Bridge maintenance decision-making method based on big data

Abstract

The invention discloses a big data-based bridge maintenance decision-making method, which comprises the following steps of: firstly, acquiring and analyzing basic data of bridge maintenance, determining a bridge maintenance decision-making standard, constructing a corresponding bridge maintenance countermeasure library, and determining a bridge maintenance demand based on the bridge maintenance countermeasure library; and based on the maintenance grading decision tree and the priority ranking model, maintenance priority ranking is carried out on the to-be-maintained bridge, a bridge maintenance decision is optimized, and finally a bridge maintenance scheme and required cost are generated. According to the scheme, in decision making, the current situation of a bridge structure is considered, various factors such as bridge maintenance historical information are also considered, the factors include the technical situation, the current situation of structure diseases and the development trend, the decision-making scheme is optimized from multiple aspects through the maintenance grading decision-making tree and the optimization sequencing model, and the bridge maintenance scheme is output according to the final decision-making result. The decision making speed is greatly improved, the actual maintenance work requirement is met, effective configuration of maintenance funds is achieved, and scientificity, high efficiency and reliability of bridge maintenance decision making are improved.

Description

Technical Field

[0001] The present invention belongs to the field of bridge maintenance and management, and specifically relates to a bridge maintenance decision-making method based on big data. Background Art

[0002] As the throat node on the transportation route, the bridge occupies an extremely important position. Its operational safety is the node where the traffic network is guaranteed to be smooth, and it is also related to the safety of the main artery of the country's macroeconomic development. During its service life, the bridge structure is affected by innate factors such as construction conditions and material properties during construction, as well as acquired factors such as running vehicles and environmental impacts. It faces the dilemma of structural design function degradation. The method and effectiveness of bridge maintenance decision-making will greatly affect the safety, applicability and durability of the structure during operation. In addition, as my country's bridge construction gradually enters the stage of "equal emphasis on construction and maintenance", the demand for bridge maintenance is increasing, and the contradiction between the supply and demand of maintenance funds has become increasingly prominent. How to quickly allocate funds through scientific and reasonable maintenance decisions has become one of the key tasks of the bridge maintenance industry.

[0003] At present, bridge maintenance decisions mostly adopt the "semi-empirical" decision-making method, which relies more on the inspection report of the current year. It is difficult to make decisions based on the technical conditions and the development history of the disease through the manual judgment of the bridge engineer on the inspection status of the bridge that year, and the decision-making has certain limitations. On the other hand, the basic laws of long-term decay of bridge structures and component performance at home and abroad are still insufficient, and it is difficult to make targeted maintenance decisions through performance trend analysis. Therefore, the current bridge industry needs to use reliable decision-making means to achieve graded and classified treatment of bridge maintenance, promote the implementation of scientific decision-making on bridge maintenance, and improve the safety and durability of structures. Summary of the invention

[0004] In view of the above problems, the purpose of the present invention is to provide a bridge maintenance decision-making method based on big data. By collecting basic bridge maintenance data and analyzing the maintenance status and development trend of the decision-making object, targeted bridge maintenance decision-making standards are established, and the maintenance and repair countermeasures library and optimization sorting model are used to select and optimize the decision-making plan, and finally a bridge maintenance and repair plan is generated. It is an efficient, targeted and implementable maintenance decision-making means, which can effectively improve the scientificity and reliability of the annual bridge plan.

[0005] The specific technical solution for achieving the purpose of the present invention is:

[0006] A bridge maintenance decision-making method based on big data includes the following steps:

[0007] Step 1: Obtain basic data on bridge maintenance and analyze it;

[0008] Step 2: Determine the bridge maintenance decision-making standard based on the basic data of bridge maintenance, and build a corresponding bridge maintenance countermeasure library;

[0009] Step 3: Determine the bridge maintenance needs based on the bridge maintenance decision criteria, including the bridges to be repaired, recommended maintenance plans and maintenance costs;

[0010] Step 4: Based on the maintenance classification decision tree and priority ranking model, the maintenance priorities of the bridges to be repaired are ranked to optimize the bridge maintenance decision;

[0011] Step 5: Generate the final bridge maintenance plan and required costs based on the optimized bridge maintenance decision.

