Method for evaluating construction quality of standardized cantilever of corrugated steel web prestressed combined box girder bridge

Abstract

The invention provides a construction quality evaluation method for a standardized cantilever of a corrugated steel web prestressed combined box girder bridge, and relates to the technical field of civil engineering bridges, S1, acquiring current construction progress data, and dividing a cantilever construction process into a No.0 block construction stage, a cantilever casting section construction stage and a closure section construction stage, calling a risk database to identify a risk factor set R of each stage; and calculating a quantitative risk value of each risk factor according to the risk factor set R, determining a weight correction coefficient according to the quantitative risk values, and constructing a five-layer dynamic index system comprising a construction stage adaptation layer. Through cooperation of the structures, compared with an existing method, the method has the following beneficial effects that by dividing construction stages and dynamically adapting an index system, evaluation indexes can be accurately matched with core risk characteristics of all the stages, the problem that a static index system is disjointed from dynamic risks of the construction stages is solved, and the construction efficiency is improved. Therefore, the stage pertinence of quality evaluation to the construction process is improved.

Description

Technical Field

[0001] This invention relates to the field of civil engineering bridge technology, and in particular to a standardized cantilever construction quality evaluation method for corrugated steel web prestressed composite box girder bridges. Background Technology

[0002] With the increasing demand for long-span bridge construction, corrugated steel web prestressed composite box girder bridges have become widely used in projects spanning rivers and canyons due to their combination of the compressive strength of concrete and the lightweight and high shear strength of corrugated steel webs. Corrugated steel web prestressed composite box girder bridges are a new type of steel-concrete composite structure, and their construction method is a key technology in building long-span bridges. To promote the standardization and normalization of construction management, establishing a matching quality evaluation system is crucial. Currently, the evaluation of the construction quality of this type of bridge largely relies on the traditional expert scoring method, which involves a simple weighted average of various evaluation indicators.

[0003] In the prior art, patent number CN118333200A discloses a method for assessing the condition of prestressed concrete continuous rigid frame bridges based on fuzzy comprehensive method. The technical solution involves establishing a four-layer condition assessment system: overall assessment, main beam / support / pier / abutment foundation, specific indicators, and bottom-level indicators. The weights of the indicators are determined using the Analytic Hierarchy Process (AHP), and a five-level fuzzy evaluation system ("excellent, good, moderate, poor, dangerous") is used to assess the bridge's health status during the operational phase. However, this technical solution, due to the use of a fixed indicator system and fixed weights during the cantilever construction of corrugated steel web prestressed composite box girder bridges, cannot quantify the correlation between the cantilever construction of corrugated steel web prestressed composite box girder bridges and the dynamic changes in quality control priorities during the construction phase. Furthermore, the dynamic changes during the construction phase affect the quality judgment boundary, and the existing method uses static fuzzy evaluation, which cannot achieve dynamic adjustment of the construction environment and fuzzy boundaries. Therefore, it is necessary to establish an integrated intelligent quality evaluation system that can adaptively adjust indicator weights and is applicable to fuzzy judgment boundaries. Summary of the Invention

[0004] To address the shortcomings of the existing technologies, the technical problem to be solved by this invention is to provide a standardized cantilever construction quality evaluation method for corrugated steel web prestressed composite box girder bridges. This method resolves technical issues in the existing technologies, such as the contradiction between static frames and dynamic construction, the contradiction between rigid boundaries and fuzzy environments, and the contradiction between general indicators and special scenarios. The goal is to achieve an integrated intelligent quality evaluation system that adaptively adjusts indicator weights and dynamically adapts fuzzy judgment boundaries.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: The present invention provides a standardized cantilever construction quality evaluation method for corrugated steel web prestressed composite box girder bridges, comprising the following steps: S1. Obtain the current construction progress data, divide the cantilever construction process into the construction stage of block 0, the construction stage of the cantilever segment, and the construction stage of the closure segment, call the risk database to identify the risk factor set R of each stage; calculate the quantitative risk value of each risk factor according to the risk factor set R, determine the weight correction coefficient according to the quantitative risk value, and construct a five-layer dynamic index system including the construction stage adaptation layer. S2. Calculate the initial weight vector using the analytic hierarchy process (AHP). Collect environmental risk factors and structural risk factors using environmental and structural sensors. Calculate the comprehensive correction coefficient based on the environmental risk factors, structural risk factors, and weight correction coefficients to correct the initial weight vector and obtain the dynamic weight vector. S3. Calculate the current risk level based on environmental risk factors and structural risk factors. Dynamically adjust the element weights of the fuzzy relation matrix based on the current risk level. Perform risk-sensitive adjustment on the membership function parameters. Perform fuzzy synthesis operation using the dynamic weight vector and the dynamic fuzzy relation matrix to obtain the final risk-weighted evaluation vector. S4. Obtain current construction scene information through GPS data and terrain analysis, match a set of special indicators based on the construction scene information, and redistribute weights based on the matched set of special indicators to obtain a scene-based weight vector. S5. Calculate the comprehensive evaluation score based on the risk-weighted evaluation vector and the scenario-based weight vector, obtain the preset threshold, determine the final evaluation result based on the preset threshold, collect actual construction quality data, and dynamically optimize the risk mapping function based on the actual construction quality data.

[0006] In the preferred embodiment, step S1 consists of the following steps: S11. The computer starts the processor, which reads the current construction progress information from the construction progress database. The processor converts the progress data into executable instructions using the instruction decoder. The program counter (PC) locates the storage address of the current construction stage based on the instructions, calls the preset construction stage division rules, and divides the cantilever construction process into three stages: the 0th block construction stage. : Steel web positioning and prestressing stage; Cantilever segment construction stage Concrete pouring and formwork support stage; Closure section construction stage Temperature stress control and alignment matching stage, output construction stage set ; According to the division of construction stages Risk factors at each stage are matched from the risk database to form a risk factor set. ,in Selected from {steel web positioning accuracy, prestressing tension control, hanging basket stability, concrete elevation control, formwork support system stability, temperature stress control, and alignment matching accuracy}, outputting a set of risk factors. ; S12, Processor on risk factor set Each risk factor The risk value is calculated using the following formula: ; in, Indicates the first Quantitative risk value of each risk factor; Indicates the first The probability of occurrence of each risk factor is rated on a scale of 1 to 5, with level 1 being extremely unlikely and level 5 being extremely likely. Indicates the first Exposure frequency of each risk factor, 1-5, Level 1: extremely low frequency, Level 5: extremely high frequency; Indicates the first The severity of the consequences of each risk factor is categorized into five levels: Level 1: minor loss; Level 5: major loss. Forming a risk value vector Output risk value vector ; S13, The processor retrieves the risk value vector. Extract the minimum value and maximum value By traversing the vector The storage address is used to calculate the weight correction coefficient for each indicator. The formula is: ; in, Indicates the first The weighting adjustment coefficients for each risk factor; This indicates the preset maximum correction factor; Represents the risk value vector The minimum risk value in; Represents the risk value vector The maximum risk value in; The processor calculates each risk factor. To form a correction coefficient vector Output correction coefficient vector ; S14. The processor reads the structural information of the target layer, criterion layer, indicator layer, and sub-indicator layer from the indicator system database, and then, based on the construction stage set... Construct a five-layer dynamic indicator system.

