Urban village existing building lap joint column reinforcement calculation method

Abstract

The invention relates to the technical field of building lap joint column reinforcement calculation, and discloses a method for calculating reinforcement of existing building lap joint columns in a village in city. According to the method, basic detection data including structure size, material performance and damage distribution are obtained, feature parameters are extracted and associated to form a parameter group, and the method is specifically realized by extracting parameter description items, distributing weights and searching for association points. And constructing a calculation model according to the data and the parameter group, integrating discrete sections by adopting a regional segmentation method to obtain a calculation path model, and generating a calculation path from an original data space to a middle hierarchy and then to a target space through hierarchical calculation division. The method can solve the problems of inaccurate data processing, unreasonable model construction and the like of the existing reinforcement calculation, and provides a scientific and efficient scheme for the reinforcement calculation of the existing building lap joint columns of the villages in the city.

Description

Technical Field

[0001] The present invention relates to the technical field of reinforcement calculation of building lap columns, and in particular to a reinforcement calculation method for lap columns of existing buildings in urban villages. Background Art

[0002] With the accelerated pace of urbanization, urban villages, a unique feature of urban development, are characterized by a large number of older buildings. Problems such as aging and damage are becoming increasingly prominent. Lap columns, as key load-bearing components in building structures, have a direct impact on the overall reliability of the building. However, existing reinforcement calculations for lap columns in existing urban village buildings have numerous shortcomings. On the one hand, traditional methods for collecting and processing basic inspection data are often incomplete and inaccurate. For example, some inspections focus solely on the cosmetic damage of lapped columns, overlooking subtle changes in structural dimensions and the degradation of material properties over time. Furthermore, the lack of a systematic mechanism for extracting and correlating characteristic parameters from the collected data prevents the full exploration of potential connections between the data, making it difficult to form a parameter set that effectively supports reinforcement calculations. Different types of data are processed independently, without considering their inherent logical relationships. This results in a lack of reliable basis for constructing subsequent calculation models. On the other hand, when it comes to computational model construction and calculation path planning, existing technologies mostly use generic building structure calculation models, failing to optimize for the unique characteristics of lapped columns in existing urban village buildings. Urban village buildings have complex layouts and diverse functions, and the stress conditions on lapped columns differ from those of conventional buildings, but existing models fail to accurately reflect these characteristics. The calculation path generation is also relatively crude, failing to fully consider the actual conditions of the building area and rationally divide it. This results in either inefficiency or failure to meet the required precision for reinforcement calculations, making it difficult to provide scientific guidance for lapped column reinforcement. Furthermore, the unique characteristics of urban village buildings, such as the long construction span and inconsistent construction standards, further complicate the calculation of reinforcement for overlapping columns in existing buildings. Existing calculation methods are ill-suited to this complex situation, resulting in a lack of targeted reinforcement solutions. This not only wastes resources but also may fail to effectively improve building safety and meet the actual needs of urban village building renovation and maintenance. Summary of the Invention

[0003] The purpose of the present invention is to provide a calculation method for reinforcing the overlapping columns of existing buildings in urban villages, so as to solve the problems raised in the above background technology.

[0004] To achieve the above-mentioned purpose, the present invention provides a calculation method for reinforcing overlapping columns of existing buildings in urban villages, the method comprising: Obtaining basic inspection data of overlapping columns of existing buildings, extracting characteristic parameters based on the basic inspection data, and correlating the extracted characteristic parameters to obtain a parameter group; wherein the basic inspection data includes structural dimension data, material performance data, and damage distribution data; correlating the extracted characteristic parameters to obtain the parameter group includes: extracting parameter description items at the characteristic parameters, assigning weight values to the characteristic parameters, and finding correlation points based on the parameter description items to obtain the correlation parameter group; wherein the parameter description items are attribute sequences that describe the characteristic parameters; Build a calculation model based on basic test data and parameter groups, and divide the calculation model into levels based on the basic test data to generate a calculation path, including: The calculation model is obtained by continuous correlation using the parameter groups obtained by mutual correlation as the reference point. The discrete segments obtained by correlation of characteristic parameters between multiple locations are used by the regional segmentation method. The complete or partial calculation path model covering the reinforcement range is obtained by expansion and integration. A hierarchical computational partitioning approach is adopted, where computation is divided into two levels. The first level divides the original data space into an intermediate level that can be expressed using a standard. The second level divides the data in the intermediate level into the target space that needs to be calculated, generating a computational path.