[0012] Compared with the prior art, the present invention has the following beneficial effects:

[0013] The solution of the present invention proposes a bridge maintenance decision-making solution based on big data. In the decision-making, not only the current status of the bridge structure is considered, but also various factors such as the historical information of bridge maintenance, including technical conditions and the current status and development trend of structural diseases, and the decision-making solution is optimized from multiple aspects through the maintenance classification decision tree and the optimization sorting model. The final decision result outputs the bridge maintenance plan, including the recommended maintenance plan and cost, which greatly improves the speed of decision-making, meets the actual maintenance work needs, realizes the effective allocation of maintenance funds, and improves the scientificity, efficiency and reliability of bridge maintenance decisions.

[0014] The present invention is further described below in conjunction with specific implementation modes. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of the bridge maintenance decision-making method based on big data of the present invention.

[0016] Figure 2 It is a schematic diagram of a decision tree for maintenance decision criteria of a prestressed concrete hollow slab bridge in an embodiment of the present invention.

[0017] Figure 3 Schematic diagram of a maintenance level classification decision tree in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] Example

[0019] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. The described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an kind" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0021] Unless otherwise specifically stated, the relative arrangement, numerical expressions and numerical values ​​of the parts and steps set forth in these embodiments do not limit the scope of the present application. At the same time, it should be understood that, for ease of description, the sizes of the various parts shown in the accompanying drawings are not drawn according to the actual proportional relationship. The technology, method and equipment known to those of ordinary skill in the relevant field may not be discussed in detail, but in appropriate cases, the technology, method and equipment should be considered as a part of the authorization specification. In all examples shown and discussed here, any specific value should be interpreted as being merely exemplary, rather than as a limitation. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so that once a certain item is defined in an accompanying drawing, it does not need to be further discussed in subsequent drawings.

[0022] Combination Figure 1 , a bridge maintenance decision-making method based on big data, comprising the following steps:

[0023] Step 1: Obtain and analyze basic data on bridge maintenance, including bridge construction information, structural information, maintenance history, inspection and assessment records, structural disease records, traffic volume and axle load, and natural environment conditions;

[0024] The bridge construction information includes: the route to which the bridge belongs, its location, design load, construction year, and management unit;

[0025] The structural construction information includes: the total length of the bridge, span combination, total span length, maximum span, bridge structure type, bridge construction material type, and main component dimensions;

[0026] The maintenance history includes: maintenance type, maintenance time, main defects, maintenance location, maintenance plan, engineering quantity, cost, and implementation effect of minor bridge repairs, major and medium-sized bridge repairs, preventive maintenance, repair maintenance, special projects, and renovation projects in the recent years (3 years):

[0027] The inspection and evaluation records include: the technical condition scores and ratings of the entire bridge and its components, component scores, and evaluation time during the bridge inspections within the last certain years (3 years);

[0028] The structural defect records include: typical defect types, defect locations, defect quantity, length, width, development trend, and detection time found in bridge inspections within the last certain years (3 years);

[0029] The traffic volume and axle load include: the annual average daily traffic volume equivalent and natural number of each type of vehicle in the most recent year (1 year), as well as axle load data such as vehicle type, gross vehicle weight, axle weight, and vehicle speed;

[0030] The natural environment conditions include: the average temperature, annual maximum temperature, annual minimum temperature, rainfall, environmental geology, and hydrological conditions in the recent year (1 year);

[0031] The basic maintenance data analysis includes: basic bridge condition analysis, technical condition analysis, structural disease analysis, maintenance and repair analysis, and traffic data analysis;

[0032] The basic analysis of bridge conditions includes: classification of functional types, classification of superstructure types, classification of material types, distribution of bridge ages, classification of design standards, and classification of general drawings during construction;

[0033] The technical condition analysis includes: the current distribution of the technical conditions of the entire bridge, parts and components, and the historical technical condition change trends of the entire bridge, parts and components;

[0034] The structural disease analysis includes: overall disease situation analysis, typical disease types and distribution patterns, and disease development and change trends over the years;