[0007] In the preferred embodiment, the five-layer dynamic indicator system is constructed in step S14 as follows: Target layer: Standardized cantilever construction quality level of corrugated steel web bridge; Criterion layer: ,in, For quality behavior, For physical quality; Construction stage layer: ,in, These correspond to the construction of block 0, the cantilevered segment, and the closure segment, respectively. Indicator layer: ,in, For personnel management, For technical management, For materials management, For subcontracting management, For construction management, For data management; ,in, It is made of corrugated steel web. For installation of corrugated steel web, For steel bar processing and installation, For the processing and tensioning of prestressed steel strands, For cantilever concrete casting; Sub-indicator layer is ,in, For the control of the web height of corrugated steel, For flange width and hole anchoring; ,in, The center distance of the corrugated steel web is... For shear connector installation; ,in, To control the spacing of reinforcing bars, For control of protective layer thickness; ,in, This represents the tensile stress value. To control the prestress loss value; ,in, For concrete strength control, For cross-sectional dimensions and elevation control; The processor stores the five-layer dynamic indicator system to a specified memory address and outputs the five-layer dynamic indicator system.

[0008] In the preferred embodiment, step S2 consists of the following steps: S21. Based on the structural information of the indicator layer and sub-indicator layer in the five-layer dynamic indicator system, convert the judgment matrix construction instruction into an executable instruction through the instruction decoder, and construct the judgment matrix according to the 1-9 scaling method. ,in, Indicates the first The first indicator is relative to the first The importance scale for each indicator, with values ​​selected from {1,2,...,9,1 / 2,...,1 / 9}; Judgment Matrix The scales and their meanings are shown in Table 1 below: Table 1. Matrix Scale and Its Meaning

[0009] Note: ={2, 4, 6, 8, 1 / 2, 1 / 4, 1 / 6, 1 / 8} indicates an importance level between ={1, 3, 5, 7, 9, 1 / 3, 1 / 5, 1 / 7, 1 / 9} S22. Calculate the judgment matrix based on the processor. Product of elements in each row ,according to calculate Root ,right Perform normalization to obtain the first... Initial weights of each indicator All Integrate into an initial weight vector The specific steps are as follows: S221. Calculate the product of elements in each row of the judgment matrix: ,in Indicates the first The product of row elements. This indicates the order of the judgment matrix, i.e., the number of indicators; S222, Calculation of The right root: ,in Indicates the first Approximate initial weight values ​​for each indicator; S223, Normalization process: ,in Indicates the first The initial weights of each indicator, This represents the sum of the approximate initial weights of all indicators; The processor will Integrate into an initial weight vector Output the initial weight vector ; S23. The processor calls the environmental sensors to collect environmental data and converts it into digital signals to form an environmental risk factor vector. ; The processor calls upon structural sensors to collect structural parameters and converts them into digital signals, forming a structural risk factor vector. ; The processor calls the progress tracker to read the current construction stage. ; S24. Obtain the environmental correction coefficient, structural correction coefficient, and stage correction coefficient based on the environmental risk factor vector E, structural risk factor vector S, and construction stage P, respectively. Calculate the comprehensive correction coefficient based on the environmental correction coefficient, structural correction coefficient, and stage correction coefficient. According to the initial weights Weighting adjustment coefficient and comprehensive correction factor Calculate the corrected weights The processor will handle all Integrate to obtain the corrected weight vector ; S25. Based on the corrected weight vector Calculate the product of the judgment matrix and the weight vector. Extract the first product vector element According to the element Calculate the largest eigenvalue According to the largest eigenvalue Calculate the consistency index According to the consistency index Calculate the consistency ratio Set a preset threshold If the consistency ratio If the judgment matrix is ​​found to have satisfactory consistency, the corrected weight vector is output. As the final weight vector ;like Then adjust the judgment matrix. elements Return to step S21 to recalculate the initial weights until... ; The specific calculation is performed as follows: S251. Calculate the largest eigenvalue: ,in This represents the largest eigenvalue of the judgment matrix; S252. Calculate the consistency index: ,in Indicators of consistency Indicates the order of the matrix to be judged; S253. Calculate the consistency ratio: ,in Indicates the consistency ratio. As the average random consistency index, according to Values: hour , hour , hour , hour , hour , hour , hour , hour , hour Its value is determined by the matrix order n, and its values ​​are shown in Table 2 below; Table 2. Values ​​of the Random Consistency Test (RI)

[0010] 1st and 2nd order judgment matrices always have complete consistency. When the order is greater than 2, the consistency ratio CR < 0.10 is considered to have satisfactory consistency. Otherwise, the judgment matrix needs to be adjusted to have satisfactory consistency.

[0011] In the preferred embodiment, the specific calculations performed in step S23 are as follows: Execute the environmental risk mapping function based on the environmental risk factor vector E: Calculate the environmental correction factor; in, This is the environmental correction factor. For environmental risk mapping function, The weighting coefficients and , For standard environmental parameters, Extreme environment parameters Execute the structural risk mapping function based on the structural risk factor vector S: Calculate the structural correction coefficient; in, This is the structural correction factor. For structural risk mapping function, These are the weighting coefficients, and , For standard structural parameters, Limiting structural parameters Execute the construction stage mapping function based on construction stage P: Correction coefficients during the calculation stage; in, This is a stage correction factor. This is a stage risk mapping function; The overall correction factor is calculated using the processor: ,in The fusion coefficient is... ,default ; According to the correction factor Execute the corrected formula: ,in Indicates the corrected number Weight of each indicator; The processor will Integrate into a modified weight vector Output corrected weight vector .

