A foundation reinforcement design method and system based on a toughness concept evaluation

Abstract

The present application relates to the technical field of engineering resilience design, and particularly relates to a foundation reinforcement design method and system based on resilience concept evaluation. A plurality of foundation reinforcement schemes are provided by a designer, a resilience decision hierarchy architecture is constructed, a judgment matrix is obtained through pairwise comparison of resilience decision elements of the resilience decision hierarchy architecture, expert discussion is carried out on the plurality of foundation reinforcement schemes based on the judgment matrix, and a weight vector of resilience evaluation of each foundation reinforcement scheme is determined; an inverse index table of a conversion formula is used to output a membership conversion formula for a resilience index attribute index of an engineering parameter of each foundation reinforcement scheme, a resilience performance parameter is generated based on the membership conversion formula, and a foundation reinforcement scheme with the highest resilience score is determined as a design scheme in combination with the weight vector. Through the present application, resilience concept evaluation design decision can be carried out on the foundation reinforcement scheme in the proposed project, and the safety and reliability of the foundation are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of engineering toughness design technology, and in particular to a foundation reinforcement design method and system based on toughness concept assessment. Background Technology

[0002] The ongoing exploration of the perennial question of "how safe is sufficient" in engineering has driven the continuous development of design concepts. As engineering design concepts have evolved from allowable stress design to load resistance partial factor design, to performance-based design, and finally to toughness-based design, the concept and process of toughness design in foundation reinforcement design are still immature. Existing design methods are unable to accurately assess the risks of a proposed project, select key toughness indicators to construct a reasonable toughness index system for subsequent foundation reinforcement design, or provide multi-level auxiliary decision-making for the toughness indicators of foundation reinforcement schemes. Furthermore, they cannot accurately quantify the normalized toughness performance parameters in foundation reinforcement schemes to obtain toughness performance parameters for design purposes. Therefore, a design method based on toughness for foundation reinforcement is urgently needed to guide foundation reinforcement design. Summary of the Invention

[0003] This invention overcomes the shortcomings of the prior art and provides a foundation reinforcement design method and system based on the concept of toughness assessment.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0005] The first aspect of this invention provides a foundation reinforcement design method based on the toughness concept assessment, comprising the following steps:

[0006] S102: Through the construction party's thorough geological survey, natural environment survey, and construction element survey of the target project, obtain detailed foundation survey parameters, centrally allocate and replace the toughness index of the toughness index dictionary with the detailed foundation survey parameters, analyze the replacement cost, and generate a decision index of detailed foundation survey parameters and toughness index.

[0007] S104: Obtain the evaluation toughness index through big data. The risk level assessment specification of each toughness index for the stability of foundation reinforcement is used. Based on the decision index, the risk assessment of the target proposed project is carried out in the risk level assessment specification with positive and negative expectations, and priority toughness indexes are selected to construct a toughness index system.

[0008] S106: The designer provides multiple foundation reinforcement schemes, constructs a resilience decision hierarchy, obtains a judgment matrix by comparing the resilience decision elements of the resilience decision hierarchy, and conducts expert discussions on multiple foundation reinforcement schemes based on the judgment matrix to determine the weight vector for the resilience assessment of each foundation reinforcement scheme.

[0009] S108: Using the inverted index table of the transformation formula, output the membership transformation formula for the toughness index attribute index of the engineering parameters of each foundation reinforcement scheme. Based on the membership transformation formula, normalize the engineering parameters to generate toughness performance parameters and combine them with the weight vector to determine the foundation reinforcement scheme with the highest toughness score as the design scheme.

[0010] More specifically, step S102 includes the following steps:

[0011] To obtain the target proposed project, the construction party conducts a thorough geological survey, natural environment survey, and construction element survey of the target proposed project to obtain detailed foundation survey parameters of the target proposed project;

[0012] Based on big data networks, a dictionary of toughness indicators necessary for geological exploration, natural environment surveys and construction element surveys is obtained. At the same time, the survey dimensions of the target proposed project are obtained. From the detailed foundation survey parameters, k multidimensional center points are randomly selected according to the survey dimensions to obtain a multidimensional center point-non-multidimensional center point community.

[0013] By linking each detailed foundation survey parameter output from geological exploration, natural environment survey, and construction element survey with each toughness index in the toughness index dictionary, a multidimensional center point affiliation cluster is constructed.

[0014] The Manhattan distance method is introduced to calculate the Manhattan distance of each non-multidimensional centroid in the multidimensional centroid-non-multidimensional centroid community relative to each multidimensional centroid, and each non-multidimensional centroid is assigned to the cluster corresponding to the multidimensional centroid with the smallest Manhattan distance.

[0015] After allocation, attempt to perform a permutation between each non-multidimensional centroid and the current multidimensional centroid, and reallocate the steps to calculate all non-multidimensional centroids to the nearest multidimensional centroid, obtain the total permutation cost, and simultaneously preset the ideal permutation cost and the limit permutation cost;

[0016] If the cost deviation between the total replacement cost and the ideal replacement cost is greater than the preset cost deviation value, the replacement of the non-multidimensional center point is retained; if the cost deviation value is less than the preset cost deviation value, the original replacement assignment is maintained.

[0017] Repeat the replacement steps of the non-multidimensional center point with the current multidimensional center point and the determination steps of the total replacement cost until the total replacement cost reaches the limit replacement cost, and generate the decision index of the foundation detailed investigation parameters and toughness index.

[0018] More specifically, step S104 includes the following steps:

[0019] The risk level assessment standard for foundation reinforcement stability of each resilience index in the dictionary of resilience indicators is obtained by big data network identification. Based on each assessment criterion of the risk level assessment standard, risk weight amplitude for different dimensions of resilience indicators is constructed.

[0020] A preset threshold for dimensional decision decomposition is set. If the decision index is greater than the threshold for dimensional decision decomposition, the resilience index of the corresponding foundation detailed survey parameter is marked as a positive resilience index. If the decision index is less than the threshold for dimensional decision decomposition, the resilience index of the corresponding foundation detailed survey parameter is marked as a negative resilience index.

