An optimization method and system based on mechanism importance and disease combination

Abstract

This invention discloses an optimization method and system based on mechanism importance and defect merging. The method includes: collecting multi-source evidence of bridge defects; determining the occurrence mechanism and impact dimensions of various defects through coupled defect model matching; classifying defects into critical defects affecting structural safety or causing systemic failure and non-critical defects affecting serviceability or durability based on the risk level of the upstream defect diagnosis module; establishing a defect importance classification list and clarifying the classification basis and judgment criteria; obtaining the preliminary defect assessment scale and the risk level and development trend given by the upstream module; maintaining the preliminary scale for critical defects and adjusting the optimization scale of non-critical defects according to a preset calibration rule set; for calibrated non-critical defects, if they belong to a preset minor defect merging pair, merging them into one item and determining their representative scale and contribution order. This ensures bridge operation safety and improves maintenance economy.

Description

Technical Field

[0001] This invention belongs to the field of bridge structural health monitoring and intelligent diagnosis technology, and more specifically, relates to an optimization method and system based on the importance of mechanism and the merging of defects. Background Technology

[0002] During long-term service, bridges are prone to various defects due to factors such as material deterioration, load fatigue, and environmental erosion. Accurately assessing bridge defects and developing scientific treatment strategies are crucial for ensuring structural safety and extending service life. Current bridge defect assessment methods largely rely on identifying superficial features, lacking in-depth analysis of the mechanisms and impact dimensions of defects. This results in a lack of precise mechanistic support for defect classification, making it difficult to accurately distinguish the different impacts of defects on bridge structural safety and functionality.

[0003] In existing technologies, disease scaling assessments often employ a uniform standard without tailoring optimization to consider disease risk levels and development trends. This frequently leads to under-assessment of critical diseases or over-assessment of non-critical diseases, affecting the reliability of assessment results and potentially causing misallocation of maintenance resources, resulting in resource waste or structural safety hazards. Furthermore, existing methods often assess and score numerous minor, non-critical diseases separately, failing to consider the correlation between similar and related diseases. This makes the assessment process cumbersome and inefficient, and prone to bias due to repeated calculations of similar diseases, affecting the accuracy of bridge technical condition scores.

[0004] Therefore, in response to the current problems in bridge defect assessment, such as insufficient mechanistic analysis, inaccurate grading, lack of scale optimization, and low efficiency in assessing minor defects, there is an urgent need to propose an optimization method that can accurately analyze defect mechanisms, scientifically grade defects, optimize scale assessments, and efficiently integrate minor defects of the same origin. This is of great practical significance for improving the accuracy and efficiency of bridge defect assessment and ensuring the safety of bridge structures. Summary of the Invention

[0005] This invention aims to address the problems of insufficient mechanism analysis, inaccurate classification, lack of scale optimization, and low efficiency in assessing minor defects in current bridge defect assessments. By accurately analyzing defect mechanisms, scientifically classifying defects, optimizing scale assessments, and efficiently integrating minor defects of the same origin, the accuracy and efficiency of assessments are improved, providing reliable technical support for ensuring the safety of bridge structures.

[0006] To address the aforementioned deficiencies or improvement needs of existing technologies, as a first aspect of this invention, the present invention provides an optimization method based on mechanistic importance and disease comorbidity, comprising:

[0007] S1. Collect multi-source evidence of bridge defects and determine the occurrence mechanism and impact dimension of each type of defect based on the multi-source evidence by coupling defect model matching;

[0008] S2. Based on the disease risk level obtained from the upstream disease diagnosis module, diseases are divided into critical diseases and non-critical diseases; among them, critical diseases are those that directly affect structural safety or may cause systemic failure; non-critical diseases are surface diseases that only affect the use function or durability; establish a disease importance classification list and clarify the classification basis and judgment criteria for each type of disease.

[0009] S3. Obtain the preliminary assessment scale of the disease; at the same time, obtain the risk level and development trend of the disease determined by the upstream disease diagnosis module; if the disease is a critical disease, maintain its preliminary scale unchanged; if the disease is a non-critical disease, calculate the calibrated scale through the non-critical disease dynamic scale correction model, which performs quantitative calculations based on the disease's preliminary scale, baseline scale, risk level, and development trend.

[0010] S4. For calibrated non-critical diseases, if there is a pre-defined pair of minor diseases to be merged, then merge the pair of diseases into one item and determine its representative scale and contribution order.

[0011] Furthermore, the multi-source evidence in S1 includes basic layer evidence reflecting the current state of the disease, historical layer evidence reflecting the evolution pattern of the disease, and knowledge layer evidence supporting the mechanism analysis.

[0012] Furthermore, the foundational layer evidence includes data on the geometric parameters, morphological characteristics, spatial distribution, and material properties of the disease; the historical layer evidence includes data on the annual rate of change of the disease, its development trend, and maintenance records; and the knowledge layer evidence includes standard provisions, expert experience, and a coupled disease model library.

[0013] Furthermore, the coupled disease model in S1 is specifically a two-dimensional coupled analysis model of disease mechanism and influence dimension. This model has a built-in library of typical bridge disease mechanisms and an influence dimension association rule library. The disease mechanism library covers the core inducing mechanisms of bridge diseases, including material deterioration, load fatigue, and environmental erosion. The influence dimension association rule library contains the influence dimension mapping relationships corresponding to different mechanisms, including structural safety, traffic performance, and durability.