[0012] In the preferred embodiment, step S3 specifically involves the following steps: S31. Based on the environmental risk factor vector Calculate the environmental risk level: ; in, Environmental risk level, For environmental risk level function, This is the threshold for environmental risk level; Based on structural risk factor vector Calculate the structural risk level: ; in, Structural risk level, For structural risk level function, The threshold for structural risk level; According to environmental risk level and structural risk level Calculate the overall risk level: ,in, Based on the overall risk level, This is a function to find the maximum value. The processor will be based on a comprehensive risk level. Store in memory and output; S32. Constructing the basic fuzzy relation matrix ,in, For the number of indicators, Corresponding to evaluation sets , The basic membership degree is preset using an expert scoring method; The processor passes the comprehensive risk level assessment. For the basic fuzzy relation matrix The elements are adjusted, and the processor integrates the adjusted elements into a dynamic fuzzy relation matrix. ; S33, Using Triangle Membership Functions ,in, The lower realm For the upper realm, Centered on the overall risk level Adjust the multi-source parameters, including the offset center point. Scaling width parameter And adjust steepness To obtain the adjusted parameters The processor will adjust the parameters Store the adjusted membership function parameter set in the membership function parameter library. The specific execution steps are as follows: S331, Center point adjustment: ,in, This is the offset of the center point; S332, Width parameter adjustment: ,in, This is the width scaling factor; S332, Steepness Adjustment: ,in, Original steepness, This is a steepness adjustment factor; the adjusted steepness is... ; S34. Based on the final weight vector and dynamic fuzzy relation matrix The formula for performing fuzzy synthesis is: ; in, For fuzzy synthesis operators, a weighted average method is adopted. , The final weight vector is the first One element, The first of the dynamic fuzzy relation matrix Line number Column elements; Based on comprehensive risk level Calculate risk weights The processor introduces risk weights Calculate and output the evaluation vector The formula is: ,in .

[0013] In the preferred embodiment, in step S32, the processor assesses the overall risk level. For the basic fuzzy relation matrix The conditions for adjusting elements are as follows: like If so, it is classified as a high-risk level: The "compliant" element is tightened: ,in, Adjust the coefficients for the "compliant" elements; The "non-compliant" elements have been relaxed: ,in, Adjust the coefficient for "non-compliant" elements; The "Not Involved" elements remain unchanged: ; like If the low-risk level is determined, the elements of the fuzzy relation matrix remain unchanged, that is... .

[0014] In the preferred embodiment, step S4 consists of the following steps: S41. Obtain GPS data and retrieve the latitude and longitude coordinates of the current construction location based on the GPS data. Call the digital elevation model database, based on latitude and longitude coordinates Extract the corresponding elevation data and terrain features; Through scene recognition function Output the current construction scene ; in, For the current construction scenario, This is a scene recognition function; S42. Based on the preset correspondence between scenarios and specific indicators, determine the set of applicable specific indicators for the current construction scenario. Perform a set of indicators and operations to obtain a merged set of indicators. : when "Crossing the canyon" ;when "Crossing the river" ;when "Regular" means The specific settings are as follows: Common scenario indicator set ; Cross-Canyon Scene Specialized Indicator Set ,in, Installation deviation of steel web plate for wind vibration resistance Wind resistance stability of the hanging basket; Cross-river scenario special indicator set ,in, Control of anti-corrosion coating thickness on steel web. : Concrete density of underwater foundation; S43. Based on the indicator set Determine the number of indicators ,in, For set The number of elements; For the index set For newly added special indicators, the processor reads their initial weights from the weight preset library. ; Weight normalization is performed by the processor: ,in For the contextualized first Each indicator weight, As an indicator The original weights, For indicator set The sum of the weights of all indicators; The processor will Integrate into a scenario-based weight vector Output contextualized weight vector .

[0015] In the preferred embodiment, step S5 specifically involves the following steps: The processor reads the contextualized weight vector of the preset evaluation set. Risk-weighted final evaluation vector and evaluation set The computer maps the evaluation set V into numerical vectors. Perform collaborative calculation of scenario-based weights and risk assessment vectors: Output the overall evaluation score Z; Where Z is the comprehensive evaluation score; m is the number of elements in the indicator set I; This is the i-th element of the contextualized weight vector; This is the i-th element of the risk-weighted evaluation vector; Let i be the i-th element of the numerical vector; The computer will combine the overall evaluation score Z with a preset threshold. , The comparison, judgment, and output of the final evaluation result are performed. The result judgment logic is as follows: like The evaluation result is "compliant"; like The evaluation result is "not in compliance"; like The evaluation result was "not involved"; In the preferred embodiment, step S5 further includes: Collect actual construction quality data The evaluation error is calculated by the processor. ,in To evaluate the error, It is an absolute value function; Preset error threshold :like Then the processor adjusts the risk mapping function parameters in S23, and the adjustment rule is that the larger the error, the larger the parameter adjustment range; like Then the parameters of the risk mapping function remain unchanged; like The processor stores the optimized risk mapping function parameters into the parameter library.

[0016] This invention provides a standardized cantilever construction quality evaluation method for corrugated steel web prestressed composite box girder bridges. Compared with existing methods, this method offers the following advantages through the coordination between the aforementioned structures: First, by dividing the construction into stages and dynamically adapting the indicator system, the evaluation indicators can accurately match the core risk characteristics of each stage, solving the problem of the disconnect between the static indicator system and the dynamic risks of the construction stage, thereby improving the stage-specific relevance of the quality evaluation to the construction process. Secondly, by dynamically adjusting the fuzzy relation matrix and membership function parameters based on the comprehensive risk level, the fuzzy evaluation process can respond to changes in construction risks in real time, solving the problem of mismatch between the traditional static fuzzy model and the actual risk scenario, thereby enhancing the sensitivity of the evaluation results to risks. Third, by identifying construction scenarios and matching them with specific indicators and scenario-based weights, the coverage blind spots of the conventional indicator system in special scenarios are made up for, and the problems of missing indicators and unreasonable weight allocation in special scenarios are solved, effectively improving the adaptability of the evaluation to different construction scenarios. Fourth, by combining environmental, structural and stage risk factors to adjust the indicator weights and conducting consistency tests, the limitations of traditional weights relying on subjective experience are overcome, the problem of insufficient scientific rigor of weights is solved, and the objective reliability of indicator weights is significantly improved. Fifth, by comparing actual quality data with evaluation results and dynamically optimizing model parameters, a closed-loop iterative mechanism for the evaluation model was constructed, which solved the problem that traditional models could not continuously improve evaluation accuracy and realized the continuous evolution of evaluation accuracy. Attached Figure Description

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1This is the main view structure diagram of the process of this invention. Detailed Implementation

[0018] To better understand the purpose, system architecture, and functional implementation of this embodiment, the embodiments and features in the embodiments of this application can be combined with each other without conflict. The exemplary embodiments disclosed in this application will be described below with reference to the accompanying drawings, which include specific technical details disclosed in this embodiment to aid understanding; however, these details should be considered exemplary rather than restrictive. Therefore, those skilled in the art should understand that various improvements and adjustments can be made to the embodiments described herein without departing from the scope and core ideas of the invention. Similarly, for clarity, detailed descriptions of well-known technologies, functions, and structures (such as standard image processing algorithms and common communication protocols) are omitted in the following description.

[0019] In the field of bridge engineering construction technology, construction quality evaluation systems are widely used in project quality control. By monitoring and analyzing various indicators during the construction process, dynamic assessment and risk warning of project quality can be achieved. In various bridge construction projects, adopting scientific quality evaluation methods can effectively reduce the incidence of quality accidents and improve the reliability and economy of project construction.