[0021] Extract the vector of the maximum value recorded for each positive resilience index in the resilience index dictionary to construct a positive expected solution, extract the vector of the minimum value recorded for each negative resilience index to construct a negative expected solution, and determine the risk weights for each positive and negative resilience index according to the risk weight magnitude to generate a positive-negative risk weighted matrix.

[0022] Calculate the distance between the positive-negative risk weighting matrix and the positive expected solution to obtain the positive bias distance value; calculate the distance between the positive-negative risk weighting matrix and the negative expected solution to obtain the negative bias distance value.

[0023] By combining positive and negative bias distance values, a relative risk approximation of the risk level of each foundation detailed survey parameter represented by the resilience index decision is performed, resulting in the relative risk approximation of each foundation detailed survey parameter.

[0024] Only the resilience indicators corresponding to the decision-making of the detailed foundation survey parameters with relative risk proximity greater than the preset relative risk proximity are extracted and marked as priority resilience indicators. A resilience indicator system is constructed using all the extracted priority resilience indicators.

[0025] More specifically, step S106 includes the following steps:

[0026] The designer provides multiple foundation reinforcement schemes, constructs a bubble accumulation framework, and uses the bubble diagram framework to cumulatively fit all the priority toughness indices in the toughness index system to generate the toughness index bubble volume value.

[0027] Based on the resilience index system, an index layer is constructed that relies solely on the priority resilience index, defined as a single criterion layer; if the bubble volume value of the resilience index is greater than the preset volume value, then an additional criterion layer that considers the resilience index system is constructed based on each resilience index, defined as a sub-criterion layer.

[0028] A scheme layer is proposed by taking multiple foundation reinforcement schemes as inputs, an overall goal decision layer is created based on resilience assessment requirements, and a resilience decision-making hierarchical architecture is constructed by combining the scheme layer, the overall goal decision layer, a single criterion layer or a sub-criterion layer.

[0029] The Saaty scaling method is introduced to compare each resilience decision element in each layer with a certain resilience decision element in the layer above it one by one, so as to evaluate the relative importance of each resilience decision element between different layers and form a judgment matrix.

[0030] The judgment matrix is ​​extracted and calculated using the matrix characteristic equation to obtain the judgment feature values ​​and the corresponding judgment feature vectors for each judgment feature value. Only the judgment weight vector corresponding to the largest judgment feature value is extracted for consistency calculation to obtain the consistency coefficient.

[0031] If the consistency coefficient is less than the preset consistency coefficient, then experts will discuss multiple foundation reinforcement schemes based on the judgment weight vector corresponding to the maximum judgment feature value to determine the weight vector for the toughness assessment of each foundation reinforcement scheme.

[0032] More specifically, step S108 includes the following steps:

[0033] Obtain the engineering requirements of the target proposed project, and obtain the corresponding engineering parameters for each foundation reinforcement scheme from the design party according to the engineering requirements;

[0034] Based on big data networks, several conversion formulas for the coordinated normalization and quantification of performance parameters under different resilience index categories in the field of building engineering are obtained. Each conversion formula is assigned an inverted index attribute prefix according to the resilience index category. An inverted index table of conversion formulas is constructed based on the index key of the inverted index prefix.

[0035] Obtain one or more fuzzy engineering index attributes implied by engineering parameters, and introduce the grey relational method to calculate the correlation between one or more of the fuzzy engineering index attributes and each priority toughness index in the toughness index system, so as to obtain the grey relational degree.

[0036] Extract only one or more priority resilience indicators with a gray relation degree greater than the preset gray relation degree, mark them as resilience indicator query terms, construct attribute index key characters for each resilience indicator query term based on the fuzzy engineering indicator attributes, and split each resilience indicator query term into N sub-term fragments based on the attribute index key characters.

[0037] The inverted index table of the transformation formula is used to find several candidate intersection prefix strings containing fragments of each sub-word group. Similarity analysis is used to obtain the membership transformation formula between each candidate intersection prefix string and each toughness index query word group. Based on the membership transformation formula, the engineering parameters are normalized to generate toughness performance parameters. The foundation reinforcement scheme with the highest toughness score is determined by combining the toughness performance parameters with the weight vector as the foundation design scheme.

[0038] More specifically, the process involves using an inverted index table with a transformation formula to find several candidate intersection prefix strings containing fragments of each sub-word group, and performing similarity analysis to obtain the membership transformation formula between each candidate intersection prefix string and each toughness index query word group. Based on the membership transformation formula, engineering parameters are normalized to generate toughness performance parameters. Finally, the foundation reinforcement scheme with the highest toughness score is determined as the foundation design scheme by combining the toughness performance parameters with the weight vector. This process specifically includes the following steps:

[0039] An intersection strategy is introduced. The inverted index table of the transformation formula is used to find the prefix set containing each of the sub-word fragments in the intersection strategy, obtain several candidate intersection prefix strings, and calculate the similarity between each candidate intersection prefix string and each resilience index query word.

[0040] If the similarity is greater than the preset similarity, the conversion formula corresponding to the inverted index attribute prefix of the candidate intersection prefix string is extracted and labeled as the membership conversion formula. The membership conversion formula is used to quantify and evaluate the engineering parameters of different foundation reinforcement schemes and obtain multiple toughness performance parameters.

[0041] The scores of each foundation reinforcement scheme are calculated by combining the weight vector with multiple toughness performance parameters to generate a toughness score for each foundation reinforcement scheme. The foundation reinforcement scheme with the highest toughness score is then extracted as the foundation design scheme for the target proposed project.

[0042] A second aspect of the present invention provides a foundation reinforcement design system based on the resilience concept assessment. The foundation reinforcement design system includes a memory and a processor. The memory stores a foundation reinforcement design method program based on the resilience concept assessment. When the foundation reinforcement design method program is executed by the processor, the foundation reinforcement design method steps described in any one of the present invention are implemented.