[0014] The model employs a three-level coupled operation—multi-source evidence feature extraction, mechanism feature matching degree calculation, and influence dimension weight allocation—to output the occurrence mechanism and influence dimensions corresponding to each type of disease, specifically:

[0015] Feature extraction was performed on the collected multi-source evidence to obtain a feature set including the disease's appearance, development, and environmental characteristics;

[0016] Calculate the matching degree between the feature set and each mechanism feature in the disease mechanism library, and determine the mechanism with the highest matching degree as the occurrence mechanism of the disease;

[0017] Based on the influence dimension association rule base, and combined with the disease occurrence mechanism and actual impact degree, the influence dimension corresponding to the disease is determined.

[0018] Furthermore, the method for determining the importance of the disease in S2 is as follows:

[0019] Disease importance is divided into two levels: critical diseases and non-critical diseases. The classification is directly based on the disease risk level results obtained from the upstream disease diagnosis module.

[0020] The upstream defect diagnosis module is a system module with bridge defect risk assessment function. It can output the risk level of the defect based on the bridge defect detection data, structural characteristic parameters and relevant specification requirements through a preset risk assessment algorithm.

[0021] Specifically, when the risk level of a disease is high, it is determined to be a critical disease; when the risk level of a disease is medium or low, it is determined to be a non-critical disease.

[0022] Furthermore, the non-critical disease dynamic scaling correction model in S3 is specifically as follows:

[0023] Comprehensive state factors The assessment is based on a combination of the disease's risk level and its development trend. Specifically, the rule is: when the disease risk level is low and the development trend is towards stabilization or improvement / recovery... When the disease risk level is low and the development trend is slow, or the risk level is medium and the development trend is stabilizing, When the disease risk level is medium and the development trend is slow, or when the development trend is rapid, ;

[0024] Nonlinear down-adjustment coefficient Based on comprehensive state factors The calculation formula is derived from the preliminary disease assessment scale. ,in, Indicates the initial assessment scale for the disease;

[0025] Scale downscaling The calculation uses a floor function to ensure that the down adjustment is an integer. The calculation formula is as follows: ,in, This represents the floor function, which rounds the result down to the largest integer not greater than the result.

[0026] Scale after calibration By constraining the maximum value function and the amplitude limit function within a reasonable range, the calculation formula is as follows: ,in, This indicates the highest permissible scale for this type of disease; This represents the maximum value function, which takes the larger of the two values ​​within the parentheses. This represents the limiting function, used to limit the value. Limited to the range inside, if Then take ,like Then take ,like Then take itself.

[0027] Furthermore, S4 also includes identifying diseases with similar functional attributes and diseases with similar material attributes as homologous diseases and incorporating them into a preset minor disease merging pair.

[0028] Furthermore, if there are pre-defined minor disease merging pairs in S4, the process of merging the pair of diseases into one item is as follows:

[0029] Multiple minor defects were pre-selected as pairs. The selection criteria were that the non-critical defects in the combination should affect the same functional area of ​​the bridge, have the same source of influence mechanism, and all belong to the functional loss type of defects.

[0030] In non-critical defects that have completed scaling calibration, the functional area attribution information and impact mechanism labels of each defect are matched to identify whether there are multiple defects belonging to the same preset minor defect pair and located on the same component, denoted as... ; among them, each disease With post-calibration scaling and standardized key parameter vectors , The number of dimensions for key parameters;

[0031] If the above situation is identified, the disease will be treated. Integrate into a single disease item And determine the disease items step by step. Representative scale The order of contribution of each disease; first calculate the contribution of each disease. Contribution weight The calculation formula is: In the formula, Represents the key parameter vector The Euclidean norm is calculated using the following formula: Subsequently, the merged disease items were determined. Representative scale The value is taken as the maximum value of the scale after calibration for each disease, and the calculation formula is:

[0032]

[0033] Finally, the contribution order of each disease was determined, firstly based on the calibrated scale of each disease. Sort in descending order; for calibrated scales For the same disease, further classification is based on its contribution weight. The defects are sorted in descending order, and the defect listed first is defined as the first defect; in the subsequent component technical condition scoring process, the merged defect item... As an independent disease item, it is included in the scoring calculation, and its corresponding standard deduction value is based on the aforementioned representative scale. Sure.

[0034] As a second aspect of the present invention, an optimization system based on the importance of mechanism and the comorbidity of diseases is also provided, comprising:

[0035] The multi-source evidence collection and mechanism matching unit for bridge defects is used to collect multi-source evidence of bridge defects and determine the occurrence mechanism and impact dimension of each type of defect based on the multi-source evidence by coupling the defect model matching.

[0036] The disease importance classification unit is used to classify diseases into critical diseases and non-critical diseases based on the disease risk level obtained from the upstream disease diagnosis module. Among them, critical diseases are those that directly affect structural safety or may cause systemic failure; non-critical diseases are surface diseases that only affect the use function or durability. A disease importance classification list is established to clarify the classification basis and judgment criteria for each type of disease.

[0037] The scaling calibration unit based on mechanism importance is used to obtain the preliminary assessment scale of the disease; at the same time, it obtains the risk level and development trend of the disease determined by the upstream disease diagnosis module; if the disease is a critical disease, its preliminary scale remains unchanged; if the disease is a non-critical disease, the calibrated scale is calculated through the non-critical disease dynamic scaling correction model, which performs quantitative calculations based on the disease's preliminary scale, baseline scale, risk level and development trend.

[0038] The homologous disease merging and final scaling determination unit is used to merge non-critical diseases that have been calibrated into a single item if there is a pre-defined minor disease merging pair, and to determine its representative scale and contribution order.