[0005] Preferably, the method further comprises: Obtain the building structure information required for calculation and generate a calculation process based on the building structure information; collect basic test data from multiple test locations according to the calculation process; Building structural information includes the target range of the reinforcement area, connection status, and load distribution data. Based on this building structural information, a calculation process is generated, including the following: Define the boundaries of the target range to build a calculation area, and plan the calculation process based on the calculation area; Establish a calculation benchmark based on the connection status and load distribution data, and collect basic test data based on the calculation benchmark.

[0006] Preferably, the calculation process according to the calculation area planning includes: Construct a two-dimensional structural analysis diagram in the target range to be calculated, and divide the target range to be calculated into a number of grid units with preset intervals as side lengths according to the two-dimensional structural analysis diagram; Based on the target range, connection status, and load distribution data, the grid cells are classified into three types: core reinforcement area, conventional connection area, and weak damage area, and the core reinforcement area is marked with priority; Randomly generate several initial calculation processes to form an initial calculation process set; Constructing an evaluation index system, wherein the evaluation index system includes a parameter matching index, a load adaptability index, and an operational feasibility index; The parameter matching index is the matching value of the characteristic parameters between each position in the initial calculation process; The load adaptability index is the sum of the loads that can be carried per unit area in each section during the initial calculation process; The operational feasibility indicator is the number of detection locations and parameter ranges that need to be adjusted in the initial calculation process; Taking the initial calculation process set as the initial sample, iterative optimization is performed according to the evaluation index system to obtain the optimal calculation process and complete the calculation process planning.

[0007] Preferably, the initial calculation process set is used as the initial sample, and iterative optimization is performed according to the evaluation index system, including: According to the evaluation index system, the evaluation value of each initial calculation process is calculated, and the evaluation value of the initial calculation process is sorted from high to low according to the three evaluation indicators to obtain a sorted set of three initial calculation processes; According to the evaluation value, several initial calculation processes are selected from the three sorted sets by probability to form sub-samples in three different directions. The probability of being selected is proportional to the evaluation value. In each sub-sample, infeasible calculation schemes are eliminated by position update, range adjustment and individual screening, and the iterative process is repeated for a preset number of times to form three sub-samples after iteration. Individuals are randomly selected from the three subsamples based on their evaluation values to form a basic sample library. Three groups of crossover subsamples are formed by crossing two of the three subsamples. For each crossover subsample, the three evaluation values of each individual are calculated. The two evaluation values involved in the crossover subsamples are sorted in multiple dimensions. Based on the sorting results, positions are updated, ranges are adjusted, and individuals are screened. Infeasible calculation schemes are then eliminated. After the three groups of cross-subsamples have iterated a preset number of times, the three subsamples are combined together, and the three evaluation indicators are weighted and summarized to obtain a comprehensive evaluation value. According to the comprehensive evaluation value, the position is updated, the range is adjusted, and the individual is screened. After iterating a preset number of times, the individual with the largest evaluation value is obtained as the optimal calculation process.

[0008] Preferably, the two evaluation values involved in the cross sub-samples are sorted in multiple dimensions, and position update, range adjustment and individual screening are performed according to the sorting results. Before the cross sub-samples are crossed, a dynamic adjustment mechanism is used to select the cross object, wherein the selection probability of the cross object is related to the position of the individual in the sorting, and the later the position of the individual in the sorting, the higher the probability of being selected.

[0009] Preferably, the method further comprises: assigning a value to the selection probability according to the sorting order of the individuals to be crossed, wherein the later the individual is sorted, the greater the selection probability value.

[0010] Preferably, the method further comprises: The associated parameter groups are screened, and the parameter groups that are incorrectly associated are screened. When constructing the calculation model, the model is constructed according to the screened parameter groups.

[0011] Preferably, the associated parameter groups are screened by using two-way verification and uniqueness verification to eliminate incorrectly associated parameter groups.

[0012] Preferably, the method further comprises the following steps: Perform data verification on the screened parameter group to verify the integrity and accuracy of the parameter group. When generating the calculation model, generate it based on the verified parameter group.