[0043] This invention addresses the technical deficiencies in the prior art, and its beneficial technical effects are as follows:

[0044] Through thorough geological surveys, natural environment investigations, and construction element surveys of the proposed project, detailed foundation survey parameters are obtained. These parameters are then centrally assigned to toughness indicators in a toughness index dictionary, and the replacement costs are analyzed to generate a decision index for the detailed foundation survey parameters and toughness indicators. Big data is used to obtain risk level assessment standards for each toughness indicator regarding the stability of foundation reinforcement. Based on the decision index, positive and negative expected risk assessments are conducted on the proposed project within the risk level assessment standards, and priority toughness indicators are selected to construct a toughness index system. Multiple foundation reinforcement schemes are provided by the design team, and a toughness decision hierarchy is constructed. A judgment matrix is ​​obtained by pairwise comparison of toughness decision elements within the hierarchy. Based on the judgment matrix, multiple foundation reinforcement schemes are discussed by experts to determine the weight vector for the toughness assessment of each scheme. Using an inverted index table of transformation formulas, a membership transformation formula is output for the toughness indicator attribute index of the engineering parameters for each foundation reinforcement scheme. Based on the membership transformation formula, the engineering parameters are normalized to generate toughness performance parameters, and combined with the weight vector, the foundation reinforcement scheme with the highest toughness score is determined as the design scheme. This invention enables the design decision-making of foundation reinforcement schemes in proposed projects by evaluating the toughness concept, so that the constructed foundation has a certain strength and toughness, which significantly improves the safety and reliability of the foundation. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained from these drawings without creative effort.

[0046] Figure 1 A flowchart of the first method for foundation reinforcement design based on the resilience concept is shown;

[0047] Figure 2 A flowchart of a second method for foundation reinforcement design based on the resilience concept is shown.

[0048] Figure 3 A system framework diagram of a foundation reinforcement design system based on the resilience concept is shown.

[0049] Figure 4 A hierarchical diagram of the three levels of resilience index system is shown;

[0050] Figure 5 A schematic diagram of the resilience decision-making hierarchy is shown. Detailed Implementation

[0051] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0052] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0053] The first aspect of this invention provides a foundation reinforcement design method based on the toughness concept assessment, such as... Figure 1 As shown, it includes the following steps:

[0054] S102: Through the construction party's thorough geological survey, natural environment survey, and construction element survey of the target project, obtain detailed foundation survey parameters, centrally allocate and replace the toughness index of the toughness index dictionary with the detailed foundation survey parameters, analyze the replacement cost, and generate a decision index of detailed foundation survey parameters and toughness index.

[0055] S104: Obtain the evaluation toughness index through big data. The risk level assessment specification of each toughness index for the stability of foundation reinforcement is used. Based on the decision index, the risk assessment of the target proposed project is carried out in the risk level assessment specification with positive and negative expectations, and priority toughness indexes are selected to construct a toughness index system.

[0056] S106: The designer provides multiple foundation reinforcement schemes, constructs a resilience decision hierarchy, obtains a judgment matrix by comparing the resilience decision elements of the resilience decision hierarchy, and conducts expert discussions on multiple foundation reinforcement schemes based on the judgment matrix to determine the weight vector for the resilience assessment of each foundation reinforcement scheme.

[0057] S108: Using the inverted index table of the transformation formula, output the membership transformation formula for the toughness index attribute index of the engineering parameters of each foundation reinforcement scheme. Based on the membership transformation formula, normalize the engineering parameters to generate toughness performance parameters and combine them with the weight vector to determine the foundation reinforcement scheme with the highest toughness score as the design scheme.

[0058] More specifically, step S102 includes the following steps:

[0059] To obtain the target proposed project, the construction party conducts a thorough geological survey, natural environment survey, and construction element survey of the target proposed project to obtain detailed foundation survey parameters of the target proposed project;

[0060] Based on big data networks, a dictionary of toughness indicators necessary for geological exploration, natural environment surveys and construction element surveys is obtained. At the same time, the survey dimensions of the target proposed project are obtained. From the detailed foundation survey parameters, k multidimensional center points are randomly selected according to the survey dimensions to obtain a multidimensional center point-non-multidimensional center point community.

[0061] By linking each detailed foundation survey parameter output from geological exploration, natural environment survey, and construction element survey with each toughness index in the toughness index dictionary, a multidimensional center point affiliation cluster is constructed.

[0062] The Manhattan distance method is introduced to calculate the Manhattan distance of each non-multidimensional centroid in the multidimensional centroid-non-multidimensional centroid community relative to each multidimensional centroid, and each non-multidimensional centroid is assigned to the cluster corresponding to the multidimensional centroid with the smallest Manhattan distance.

[0063] After allocation, attempt to perform a permutation between each non-multidimensional centroid and the current multidimensional centroid, and reallocate the steps to calculate all non-multidimensional centroids to the nearest multidimensional centroid, obtain the total permutation cost, and simultaneously preset the ideal permutation cost and the limit permutation cost;

[0064] If the cost deviation between the total replacement cost and the ideal replacement cost is greater than the preset cost deviation value, the replacement of the non-multidimensional center point is retained; if the cost deviation value is less than the preset cost deviation value, the original replacement assignment is maintained.

[0065] Repeat the replacement steps of the non-multidimensional center point with the current multidimensional center point and the determination steps of the total replacement cost until the total replacement cost reaches the limit replacement cost, and generate the decision index of the foundation detailed investigation parameters and toughness index.

[0066] It should be noted that for a specific proposed project, a thorough and detailed risk assessment must be conducted first, based on a comprehensive geological survey, natural environment investigation, and construction element investigation. This assessment is then used to select the most important indicators for the foundation reinforcement project within the resilience index system. However, traditional risk assessment methods for construction projects often struggle to analyze the dependence of investigated parameters on related risks at the indicator level. For example, if a specific foundation investigation parameter relies on the risk assessment value of a particular resilience index, the accuracy of the risk assessment results for selecting subsequent resilience indicators will significantly decrease, making it impossible to select reasonable resilience indicators to construct an accurate resilience index system. To address this, this method first randomly selects k multidimensional centroids from the foundation investigation parameters according to the investigation dimensions of the target proposed project. These multidimensional centroids are representative points following different investigation dimensions, ensuring that the remaining foundation investigation parameters share a common dimensional hierarchical basis, thus accelerating the decision-making clustering rate between the remaining foundation investigation parameters and the resilience indicators. The investigation dimensions include investigation time, investigation area, and investigation method. The clustering of multidimensional centroids indicates the clusters to which each detailed foundation survey parameter belongs to different resilience indices. Each non-multidimensional centroid is assigned to the cluster corresponding to the multidimensional centroid with the minimum Manhattan distance. This aims to ensure that each survey parameter is reasonably assigned under the current multidimensional centroid structure, so that detailed foundation survey parameters belonging to the same resilience index can attract each other and improve the accuracy of decision response between resilience indices and survey parameters.