[0039] As a third aspect of the invention, a computer-readable storage medium is also provided, on which a computer program is stored, which is executed by a processor as an optimization method based on mechanism importance and disease merging as described in any one of the claims.

[0040] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0041] 1. The optimization method based on mechanism importance and disease merging of this invention collects multi-source evidence of bridge diseases and determines the occurrence mechanism and impact dimensions of various diseases by matching coupled disease models. This core technical feature realizes the transformation of disease diagnosis from single-observation identification to in-depth mechanistic analysis. Through the comprehensive integration of multi-source evidence and matching of coupled models, it effectively improves the accuracy of determining the occurrence mechanism and impact dimensions of diseases, providing reliable mechanistic support for subsequent disease classification and optimization treatment. This avoids deviations in subsequent treatment strategies due to mechanistic misjudgment, ensuring the scientific validity and rationality of the entire optimization method.

[0042] 2. The optimization method based on mechanism importance and disease merging of this invention classifies critical and non-critical diseases according to the risk level of the upstream disease diagnosis module, establishes a graded list and clarifies the judgment criteria, and simultaneously optimizes the scale of non-critical diseases by specifically downgrading it based on the disease development trend and type. This core technical feature achieves accurate classification of disease importance and scientific calibration of the scale. Maintaining the scale of critical diseases ensures priority protection of core structural safety requirements, while optimizing the scale of non-critical diseases avoids resource waste caused by over-assessment, enabling the scale to truly reflect the actual risk level of the disease and improving the accuracy and relevance of disease assessment.

[0043] 3. The optimization method based on mechanistic importance and disease merging of this invention merges calibrated non-critical diseases of the same origin by pre-setting minor disease merging pairs, accumulating key parameters, and completing the final scaling calculation. This core technical feature achieves efficient integration of non-critical diseases. Through targeted merging of diseases of the same origin, it reduces the number of disease items in the subsequent scoring process, simplifies the scoring process, and improves assessment efficiency. Simultaneously, based on the accumulation of key parameters and reasonable scaling calculation, it ensures the accuracy of the disease assessment after merging, avoids assessment bias caused by repeated calculation of similar minor diseases, and further guarantees the reliability of the bridge technical condition scoring results. Attached Figure Description

[0044] Figure 1 This is a flowchart of the optimization method based on the importance of mechanism and the merging of diseases according to an embodiment of the present invention;

[0045] Figure 2 This is a schematic diagram of beam-slab bending cracks according to an embodiment of the present invention;

[0046] Figure 3 This is a schematic diagram of a transverse crack near the mid-span in an embodiment of the present invention;

[0047] Figure 4 This is a schematic diagram illustrating a partial damage example of the asphalt pavement layer on the bridge deck according to an embodiment of the present invention;

[0048] Figure 5 This is a system unit diagram of an embodiment of the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0050] Example 1

[0051] Please refer to Figure 1 This embodiment 1 provides an optimization method based on the importance of mechanism and the merging of diseases, including:

[0052] S1. Collect multi-source evidence of bridge defects and determine the occurrence mechanism and impact dimension of each type of defect based on the multi-source evidence by coupling defect model matching;

[0053] S2. Based on the disease risk level obtained from the upstream disease diagnosis module, diseases are divided into critical diseases and non-critical diseases; among them, critical diseases are those that directly affect structural safety or may cause systemic failure; non-critical diseases are surface diseases that only affect the use function or durability; establish a disease importance classification list and clarify the classification basis and judgment criteria for each type of disease.

[0054] S3. Obtain the preliminary assessment scale of the disease; at the same time, obtain the risk level and development trend of the disease determined by the upstream disease diagnosis module; if the disease is a critical disease, maintain its preliminary scale unchanged; if the disease is a non-critical disease, calculate the calibrated scale through the non-critical disease dynamic scale correction model, which performs quantitative calculations based on the disease's preliminary scale, baseline scale, risk level, and development trend.

[0055] S4. For calibrated non-critical diseases, if there is a pre-defined pair of minor diseases to be merged, then merge the pair of diseases into one item and determine its representative scale and contribution order.

[0056] This embodiment further elaborates on the above steps.

[0057] (1) Collection of evidence from multiple sources of disease and matching of mechanisms

[0058] In the field of bridge defect assessment, traditional assessment methods often rely on single-dimensional detection data, making it difficult to comprehensively capture the complex characteristics of defects. This leads to superficial judgments about the mechanisms of defect occurrence, affecting the scientific rigor of subsequent assessments and treatments. To overcome this limitation, this method constructs a multi-source evidence fusion framework. By comprehensively collecting and integrating multi-dimensional evidence, and combining it with a coupled defect model, it achieves accurate determination of the mechanisms of defect occurrence and the dimensions of their impact.

[0059] The system's three-layer evidence fusion architecture covers three types of evidence: the foundational layer, the historical layer, and the knowledge layer, forming a complete evidence support system.

[0060] Among them, the basic layer is direct detection evidence, focusing on the current state of the disease, specifically including the current quantitative data of the disease, spatial distribution characteristics and appearance morphological characteristics. The current quantitative data of the disease can accurately reflect the geometric and material parameters of the disease, the spatial distribution characteristics can clearly identify the location, range and density of the disease, and the appearance morphological characteristics can intuitively present the external manifestation of the disease through color, texture and accompanying phenomena.

[0061] The historical layer provides time-series evidence, focusing on the evolution of diseases. It includes trend analysis of annual testing data, impact assessment of maintenance records, and historical correlation of environmental loads. By tracing the changes of diseases over time and combining the actual effects of maintenance measures with the course of environmental loads, it provides support for analyzing the development logic of diseases.