[0013] Preferably, data verification is performed on the screened parameter group, including: using historical data comparison and cross-validation to confirm the integrity and accuracy of the parameter group.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The calculation method for reinforcing overlapping columns in existing urban village buildings, provided by this invention, improves and optimizes existing technologies from multiple perspectives. In terms of data processing, it comprehensively acquires basic test data covering structural dimensions, material properties, and damage distribution. By extracting attribute sequences from characteristic parameters as parameter descriptions, assigning weights to the characteristic parameters, and finding association points based on the parameter descriptions, it achieves deep correlation of the characteristic parameters, thereby obtaining a precise parameter set. This data processing method fully explores the potential connections between data, providing a rich and accurate information foundation for subsequent calculations. During the computational model construction phase, the associated parameter groups served as reference points. A continuous correlation model was constructed, and a regional segmentation approach was employed to expand and integrate the discrete segments derived from the correlation of characteristic parameters across multiple locations, forming a complete or partial computational path model covering the reinforcement range. This construction approach closely aligned with the actual conditions of existing building lap columns in urban villages, accurately simulating the stress state and structural characteristics of lap columns in complex building environments. A hierarchical calculation approach divides the calculation process into two levels: the original data space, an intermediate level where standard expressions can be used, and finally the target space to be calculated. This makes the calculation process clearer and more organized, ensuring both standardized data processing and improved computational efficiency. This allows for the rapid and accurate generation of calculation paths, meeting the dual requirements of efficiency and accuracy for the reinforcement calculation of lapped columns in existing buildings in urban villages. The present invention can effectively solve the problems of inaccurate data processing, unreasonable calculation models, and imperfect calculation path planning in the existing technology, and provide a scientific, reliable and efficient solution for the reinforcement calculation of the lap columns of existing buildings in urban villages, ensuring that the reinforcement plan is more targeted and effective, thereby improving the safety and stability of existing buildings in urban villages. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a working principle diagram of a calculation method for reinforcing overlapping columns of existing buildings in urban villages according to the present invention; Figure 2 This is a working principle diagram of the present invention for generating a calculation process based on building structure information. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0017] See also Figure 1-Figure 2 The present invention provides a calculation method for reinforcing overlapping columns of existing buildings in urban villages, the method comprising: Basic inspection data of lapped columns of existing buildings are obtained, characteristic parameters are extracted according to the basic inspection data, and parameter groups are obtained by association according to the extracted characteristic parameters; the basic inspection data include structural dimension data, material performance data and damage distribution data; the parameter groups are obtained by association according to the extracted characteristic parameters, including: extracting parameter description items at the characteristic parameters, assigning weight values to the characteristic parameters, and finding association points according to the parameter description items to obtain the association parameter group; the parameter description items are attribute sequences that describe the characteristic parameters.

[0018] Build a calculation model based on basic test data and parameter groups, and divide the calculation model into levels based on the basic test data to generate a calculation path, including: The calculation model is obtained by continuous correlation using the parameter groups obtained by mutual correlation as the reference point. The discrete segments obtained by correlation of characteristic parameters between multiple locations are used by the regional segmentation method. The complete or partial calculation path model covering the reinforcement range is obtained by expansion and integration. A hierarchical computational partitioning approach is adopted, where computation is divided into two levels. The first level divides the original data space into an intermediate level that can be expressed using a standard. The second level divides the data in the intermediate level into the target space that needs to be calculated, generating a computational path.

[0019] Example 1: During the basic inspection data acquisition phase for existing building lap columns, the collection of structural dimension data covers geometric dimensions such as the length, width, and height of the lap columns, as well as the cross-sectional shape and dimensional details of the columns, and the dimensions of the connection points between the columns and the surrounding structure. Material performance data is acquired for the various materials that comprise the lap columns, such as the strength grade and elastic modulus of concrete, and the type, yield strength, and ultimate strength of the steel bars. Damage distribution data, through various testing methods, records the distribution, depth, and width of cracks on the column surface, as well as the location and area of concrete spalling and the corrosion of the steel bars.

[0020] After completing the basic inspection data collection, the characteristic parameter extraction phase begins. Extract key characteristic parameters from the structural dimension data, such as the slenderness ratio of the column, the moment of inertia of the section, and other parameters that can reflect the structural characteristics of the column; for the material performance data, extract core performance parameters such as the compressive strength of concrete and the tensile strength of steel bars; in the damage distribution data, extract damage-related parameters such as the number of cracks, the maximum crack width, and the percentage of spalling area. Extract parameter description items at the characteristic parameters. Taking the concrete strength grade as an example, its parameter description items may include attribute sequences such as the specific value of the strength grade, the test standard, and the test location. For each characteristic parameter, assign a corresponding weight value based on its influence on the performance of the lap column and the reinforcement calculation. Find correlation points based on the parameter description items. For example, columns in a certain area have similar structural dimensions, similar material properties, and damage distribution has a certain pattern. Combine these characteristic parameters with correlation points to obtain a correlation parameter group.

[0021] The parameter groups obtained by mutual correlation are used as reference points, and the calculation model is obtained through continuous correlation. Based on the regional segmentation method, the discrete segments obtained by the correlation of characteristic parameters between multiple locations are analyzed. Taking the lap columns in a certain urban village building complex as an example, the lap columns of different buildings are divided into multiple segments according to factors such as geographical location and structural type. For the correlation results of discrete characteristic parameters in each segment, the parameters of adjacent segments are fused and supplemented through expansion and integration to obtain a complete or partial calculation path model covering the reinforcement range. When constructing the calculation path model, if the main problem of the lap columns in a certain segment is concentrated on the bottom damage, and there are certain defects on the top of the column in the adjacent segment, the parameters of the two segments are comprehensively considered during the integration process, so that the calculation path model can fully reflect the situation within the entire reinforcement range.