[0035] Among them, the development trend of defects over the years refers to whether the total number of defects or the total increase in seam width of the corresponding defective components in this inspection compared with the previous inspection exceeds the set threshold. For example, if the increase exceeds 20%, in actual operation, it is necessary to eliminate some cases where the increase is 1 to 2, 1 to 3, 2 to 3, 2 to 4, and 3 to 4 to remove miscellaneous influences;

[0036] The maintenance and repair analysis includes: the scale and cost of different maintenance measures over the years, the changes in technical conditions and diseases under different maintenance measures, and the implementation effects;

[0037] The traffic data analysis includes: the development law and growth rate changes of cross-sectional traffic flow in the road section over the years, the distribution status and change trend of different vehicle types, the cumulative number of equivalent axle load actions, and traffic volume forecast analysis.

[0038] Step 2: Determine the bridge maintenance decision-making standard based on the basic data of bridge maintenance, and build a corresponding bridge maintenance countermeasure library;

[0039] The bridge maintenance decision-making standard is determined based on the bridge structure type, material type, and disease type, and the bridge maintenance decision-making index is determined. The decision-making index is graded according to the disease distribution location and disease development trend to form a maintenance decision-making standard and realize the classification of bridge diseases, which are divided into Class 1 structural disease, Class 2 structural disease, Class 1 non-structural disease, and Class 2 non-structural disease;

[0040] Among them, bridge structure types include hollow slab, solid slab, T-beam, I-beam, π-beam, composite box girder, continuous box girder with constant cross-section, continuous box girder with variable cross-section, T-shaped rigid frame, continuous rigid frame, steel plate girder, steel box girder, steel truss, and steel-concrete composite beam;

[0041] Material types include reinforced concrete, prestressed concrete, and steel;

[0042] The types of defects include oblique cracks in the web, transverse cracks in the bottom plate, oblique cracks in the bottom plate, vertical cracks in the web, concrete damage, and steel corrosion;

[0043] The location of the defects on the bridge includes the distance from the beam end and the distance from the upper / lower part;

[0044] In the disease development trend, common diseases are reflected by the increase or decrease in the total number of diseases, and crack diseases are reflected by the increase in the total width of cracks of the same type.

[0045] In this embodiment, a prestressed concrete hollow slab beam is taken as an example to construct a bridge maintenance decision standard in the form of a decision tree, such as Figure 2 As shown;

[0046] The bridge maintenance countermeasures database sets corresponding bridge maintenance countermeasures for each bridge maintenance decision indicator. The maintenance countermeasures include optional maintenance plan names, cost calculation formulas, and recommended maintenance plans for each decision standard.

[0047] The bridge maintenance and repair countermeasure library and the bridge maintenance decision criteria and repair countermeasure recommendations in this embodiment are shown in Table 1 and Table 2:

[0048] Table 1 Bridge maintenance and repair strategy database

[0049]

[0050] Table 2 Bridge maintenance decision-making standards and recommended maintenance strategies

[0051]

[0052] Step 3: Determine the bridge maintenance needs based on the bridge maintenance decision criteria, including the bridges to be repaired, recommended maintenance plans and maintenance costs;

[0053] Among them, determining the maintenance demand of bridges is to compare the bridge disease index with the bridge maintenance decision standard. When the bridge disease index meets the requirements of the maintenance decision standard, the bridge is included in the list of bridges that need maintenance and repair, and at the same time, the recommended maintenance and repair plan is matched through the bridge maintenance countermeasure library, and the maintenance cost of the corresponding plan is calculated;

[0054] The comparison between the bridge disease index and the bridge maintenance decision-making standard includes whether the disease type matches, whether the location of the disease matches, and whether the disease has a development trend. Whether the disease develops refers to whether the total number of similar diseases or the total increase in seam width of the corresponding component compared with the previous inspection exceeds the set threshold.

[0055] In this embodiment, a prestressed concrete hollow slab beam bridge is taken as an example. Last year, 5 oblique cracks with a width of 0.08mm were found at 0.1L of the 1# web of the 1# beam of this bridge. This year, 8 oblique cracks with a width of 0.1mm were found at 0.1L of the 1# web of the 1# beam of this bridge. There were 2 vertical cracks with a width of 0.1mm at 0.5L of the 1# web, and 2 oblique cracks with a width of 0.2mm at 0.4L of the 2# web of the 11# beam.