[0067] It should be noted that the process iterates through each pair of combinations containing one multidimensional centroid and one non-multidimensional centroid, attempting to replace both. Then, the sum of the distances from all non-multidimensional centroids to their nearest multidimensional centroid is calculated. This evaluation assesses whether the replacement reduces the total clustering cost, i.e., the total replacement cost. This allows the detailed foundation survey parameters to find better combinations of multidimensional centroids for clustering. Each exchange that helps reduce costs is accepted, gradually improving the clustering effect between the detailed foundation survey parameters and resilience indicators. This effectively avoids local ranges caused by poor initial selection and significantly improves the accuracy of the decision-making correspondence between the detailed foundation survey parameters and resilience indicators. If the cost deviation between the total replacement cost and the ideal replacement cost is greater than the preset cost deviation, it indicates that the replacement of the multidimensional centroid and non-multidimensional centroid reduces the total cost, suggesting that the detailed foundation survey parameter of that multidimensional centroid has a better resilience indicator decision membership. Therefore, the replacement with the better decision membership is retained. Conversely, if the replacement fails to reduce the total cost, it indicates that the resilience index is the optimal decision-making attribute for the detailed foundation investigation parameters. This method clarifies the decision-making attribute of different resilience indices during the risk assessment of a proposed project, providing an effective benchmark for selecting important resilience indices in subsequent preliminary risk assessments, thus making the selection of important resilience indices after risk assessment more reasonable and accurate.

[0068] It should be noted that, specifically regarding foundation toughness, the toughness index system includes three levels, such as... Figure 4 The indicators are as follows: Absorption capacity: the ability of the foundation and superstructure to absorb external load impacts and maintain normal operation. Resistance capacity: the ability of the foundation to ensure the stability of the main functions of the foundation and superstructure under huge impacts, i.e., global safety under the ultimate bearing capacity, allowing for local damage and providing a basis for subsequent repair of the foundation and structure. Recovery capacity: the maintainability of the foundation, divided into routine reinforcement and post-disaster maintenance. Routine reinforcement measures are used to address the gradual decline in the performance of the foundation system under natural conditions, while emergency repair measures are used after the foundation suffers damage from sudden events. Resistance capacity indicators include foundation bearing capacity, underlying weak layer bearing capacity, overall anti-sliding safety factor, horizontal sliding safety factor, strength of the foundation reinforcement structure, impermeability coefficient, anti-buoyancy coefficient, soil between piles in composite foundations, shear strength of the pile body, and liquefaction coefficient. Absorption capacity indicators include foundation settlement, differential settlement, foundation erosion resistance, foundation lateral deformation, construction period stability, degree of foundation softening, and soft soil subsidence. The indicators of repair capacity include the cost of foundation reinforcement, the difficulty of foundation monitoring, the time required for post-disaster repair, and the cost of post-disaster repair.

[0069] More specifically, step S104, as follows: Figure 2 As shown, the specific steps include:

[0070] S202: Identify and obtain the risk level assessment specifications for each resilience index in the dictionary of resilience indicators for foundation reinforcement stability through big data network identification, and construct risk weight amplitudes for different dimensions of resilience indicators based on each assessment criterion of the risk level assessment specifications.

[0071] S204: Preset dimensional decision boundary threshold. If the decision index is greater than the dimensional decision decomposition threshold, the resilience index of the corresponding foundation detailed survey parameter is marked as a positive resilience index. If the decision index is less than the dimensional decision decomposition threshold, the resilience index of the corresponding foundation detailed survey parameter is marked as a negative resilience index.

[0072] S206: Extract the composition vector of the maximum value recorded for each positive resilience index in the resilience index dictionary to construct a positive expected solution, extract the composition vector of the minimum value recorded for each negative resilience index to construct a negative expected solution, and determine the risk weights for each positive and negative resilience index according to the risk weight magnitude to generate a positive-negative risk weighted matrix.

[0073] S208: Calculate the distance between the positive-negative risk weighting matrix and the positive expected solution to obtain the positive bias distance value; calculate the distance between the positive-negative risk weighting matrix and the negative expected solution to obtain the negative bias distance value.

[0074] S210: By combining the positive and negative bias distance values, a relative approximation of the risk level is made for each foundation detailed survey parameter represented by the resilience index decision, and the relative risk approximation of each foundation detailed survey parameter is obtained.

[0075] S212: Extract only the resilience indicators corresponding to the decision-making of the detailed foundation survey parameters whose relative risk proximity is greater than the preset relative risk proximity, mark them as priority resilience indicators, and construct a resilience indicator system using all the extracted priority resilience indicators.

[0076] It should be noted that traditional risk assessment models applied to engineering infrastructure rely on historical risk planning experience, leading to significant subjective biases in risk assessment results and a substantial decrease in accuracy. This results in inaccurate selection errors for key resilience indicators. To address this, this method defines risk weight amplitudes for different resilience indicator dimensions based on the risk level assessment criteria for evaluating the impact of resilience indicators on foundation reinforcement stability. Then, it sets dimensional decision boundary thresholds to further identify the directional dimension of the resilience indicators—positive and negative—based on the obtained decision indices. This unifies all indicators to the same dimensionless order of magnitude, eliminating the influence of different indicator dimensions and units, making all indicators comparable, and preventing certain resilience indicators from erroneously dominating indicator selection decisions due to unreasonable risk assessment values. Based on the risk weight amplitude, risk weights are determined for each positive and negative resilience index, generating a positive-negative risk weighting matrix. This matrix assigns different influences to each resilience index, reflecting their importance in the risk assessment of the proposed project. This makes the targeted risk assessment of each foundation detailed survey parameter, based on the known high membership of each resilience index, more consistent with actual preferences or expert judgment. The positive expected value represents the optimal solution, indicating that the maximum risk level is emphasized (optimal) for each resilience index, while the negative expected value represents the worst solution, indicating that the minimum risk level is emphasized (worst). The distances between the positive-negative risk weighting matrix and the positive and negative expected values ​​are then measured. The distances between each foundation detailed survey parameter and the maximum and minimum risk level emphasized values ​​are measured, reflecting the degree to which each foundation detailed survey parameter for a given resilience index approaches the optimal or worst risk. A smaller positive bias distance value indicates that the detailed foundation investigation parameters are closer to the most reasonable risk level results, while a larger negative bias distance value indicates that the detailed foundation investigation parameters are further away from the unreasonable risk level results.