[0020] With the development of intelligent monitoring technology, bridge construction quality evaluation is gradually evolving from manual experience-based judgment to data-driven intelligent evaluation to adapt to the refined management needs under complex working conditions. Intelligent evaluation models can integrate multi-source information such as sensor monitoring data, process acceptance records, and environmental parameters for comprehensive analysis, typically yielding more objective and efficient evaluation results than traditional manual evaluation. In the construction of special-structure bridges such as corrugated steel web prestressed composite box girder bridges, quality evaluation models often need to consider the characteristics of different construction stages and scenario-based risk factors to make accurate judgments. However, common evaluation systems often have fixed indicator settings and rigid weight allocations, making it difficult to dynamically adapt to changes in construction stages and risk characteristics under special scenarios.

[0021] When evaluating bridge construction quality, related technologies often employ a fixed-level framework for evaluation indicators at the structural level and rely heavily on static empirical values ​​for weight calculation. Therefore, the quality evaluation methods in these technologies have certain limitations and are ill-suited to the quality control needs of complex bridge structures at different construction stages and under special circumstances.

[0022] Example 1 like Figure 1As shown, this embodiment is used to construct a standardized cantilever construction quality evaluation model for corrugated steel web prestressed composite box girder bridges. Through data collection, risk quantification, index system construction, weight calculation, fuzzy evaluation model training, and parameter optimization, a pre-trained model that can be directly used for actual engineering evaluation is formed. The specific implementation process is as follows: S1. Construction stage division and risk factor collection: In this embodiment, a 120m span corrugated steel web prestressed composite box girder bridge is used as the training object, and the construction data comes from the monitoring system of the entire cantilever construction process of the bridge.

[0023] Specifically, the computer uses an Intel Core i7 12700K processor (3.6GHz), runs Windows Server 2019, and connects to the construction management database (MySQL 8.0) via an ODBC interface. The program counter (PC) locates the storage address (physical address 0x7F8A2000) of the "Construction Schedule" in the database, reads fields including "Segment Number," "Pouring Time," and "Process Completion Rate," and executes the stage division using a Python script (version 3.13.7): When "segment number = 0" and "process completion rate ≥ 90%", it is determined to be the construction stage of block 0. ; When "segment number = 1~10" and "pouring time is within the cantilever casting period", it is determined to be in the cantilever casting segment construction stage. ; When "segment number = closure segment" and "temperature monitoring value is between 15 and 25 degrees Celsius" When "C" is reached, it is determined to be in the closure section construction stage. ; Output construction phase set It is stored at memory address 0x7F8B3000.

[0024] In the preferred scheme, risk factor identification employs a rule-based matching mechanism. A risk database (SQL Server 2019) pre-stores 300 historical project risk records, each containing fields for "Construction Stage," "Risk Factor Name," and "Occurrence Probability." The computer calls the risk identification module, written in C++ and using the dynamic link library file risk_match.dll, to match the risk factors based on the construction stage set. Perform fuzzy matching based on the query criteria: Stage matching to determine template installation verticality Thickness of concrete cover for reinforcing bars (; Stage matching to determine the elastic deformation of the hanging basket Prestressing tension control force ; Stage matching to determine the height difference of the closure section axial misalignment ; Forming a set of risk factors Output in JSON format (file path . / risk_set.json).

[0025] S2. Risk factor quantification and correction coefficient calculation: In this embodiment, the LEC method is used to quantify risk factors. Data acquisition is achieved through the following devices: possibility The scores were based on ratings from 5 senior engineers (levels 1-5), summarized using the Delphi method, and recorded using a rating tool (self-developed software RiskScoreV1.0). The rating interface included a risk description dropdown. Exposure frequency The data is automatically collected from the construction logs. Daily exposure is recorded as Level 4 and weekly exposure as Level 2. This data is extracted from the "Work Process Record Table" by a Python script. Severity of consequences According to the "Highway Engineering Quality Inspection and Evaluation Standard", partial rework is recorded as Level 3 and overall rework as Level 5, which is automatically mapped by the quality supervision system (Java development).

[0026] In practice, a quantitative risk value is calculated for each risk factor: : (may happen) (Daily exposure) (Partial rework), executed by the Arithmetic Logic Unit (ALU). ; : , , ,have to ; : , , ,have to ; : , , ,have to ; : , , ,have to ; : , , ,have to ; extract ( ), ( (Preset maximum correction factor) Calculate the correction factor:

[0027] Substituting, we get: , , , , , To form a correction coefficient vector The data is stored in an Excel spreadsheet at . / correction_coefficients.xlsx.

[0028] S3. Construction of a five-layer dynamic indicator system. In this embodiment, the basic structure of the indicator system is derived from the "Technical Specifications for Highway Bridge and Culvert Construction" and stored in the database table "index_system," which includes fields for "Level," "Indicator Name," and "Parent Node ID." The computer calls the indicator system construction module (developed using the Python Django framework) to perform the following operations: Specifically, at the criteria level (quality behavior) Entity quality A construction phase adaptation layer is inserted between the indicator layer and the construction phase adaptation layer, forming a five-layer structure: Target layer: Construction quality level of cantilever bridge with corrugated steel web prestressed composite box girder; Criterion layer: (Quality behavior) (Physical quality); Construction phase adaptation layer: (Block 0) (Cantilevered segment) (The closing section); Indicator layer: subordinate (Percentage of personnel holding certificates) (Process acceptance rate); subordinate (Steel web plate installation accuracy) (Prestressing tension quality); Sub-indicator layer: subordinate (Plane position deviation) (Verticality); subordinate (Tension stress deviation) (Elongation deviation).

[0029] In the preferred scheme, the indicator system is displayed through a visualization tool (Power BI), and the relationships between each level are stored in memory in a tree structure (the data structure is an adjacency list), which facilitates quick retrieval during subsequent weight calculations.

[0030] S4. Initial weight calculation and dynamic correction: In this embodiment, the initial weights are calculated using the Analytic Hierarchy Process (AHP). The steps are as follows: In practice, seven bridge engineering experts were invited to construct a judgment matrix. Subordinate indicators (Steel web plate installation accuracy) (Prestressing tension quality) Taking concrete strength as an example, experts input pairwise comparison results (on a scale of 1-9) through a scoring interface (developed based on Vue) to form a judgment matrix. : ; The computer calls the AHP calculation module (written in MATLAB R2022a) to execute: 1. Calculate the product of elements in each row: , , ; 2. Find The root of the power ( ): , , ; 3. Normalization: , , Output the initial weight vector .

[0031] In this embodiment, environmental and structural parameters are collected using the following sensors: Environmental sensors: Temperature and humidity sensor and wind speed sensor installed on the top of the hanging basket. The temperature and humidity sensor is model SHT30 and the wind speed sensor is model FS300. Structural sensors: strain gauges and total station mounted on the steel web; the strain gauge model is BX1203AA, and the total station model is LeicaTS60. The collected data is converted into digital signals by an AD converter (model ADC0809) to form an environmental risk factor vector. Structural risk factor vector .