[0062] The knowledge layer provides theoretical and practical basis for expert experience evidence, covering compliance with normative provisions, matching of basic models of coupled defects, and analogical reasoning of engineering cases. Among them, the matching of basic models of coupled defects can be based on the "Illustrated Manual for Diagnosis and Treatment of Typical Defects in Highway Bridges" to ensure the authority and applicability of the model matching, while normative provisions and engineering cases further enhance the rigor of mechanism analysis.

[0063] After completing the collection of multi-source evidence, matching analysis was conducted through a coupled disease model. This model is specifically a two-dimensional coupled analysis model of disease mechanism and impact dimension, with two core databases built-in: a typical bridge disease mechanism database and an impact dimension association rule database. The disease mechanism database covers the core inducing mechanisms of bridge diseases, such as material degradation, load fatigue, and environmental erosion, comprehensively covering the key factors that may lead to disease during bridge service. The impact dimension association rule database establishes the correspondence between different mechanisms and impact dimensions, clarifying the impact mapping of different mechanisms such as material degradation and load fatigue on dimensions such as structural safety, traffic performance, and durability.

[0064] The model runs through a three-level coupled operation. First, feature extraction is performed on the collected multi-source evidence. Core feature sets such as the appearance, development, and environmental correlation features of the disease are extracted from various types of evidence in the basic, historical, and knowledge layers to achieve accurate extraction of evidence information.

[0065] Subsequently, the matching degree between this feature set and each mechanism feature in the disease mechanism library is calculated. By comparative analysis, the mechanism with the highest matching degree is determined as the occurrence mechanism of the disease, ensuring the accuracy of mechanism determination. Finally, based on the influence dimension association rule library, combined with the determined disease occurrence mechanism and the actual degree of disease impact, the influence dimension corresponding to the disease is clarified. Finally, the occurrence mechanism and influence dimension determination results of each type of disease are output, providing reliable mechanism-level support for subsequent disease classification and optimization treatment.

[0066] (2) Disease importance classification determination

[0067] In bridge defect assessment, the importance level of a defect directly determines the priority of subsequent treatment. Traditional assessment methods often lack clear and unified classification criteria, leading to inaccurate differentiation of defects with different risk levels. This results in either over-focusing on minor defects, wasting resources, or neglecting critical defects that create safety hazards. To address this issue, this method uses the risk level output by the upstream defect diagnosis module as the core basis to classify the importance of defects, achieving a scientific distinction between critical and non-critical defects.

[0068] The upstream defect diagnosis module here is a system module with professional bridge defect risk assessment function. Its core advantage lies in its ability to integrate multi-dimensional data for comprehensive analysis. Specifically, based on the detection data of bridge defects and the structural characteristic parameters of the bridge itself, and strictly in accordance with relevant industry standards, it uses a built-in preset risk assessment algorithm to quantitatively assess the risk level of each type of defect and output the corresponding risk level, providing reliable data support for subsequent classification.

[0069] Based on the risk level results, this method clearly classifies the importance of defects into two levels: critical defects and non-critical defects, and establishes clear classification criteria. Critical defects specifically refer to those that directly affect the structural safety of the bridge or may cause systemic failure of the bridge. Once these defects worsen, they directly threaten the bridge's service safety. Therefore, when the risk level output by the upstream module is high risk, the defect is determined to be a critical defect. Non-critical defects, on the other hand, are superficial defects that only affect the bridge's functionality or structural durability. They do not pose a direct and serious threat to the bridge's structural safety. The corresponding criterion is that when the risk level output by the upstream module is medium risk or low risk, the defect is determined to be a non-critical defect.

[0070] After completing the classification determination, this method further establishes a disease importance classification list, which systematically compiles and includes the name of each disease category, its corresponding risk level, classification result, and specific judgment criteria. The establishment of this list not only achieves systematic management of disease classification information but also provides clear criteria for subsequent assessors to quickly query and verify disease levels, ensuring the entire classification process is traceable and verifiable. This lays the foundation for subsequent differentiated scaling calibration and disease merging work.

[0071] (3) Scaling calibration based on the importance of mechanism

[0072] In the bridge defect scaling assessment process, traditional methods often employ fixed and uniform assessment standards without considering the actual risk level and development trend of the defects. This easily leads to problems such as the scale for critical defects being insufficient to reflect the risk level, and the scale for non-critical defects being too high, resulting in over-assessment and affecting the rationality of subsequent maintenance strategies. To solve this problem, this method conducts differentiated scaling calibration based on the importance level of the defects, achieving more accurate scaling assessment.

[0073] In practice, the initial assessment scale for each type of disease is first obtained. This scale is based on the initial assessment results derived from conventional testing standards. Simultaneously, the risk level and development trend of the corresponding disease, determined by the upstream disease diagnosis module, are also acquired. These two data points are the core basis for scale calibration. Differentiated treatment strategies are adopted for diseases of different importance levels. For critical diseases, because they directly relate to the safety of the bridge structure, insufficient assessment could create serious safety hazards; therefore, their initial scale is maintained to ensure that their risk level is fully reflected. For non-critical diseases, the calibrated scale is calculated using a non-critical disease dynamic scale correction model. This model performs quantitative calculations based on the disease's initial scale, baseline scale, risk level, and development trend.