[0077] It should be noted that the relative risk proximity comprehensively considers the degree to which each detailed foundation survey parameter approaches the most reasonable risk level solution and the unreasonable risk level solution. If the relative risk proximity is greater than the preset relative risk proximity, it indicates that the risk assessment level of the detailed foundation survey parameter is excellent. Therefore, the resilience index based on the detailed foundation survey parameter is the best index to evaluate the reinforcement performance of the proposed project and has a high selection priority, thus it is selected. Conversely, if the relative risk proximity is less than the preset relative risk proximity, it indicates that the risk assessment level of the detailed foundation survey parameter is not ideal, and the corresponding resilience index is not the best index to evaluate the reinforcement performance and importance of the proposed project, and is not selected. This method can conduct positive and negative risk level assessments of detailed foundation survey parameters based on the prior knowledge of resilience index-based decision-making. Compared with the cumbersome steps of first assessing risk and then selecting resilience indexes, its advantage lies in realizing the selection of indicators in the process of determining the reasonableness of the proposed project's risk assessment, significantly improving the efficiency of resilience index selection, ensuring the accuracy of risk assessment, and making the construction of the resilience index system more precise and reasonable.

[0078] More specifically, step S106 includes the following steps:

[0079] The designer provides multiple foundation reinforcement schemes, constructs a bubble accumulation framework, and uses the bubble diagram framework to cumulatively fit all the priority toughness indices in the toughness index system to generate the toughness index bubble volume value.

[0080] Based on the resilience index system, an index layer is constructed that relies solely on the priority resilience index, defined as a single criterion layer; if the bubble volume value of the resilience index is greater than the preset volume value, then an additional criterion layer that considers the resilience index system is constructed based on each resilience index, defined as a sub-criterion layer.

[0081] A scheme layer is proposed by taking multiple foundation reinforcement schemes as inputs, an overall goal decision layer is created based on resilience assessment requirements, and a resilience decision-making hierarchical architecture is constructed by combining the scheme layer, the overall goal decision layer, a single criterion layer or a sub-criterion layer.

[0082] The Saaty scaling method is introduced to compare each resilience decision element in each layer with a certain resilience decision element in the layer above it one by one, so as to evaluate the relative importance of each resilience decision element between different layers and form a judgment matrix.

[0083] The judgment matrix is ​​extracted and calculated using the matrix characteristic equation to obtain the judgment feature values ​​and the corresponding judgment feature vectors for each judgment feature value. Only the judgment weight vector corresponding to the largest judgment feature value is extracted for consistency calculation to obtain the consistency coefficient.

[0084] If the consistency coefficient is less than the preset consistency coefficient, then experts will discuss multiple foundation reinforcement schemes based on the judgment weight vector corresponding to the maximum judgment feature value to determine the weight vector for the toughness assessment of each foundation reinforcement scheme.

[0085] It should be noted that after establishing the resilience index system, this method constructs a hierarchical architecture for resilience decision-making by building a scheme layer with multiple foundation reinforcement schemes as the scheme layer, resilience assessment requirements as the overall objective decision layer, and a single criterion layer or sub-criterion layer based on the resilience index system. Figure 5 As shown, this method is used to assess the resilience of various foundation reinforcement schemes and assist in decision-making. The Saaty scaling method, also known as the Hierarchical Analysis scaling method, is used to determine and quantify the relative importance of elements at different hierarchical levels. For example, 1 indicates two factors are equally important, 3 indicates one factor is slightly important, and 7 indicates one factor is very important, thus forming a judgment matrix. Next, feature extraction processing is performed on the judgment matrix, and the matrix is ​​checked for logical contradictions or excessive subjective bias. The relative importance of each matrix factor is calculated. If the consistency coefficient is less than the preset consistency coefficient, it indicates that the subjective resilience assessment decision-making bias of the judgment matrix is ​​low, meeting the consistency requirement and ensuring the reliability and rationality of the resilience assessment judgment. Finally, through expert discussions with the construction party, operation and maintenance party, and regulatory agencies based on this judgment matrix, the weight vector for the resilience assessment of each foundation reinforcement scheme is determined. This method enables the use of a multi-criteria, multi-level target analysis architecture to assess the resilience of each foundation reinforcement scheme, effectively improving the reliability of the judgment matrix and weight coefficient vector of each resilience index, and ensuring the design accuracy of the foundation reinforcement scheme.

[0086] It should be noted that this method uses a bubble chart framework to cumulatively fit all priority toughness indices and generate a volume value of the toughness index bubble that quantitatively expresses the scale of the toughness index system. If the volume value of the toughness index bubble is greater than the preset volume value, it indicates that the toughness index system covers a large scale and involves a large number of toughness indices. A single criterion layer may be insufficient to handle the toughness assessment decision of foundation reinforcement schemes with a large number of toughness indices. Therefore, a multi-criterion layer multi-level target analysis method is required. Based on the number of toughness indices, an additional sub-criterion layer is constructed to consider the classification of toughness indices to solve the problem of the large scale of toughness indices. This method is simple and practical, and therefore suitable for decision-making on target schemes when multiple parties are involved and the degree of importance attached to each evaluation factor varies.