[0022] When using hierarchical calculation division, the first level converts the original collected basic inspection data, such as structural dimension data containing a large amount of detailed information, complex material performance test data, and various damage distribution records, from the original data space to the intermediate level where standard expressions can be used. In this process, the original data is formatted, units are converted, and data is standardized to conform to the standard format for subsequent calculations and analyses. The second level further divides the intermediate level data into the target space that needs to be calculated. In combination with the goals and requirements of the reinforcement design, the intermediate level data is screened, classified, and reorganized to generate a calculation path that clarifies the specific process and steps from basic data to the final calculation results.

[0023] Example 2: This embodiment is used to describe the process of acquiring building structure information, constructing a calculation area, planning a calculation process, and collecting basic test data, which specifically includes: The first step is to obtain the building structure information required for calculation, including the target range of the area requiring reinforcement, the connection status, and load distribution data. Determining the target range requires a comprehensive on-site survey of existing buildings in the urban village to determine which areas of the lap columns require reinforcement. For example, in an older urban village complex, a preliminary inspection revealed significant damage to the lap columns of several buildings. The area containing the lap columns in these buildings can then be designated as the target range. To obtain the connection status, detailed records must be kept of the connection between the lap columns and the surrounding building structures, including whether they are rigid or flexible, and whether the connection is loose or cracked. The collection of load distribution data needs to take into account the building's function. For residential buildings, the dead and live loads generated by the number of residents and furniture arrangement must be counted. For commercial buildings, load changes caused by cargo stacking and crowd movement must also be considered.

[0024] The calculation area is constructed by defining boundaries based on the target range. After determining the specific areas requiring reinforcement, the boundaries of the calculation area are precisely defined through measurement and drawing. For example, using a measuring instrument such as a total station, the boundary coordinates of the buildings within the target range are measured and then marked on the drawing to form a clear calculation area. At the same time, a calculation benchmark is established based on the connection status and load distribution data. The connection status determines the transmission method and collaborative working performance of the lap column when subjected to force. Different connection statuses affect the setting of the calculation benchmark. Load distribution data is the foundation of the calculation. Only by accurately understanding the load conditions can a reasonable calculation benchmark be established. For example, if the connection between a lap column and the surrounding structure is relatively weak, the bearing capacity value of the connection part should be appropriately reduced when establishing the calculation benchmark. If the load in a certain area is large, the load consideration for this area should also be increased accordingly in the calculation benchmark.

[0025] Plan the calculation process according to the calculation area. First, construct a two-dimensional structural analysis diagram in the target range to be calculated. Using architectural drawings and field measurement data, use drawing software to draw a two-dimensional plan view including the lap columns and surrounding structures. This diagram can clearly show the position, size, and connection relationship of each lap column with other components. Then, according to the two-dimensional structural analysis diagram, divide the target range to be calculated into several grid units with preset intervals as the side length. The selection of the preset interval should take into account the calculation accuracy and efficiency. Generally speaking, for areas with complex structures and severe damage, the preset interval can be smaller to ensure the accuracy of the calculation; for areas with relatively simple structures, the preset interval can be appropriately increased.

[0026] Based on the target range, connection status, and load distribution data, the grid elements are classified into three types: core reinforcement areas, conventional connection areas, and weak damage areas. The core reinforcement areas are also prioritized. Core reinforcement areas are generally those areas that play a key role in the overall safety of the building and are severely damaged; conventional connection areas are areas where the connection status and stress conditions are relatively stable; and weak damage areas are areas where there is some damage but it has not yet reached the severity of the core reinforcement areas. For example, on the ground floor of a building, the lap columns bear the main load of the upper floors, and some columns have severe cracks and concrete spalling. In this case, the grid elements where these lap columns are located can be classified as core reinforcement areas and prioritized so that they can be given priority in subsequent calculations and reinforcement processes.

[0027] Several initial calculation processes are then randomly generated to form a set of initial calculation processes. These initial calculation processes can be randomly generated using a computer program. Each process includes the inspection sequence and calculation method for different grid cells. An evaluation index system is then constructed, consisting of a parameter matching index, a load adaptability index, and an operational feasibility index. The parameter matching index measures the degree of conformity between characteristic parameters at each location in the initial calculation process; the load adaptability index reflects the total load that can be carried per unit area in each section of the initial calculation process; and the operational feasibility index indicates the number of inspection locations and parameter ranges that require adjustment in the initial calculation process. Using the set of initial calculation processes as the initial sample, an iterative optimization process is performed according to the evaluation index system. By continuously adjusting the various parameters and steps in the calculation process, the optimal calculation process is gradually obtained, thus completing the calculation process planning. Finally, based on the determined optimal calculation process, basic inspection data is collected from multiple inspection locations to ensure comprehensiveness and accuracy of data collection.