[0032] Specifically, the risk mapping function uses a piecewise linear function: Environmental correction factor Substituting into ; Structural correction factor Substituting into ; Stage correction coefficient : Take 1.0, Take 1.2, Take 1.1 (because) (Highest risk) Comprehensive correction coefficient , where the fusion coefficient , , (Determined through grid search optimization), calculated as follows .

[0033] Adjusted weights Substituting, we get: , , This forms a corrected weight vector. .

[0034] In the preferred scheme, the consistency check is calculated using MATLAB: the largest eigenvalue. Consistency indicators Random Consistency Index Consistency ratio To meet consistency requirements, the final weight vector is output. .

[0035] S5. Training of the risk-sensitive fuzzy evaluation model. In this embodiment, the fuzzy evaluation model is constructed through the following steps: Specifically, the comprehensive risk level calculation includes: environmental risk level. Depend on Mapping; Structural risk level Depend on Mapping; Comprehensive risk level .

[0036] Construction of dynamic fuzzy relation matrix: Basic fuzzy relation matrix Based on statistics from 500 sets of historical qualified data, the membership degree of "compliant" is as follows. "Does not comply" "Not involved" .because (Medium-high risk), adjustments to be implemented: The membership degree of "meets" is tightened: ; The membership degree of "not conforming" is relaxed: ; Output dynamic fuzzy relation matrix .

[0037] In this embodiment, the membership function is a triangular membership function, and the initial parameter value is... (Lower Boundary) (Upper Boundary) (Center point). Based on Adjust parameters: Center point offset: ; Width scaling: , ; The adjusted membership function is: ; Fuzzy synthesis employs a weighted average method, executed by the computer using the scikitfuzzy library (version 0.4.2).

[0038] Risk weighting coefficient ,right Weighted This serves as the final evaluation vector for risk weighting.

[0039] S6. Model parameter optimization. In this embodiment, model optimization is based on actual quality data feedback, and the steps are as follows: In practice, actual construction quality data Obtain it through the following methods: Steel web plate installation accuracy: 30 points were measured with a total station, the average deviation was taken, and standardized to 01 (0 indicates no deviation, 1 indicates deviation exceeds the standard); Prestressing tension quality: Ten sets of tension values ​​were collected by stress sensors, and the deviation rate from the design value was calculated and standardized to 0.1. comprehensive Weighted summation is used (weights are the same). ),have to .

[0040] Calculate evaluation error Preset threshold ,because No parameter adjustments are required. The fusion coefficient is then optimized using the gradient descent method. (Learning rate 0.01, number of iterations 100), until .

[0041] Finally, the model parameters are stored in the model file (path . / quality_evaluation_model.pkl) for subsequent scenario-based applications.

[0042] Example 2 This embodiment focuses on the cantilever construction quality evaluation of a prestressed composite box girder bridge with corrugated steel web spanning a river. Based on the pre-trained model trained in Embodiment 1, it evaluates the river-crossing environment through scene recognition, specific indicator matching, dynamic weight adjustment, and comprehensive scoring. The specific process is as follows: In this embodiment, scene recognition relies on the fusion analysis of geographic information and terrain features. The hardware and software configurations are as follows: The GPS module uses a Trimble R10 GNSS receiver, which is installed on the top of the basket and has a sampling frequency of 1Hz. It transmits real-time coordinates to the industrial control computer via Bluetooth. The terrain analysis software is a terrain feature extraction module developed based on ArcGIS Pro 3.0, which integrates a digital elevation model (DEM) database; Data processing environment: Windows 10 operating system, Python 3.11.9.

[0043] In practice, the scene recognition steps are as follows: Coordinate acquisition: The GPS module obtains the coordinates of the center position of the current construction segment. Converted to plane coordinates via Gauss-Kruger projection , stored in the memory buffer (address 0x7F9C4000); Elevation Extraction: The terrain analysis module is invoked to query the DEM database using planar coordinates as an index, and the elevation at that location is extracted. And obtain the terrain profile within a 500m radius; Scene determination: Matching based on a preset rule base (stored in . / scene_rules.json): When "distance from the river boundary" "And "elevation difference" "At that time, it was determined to be a scene spanning a river." Output scene labels to the log file (. / scene_log.txt).

[0044] In this embodiment, the indicator matching is based on the special characteristics of the cross-river scenario, calling the basic indicator system constructed in Embodiment 1 and expanding it through a special indicator library: In the preferred scheme, the special indicator library (MySQL database table "special_index") contains fields for "scenario type", "indicator name", and "detection standard", targeting cross-river scenarios. The following specific indicators are predefined: Anti-corrosion coating specifications: , ; Underwater concrete specifications: , .

[0045] Specifically, the computer invokes the indicator matching module to perform the following operations: S1. Read the basic index set of Example 1 , which are the basic indicators for quality behavior and physical quality; S2. Using scene tags Based on the query criteria, match from the special indicator database. , , , ; S3. Merge to form a scenario-based indicator set The hierarchical relationship of indicators is displayed through a visual interface (developed with Qt5.15) (basic indicators are white, and newly added special indicators are blue).

[0046] In this embodiment, the weight adjustment needs to take into account both the original importance of the basic indicators and the specific scenarios of the indicators. The steps are as follows: In practice, the weights of the basic indicators will follow the final weight vector output in Example 1. (correspond The initial weights of the newly added special indicators are determined in the following ways: Three senior engineers with ≥15 years of experience in waterway bridge construction were invited to score the samples using a 1-9 scale to form a judgment matrix. :

[0047] Call the AHP module to calculate the initial weights: ( Consistency check The requirements are met.

[0048] During normalization, the computer performs the following steps through the arithmetic logic unit: S1. Calculate the total weight sum: ; S2. Contextualized Weights ,have to:

[0049] Right now ; S3. Output the scene-based weight vector to an Excel file (. / scene_weights.xlsx), including specific metrics. (Anti-corrosion coating thickness) has the highest weight, reflecting the high requirements for anti-corrosion performance in cross-river scenarios.

[0050] In this embodiment, the indicator data collection adopts a combination of automated detection and manual verification. The specific equipment and methods are as follows: Basic indicator data: The sensor network used in Example 1 is adopted, such as temperature and humidity sensors, stress sensors, etc. (Steel web plate installation accuracy) was measured using a total station (Leica TS60); Specific indicator data: (Coating thickness): Using a magnetic thickness gauge, 20 measuring points were taken on the surface of the steel web, and the average value was 215 μm (normalized to 0.85). (Adhesion): The pull-off tester was used to test 3 points, and the average value was 5.8 MPa (normalized to 0.90). (Concrete slump): 200mm was obtained by manual testing with a slump cone (standardized to 0.80). (Permeability grade): According to the "Standard for Test Methods of Long-term Performance and Durability of Ordinary Concrete", the stepwise pressure method was used for testing, and the result was P10 (standardized to 0.95).