[0074] The specific dynamic scaling correction model for non-critical diseases is as follows:

[0075] Comprehensive state factors The assessment is based on a combination of the disease's risk level and its development trend. Specifically, the rule is: when the disease risk level is low and the development trend is towards stabilization or improvement / recovery... When the disease risk level is low and the development trend is slow, or the risk level is medium and the development trend is stabilizing, When the disease risk level is medium and the development trend is slow, or when the development trend is rapid, ;

[0076] Nonlinear down-adjustment coefficient Based on comprehensive state factors The calculation formula is derived from the preliminary disease assessment scale. ,in, Indicates the initial assessment scale for the disease;

[0077] Scale downscaling The calculation uses a floor function to ensure that the down adjustment is an integer. The calculation formula is as follows: ,in, This represents the floor function, which rounds the result down to the largest integer not greater than the result.

[0078] Scale after calibration By constraining the maximum value function and the amplitude limit function within a reasonable range, the calculation formula is as follows: ,in, This indicates the highest permissible scale for this type of disease; This represents the maximum value function, which takes the larger of the two values ​​within the parentheses. This represents the limiting function, used to limit the value. Limited to the range inside, if Then take ,like Then take ,like Then take itself.

[0079] (4) Merging of diseases with the same origin and determination of the final scale

[0080] After scaling non-critical defects, traditional assessment methods still score each type of defect separately. For the numerous minor, non-critical defects present during bridge service, this separate assessment leads to a large number of assessment items, increasing the workload of subsequent scoring, reducing assessment efficiency, and easily causing assessment bias due to repeated calculations of similar defects, thus affecting the accuracy of the bridge's technical condition score. To address this issue, this method pre-sets minor defect merging pairs, merging calibrated non-critical defects that meet the criteria, thereby simplifying the assessment process and optimizing the assessment results.

[0081] Before merging defects, it is necessary to pre-determine the merging pairs of minor defects. The selection of merging pairs follows clear criteria, requiring that the non-critical defects in the combination affect the same functional area of ​​the bridge, have a common source of influence mechanism, and both belong to the category of functional loss defects. Among them, the determination of common-source defects is based on the similarity of functional attributes and material attributes. Defects that meet these two similarity conditions are identified as common-source defects and included in the pre-determined scope of minor defect merging pairs.

[0082] The specific process is as follows: multiple minor defects are pre-selected and combined. The selection criteria are that the non-critical defects in the combination must affect the same functional area of ​​the bridge, have the same source of influence mechanism, and all belong to the functional loss type of defects.

[0083] In non-critical defects that have completed scaling calibration, the functional area attribution information and impact mechanism labels of each defect are matched to identify whether there are multiple defects belonging to the same preset minor defect pair and located on the same component, denoted as... ; among them, each disease With post-calibration scaling and the standardized key parameter vector , The number of dimensions for key parameters;

[0084] If the above situation is identified, the disease will be treated. Integrate into a single disease item And determine the disease items step by step. Representative scale The order of contribution of each disease; first calculate the contribution of each disease. Contribution weight The calculation formula is: In the formula, Represents the key parameter vector The Euclidean norm is calculated using the following formula: Subsequently, the merged disease items were determined. Representative scale The value is taken as the maximum value of the scale after calibration for each disease, and the calculation formula is:

[0085]

[0086] Finally, the contribution order of each disease was determined, firstly based on the calibrated scale of each disease. Sort in descending order; for calibrated scales For the same disease, further classification is based on its contribution weight. The defects are sorted in descending order, and the defect listed first is defined as the first defect; in the subsequent component technical condition scoring process, the merged defect item... As an independent disease item, it is included in the scoring calculation, and its corresponding standard deduction value is based on the aforementioned representative scale. Sure.

[0087] To make the objectives, technical solutions, and advantages of this embodiment clearer, the implementation process of this method will be described in detail below with reference to a specific bridge defect diagnosis case.

[0088] Please refer to Figure 2 , Figure 3 as well as Figure 4Taking an operational reinforced concrete hollow slab bridge as the evaluation object, after the bridge was inspected, the preliminary analysis results of three defects were obtained through the upstream defect diagnosis module, namely defect A (transverse cracks in the bottom slab at the mid-span of the No. 1 hollow slab), defect B (grinding and exposing the aggregate in the asphalt pavement layer of the bridge deck), and defect C (local damage to the asphalt pavement layer of the bridge deck).

[0089] Among them, disease A is initially assessed with a scale of 4, a risk level of high risk, and a development trend of slow development. The key parameters are crack length of 2.5 meters and maximum width of 0.3 millimeters.

[0090] Disease B is preliminarily assessed with a scale of 4, a risk level of low risk, and a development trend of stabilization. The key parameter is an area of ​​8 square meters.

[0091] Disease C is preliminarily assessed with a scale of 3, a risk level of low risk, and a development trend of stabilization. The key parameter is an area of ​​2 square meters.

[0092] The first step in implementing this method is mechanism matching and evidence confirmation. This method receives the preliminary analysis results of the above three diseases as input. The system confirms that the upstream module has completed the matching and analysis of the coupled disease model based on multi-source evidence (basic test data, historical data, and standard knowledge). Among them, it is determined that disease A may belong to the "mid-span bending crack-insufficient bearing capacity model", while diseases B and C belong to surface function degradation.

[0093] The second step is to classify the importance of the disease. This is done directly based on the "risk level" output by the upstream module. Disease A has a risk level of "high risk" and is therefore classified as a critical disease. Diseases B and C both have a risk level of "low risk" and are therefore classified as non-critical diseases.