[0028] Example 3: This embodiment is used to describe the iterative optimization process of the initial computing process set, which specifically includes: Based on the established evaluation index system, namely the parameter matching index, load adaptability index, and operational feasibility index, an evaluation value is calculated for each initial calculation process. For the parameter matching index, in a specific initial calculation process, the structural dimensions, material properties, and other characteristic parameters of multiple lap columns are involved. By comparing the corresponding parameters of different lap columns, a specific algorithm is used to determine the degree of conformity between these parameters. This value is the parameter matching index value for that process. The calculation of the load adaptability index requires combining the load distribution data in the building structure information and the load-bearing capacity of different areas. First, the load value that can be carried per unit area in each section of the initial calculation process is calculated, and then the values for all sections are accumulated to obtain the load adaptability index value. The operational feasibility index is calculated by analyzing the inspection plan and parameter settings involved in the initial calculation process, counting the number of inspection locations and parameter ranges that need to be adjusted, and using this as the operational feasibility index value.

[0029] After obtaining the values of each initial calculation process under the three evaluation indicators, the initial calculation processes are sorted from high to low according to the evaluation values of the three evaluation indicators, and then a sorted set of three initial calculation processes is obtained. Assume that the initial calculation process set is Indicates that Represents the first An initial calculation process, The value range is from arrive ; The sorted set corresponding to the parameter matching index is recorded as ,in Indicates that the parameter matching index is ranked first. The initial calculation process of the load adaptability index is ; The sorted set corresponding to the operation feasibility index is .

[0030] According to the evaluation value, a number of initial calculation processes are selected from the three sorting sets in a probabilistic manner to form three subsamples in different directions. Among them, the probability of being selected is proportional to the evaluation value. The sorting set corresponding to the parameter matching index For example, for the initial calculation process of the top ranking and behind , the probability of the former being selected is greater than the latter. Using the formula (Formula 1) to calculate the probability, in which Indicates the first The probability that an initial calculation process is selected, It is The evaluation value of the initial calculation process under the corresponding evaluation index, and are the maximum and minimum evaluation values in the sorted set, respectively. is an adjustable coefficient, which is used to control the value range of probability. According to the same method, the probability selection operation is performed in the sorted sets corresponding to the load adaptability index and the operation feasibility index, and finally three sub-samples in different directions are obtained. 、 、 .

[0031] Within each subsample, infeasible calculation solutions are eliminated through operations such as position updating, range adjustment, and individual screening. Position updating changes the order of inspection locations in the initial calculation process. For example, the original plan was to inspect the lapped columns on one side of the building first, but after updating, the lapped columns on the other side will be inspected first. Range adjustment refers to expanding or narrowing the inspection range involved in the calculation process. For example, originally only the upper half of the lapped column was inspected, but after adjustment, the inspection range was expanded to the entire column. Individual screening, based on pre-set rules, removes from the subsample initial calculation processes that have problems such as difficult-to-reach inspection locations or obviously unreasonable calculation parameters. Each subsample is iterated a pre-set number of times, ultimately resulting in three subsamples after iteration.

[0032] Then a crossover operation is performed. Individuals are randomly selected from the three sub-samples based on the evaluation values and combined to form a basic sample library. Then, crossover is performed between the three sub-samples in pairs to form three groups of crossover sub-samples. For each group of crossover sub-samples, the three evaluation values of each individual are calculated, and then the two evaluation index values involved in the crossover sub-samples are sorted in multiple dimensions. For example, when a group of crossover sub-samples involves a parameter matching index and a load adaptability index, the values of all individuals in the crossover sub-sample under these two indicators are comprehensively considered and sorted, and the different weights of the two indicators are taken into account during the sorting process. Based on the sorting results, position updates, range adjustments, and individual screening operations are performed again to eliminate infeasible calculation schemes. After a preset number of iterations of the three groups of crossover sub-samples, the three sub-samples are combined.