[0051] Specifically, the evaluation vector construction steps are as follows: S1. The basic indicator evaluation vector follows the risk weighting vector of Example 1. ,correspond ; S2. The evaluation vector for specific indicators is calculated using fuzzy membership functions: for example... The measured value of 215 μm corresponds to a membership degree of 0.85 for "compliant", 0.10 for "non-compliant", and 0.05 for "not involved", forming... ; S3. Combine to obtain the overall evaluation vector. .

[0052] In this embodiment, the overall score is determined by a weighted summation method combined with a preset threshold to achieve the quality level determination. In practice, the computer calls the comprehensive evaluation module, which is written in Python and executed using NumPy library version 1.21.0: S1. Overall Score Substitute the data:

[0053] Step-by-step calculation yields: ; S2. Threshold Setting: Referring to the "Highway Engineering Quality Inspection and Evaluation Standards" and considering the strict requirements of cross-river scenarios, a threshold is preset. (conform to), (Does not comply); S3. Result Determination: Because The evaluation result is "Compliant" and a PDF report containing the scores of each indicator (path . / evaluation_report_S1.pdf) is generated, with special annotations for specific indicators in the report. , Excellent score (contributing 31% to the total score).

[0054] Example 3 This embodiment focuses on the quality evaluation of cantilever construction of a prestressed composite box girder bridge with corrugated steel web spanning a canyon. Based on the pre-trained model trained in Embodiment 1, and considering the environmental characteristics of the canyon—high wind speeds and large elevation differences—it achieves targeted quality evaluation through scenario-specific index expansion, wind resistance risk quantification, and dynamic weight adaptation. The specific process is as follows: In this embodiment, scene recognition relies on the fusion of high-precision geospatial data and meteorological features. The hardware and software configurations are as follows: Positioning equipment: A Beidou BD970 GNSS receiver is used, installed on the top of the bridge block 0, with a sampling frequency of 2Hz, and the coordinates are transmitted to the monitoring center server through a 4G module; Topographic data source: The data was obtained from the space remote sensing DEM database, covering a 3km×3km area of ​​the canyon, and the data format was GeoTIFF. Analysis software: The terrain factor extraction module, developed based on ENVI 5.6, can calculate parameters such as slope, aspect, and elevation difference; Operating environment: Linux CentOS 7 operating system, Python 3.11.9.

[0055] In practice, the scene recognition steps are as follows: S1. Coordinate Acquisition: The BeiDou module acquires the center coordinates of the construction area. Convert to Gauss-Kruger plane coordinates , stored in a PostgreSQL database (table name "construction_coords"); S2. Terrain Analysis: The terrain factor extraction module is invoked to extract DEM data within a 500m × 500m area centered on the plane coordinates. The calculated values ​​are: maximum elevation 890m, minimum elevation 770m, and elevation difference. average slope ; S3. Scene Determination: Match the preset scene rule library (. / canyon_rules.json), and if "elevation difference" is found... "and average slope" "At that time, it was determined to be a scene crossing a canyon." The section marked "Deep Canyon Crossing Section" was recorded simultaneously with the prevailing wind direction of the canyon.

[0056] In this embodiment, the indicator system, based on the basic indicator set of Embodiment 1, expands to include specific indicators for wind resistance and structural stability, taking into account the characteristics of strong winds and steep terrain in canyons: In the preferred solution, the specialized indicator library (MySQL table "canyon_special_index") predefines core indicators for cross-canyon scenarios: Wind vibration resistance index: (Critical wind speed for basket flutter) (Steel web vibration acceleration, (Windbreak installation accuracy); Structural stability indicators: (Preload of anchoring system) (Foundation settlement rate).

[0057] Specifically, the indicator matching process: S1. Read the basic index set of Example 1 , which are the basic indicators for quality behavior and physical quality; S2. Using scene tags The above five indicators are matched from the special indicator database as search criteria. S3. Merge to form a scenario-based indicator set The data is displayed through a web visualization platform (developed based on React), with the wind resistance index highlighted in red.

[0058] In this embodiment, the weight adjustment needs to highlight the importance of wind vibration resistance and structural stability indicators. The steps are as follows: In practical implementation, the weights of the basic indicators will follow those in Example 1. (correspond The initial weights of the newly added special indicators were determined by a combination of expert scoring and risk quantification. Five bridge wind resistance experts were invited to construct a judgment matrix using the analytic hierarchy process (AHP). ,against Calculate the initial weights: ; Consistency check: This satisfies the consistency requirements.

[0059] Normalization process: S1. Total weights: ; S2. Contextualized Weights ,have to:

[0060] in With basic indicators The weights are tied for the highest at 0.15, reflecting their core importance in wind resistance; S3. Output the weight vector to a CSV file (. / canyon_weights.csv) and generate a weight percentage pie chart (stored in . / weight_chart.png).

[0061] In this embodiment, professional monitoring equipment is used to collect wind resistance and stability index data, as detailed below: Wind vibration resistance index: (Critical flutter wind speed): The critical wind speed was measured to be 22.5 m / s through wind tunnel simulation (model scale ratio 1:50). (Vibration acceleration): A triaxial accelerometer was used, installed in the middle of the steel web, with a sampling frequency of 100Hz and a 10-minute average of 0.08g (normalized to 0.90). (Wind barrier installation accuracy): The total station measured 20 wind barrier posts, with a maximum deviation of 42mm (normalized to 0.88). Structural stability indicators: (Anchoring preload): Measured at 30 anchoring points using a torque wrench; (Foundation settlement): The average daily settlement was 1.2 mm after 7 days of monitoring with a level instrument.

[0062] The basic indicator data were collected using the same method as in Example 1.

[0063] Specifically, the evaluation vector is constructed as follows: S1. Basic Indicator Evaluation Vector ; S2. The evaluation vector for specific indicators is calculated using fuzzy membership functions: for example... The value of 22.5 m / s corresponds to a membership degree of 0.85 for "compliant" and 0.12 for "non-compliant", forming... ; S3. Combine to obtain the overall evaluation vector: .

[0064] In this embodiment, the overall score adopts a weighted summation method, and a strict threshold is set in combination with the high-risk characteristics of the canyon scene: In practice, the comprehensive evaluation module (written based on MATLAB R2023a) is called to calculate: S1. Overall Score :

[0065] Step-by-step calculation yields: ; S2. Threshold Setting: Refer to the "Code for Wind Resistance Design of Highway Bridges" for preset thresholds in canyon scenarios. (conform to), (Not compliant), 10% higher than in typical scenarios; S3. Result Determination: Because It was judged as "compliant", but the specific annotation was not included. The score for (critical flutter wind speed) is slightly low, and it is recommended to strengthen real-time monitoring of wind-induced vibration.