[0056] According to the bridge maintenance decision-making standards, it is judged that the 1# beam of this bridge has structural defects of diagonal web cracks (Type 1) and vertical web cracks (Type 2), and the 11# beam has non-structural defects of diagonal web cracks (Type 1).

[0057] Therefore, this bridge is in need of repair. Recommended repair plan 1 is to use cavity "rebar embedding + high-performance concrete" and seal and grout for repair of 1# beam, and seal and grout for repair of 11# beam. Recommended plan 2 is to use glued steel plates for 1# beam (vertical cracks in the web continue to be observed), and continue to observe 11# beam without repair.

[0058] Step 4: Based on the maintenance classification decision tree and priority ranking model, the maintenance priorities of the bridges to be repaired are ranked to optimize the bridge maintenance decision:

[0059] Step 4-1: Classify the maintenance level of the bridges to be maintained based on the maintenance classification decision tree:

[0060] The bridges are graded for maintenance based on whether there are serious driving safety defects, bridge technical conditions, component technical conditions, structural defects, and serious apparent defects. The maintenance priority is from high to low: first-level maintenance, second-level maintenance, third-level maintenance, fourth-level maintenance, and fifth-level maintenance.

[0061] In this embodiment, the bridge maintenance level is graded in the form of a decision tree, such as Figure 3 As shown;

[0062] Among them, serious driving safety diseases refer to the diseases in Table 3:

[0063] Table 3 List of serious driving safety hazards

[0064]

[0065] The technical condition of the bridge and its components refers to the technical condition assessment carried out in accordance with the "Highway Bridge Technical Condition Assessment Standard" (JTG / H21-2011) and the "Technical Standard for Assessment of Rubber Bearing Defects of Highway Bridges" (DB32 / T 2172-2012), to obtain the bridge technical condition score and grade, and the component technical condition score and grade.

[0066] The main components and secondary components mentioned above refer to the main components and secondary components of bridges classified in accordance with the "Technical Condition Assessment Standards for Highway Bridges" (JTG / H21-2011).

[0067] The structural damage (category 1) and structural damage (category 2) mentioned above refer to Figure 2 The bridge maintenance decision tree determines the defects that may affect the overall bearing capacity of the bridge, including but not limited to transverse cracks in the bottom plate of the main beam of the beam bridge, oblique cracks in the bottom plate, vertical cracks in the web, oblique cracks in the web, single plate stress defects (hinge joint defects), etc.

[0068] The serious apparent defects mentioned above refer to cracks and concrete defects with a scale of 4 or above other than structural defects.

[0069] Whether the damage mentioned exceeds the limit refers to whether it exceeds the crack width limit of the bridge structure or component under permanent conditions specified in the "Highway Bridge Bearing Capacity Inspection and Assessment Procedure" (JTG / TJ21-2011).

[0070] Step 4-2: Prioritize the maintenance of bridges in each maintenance level based on the priority ranking model;

[0071] The maintenance priorities of multiple bridges in each maintenance level are re-ranked, and the optimal ranking is determined using the linear weighted method:

[0072]

[0073] Among them, F(x) is the ranking evaluation value, ω k is the weight coefficient of the k-th sorting index, ω k ∈[0,1], f k (x) is the evaluation value of the k-th sorting index, which is a dimensionless value ranging from 0 to 100 and is determined according to the attribute; n is the number of sorting indicators.

[0074] The ranking index in this embodiment includes a primary index and a secondary index;

[0075] The first-level indicators include bridge importance, technical condition, bridge suitability and traffic capacity;

[0076] The bridge importance includes secondary indicators such as the maximum span of a single span of the bridge, the technical grade of the route and the structural type;

[0077] The technical status includes secondary indicators of the technical status of the entire bridge, the technical status of the entire bridge, the development trend of the technical status, and the development trend of key diseases;

[0078] The bridge serviceability includes secondary indicators of bridge age and design load;

[0079] The traffic capacity includes secondary indicators of traffic volume and degree of heavy load.