[0087] It should be noted that, taking the post-disaster repair and reinforcement design scheme of a soft soil foundation in a coastal offshore island group as an example, the toughness reinforcement index is first determined. , , , Next, the interrelationships among the four resilience indicators are determined using a resilience decision hierarchy framework: foundation bearing capacity > post-construction settlement > reinforcement cost > post-disaster repair time. Based on this, a judgment matrix for the four resilience indicators is constructed.

[0088] ,

[0089] The judgment matrix is ​​analyzed and calculated to find the largest eigenvalue. 3.0385, then the consistency coefficient is calculated using the following formula:

[0090] ,

[0091] The consistency coefficient is 0.033 < 0.1, indicating a low degree of inconsistency, which meets the consistency requirement, and the final weight vector is obtained. [0.637, 0.258, 0.105].

[0092] More specifically, step S108 includes the following steps:

[0093] Obtain the engineering requirements of the target proposed project, and obtain the corresponding engineering parameters for each foundation reinforcement scheme from the design party according to the engineering requirements;

[0094] Based on big data networks, several conversion formulas for the coordinated normalization and quantification of performance parameters under different resilience index categories in the field of building engineering are obtained. Each conversion formula is assigned an inverted index attribute prefix according to the resilience index category. An inverted index table of conversion formulas is constructed based on the index key of the inverted index prefix.

[0095] Obtain one or more fuzzy engineering index attributes implied by engineering parameters, and introduce the grey relational method to calculate the correlation between one or more of the fuzzy engineering index attributes and each priority toughness index in the toughness index system, so as to obtain the grey relational degree.

[0096] Extract only one or more priority resilience indicators with a gray relation degree greater than the preset gray relation degree, mark them as resilience indicator query terms, construct attribute index key characters for each resilience indicator query term based on the fuzzy engineering indicator attributes, and split each resilience indicator query term into N sub-term fragments based on the attribute index key characters.

[0097] The inverted index table of the transformation formula is used to find several candidate intersection prefix strings containing fragments of each sub-word group. Similarity analysis is used to obtain the membership transformation formula between each candidate intersection prefix string and each toughness index query word group. Based on the membership transformation formula, the engineering parameters are normalized to generate toughness performance parameters. The foundation reinforcement scheme with the highest toughness score is determined by combining the toughness performance parameters with the weight vector as the foundation design scheme.

[0098] It should be noted that due to the diversity of toughness indices, a normalization method for converting engineering parameters to toughness indices is needed to ensure consistent performance parameters across all toughness indices. However, different index categories correspond to different conversion formulas for normalization, so the corresponding conversion formula must be applied based on the attributes of each toughness index. To improve normalization efficiency, this method constructs an inverted index table of conversion formulas, where each formula has an inverted index attribute prefix defined according to the toughness index category. This provides a reliable index root for subsequent toughness index queries. The grey relational analysis method is used to calculate the grey relational degree between fuzzy engineering index attributes and each priority toughness index. Here, the fuzzy engineering index attributes are the baseline attributes of the index category, such as safety factor categories, settlement or lateral deformation service state limit categories, etc., thereby obtaining the necessary priority toughness indices for engineering parameters, i.e., the toughness index query terms. At this point, the attribute index keywords constructed based on the fuzzy engineering indicator attributes decompose each toughness indicator query phrase into fixed-length sub-fragments for indexing or matching. For example, the attribute index keywords constructed based on the state limit class yield the state limit toughness indicator, and the safety factor toughness indicator is further decomposed into sub-fragments such as settlement, lateral deformation, state, and limit. Subsequently, the inverted index table of the transformation formula is used to perform inverted index lookup analysis on these sub-fragments, which can quickly index the transformation formula that conforms to the normalization quantification of engineering parameters, i.e., the membership transformation formula, achieving efficient retrieval without brute-force matching of each phrase fragment, significantly improving the query performance of quantifiable assessments. By using this membership transformation formula to normalize the engineering parameters, the corresponding precise toughness performance parameters can be obtained. Using these toughness performance parameters and weight vectors, the foundation reinforcement scheme with the highest toughness score is determined as the foundation design scheme. This method allows for the selection of appropriate quantifiable evaluation formulas to convert engineering parameters into normalized toughness performance parameters. After weighted vector calculation, the toughness scores of each scheme are obtained for design decisions on reinforcement schemes, thereby achieving accurate design of foundation reinforcement schemes based on the concept of toughness assessment and improving the reliability and safety of foundation reinforcement.

[0099] It should be noted that, taking the post-disaster repair and reinforcement design scheme of a soft soil foundation in a coastal offshore island group as an example, the design unit proposed three soft soil foundation reinforcement schemes according to the usage requirements: dynamic compaction, surcharge preloading, and cement mixing pile method, and estimated the cost of the three schemes. A comparison example of the foundation reinforcement schemes is shown in the table below:

[0100]

[0101] The first three indicators mentioned above can be quantified for analysis. Therefore, the above engineering parameters can be normalized to obtain the toughness performance parameters. An example of the toughness performance parameters of the foundation reinforcement scheme is shown in the table below:

[0102]

[0103] Note: The values ​​in parentheses are normalized weight values; the weight coefficients for post-disaster recovery difficulty are determined by the judgment matrix. Calculated.

[0104] The weighted score for each scheme is calculated using normalized weighting coefficients as follows:

[0105] Score for dynamic compaction method: =0.299*0.607+0.25*0.243+0.556*0.101+0.637*0.049=0.3296;

[0106] Score for surcharge preloading method: =0.328*0.607+0.25*0.243+0.333*0.101+0.258*0.049=0.3061;

[0107] Score for cement mixing pile method: =0.373*0.607+0.5*0.243+0.111*0.101+0.105*0.049=0.3643.

[0108] It is evident that among the three foundation reinforcement schemes, cement mixing piles scored the highest. Therefore, from the perspective of toughness reinforcement, the cement mixing pile scheme is the best design option.