[0066] Example 4 This embodiment focuses on the quality evaluation of cantilever construction of a corrugated steel web prestressed composite box girder bridge in a plain area. Based on the pre-trained model trained in Embodiment 1, it adopts the basic index system and weight configuration, and achieves efficient quality assessment under conventional scenarios through standardized data collection and automated evaluation processes. The specific process is as follows: In this embodiment, the determination of a conventional scene relies on terrain flatness and environmental stability analysis, and the hardware and software configurations are as follows: Positioning equipment: A UFORTK receiver from UniStrong is used, installed on the top of the construction trestle, with a sampling frequency of 1Hz, and transmits data to a portable industrial tablet via Bluetooth; Topographic data: Access the regional topographic map database; Analysis tool: A lightweight terrain analysis script developed based on Python 3.11.9, which can quickly calculate elevation difference and slope.

[0067] In practice, the scene recognition steps are as follows: S1. Coordinate Acquisition: The RTK receiver acquires the center coordinates of the construction area. Convert to planar coordinates Stored in a local SQLite database; S2. Environmental Feature Extraction: The script reads topographic map data and calculates the maximum elevation of 125.3m and the minimum elevation of 121.5m within a 500m range of the construction area, as well as the elevation difference. average slope ; S3. Scene Determination: Match the regular scene rule base (. / normal_rules.json), and if "elevation difference" is found... "and average slope" "And "the distance from the boundary of special landforms (rivers, canyons) When this occurs, it is determined to be a routine scenario. (Labeled as "open plain section"), environmental parameters were recorded simultaneously, with no significant wind direction preference.

[0068] In this embodiment, since there are no special risk factors in the conventional scenario, the basic indicator system constructed in Implementation 1 is directly used without the need to expand specific indicators, as follows: Specifically, the indicator matching process: S1. Call the five-layer dynamic indicator system output in Example 1 to extract the basic indicator set. ,in: Quality Behavior Indicators : (Percentage of personnel holding certificates) (Process acceptance rate); Entity quality indicators : (Deviation of planar position of steel web) (Prestressing tension deviation) (Concrete strength compliance rate); S2. Verification of indicator applicability: Through expert review, it was confirmed that the above indicators fully cover the core elements of construction quality in conventional scenarios, without omissions or redundancy. S3. Output Indicator Set The data is transferred to an Excel spreadsheet (. / normal_indexes.xlsx), which specifies the testing standards and frequencies for each indicator.

[0069] In this embodiment, the weights for conventional scenarios directly reuse the final weight vector obtained from training in Implementation 1, without any additional adjustments. The specific process is as follows: In the preferred solution, the weight reuse step is as follows: S1. Read the final weight vector output from Example 1. Corresponding indicators ,in (Prestressing tensioning quality) has the highest weight (0.30) because prestressing construction is the core of quality control in conventional scenarios; S2. Consistency Check: Recalculate the consistency ratio of the judgment matrix. This confirms that the weights still meet scientific requirements in typical scenarios; S3. Weight Visualization: Generate a weight percentage chart from an Excel pivot table and store it in . / normal_weight_chart.png.

[0070] In this embodiment, basic indicator data collection utilizes conventional engineering testing equipment, balancing automation and manual verification, as detailed below: Quality behavior indicators: (Personnel Certification Rate): The list of certified special operation personnel such as welders and crane operators was exported through the construction management system. A total of 32 people were certified. (Process acceptance pass rate): Statistics show that out of 95 inspection batches, 93 out of the 10 completed cantilevered sections passed the acceptance records. Entity quality indicators: (Deviation of planar position of steel web): Four corner points of each segment were measured using a total station; (Prestressing tension stress deviation): Stress sensors are used to monitor 20 bundles of steel strands; (Concrete strength compliance rate): 50 test areas were tested using a rebound hammer, combined with the compressive strength of the test blocks.

[0071] Specifically, the data standardization rules are as follows: for indicators that are "larger is better", the standardized value = measured value / target value; for indicators that are "smaller is better", the standardized value = 1 (measured value / limit value), ensuring that all indicator values ​​are mapped to the [0,1] interval.

[0072] In this embodiment, the evaluation vector is generated based on the fuzzy evaluation model of Embodiment 1, and the comprehensive score adopts a weighted summation method, as follows: In practice, the evaluation process is as follows: S1. Basic Fuzzy Relationship Matrix: Following the conventional risk levels in Example 1 ( The matrix corresponding to ) ; S2. Fuzzy Synthesis: Call the fuzzy synthesis module of Example 1 and execute... The basic evaluation vector is calculated as follows: ; S3. Risk Weighting: Comprehensive Risk Level in Typical Scenarios Risk weighting coefficient , thus obtaining the risk-weighted evaluation vector ; S4. Overall Score: : .

[0073] In this embodiment, the threshold for conventional scenarios is set based on industry standards, and the judgment result directly guides construction acceptance, as detailed below: In practical implementation, the result is determined as follows: S1. Threshold Setting: Refer to the "Highway Engineering Quality Inspection and Evaluation Standards" for preset thresholds in typical scenarios. (conform to), (Not applicable), slightly more lenient in special scenarios; S2. Result Determination: Because The result was determined to be "compliant". (Prestressed tensioning) and (Concrete strength) scored exceptionally well, contributing 40% to the total score; S3. Report Generation: Automatically generate a PDF evaluation report (. / normal_evaluation_report.pdf), which includes the measured values, standardized values, weighting percentages, and overall scores of each indicator. The report is archived after being electronically signed by the supervision unit.

[0074] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0075] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A quality evaluation method for the standardized cantilever construction of a corrugated steel web prestressed composite box girder bridge, characterized in that, Comprise the following steps: S1, acquire current construction progress data, divide the cantilever construction process into 0 block construction stage, cantilever segment construction stage and closure section construction stage, call risk database to identify the risk factor set R of each stage; according to the risk factor set R, calculate the quantitative risk value of each risk factor, determine the weight correction coefficient according to the quantitative risk value, and build a five-layer dynamic index system including construction stage adaptation layer; S2, calculate the initial weight vector by analytic hierarchy process, collect environmental risk factors and structural risk factors through environmental sensors and structural sensors, calculate the comprehensive correction coefficient according to the environmental risk factors, structural risk factors and weight correction coefficient, correct the initial weight vector, and obtain the dynamic weight vector; S3, calculate the current risk level according to the environmental risk factors and structural risk factors, dynamically adjust the element weight of fuzzy relation matrix according to the current risk level, adjust the membership function parameter risk sensitive, and execute fuzzy synthesis operation by using dynamic weight vector and dynamic fuzzy relation matrix, obtain risk weighted evaluation vector; S4, obtain the current construction scene information through GPS data and terrain analysis, match the special index set according to the construction scene information, reallocate the weight according to the matched special index set, and obtain the scene weight vector; S5, calculate the comprehensive evaluation score according to the risk weighted evaluation vector and the scene weight vector, acquire the preset threshold, determine the final evaluation result according to the preset threshold, collect the actual construction quality data, and dynamically optimize the risk mapping function according to the actual construction quality data.