[0094] The third step is scaling based on the importance of the mechanism, which involves differentiated optimization of the scaling rules applied to each defect. For critical defect A (transverse cracks in the bottom slab at mid-span of the #1 hollow slab), since it is directly related to the safety of the bridge structure, its risk level needs to be fully reflected. According to the rule "if a defect is a critical defect, its preliminary scale is directly taken," its calibrated scale is... .

[0095] For non-critical defects B (polished asphalt pavement layer exposing aggregate) and C (localized damage to the asphalt pavement layer), a nonlinear scaling correction formula is used for accurate calculation. The specific form of this formula is as follows: In the formula, , As a comprehensive state factor; diseases B and C both belong to the surface functional type of disease, and the highest allowable scale for this type of disease is set. .

[0096] Preliminary scale of disease B The risk level is low, and the development trend is towards stability. According to the comprehensive state factor judgment rules, low-risk and stable diseases correspond to... The specific calculation process is as follows: First, calculate the nonlinear down-adjustment coefficient. Then calculate the scale down adjustment. Next, calculate the intermediate value. Finally, the calibrated scale is obtained through a limiting function. .

[0097] Preliminary scale of disease C The risk level is low, and the development trend is towards stability, which also corresponds to the comprehensive state factor. The specific calculation process is as follows: Calculate the nonlinear down-adjustment coefficient. ; Calculate the scale down adjustment ; Calculate the intermediate value ; Final calibration scale After calibration, the scales for the three diseases are as follows: Disease A=4, Disease B=2, Disease C=2.

[0098] The fourth step involves merging minor defects and determining the representative scale and contribution order. The system traverses and searches all non-critical defects that have completed scale calibration, matching them with predefined minor defect merging pairs. According to the rules, {police deck pavement polished and exposed aggregate, pavement damage} is a predefined merging pair. Defect B (police deck asphalt pavement layer polished and exposed aggregate) and defect C (police deck asphalt pavement layer partially damaged) belong to the same merging pair and are located in the same component, so the system initiates the merging process.

[0099] First, data preparation was carried out. The key parameters (area) of defects B and C have been standardized. Assuming the total area of ​​the bridge deck pavement is 50 square meters, a standardized parameter vector was formed accordingly. (The calculation logic is 8 square meters / 50 square meters = 0.16). (The calculation logic is 2 square meters / 50 square meters = 0.04).

[0100] Next, the contribution weights are calculated. First, the Euclidean norms of the two standardized parameter vectors of the disease are calculated separately. Since the parameter vectors are one-dimensional, the norm is the parameter itself. , Then, the contribution weight is calculated based on the norm, and the contribution weight of disease B is calculated. Contribution weight of disease C .

[0101] Next, determine the representative scale for the merged disease item by taking the maximum value of the calibrated scales of the two diseases, i.e.:

[0102]

[0103] Finally, the order of contributions was determined and the names were completed. Since the two diseases had the same scale after calibration (both were 2), they were arranged in descending order of contribution weight. Therefore, defect B (polished and exposed aggregate of bridge deck asphalt pavement) is the primary defect; after merging, the defect item adopts the structured naming rule of "functional area · core defect type - related defect type", which is "polished and exposed aggregate of bridge deck pavement + damage".

[0104] After optimization using this method, the original three disease records were simplified to two: the critical disease "transverse crack in the bottom slab of the #1 hollow slab at mid-span", with a scale of 4, and the key parameters (crack length 2.5 meters, maximum width 0.3 mm) remained unchanged; the merged non-critical disease "polish-exposed aggregate and damage to the bridge deck pavement", with a scale of 2 and a total area of ​​10 square meters, of which the contribution of the polished aggregate of the bridge deck asphalt pavement layer was 80%, and the contribution of the local damage to the bridge deck asphalt pavement layer was 20%, providing a precise basis for subsequent maintenance priority division and rational allocation of resources.

[0105] Example 2

[0106] Please refer to Figure 5 This embodiment 2 provides an optimization system based on the importance of mechanism and the merging of diseases, including:

[0107] The multi-source evidence collection and mechanism matching unit for bridge defects is used to collect multi-source evidence of bridge defects and determine the occurrence mechanism and impact dimension of each type of defect based on the multi-source evidence by coupling the defect model matching.

[0108] The disease importance classification unit is used to classify diseases into critical diseases and non-critical diseases based on the disease risk level obtained from the upstream disease diagnosis module. Among them, critical diseases are those that directly affect structural safety or may cause systemic failure; non-critical diseases are surface diseases that only affect the use function or durability. A disease importance classification list is established to clarify the classification basis and judgment criteria for each type of disease.

[0109] The scaling calibration unit based on mechanism importance is used to obtain the preliminary assessment scale of the disease; at the same time, it obtains the risk level and development trend of the disease determined by the upstream disease diagnosis module; if the disease is a critical disease, its preliminary scale remains unchanged; if the disease is a non-critical disease, the calibrated scale is calculated through the non-critical disease dynamic scaling correction model, which performs quantitative calculations based on the disease's preliminary scale, baseline scale, risk level and development trend.

[0110] The homologous disease merging and final scaling determination unit is used to merge non-critical diseases that have been calibrated into a single item if there is a pre-defined minor disease merging pair, and to determine its representative scale and contribution order.

[0111] Example 3

[0112] This embodiment 3 also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement any step of an optimization method based on the importance of mechanism and the merging of diseases.

[0113] The computer-readable storage medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0114] For a description of the computer-readable storage medium provided in this application, please refer to the above method embodiments; further details will not be repeated here.