[0080] As shown in Table 4:

[0081] Table 4 Prioritization algorithm indicators

[0082]

[0083] The weight of each indicator is determined by the entropy weight method:

[0084] Taking the weight of secondary indicators as an example, there are 10 bridges in this embodiment, and the values ​​of each indicator are: the maximum span of a single span of the bridge is the maximum span of the single span of the bridge; the route technical grade indicator: the expressway is 6, the first-class highway is 5, the second-class highway is 4, the third-class highway is 3, the fourth-class highway is 2, and the non-standard highway is 1; the structural type indicator beam bridge is 1, the arch bridge is 2, and the cable-stayed bridge / suspension bridge is 3; the technical condition grade of the whole bridge is the technical condition score of the bridge; the worst technical condition indicator of the main components is the lowest value of the technical condition score of the main components; the technical condition development trend indicator is the technical condition of this year. The difference between the technical condition score and the technical condition score of last year; the development trend index of key defects is the growth rate of the same type of key defects of the same component relative to last year; the bridge age index is the year of the bridge when the decision was made; the design load index: 5 for automobiles below level 15, 4 for automobiles level 15, 3 for automobiles level 20, 2 for highway level II, 1 for highway level I and automobiles level 20; the traffic volume index: 5 for extremely heavy traffic, 4 for extra heavy traffic, 3 for heavy traffic, 2 for medium traffic, and 1 for light traffic; the heavy load degree index: the proportion of heavy-loaded vehicles is the proportion of vehicles with a total weight of more than 49t or an axle weight of more than 14t;

[0085] First, the original matrix R of each secondary index is constructed, which is:

[0086]

[0087] And standardize it to get the standardized matrix R ′ :

[0088]

[0089] Among them, r ij is the jth index value of the i-th bridge, r i ′ j represents the jth index value of the i-th bridge after standardization;

[0090] Determine the weight of each secondary indicator:

[0091]

[0092] Among them, m is the number of bridges, n is the number of indicators, P ij Represents r i ′ j The proportion, e j represents the entropy value of the jth indicator, ω j represents the weight of the j-th indicator;

[0093] After obtaining the weights and values ​​of each secondary indicator, the entropy weight method is repeated to determine the weights of each primary indicator.

[0094] In this embodiment, the weights of the secondary indicators under the final bridge importance index are ω=(0.37, 0.15, 0.48); the weights of the secondary indicators under the technical condition index are ω=(0.25, 0.26, 0.26, 0.24); the weights of the secondary indicators under the bridge suitability index are ω=(0.41, 0.59); and the weights of the secondary indicators under the traffic capacity index are ω=(0.57, 0.43).

[0095] The logic of maintenance priority sorting is:

[0096] First, determine whether there is a funding restriction. If there is no funding restriction, repair all types of defects in all bridges that need to be repaired according to the recommended plan with high maintenance standards and generate the total cost of repair;

[0097] If there is a funding restriction, the bridges with the first-level maintenance level will be repaired according to the recommended plan with low maintenance standards to generate the minimum maintenance cost of the first-level maintenance bridges. At this time, if the set maintenance funds are less than the minimum maintenance cost, the bridge maintenance decision system will be triggered to prompt "Maintenance funds are less than the minimum maintenance cost, please check"; if the set maintenance funds are higher than the minimum maintenance cost of the first-level maintenance bridges, the first-level maintenance bridges will all be repaired and calculated according to the recommended plan with high maintenance standards. If the funds are insufficient to support the high-standard maintenance of all first-level bridges, the first-level bridges will be sorted by the priority sorting algorithm to determine the order of the bridges to be repaired with high maintenance standards, and the rest will be repaired with low maintenance standards;

[0098] If the set funds are higher than the total cost of the high maintenance standard maintenance plan for the first-level maintenance bridges, the cost will be calculated according to the recommended maintenance plan of the high maintenance standard for all the first-level maintenance bridges and the recommended maintenance plan of the low maintenance standard for all the second-level maintenance bridges. If the funds are insufficient to support the maintenance of all the second-level maintenance bridges according to the recommended plan of the low maintenance standard, the second-level maintenance bridges will use the priority sorting algorithm to sort the bridges of the same level to determine the final maintenance order;