[0109] More specifically, the process involves using an inverted index table with a transformation formula to find several candidate intersection prefix strings containing fragments of each sub-word group, and performing similarity analysis to obtain the membership transformation formula between each candidate intersection prefix string and each toughness index query word group. Based on the membership transformation formula, engineering parameters are normalized to generate toughness performance parameters. Finally, the foundation reinforcement scheme with the highest toughness score is determined as the foundation design scheme by combining the toughness performance parameters with the weight vector. This process specifically includes the following steps:

[0110] An intersection strategy is introduced. The inverted index table of the transformation formula is used to find the prefix set containing each of the sub-word fragments in the intersection strategy, obtain several candidate intersection prefix strings, and calculate the similarity between each candidate intersection prefix string and each resilience index query word.

[0111] If the similarity is greater than the preset similarity, the conversion formula corresponding to the inverted index attribute prefix of the candidate intersection prefix string is extracted and labeled as the membership conversion formula. The membership conversion formula is used to quantify and evaluate the engineering parameters of different foundation reinforcement schemes and obtain multiple toughness performance parameters.

[0112] The scores of each foundation reinforcement scheme are calculated by combining the weight vector with multiple toughness performance parameters to generate a toughness score for each foundation reinforcement scheme. The foundation reinforcement scheme with the highest toughness score is then extracted as the foundation design scheme for the target proposed project.

[0113] It should be noted that, for the search and analysis steps of the inverted index sub-word fragments using the conversion formula, this method searches for prefix sets that overlap with at least one or more sub-word fragments in the intersection strategy using the inverted index table. These candidate intersection prefix strings have a high probability of satisfying the normalization quantization of the necessary priority toughness indicators for engineering parameters. Therefore, it is necessary to further calculate the similarity between each candidate intersection prefix string and each toughness indicator query word to clarify the compatibility between the two. If the similarity is greater than the preset similarity, it means that the conversion formula corresponding to the inverted index attribute prefix of the candidate intersection prefix string can meet the normalization quantization calculation of the priority toughness indicator. Therefore, the conversion formula of the candidate intersection prefix string is extracted to obtain the membership conversion formula.

[0114] A second aspect of this invention provides a foundation reinforcement design system based on the toughness concept for assessment, such as... Figure 3 As shown, the foundation reinforcement design system includes a memory 31 and a processor 32. The memory 31 stores a foundation reinforcement design method program based on the resilience concept assessment. When the foundation reinforcement design method program is executed by the processor 32, it implements any of the foundation reinforcement design method steps described above.

[0115] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A foundation reinforcement design method based on resilience assessment, characterized in that, Includes the following steps: S102: By conducting geological surveys, natural environment investigations, and construction element investigations of the construction party, the foundation survey parameters are obtained for the proposed project. The foundation survey parameters are then centrally allocated to replace the toughness indexes in the toughness index dictionary, and the replacement cost is analyzed to generate a decision index for the foundation survey parameters and toughness indexes. S104: Obtain the evaluation toughness index through big data. The risk level assessment specification of each toughness index for the stability of foundation reinforcement is used. Based on the decision index, the risk assessment of the target proposed project is carried out in the risk level assessment specification with positive and negative expectations, and priority toughness indexes are selected to construct a toughness index system. S106: The designer provides multiple foundation reinforcement schemes, constructs a resilience decision hierarchy, obtains a judgment matrix by comparing the resilience decision elements of the resilience decision hierarchy, and conducts expert discussions on multiple foundation reinforcement schemes based on the judgment matrix to determine the weight vector for the resilience assessment of each foundation reinforcement scheme. S108: Using the inverted index table of the transformation formula, output the membership transformation formula for the toughness index attribute index of the engineering parameters of each foundation reinforcement scheme. Based on the membership transformation formula, normalize the engineering parameters to generate toughness performance parameters and combine them with the weight vector to determine the foundation reinforcement scheme with the highest toughness score as the design scheme. In step S102, the foundation survey parameters are obtained by conducting geological surveys, natural environment investigations, and construction element investigations of the target project. The foundation survey parameters are then centrally assigned to replace the toughness indexes in the toughness index dictionary. This process specifically includes the following steps: To obtain the target proposed project, the construction party conducts a thorough geological survey, natural environment survey, and construction element survey of the target proposed project to obtain the foundation survey parameters of the target proposed project; Based on big data networks, a dictionary of toughness indicators necessary for geological exploration, natural environment surveys and construction element surveys is obtained. At the same time, the survey dimensions of the target proposed project are obtained. From the foundation survey parameters, k multidimensional center points are randomly selected according to the survey dimensions to obtain a multidimensional center point-non-multidimensional center point community. By associating each foundation survey parameter output from geological exploration, natural environment survey, and construction element survey with each toughness index in the toughness index dictionary, a multidimensional center point affiliation cluster is constructed. The Manhattan distance method is introduced to calculate the Manhattan distance of each non-multidimensional centroid in the multidimensional centroid-non-multidimensional centroid community relative to each multidimensional centroid, and each non-multidimensional centroid is assigned to the cluster corresponding to the multidimensional centroid with the smallest Manhattan distance. After allocation, attempt to perform a permutation between each non-multidimensional centroid and the current multidimensional centroid, and reallocate the steps to calculate all non-multidimensional centroids to the nearest multidimensional centroid, obtain the total permutation cost, and simultaneously preset the ideal permutation cost and the limit permutation cost.

2. The foundation reinforcement design method based on the toughness concept assessment according to claim 1, characterized in that, In step S102, the replacement cost is analyzed, and a decision index for foundation investigation parameters and toughness indicators is generated. This specifically includes the following steps: If the cost deviation between the total replacement cost and the ideal replacement cost is greater than the preset cost deviation value, the replacement of the non-multidimensional center point is retained; if the cost deviation value is less than the preset cost deviation value, the original replacement assignment is maintained. Repeat the replacement steps of the non-multidimensional center point with the current multidimensional center point and the determination steps of the total replacement cost until the total replacement cost reaches the limit replacement cost, and generate the decision index of foundation investigation parameters and toughness indicators.