[0115] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An optimization method based on the importance of mechanism and the merging of diseases, characterized in that, include: S1. Collect multi-source evidence of bridge defects and determine the occurrence mechanism and impact dimension of each type of defect based on the multi-source evidence by coupling defect model matching; S2. Based on the disease risk level obtained from the upstream disease diagnosis module, diseases are divided into critical diseases and non-critical diseases; among them, critical diseases are those that directly affect structural safety or may cause systemic failure; non-critical diseases are surface diseases that only affect the use function or durability; establish a disease importance classification list and clarify the classification basis and judgment criteria for each type of disease. S3. Obtain the preliminary assessment scale of the disease; at the same time, obtain the risk level and development trend of the disease determined by the upstream disease diagnosis module; if the disease is a critical disease, maintain its preliminary scale unchanged; if the disease is a non-critical disease, calculate the calibrated scale through the non-critical disease dynamic scale correction model, which performs quantitative calculations based on the disease's preliminary scale, baseline scale, risk level, and development trend. S4. For calibrated non-critical diseases, if there are pre-defined minor disease merging pairs, merge the pair of diseases into one item and determine its representative scale and contribution order. The coupled disease model is specifically a dual-dimensional coupled analysis model of disease mechanism and influence dimension, with two core databases built-in: a typical bridge disease mechanism library and an influence dimension association rule library. The disease mechanism library covers the core inducing mechanisms of bridge diseases, including material degradation, load fatigue, and environmental erosion, comprehensively covering the factors that lead to disease during bridge service. The influence dimension association rule library establishes the correspondence between different mechanisms and influence dimensions, clarifying the influence mapping of different mechanisms, including material degradation and load fatigue, on structural safety, traffic performance, and durability dimensions. The upstream defect diagnosis module is a system module with professional bridge defect risk assessment function. It is used to integrate multi-dimensional data for comprehensive analysis. Specifically, based on the detection data of bridge defects, the structural characteristic parameters of the bridge itself, and in accordance with relevant industry standards, it uses a built-in preset risk assessment algorithm to quantitatively assess the risk level of each type of defect and output the corresponding risk level. The specific dynamic scaling correction model for non-critical diseases in S3 is as follows: Comprehensive state factors The assessment is based on a combination of the disease's risk level and its development trend. Specifically, the rule is: when the disease risk level is low and the development trend is towards stabilization or improvement / recovery... When the disease risk level is low and the development trend is slow, or the risk level is medium and the development trend is stabilizing, When the disease risk level is medium and the development trend is slow, or when the development trend is rapid, ; Nonlinear down-adjustment coefficient Based on comprehensive state factors The calculation formula is derived from the preliminary disease assessment scale. ,in, Indicates the initial assessment scale for the disease; Scale downscaling The calculation uses a floor function to ensure that the down adjustment is an integer. The calculation formula is as follows: ,in, This represents the floor function, which rounds the result down to the largest integer not greater than the result. Scale after calibration By constraining the maximum value function and the amplitude limit function within a reasonable range, the calculation formula is as follows: ,in, This indicates the highest permissible scale for this type of disease; This represents the maximum value function, which takes the larger of the two values ​​within the parentheses. This represents the limiting function, used to limit the value. Limited to the range inside, if Then take ,like Then take ,like Then take itself.

2. The optimization method based on the importance of mechanism and synergy of diseases according to claim 1, characterized in that, The multi-source evidence in S1 includes basic layer evidence reflecting the current state of the disease, historical layer evidence reflecting the evolution pattern of the disease, and knowledge layer evidence supporting the mechanism analysis.

3. The optimization method based on the importance of mechanism and synergy of diseases according to claim 2, characterized in that, The foundational evidence includes data on the geometric parameters, morphological characteristics, spatial distribution, and material properties of the disease; the historical evidence includes data on the annual rate of change of the disease, its development trend, and maintenance records; and the knowledge-based evidence includes standard provisions, expert experience, and a coupled disease model library.

4. The optimization method based on the importance of mechanism and synergy of diseases according to claim 1, characterized in that, The coupled disease model in S1 is specifically a two-dimensional coupled analysis model of disease mechanism and influence dimension. This model has a built-in library of typical bridge disease mechanisms and an influence dimension association rule library. The disease mechanism library covers the core inducing mechanisms of bridge diseases, including material deterioration, load fatigue, and environmental erosion. The influence dimension association rule library contains the influence dimension mapping relationships corresponding to different mechanisms, including structural safety, traffic performance, and durability. The model employs a three-level coupled operation—multi-source evidence feature extraction, mechanism feature matching degree calculation, and influence dimension weight allocation—to output the occurrence mechanism and influence dimensions corresponding to each type of disease, specifically: Feature extraction is performed on the collected multi-source evidence to obtain a feature set of the disease, including appearance features, development features, and environmental correlation features; Calculate the matching degree between the feature set and each mechanism feature in the disease mechanism library, and determine the mechanism with the highest matching degree as the occurrence mechanism of the disease; Based on the influence dimension association rule base, and combined with the disease occurrence mechanism and actual impact degree, the influence dimension corresponding to the disease is determined.

5. The optimization method based on the importance of mechanism and the merging of diseases according to claim 1, characterized in that, The method for determining the importance of the disease in S2 is as follows: Disease importance is divided into two levels: critical diseases and non-critical diseases. The classification is directly based on the disease risk level results obtained from the upstream disease diagnosis module. The upstream defect diagnosis module is a system module with bridge defect risk assessment function. It can output the risk level of the defect based on the bridge defect detection data, structural characteristic parameters and relevant specification requirements through a preset risk assessment algorithm. Specifically, when the risk level of a disease is high, it is determined to be a critical disease; when the risk level of a disease is medium or low, it is determined to be a non-critical disease.