[0099] If the funds are higher than the total cost of the high maintenance standard maintenance plan for the first-level maintenance bridges and the low maintenance standard maintenance plan for the second-level maintenance bridges, the cost will be calculated based on the high maintenance standard maintenance plan for both the first-level and second-level maintenance bridges. If the funds are insufficient, the first-level maintenance bridges will be repaired with the high maintenance standard maintenance plan, and the second-level maintenance bridges will be sorted by the priority sorting algorithm for the same level of maintenance bridges. The bridges at the front of the list will be repaired with the recommended plan with the high maintenance standard, and the bridges at the back of the list will be repaired with the recommended plan with the low maintenance standard.

[0100] Consider in turn that all the first, second and third level maintenance bridges are maintained according to the recommended high maintenance standard recommended plan, and all the fourth level maintenance bridges are maintained according to the recommended low maintenance standard recommended plan, and all the first, second, third and fourth level maintenance bridges are maintained according to the recommended high maintenance standard recommended plan, and all the fifth level maintenance bridges are maintained according to the recommended low maintenance standard recommended plan. Iterate according to the above steps, and the decision-making plan optimization ends when the final bridge maintenance cost is the smallest difference from the set absolute value of the maintenance fund.

[0101] Step 5: Generate the final bridge maintenance plan and required costs based on the optimized bridge maintenance decision, including the bridge structure type, current technical status, maintenance plan, maintenance cost, maintenance year, etc. included in the maintenance plan.

[0102] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A bridge maintenance decision-making method based on big data, characterized in that: The following steps are involved: Step 1: Obtain basic data on bridge maintenance and analyze it; Step 2: Determine the bridge maintenance decision-making standard based on the basic data of bridge maintenance, and build a corresponding bridge maintenance countermeasure library; Step 3: Determine the bridge maintenance needs based on the bridge maintenance decision criteria, including the bridges to be repaired, recommended maintenance plans and maintenance costs; Step 4: Based on the maintenance classification decision tree and priority ranking model, the maintenance priorities of the bridges to be repaired are ranked to optimize the bridge maintenance decision; Step 5: Generate the final bridge maintenance plan and required costs based on the optimized bridge maintenance decision.

2. The bridge maintenance decision-making method based on big data according to claim 1 is characterized in that: The basic data of bridge maintenance in step 1 includes bridge construction information, structural information, maintenance history, inspection and assessment records, structural disease records, traffic volume and axle load, and natural environment conditions; The bridge construction information includes: the route to which the bridge belongs, its location, design load, construction year, and management unit; The structural construction information includes: the total length of the bridge, span combination, total span length, maximum span, bridge structure type, bridge construction material type, and main component dimensions; The maintenance history includes: maintenance type, maintenance time, main defects, maintenance location, maintenance plan, engineering quantity, cost, and implementation effect of minor bridge repairs, major and medium-sized bridge repairs, preventive maintenance, repair maintenance, special projects, and renovation projects in the recent years: The inspection and evaluation records include: the technical condition scores and ratings of the entire bridge and its components, component scores, and evaluation time during the bridge inspections in the most recent year; The structural defect records include: typical defect types, defect locations, defect quantity, length, width, development trend, and detection time found in bridge inspections in recent years; The traffic volume and axle load include: the annual average daily traffic volume equivalent and natural number of each type of vehicle in the recent years, as well as axle load data such as vehicle type, gross vehicle weight, axle weight, and vehicle speed; The natural environment conditions include: the average temperature, annual maximum temperature, annual minimum temperature, rainfall, environmental geology, and hydrological conditions in the recent years; The basic maintenance data analysis includes: basic bridge condition analysis, technical condition analysis, structural disease analysis, maintenance and repair analysis, and traffic data analysis; The basic analysis of bridge conditions includes: classification of functional types, classification of superstructure types, classification of material types, distribution of bridge ages, classification of design standards, and classification of general drawings during construction; The technical condition analysis includes: the current distribution of the technical conditions of the entire bridge, parts and components, and the historical technical condition change trends of the entire bridge, parts and components; The structural disease analysis includes: overall disease situation analysis, typical disease types and distribution patterns, and disease development and change trends over the years; The maintenance and repair analysis includes: the scale and cost of different maintenance measures over the years, the changes in technical conditions and diseases under different maintenance measures, and the implementation effects; The traffic data analysis includes: the development law and growth rate changes of cross-sectional traffic flow in the road section over the years, the distribution status and change trend of different vehicle types, the cumulative number of equivalent axle load actions, and traffic volume forecast analysis.