[0033] Finally, the three evaluation indicators are weighted and summarized to obtain the comprehensive evaluation value. The weight of the parameter matching indicator is set to , the weight of the load adaptability index is , the weight of the operational feasibility index is , and satisfy For an individual in the basic sample library (in , is the total number of individuals), its comprehensive evaluation value ,in 、 、 Individual The evaluation values under the parameter matching index, load adaptability index, and operational feasibility index are calculated. Based on the comprehensive evaluation value, the position update, range adjustment, and individual screening operations are continued. After a preset number of iterations, the individual with the largest evaluation value is finally obtained. This is determined as the optimal calculation process and used for data collection and related calculations in the subsequent reinforcement calculation of the overlapped columns of existing buildings in urban villages.

[0034] Example 4: This embodiment mainly describes the dynamic adjustment mechanism during the cross-subsample operation to optimize the cross-object selection process. The following describes this embodiment in detail through specific examples.

[0035] Assume that, in an urban village renovation project, a lapped column reinforcement calculation is performed on an area consisting of 10 old buildings. After the operations in Example 3, three subsamples are obtained, each containing 15 individual initial calculation processes. For example, a set of intersection subsamples involving parameter matching and load adaptability indicators must be identified before performing multi-dimensional sorting on these intersection subsamples.

[0036] First, the 30 individuals in the crossover subsample are ranked based on the combined evaluation values of the parameter matching index and the load adaptability index. After the ranking is complete, each individual is assigned a selection probability value based on its ranking order. For example, the individual ranked first is often selected first in traditional crossover selection due to its high comprehensive evaluation value, but under this dynamic adjustment mechanism, it is assigned a lower selection probability value. On the other hand, the individual ranked 30th is assigned a higher selection probability value due to its relatively low comprehensive evaluation value. The specific assignment process can use a simple linear relationship to establish a connection between the ranking order and the selection probability. Assume that the selection probability of the individual ranked first is 0.1, the selection probability of the individual ranked 30th is 0.9, and the selection probability of the intermediate individuals increases linearly between the two according to the ranking order.

[0037] When selecting a crossover target, a random selection is performed based on the assigned selection probability. For example, if a random number generator generates a number between 0 and 1 and the generated number is 0.75, and the individual selection probability is compared, it is found that this random number falls within the selection probability range of individuals ranked 25-26, and is closer to the selection probability range of individuals ranked 25, so the individual ranked 25 is selected as one of the crossover targets. Next, a random number is generated again to select another crossover target, and the selection is also performed based on the selection probability. For example, the individual ranked 18 is selected this time.

[0038] After selecting two intersecting objects, perform an intersecting operation on the calculation process content contained in these two individuals. For example, swap the content related to the dimensional inspection sequence of the overlapped column structure in the 25th individual with the content related to the material property inspection location settings in the 18th individual, forming two new individuals. During the intersecting process, carefully check the logical coherence and rationality of the calculation process in the newly generated individuals to ensure that the inspection locations and parameter settings meet actual operational requirements.

[0039] After completing the crossover operation, the evaluation value of the newly generated individuals is calculated, and their values under the parameter matching index and load adaptability index are recalculated. Then, these new individuals are sorted again in multiple dimensions together with other individuals in the crossover sub-sample. According to the sorting results, position update, range adjustment and individual screening operations are performed. For example, in terms of position update, if there is an unreasonable detection order in the new individual, such as first detecting the high-rise overlapping columns that are difficult to reach, and then detecting the overlapping columns that are easy to detect near the ground, the detection order will be adjusted; in terms of range adjustment, if the detection range of an individual is too large, resulting in increased difficulty in operation, the detection range will be appropriately narrowed; in the individual screening link, if the detection parameter settings of the new individual are unclear or cannot be implemented, the individual will be removed from the crossover sub-sample.

[0040] Following the above process, individuals in the crossover subsample are continuously crossovered, evaluated, sorted, adjusted, and screened. After a preset number of iterations, a more optimal calculation process individual is obtained. Throughout this process, a dynamic adjustment mechanism is used to allow individuals with low evaluation values that may have been overlooked to have more opportunities to participate in the crossover, avoiding the premature elimination of promising calculation processes. This allows for greater exploration of the optimization space for the calculation process, increases the possibility of obtaining a more optimal calculation process, and provides a more reasonable process solution for the reinforcement calculation of lap columns in existing buildings in urban villages. In subsequent crossover operations and iterations, the selection probability is continuously dynamically adjusted based on the sorting order of the individuals, ensuring that each crossover fully exploits the advantages of individuals with different calculation processes, gradually improving the calculation process to make it more suitable for actual reinforcement calculation needs.

