure; the dominance discrimination algorithm calculates the dominance index based on the relative magnitude of the bearing capacity of each failure mode, and uses a preset sharpness parameter to amplify the distinguishability of the most unfavorable failure mode; the judgment rule is: if there is a dominance index greater than the preset dominance threshold, it is judged as a single dominant failure mode. Otherwise, if the difference between the first two dominance indices is less than the preset difference threshold, it is determined to be a composite failure mode and a composite failure correction coefficient is applied; otherwise, it is determined to be a mixed failure mode and a complete dominance distribution is output.

5. The intelligent calculation method for anchor bearing capacity based on multi-parameter adaptive method according to claim 1, characterized in that: The structural failure modes include single dominant failure modes, composite failure modes, or mixed failure modes.

6. The intelligent calculation method for anchor bearing capacity based on multi-parameter adaptive method according to claim 1, characterized in that: The data source 2 includes load duration, service temperature, stress ratio, and seismic fortification intensity parameters; the creep reduction factor is calculated based on the load duration, service temperature, and stress ratio; the fatigue life is obtained by cyclically counting the variable amplitude fatigue load using the fatigue SN curve and rainflow counting method; the seismic force amplification factor is determined by looking up a table or function based on the fortification intensity and the structural location of the anchor bolt.

7. The intelligent calculation method for anchor bearing capacity based on multi-parameter adaptive method according to claim 1, characterized in that: The measured bearing capacity is uploaded to the server via the wireless module of the testing instrument; the deviation is the relative deviation between the calculated bearing capacity and the measured bearing capacity; when the absolute value of the deviation does not exceed the preset deviation threshold, the test data is marked as normal and stored in the historical engineering database for subsequent sample accumulation; when the absolute value of the deviation exceeds the preset deviation threshold, the test data is marked as abnormal and stored in the historical engineering database and a parameter self-learning mechanism is triggered, the reverse regression model is called to correct the base material parameters or correction coefficients, and the correction results are written into the parameter library.

8. The intelligent calculation method for anchor bearing capacity based on multi-parameter adaptive method according to claim 1, characterized in that: The utilization rate is the ratio of the actual design load effect borne by the anchor bolt to the design bearing capacity determined after multiple specifications are applied. A preset threshold is used to classify the utilization rate into different warning levels.

9. The intelligent calculation method for anchor bearing capacity based on multi-parameter adaptive method according to claim 1, characterized in that: The warning level and non-pass level automatically retrieve the corresponding optimization suggestion library based on the dominant damage mode, including optimization measures for different dominant damage modes, estimate the modification cost and project time impact for each optimization scheme, and output a scheme comparison table for selection.

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