3. The bridge maintenance decision-making method based on big data according to claim 1 is characterized in that: Determining the bridge maintenance decision standard in step 2 is to determine the bridge maintenance decision index according to the bridge structure type, material type, and disease type, and classify the decision index according to the disease distribution location and disease development trend to form a maintenance decision standard, and realize the classification of bridge diseases into Class 1 structural disease, Class 2 structural disease, Class 1 non-structural disease, and Class 2 non-structural disease; Among them, bridge structure types include hollow slab, solid slab, T-beam, I-beam, π-beam, composite box girder, continuous box girder with constant cross-section, continuous box girder with variable cross-section, T-shaped rigid frame, continuous rigid frame, steel plate girder, steel box girder, steel truss, and steel-concrete composite beam; Material types include reinforced concrete, prestressed concrete, and steel; The types of defects include oblique cracks in the web, transverse cracks in the bottom plate, oblique cracks in the bottom plate, vertical cracks in the web, concrete damage, and steel corrosion; The distribution location of the defects includes the distance from the beam end and the distance from the upper / lower part; In the disease development trend, common diseases are reflected by the increase or decrease in the total number of diseases, and crack diseases are reflected by the increase in the total width of cracks of the same type.

4. The bridge maintenance decision-making method based on big data according to claim 3 is characterized in that: In the bridge maintenance strategy library in step 2, a corresponding bridge maintenance strategy is set for each bridge maintenance decision indicator. The maintenance strategy includes an optional maintenance plan name, a cost calculation formula, and a recommended maintenance plan is set for each decision standard.

5. The bridge maintenance decision-making method based on big data according to claim 1 is characterized in that: Determining the bridge maintenance demand in step 3 is to compare the bridge disease index with the bridge maintenance decision standard. When the bridge disease index meets the requirements of the maintenance decision standard, the bridge is included in the list of bridges that need maintenance and repair, and at the same time, a recommended maintenance and repair plan is matched through the bridge maintenance countermeasure library, and the maintenance cost of the corresponding plan is calculated; The comparison between the bridge disease index and the bridge maintenance decision-making standard includes whether the disease type matches, whether the location of the disease matches, and whether the disease has a development trend. Whether the disease develops refers to whether the total number of similar diseases or the total increase in seam width of the corresponding component compared with the previous inspection exceeds the set threshold.

6. The bridge maintenance decision-making method based on big data according to claim 1 is characterized in that: The maintenance priorities in step 4 are sorted as follows: Step 4-1: Classify the maintenance level of bridges that need maintenance based on the maintenance classification decision tree: The bridges are graded for maintenance based on whether there are serious driving safety defects, bridge technical conditions, component technical conditions, structural defects, and serious apparent defects. The maintenance priority is from high to low: first-level maintenance, second-level maintenance, third-level maintenance, fourth-level maintenance, and fifth-level maintenance. Step 4-2: Prioritize the maintenance of bridges within each maintenance level based on the priority ranking model.

7. The bridge maintenance decision-making method based on big data according to claim 6 is characterized in that: The maintenance priority of bridges in each maintenance level is prioritized based on the priority ranking model in step 4-2, specifically: The maintenance priorities of multiple bridges in each maintenance level are ranked, and the optimal ranking is determined using the linear weighting method: Among them, F(x) is the ranking evaluation value, ω k is the weight coefficient of the k-th sorting index, ω k ∈[0,1], f k (x) is the evaluation value of the k-th ranking index, which is a dimensionless value ranging from 0 to 100 and is determined according to the attribute; n is the number of ranking indicators.