6. The optimization method based on the importance of mechanism and synergy of diseases according to claim 1, characterized in that, The S4 further includes identifying diseases with similar functional attributes and diseases with similar material attributes as homologous diseases and incorporating them into a preset minor disease merging pair.

7. The optimization method based on the importance of mechanism and the merging of diseases according to claim 1, characterized in that, If there is a preset pair of minor disease merging in S4, the process of merging the pair of diseases into one item is as follows: Multiple minor defects were pre-selected as pairs. The selection criteria were that the non-critical defects in the combination should affect the same functional area of ​​the bridge, have the same source of influence mechanism, and all belong to the functional loss type of defects. In non-critical defects that have completed scaling calibration, the functional area attribution information and impact mechanism labels of each defect are matched to identify whether there are multiple defects belonging to the same preset minor defect pair and located on the same component, denoted as... ; among them, each disease With post-calibration scaling and standardized key parameter vectors , The number of dimensions for key parameters; If the above situation is identified, the disease will be treated. Integrated into merged disease items And determine the disease items step by step. Representative scale The order of contribution of each disease; first calculate the contribution of each disease. Contribution weight The calculation formula is: In the formula, Represents the key parameter vector The Euclidean norm is calculated using the following formula: Subsequently, the merged disease items were determined. Representative scale The value is taken as the maximum value of the scale after calibration for each disease, and the calculation formula is: Finally, the order of contribution of each disease was determined, firstly based on the calibrated scale of each disease. Sort in descending order; for calibrated scales For the same disease, further classification is based on its contribution weight. The defects are sorted in descending order, and the defect listed first is defined as the first defect; in the subsequent component technical condition scoring process, the merged defect items... As an independent disease item, it is included in the scoring calculation, and its corresponding standard deduction value is based on the aforementioned representative scale. Sure.

8. An optimization system based on the importance of mechanism and the merging of diseases, characterized in that, include: The multi-source evidence collection and mechanism matching unit for bridge defects is used to collect multi-source evidence of bridge defects and determine the occurrence mechanism and impact dimension of each type of defect based on the multi-source evidence by coupling the defect model matching. The disease importance classification unit is used to classify diseases into critical diseases and non-critical diseases based on the disease risk level obtained from the upstream disease diagnosis module. Among them, critical diseases are those that directly affect structural safety or may cause systemic failure; non-critical diseases are surface diseases that only affect the use function or durability. A disease importance classification list is established to clarify the classification basis and judgment criteria for each type of disease. The scaling calibration unit based on mechanism importance is used to obtain the preliminary assessment scale of the disease; at the same time, it obtains the risk level and development trend of the disease determined by the upstream disease diagnosis module; if the disease is a critical disease, its preliminary scale remains unchanged; if the disease is a non-critical disease, the calibrated scale is calculated through the non-critical disease dynamic scaling correction model, which performs quantitative calculations based on the disease's preliminary scale, baseline scale, risk level and development trend. The homologous disease merging and final scaling determination unit is used to merge non-critical diseases that have been calibrated into a single item if there is a pre-defined pair of minor diseases to be merged, and to determine its representative scale and contribution order. The coupled disease model is specifically a dual-dimensional coupled analysis model of disease mechanism and influence dimension, with two core databases built-in: a typical bridge disease mechanism library and an influence dimension association rule library. The disease mechanism library covers the core inducing mechanisms of bridge diseases, including material degradation, load fatigue, and environmental erosion, comprehensively covering the factors that lead to disease during bridge service. The influence dimension association rule library establishes the correspondence between different mechanisms and influence dimensions, clarifying the influence mapping of different mechanisms, including material degradation and load fatigue, on structural safety, traffic performance, and durability dimensions. The upstream defect diagnosis module is a system module with professional bridge defect risk assessment function. It is used to integrate multi-dimensional data for comprehensive analysis. Specifically, based on the detection data of bridge defects, the structural characteristic parameters of the bridge itself, and in accordance with relevant industry standards, it uses a built-in preset risk assessment algorithm to quantitatively assess the risk level of each type of defect and output the corresponding risk level. The specific dynamic scaling correction model for non-critical diseases in S3 is as follows: Comprehensive state factors The assessment is based on a combination of the disease's risk level and its development trend. Specifically, the rule is: when the disease risk level is low and the development trend is towards stabilization or improvement / recovery... When the disease risk level is low and the development trend is slow, or the risk level is medium and the development trend is stabilizing, When the disease risk level is medium and the development trend is slow, or when the development trend is rapid, ; Nonlinear down-adjustment coefficient Based on comprehensive state factors The calculation formula is derived from the preliminary disease assessment scale. ,in, Indicates the initial assessment scale for the disease; Scale downscaling The calculation uses a floor function to ensure that the down adjustment is an integer. The calculation formula is as follows: ,in, This represents the floor function, which rounds the result down to the largest integer not greater than the result. Scale after calibration By constraining the maximum value function and the amplitude limit function within a reasonable range, the calculation formula is as follows: ,in, This indicates the highest permissible scale for this type of disease; This represents the maximum value function, which takes the larger of the two values ​​within the parentheses. This represents the limiting function, used to limit the value. Limited to the range inside, if Then take ,like Then take ,like Then take itself.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is executed by a processor as an optimization method based on mechanism importance and disease merging as described in any one of claims 1-7.

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