3. The foundation reinforcement design method based on the toughness concept assessment according to claim 1, characterized in that, Step S104 specifically includes the following steps: The risk level assessment standard for foundation reinforcement stability of each resilience index in the dictionary of resilience indicators is obtained by big data network identification. Based on each assessment criterion of the risk level assessment standard, risk weight amplitude for different dimensions of resilience indicators is constructed. A preset threshold for dimensional decision decomposition is set. If the decision index is greater than the threshold for dimensional decision decomposition, the resilience index of the corresponding foundation survey parameter is marked as a positive resilience index. If the decision index is less than the threshold for dimensional decision decomposition, the resilience index of the corresponding foundation survey parameter is marked as a negative resilience index. Extract the vector of the maximum value recorded for each positive resilience index in the resilience index dictionary to construct a positive expected solution, extract the vector of the minimum value recorded for each negative resilience index to construct a negative expected solution, and determine the risk weights for each positive and negative resilience index according to the risk weight magnitude to generate a positive-negative risk weighted matrix. Calculate the distance between the positive-negative risk weighting matrix and the positive expected solution to obtain the positive bias distance value; calculate the distance between the positive-negative risk weighting matrix and the negative expected solution to obtain the negative bias distance value. By combining positive and negative bias distance values, a relative risk proximity estimate is made for each foundation survey parameter represented by the resilience index decision, thus obtaining the relative risk proximity of each foundation survey parameter. Only the resilience indicators corresponding to the decision-making of foundation survey parameters whose relative risk proximity is greater than the preset relative risk proximity are extracted and marked as priority resilience indicators. A resilience indicator system is constructed using all the extracted priority resilience indicators.

4. The foundation reinforcement design method based on the toughness concept assessment according to claim 1, characterized in that, Step S106 specifically includes the following steps: The designer provides multiple foundation reinforcement schemes, constructs a bubble accumulation framework, and uses the bubble diagram framework to cumulatively fit all the priority toughness indices in the toughness index system to generate the toughness index bubble volume value. Based on the resilience index system, an index layer is constructed that relies solely on the priority resilience index, defined as a single criterion layer; if the bubble volume value of the resilience index is greater than the preset volume value, then an additional criterion layer that considers the resilience index system is constructed based on each resilience index, defined as a sub-criterion layer. A scheme layer is proposed by taking multiple foundation reinforcement schemes as inputs, an overall goal decision layer is created based on resilience assessment requirements, and a resilience decision-making hierarchical architecture is constructed by combining the scheme layer, the overall goal decision layer, a single criterion layer or a sub-criterion layer. The Saaty scaling method is introduced to compare each resilience decision element in each layer with a certain resilience decision element in the layer above it one by one, so as to evaluate the relative importance of each resilience decision element between different layers and form a judgment matrix. The judgment matrix is ​​extracted and calculated using the matrix characteristic equation to obtain the judgment feature values ​​and the corresponding judgment feature vectors for each judgment feature value. Only the judgment weight vector corresponding to the largest judgment feature value is extracted for consistency calculation to obtain the consistency coefficient. If the consistency coefficient is less than the preset consistency coefficient, then experts will discuss multiple foundation reinforcement schemes based on the judgment weight vector corresponding to the maximum judgment feature value to determine the weight vector for the toughness assessment of each foundation reinforcement scheme.

5. The foundation reinforcement design method based on the toughness concept assessment according to claim 1, characterized in that, Step S108 specifically includes the following steps: Obtain the engineering requirements of the target proposed project, and obtain the corresponding engineering parameters for each foundation reinforcement scheme from the design party according to the engineering requirements; Based on big data networks, several conversion formulas for the coordinated normalization and quantification of performance parameters under different resilience index categories in the field of building engineering are obtained. Each conversion formula is assigned an inverted index attribute prefix according to the resilience index category. An inverted index table of conversion formulas is constructed based on the index key of the inverted index prefix. Obtain one or more fuzzy engineering index attributes implied by engineering parameters, and introduce the grey relational method to calculate the correlation between one or more of the fuzzy engineering index attributes and each priority toughness index in the toughness index system, so as to obtain the grey relational degree. Extract only one or more priority resilience indicators with a gray relation degree greater than the preset gray relation degree, mark them as resilience indicator query terms, construct attribute index key characters for each resilience indicator query term based on the fuzzy engineering indicator attributes, and split each resilience indicator query term into N sub-term fragments based on the attribute index key characters. The inverted index table of the transformation formula is used to find several candidate intersection prefix strings containing fragments of each sub-word group. Similarity analysis is used to obtain the membership transformation formula between each candidate intersection prefix string and each toughness index query word group. Based on the membership transformation formula, the engineering parameters are normalized to generate toughness performance parameters. The foundation reinforcement scheme with the highest toughness score is determined by combining the toughness performance parameters with the weight vector as the foundation design scheme.

6. The foundation reinforcement design method based on the toughness concept assessment according to claim 5, characterized in that, The process involves using an inverted index table with a transformation formula to find several candidate intersection prefix strings containing fragments of each sub-word group, and performing similarity analysis to obtain the membership transformation formula between each candidate intersection prefix string and each toughness index query word group. Based on the membership transformation formula, engineering parameters are normalized to generate toughness performance parameters. Finally, the foundation reinforcement scheme with the highest toughness score is determined as the foundation design scheme by combining the toughness performance parameters with the weight vector. The specific steps include: An intersection strategy is introduced. The inverted index table of the transformation formula is used to find the prefix set containing each of the sub-word fragments in the intersection strategy, obtain several candidate intersection prefix strings, and calculate the similarity between each candidate intersection prefix string and each resilience index query word. If the similarity is greater than the preset similarity, the conversion formula corresponding to the inverted index attribute prefix of the candidate intersection prefix string is extracted and labeled as the membership conversion formula. The membership conversion formula is used to quantify and evaluate the engineering parameters of different foundation reinforcement schemes and obtain multiple toughness performance parameters. The scores of each foundation reinforcement scheme are calculated by combining the weight vector with multiple toughness performance parameters to generate a toughness score for each foundation reinforcement scheme. The foundation reinforcement scheme with the highest toughness score is then extracted as the foundation design scheme for the target proposed project.

7. A foundation reinforcement design system based on resilience assessment, characterized in that, The foundation reinforcement design system includes a memory and a processor. The memory stores a foundation reinforcement design method program based on the resilience concept assessment. When the foundation reinforcement design method program is executed by the processor, the foundation reinforcement design method steps as described in any one of claims 1-6 are implemented.

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