[0041] Example 5: This embodiment is used to describe the screening and verification of the associated parameter group. The specific method is as follows: After obtaining the associated parameter set, screening begins. Bidirectional verification is a key method of screening. For example, the parameter set for a lap column in an old three-story building in an urban village includes multiple parameters, including structural dimensions, material properties, and damage distribution. During this bidirectional verification, the structural dimension data is first examined to determine the logical relationship between it, the material properties data, and the damage distribution data. For example, if the structural dimensions of a lap column indicate a small load-bearing area, but the material properties data indicate that the material used is extremely strong, far exceeding conventional combinations, this requires re-examination of the data source and calculation process to determine if there are any incorrect associations. Furthermore, the structural dimension and material property data are reverse-checked against the damage distribution data. If a lap column is found to have multiple severe cracks, but the structural dimensions and material property data do not reflect anomalies corresponding to this degree of damage, the parameter set also requires further inspection.

[0042] Uniqueness verification is also a key operation for screening out incorrectly associated parameter groups. When processing a large amount of data on overlapping columns in urban village buildings, there may be similarities in the overlapping column parameter groups of different buildings or different parts of the same building, but each parameter group should have its own uniqueness. For example, after inspecting multiple buildings in an urban village area, numerous overlapping column parameter groups were obtained. During the uniqueness verification process, all parameter items of each parameter group are compared. If it is found that the structural dimension data of two parameter groups are exactly the same, but there are differences in the material performance data and damage distribution data, and this difference cannot be explained by reasonable building design or usage, then it is necessary to further determine that one of the parameter groups may be incorrectly associated and mark it for rechecking and verification. Through two-way verification and uniqueness verification, incorrectly associated parameter groups are gradually eliminated to obtain a relatively accurate set of parameter groups.

[0043] After the screening is completed, the data verification phase for the screened parameter group begins. Historical data comparison is an important way to verify data. In urban village building renovation projects, a certain amount of historical data on existing building inspections is usually accumulated. Compare the screened parameter group with the historical data and analyze from multiple dimensions. For example, for the material performance parameter group of the lap column of a certain type of building, compare the material performance data of previous buildings of the same type under similar service years and environments to see if the data in the new parameter group is within a reasonable fluctuation range. If the concrete strength data in the new parameter group is significantly higher than the average value of the historical data, and there is no reasonable explanation such as the upgrade of building materials, the accuracy of the parameter group needs to be further verified.

[0044] Cross-validation also plays an important role in data verification. The filtered parameter group is divided into multiple subsets according to certain rules, and the data between different subsets are used to verify each other. For example, the parameter group of the lap columns of a building in an urban village area is divided into two subsets, one subset is used to build a preliminary calculation model, and the other subset is used to verify the rationality of the model. After using the first subset to build the calculation model, the parameter data in the second subset is substituted into the model for calculation to observe whether the calculation results are consistent with the actual situation. If there is a large deviation between the calculation results and the actual building structure performance, damage conditions, etc., it means that there may be problems with the integrity or accuracy of the parameter group, and the parameter group needs to be re-checked and corrected.

[0045] After comparing and cross-validating historical data to confirm the completeness and accuracy of the parameter set, it was used to generate the calculation model. During the model generation process, each parameter in the parameter set was set strictly in accordance with the parameters to ensure that the model can truly reflect the actual situation of the overlapping columns of existing buildings in urban villages, providing a reliable data foundation for subsequent reinforcement calculations and ensuring that the reinforcement calculation results are more in line with actual needs.

[0046] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0047] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A calculation method for strengthening overlapping columns of existing buildings in urban villages, characterized by: Includes the following: Obtain basic inspection data of the existing building overlap columns, extract characteristic parameters based on the basic inspection data, and associate the extracted characteristic parameters to obtain a parameter group; The basic detection data includes structural dimension data, material property data, and damage distribution data; wherein the parameter group is obtained by association based on the extracted characteristic parameters, including: extracting parameter description items at the characteristic parameters, assigning weight values to the characteristic parameters, and finding association points based on the parameter description items to obtain the association parameter group; wherein the parameter description items are attribute sequences that describe the characteristic parameters; Build a calculation model based on basic test data and parameter groups, and divide the calculation model into levels based on the basic test data to generate a calculation path, including: The calculation model is obtained by continuous correlation using the parameter groups obtained by mutual correlation as the reference point. The discrete segments obtained by correlation of characteristic parameters between multiple locations are used by the regional segmentation method. The complete or partial calculation path model covering the reinforcement range is obtained by expansion and integration. A hierarchical computational partitioning approach is adopted, where computation is divided into two levels. The first level divides the original data space into an intermediate level that can be expressed using a standard. The second level divides the data in the intermediate level into the target space that needs to be calculated, generating a computational path.