8. The bridge maintenance decision-making method based on big data according to claim 7 is characterized in that: The ranking index includes primary index and secondary index; The first-level indicators include bridge importance, technical condition, bridge suitability and traffic capacity; The bridge importance includes secondary indicators such as the maximum span of a single span of the bridge, the technical grade of the route and the structural type; The technical status includes secondary indicators of the technical status of the entire bridge, the technical status of the entire bridge, the development trend of the technical status, and the development trend of key diseases; The bridge serviceability includes secondary indicators of bridge age and design load; The traffic capacity includes secondary indicators of traffic volume and degree of heavy load.

9. The bridge maintenance decision-making method based on big data according to claim 8 is characterized in that: The weights of the indicators are determined using the entropy weight method: First, construct the original matrix R of each secondary index and standardize it to obtain the standardized matrix R ′ : Among them, r ij is the jth index value of the i-th bridge, r i ′ j represents the jth index value of the i-th bridge after standardization; Determine the weight of each secondary indicator: Among them, m is the number of bridges, n is the number of indicators, P ij Represents r i ′ j The proportion, e j represents the entropy value of the jth indicator, ω j represents the weight of the j-th indicator; After obtaining the weights and values ​​of each secondary indicator, the entropy weight method is repeated to determine the weights of each primary indicator.

10. The bridge maintenance decision-making method based on big data according to claim 6 is characterized in that: The maintenance priority ranking in step 4 is specifically as follows: First, determine whether there is a funding restriction. If there is no funding restriction, repair all types of defects in all bridges that need to be repaired according to the recommended plan with high maintenance standards and generate the total cost of repair; If there is a funding restriction, the bridges with the first-level maintenance level will be repaired according to the recommended plan with low maintenance standards to generate the minimum maintenance cost of the first-level maintenance bridges. At this time, if the set maintenance funds are less than the minimum maintenance cost, a prompt "Maintenance funds are less than the minimum maintenance cost, please check" will be displayed; if the set maintenance funds are higher than the minimum maintenance cost of the first-level maintenance bridges, the maintenance costs of all the first-level maintenance bridges will be calculated according to the recommended plan with high maintenance standards. If the funds are insufficient to support the high-standard maintenance of all the first-level bridges, the first-level bridges will be sorted by the priority sorting algorithm to determine the order of the bridges to be repaired with high maintenance standards, and the rest will be repaired with low maintenance standards; If the set funds are higher than the total cost of the high maintenance standard maintenance plan for the first-level maintenance bridges, the cost will be calculated according to the recommended maintenance plan of the high maintenance standard for all the first-level maintenance bridges and the recommended maintenance plan of the low maintenance standard for all the second-level maintenance bridges. If the funds are insufficient to support the maintenance of all the second-level maintenance bridges according to the recommended plan of the low maintenance standard, the second-level maintenance bridges will use the priority sorting algorithm to sort the bridges of the same level to determine the final maintenance order; If the funds are higher than the total cost of the high maintenance standard maintenance plan for the first-level maintenance bridges and the low maintenance standard maintenance plan for the second-level maintenance bridges, the cost will be calculated based on the high maintenance standard maintenance plan for both the first-level and second-level maintenance bridges. If the funds are insufficient, the first-level maintenance bridges will be repaired with the high maintenance standard maintenance plan, and the second-level maintenance bridges will be sorted by the priority sorting algorithm for the same level of maintenance bridges. The bridges at the front of the list will be repaired with the recommended plan with the high maintenance standard, and the bridges at the back of the list will be repaired with the recommended plan with the low maintenance standard. Consider in turn that all the first, second and third level maintenance bridges are maintained according to the recommended high maintenance standard recommended plan, and all the fourth level maintenance bridges are maintained according to the recommended low maintenance standard recommended plan, and all the first, second, third and fourth level maintenance bridges are maintained according to the recommended high maintenance standard recommended plan, and all the fifth level maintenance bridges are maintained according to the recommended low maintenance standard recommended plan. Iterate according to the above steps, and the decision-making plan optimization ends when the final bridge maintenance cost is the smallest difference from the set absolute value of the maintenance fund.

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