2. The calculation method for reinforcing overlapping columns of existing buildings in urban villages according to claim 1 is characterized in that: Also includes: Obtain the building structure information required for calculation and generate a calculation process based on the building structure information; Various test locations are collected according to the calculation process to obtain basic test data; Building structural information includes the target range of the reinforcement area, connection status, and load distribution data. Based on this building structural information, a calculation process is generated, including the following: Define the boundaries of the target range to build a calculation area, and plan the calculation process based on the calculation area; Establish a calculation benchmark based on the connection status and load distribution data, and collect basic test data based on the calculation benchmark.

3. The calculation method for strengthening overlapping columns of existing buildings in urban villages according to claim 2 is characterized in that: The calculation process according to the calculation area planning includes: Construct a two-dimensional structural analysis diagram in the target range to be calculated, and divide the target range to be calculated into a number of grid units with preset intervals as side lengths according to the two-dimensional structural analysis diagram; Based on the target range, connection status, and load distribution data, the grid cells are classified into three types: core reinforcement area, conventional connection area, and weak damage area, and the core reinforcement area is marked with priority; Randomly generate several initial calculation processes to form an initial calculation process set; Constructing an evaluation index system, wherein the evaluation index system includes a parameter matching index, a load adaptability index, and an operational feasibility index; The parameter matching index is the matching value of the characteristic parameters between each position in the initial calculation process; The load adaptability index is the sum of the loads that can be carried per unit area in each section during the initial calculation process; The operational feasibility indicator is the number of detection locations and parameter ranges that need to be adjusted in the initial calculation process; Taking the initial calculation process set as the initial sample, iterative optimization is performed according to the evaluation index system to obtain the optimal calculation process and complete the calculation process planning.

4. The calculation method for reinforcing overlapping columns of existing buildings in urban villages according to claim 3 is characterized in that: Using the initial set of computational processes as the initial sample, iterative optimization is performed based on the evaluation indicator system, including: According to the evaluation index system, the evaluation value of each initial calculation process is calculated, and the evaluation value of the initial calculation process is sorted from high to low according to the three evaluation indicators to obtain a sorted set of three initial calculation processes; According to the evaluation value, several initial calculation processes are selected from the three sorted sets by probability to form sub-samples in three different directions. The probability of being selected is proportional to the evaluation value. In each sub-sample, infeasible calculation schemes are eliminated by position update, range adjustment and individual screening, and the iterative process is repeated for a preset number of times to form three sub-samples after iteration. Individuals are randomly selected from the three subsamples based on their evaluation values to form a basic sample library. Three groups of crossover subsamples are formed by crossing two of the three subsamples. For each crossover subsample, the three evaluation values of each individual are calculated. The two evaluation values involved in the crossover subsamples are sorted in multiple dimensions. Based on the sorting results, positions are updated, ranges are adjusted, and individuals are screened. Infeasible calculation schemes are then eliminated. After the three groups of cross-subsamples have iterated a preset number of times, the three subsamples are combined together, and the three evaluation indicators are weighted and summarized to obtain a comprehensive evaluation value. According to the comprehensive evaluation value, the position is updated, the range is adjusted, and the individual is screened. After iterating a preset number of times, the individual with the largest evaluation value is obtained as the optimal calculation process.

5. The calculation method for reinforcing overlapping columns of existing buildings in urban villages according to claim 4 is characterized by: The two evaluation values involved in the cross sub-sample are sorted in multiple dimensions, and position update, range adjustment and individual screening are performed according to the sorting results. Before the cross sub-samples are crossed, a dynamic adjustment mechanism is used to select the cross object, wherein the selection probability of the cross object is related to the position of the individual in the sorting. The later the position of the individual in the sorting, the higher the probability of being selected.

6. The calculation method for reinforcing overlapping columns of existing buildings in urban villages according to claim 5 is characterized in that: Also includes: The selection probability is assigned according to the sorting order of the individuals to be crossed. The later the individual is sorted, the greater the selection probability value.

7. The calculation method for reinforcing overlapping columns of existing buildings in urban villages according to claim 1 is characterized in that: Also includes: The associated parameter groups are screened, and the parameter groups that are incorrectly associated are screened. When constructing the calculation model, the model is constructed according to the screened parameter groups.

8. The calculation method for reinforcing overlapping columns of existing buildings in urban villages according to claim 7 is characterized by: The associated parameter groups are screened by using two-way verification and uniqueness verification to eliminate incorrectly associated parameter groups.

9. The calculation method for reinforcing overlapping columns of existing buildings in urban villages according to claim 8 is characterized by: Also included: Perform data verification on the screened parameter group to verify the integrity and accuracy of the parameter group. When generating the calculation model, generate it based on the verified parameter group.

10. The calculation method for reinforcing overlapping columns of existing buildings in urban villages according to claim 9 is characterized in that: Perform data verification on the screened parameter groups, including: using historical data comparison and cross-validation to confirm the completeness and accuracy of the parameter groups.

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