2. The quality evaluation method for the standardized cantilever construction of the prestressed composite box girder bridge with corrugated steel webs according to claim 1, characterized in that, The specific steps of step S1 are: S11, the computer starts the processor, the processor reads the current construction progress information from the construction progress database, converts the progress data into program executable instructions through the instruction decoder, the program counter is positioned to the storage address of the current construction stage according to the instructions, calls the preset construction stage division rule, divides and outputs the cantilever construction process into a construction stage set ; According to the division of construction stage Match the risk factors of each stage from the risk database to form a risk factor set ; S12, the processor computes a risk value for each risk factor in the set of risk factors ;​​​ S13, The processor reads the risk value vector. Extract the minimum value and maximum value Calculate the weight adjustment coefficient for each indicator. To form a correction coefficient vector ; S14, the processor reads the structure information of the target layer, the criterion layer, the index layer and the sub-index layer from the index system database, and according to the construction phase set A five-layer dynamic index system is constructed.

3. The quality evaluation method for the standardization of the cantilever construction of the prestressed composite box girder bridge with corrugated steel webs according to claim 2, characterized in that, In step S14, the five-layer dynamic index system is constructed as follows: Target layer: standardization of cantilever construction quality level of corrugated steel web bridge; Criteria layer: wherein, is a quality behavior, is an entity quality; Construction stage layer: wherein, corresponding to the construction of the 0th block, the cantilever segment, and the closure segment, respectively. Index layer: wherein, for personnel management, for technical management, for material management, for subcontract management, for construction management, for data management; wherein, fabricating corrugated steel webs, erecting corrugated steel webs, fabricating and erecting reinforcing steel, fabricating and tensioning prestressing steel tendons, casting concrete cantilevers; The sub-index layer is wherein, is the height control of the corrugated steel web, is the flange plate width and opening anchoring; wherein, is the wave-shaped steel web center distance, is the shear connector installation; wherein, for the control of the spacing between the stressed reinforcement, for the control of the thickness of the protective layer; wherein, is the tension stress value, is the prestress loss value control; wherein, for concrete strength control, for cross-section size and elevation control; The processor stores the five-layer dynamic index system to the specified address of the memory, and outputs the five-layer dynamic index system.

4. The quality evaluation method for the standardization of the cantilever construction of the prestressed composite box girder bridge with corrugated steel webs according to any one of claims 1 to 3, characterized in that, The specific steps of step S2 are: S21, according to the structure information of the reading index layer and the sub-index layer in the five-layer dynamic index system, the judgment matrix construction instruction is converted into executable instruction through the instruction decoder, and the judgment matrix is constructed according to the 1-9 scale method ; S22, according to processor calculation judgment matrix Each row element product , according to Calculation Root of power , to Normalization processing, get the initial weight of the first Index , all Integrated into the initial weight vector ; S23, the processor calls the environment sensor to collect environment data and convert it into a digital signal to form an environment risk factor vector ; The processor calls the structure sensor to collect the structure parameters and convert them into digital signals to form a structure risk factor vector ; The processor invokes the progress tracker to read the current construction phase ; S24. Obtain the environmental correction coefficient, structural correction coefficient, and stage correction coefficient based on the environmental risk factor vector E, structural risk factor vector S, and construction stage P, respectively. Calculate the comprehensive correction coefficient based on the environmental correction coefficient, structural correction coefficient, and stage correction coefficient. According to the initial weights Weighting adjustment coefficient and comprehensive correction factor Calculate the corrected weights The processor will handle all Integrate to obtain the corrected weight vector ; S25. Based on the corrected weight vector Calculate the product of the judgment matrix and the weight vector. Extract the first product vector element According to the element Calculate the largest eigenvalue According to the largest eigenvalue Calculate the consistency index According to the consistency index Calculate the consistency ratio Set a preset threshold If the consistency ratio If the judgment matrix is ​​found to have satisfactory consistency, the corrected weight vector is output. As the final weight vector ;like Then adjust the judgment matrix. elements Return to step S21 to recalculate the initial weights until... .

5. The quality evaluation method for the standardized cantilever construction of the prestressed composite box girder bridge with corrugated steel webs according to claim 4, characterized in that, In step S23, the specific calculation is executed as follows: An environmental risk mapping function is performed according to the environmental risk factor vector E: , to calculate an environmental correction coefficient; wherein, is the environmental risk mapping function; According to the structural risk factor vector S, a structural risk mapping function is performed: , a structural correction coefficient is calculated; wherein, is a structural risk mapping function; According to the construction phase P, a construction phase mapping function is performed: , a phase correction coefficient is calculated; wherein, is the phase correction coefficient, is a phase risk mapping function; The integrated correction coefficient is calculated by the processor: wherein is the fusion coefficient, i.e. ; According to the correction coefficient The correction formula is executed: wherein represents the corrected first indicator weight; The processor integrates all into a modified weight vector , outputs the modified weight vector .

6. The quality evaluation method for the standardized cantilever construction of the prestressed composite box girder bridge with corrugated steel webs according to any one of claims 4 or 5, characterized in that, The specific steps of step S3 are: S31, determining an environmental risk factor vector calculating an environmental risk level: ; wherein, is an environmental risk ranking function; According to the structural risk factor vector Calculating the structural risk level: ; wherein is a structured risk level function; According to the environmental risk level and the structural risk level The integrated risk level is calculated as: wherein, is a max function; the processor stores the integrated risk level to the memory and outputs it; S32. Constructing the basic fuzzy relation matrix The processor passes the comprehensive risk level assessment. For the basic fuzzy relation matrix The elements are adjusted, and the processor integrates the adjusted elements into a dynamic fuzzy relation matrix. ; S33, adopting a triangular membership function , according to the comprehensive risk level adjusting multi-source parameters, the multi-source parameters including offset center points , scaling width parameters and adjusting steepness , to obtain adjusted parameters , the processor stores the adjusted parameters to a membership function parameter library, and outputs an adjusted membership function parameter set; S34. The most weighted vector and a dynamic fuzzy relation matrix Performing a fuzzy composition operation with the formula: ; in, For fuzzy synthesis operators, a weighted average method is adopted. , The final weight vector is the first One element, The first of the dynamic fuzzy relation matrix Line number Column elements; According to the integrated risk level Computing the risk weight The processor computes and outputs the evaluation vector by introducing the risk weight Computing and outputting the evaluation vector The formula is: Wherein .

7. The method of quality evaluation of the standardization of the cantilever construction of the prestressed composite box girder bridge with corrugated steel webs according to claim 6, characterized in that, In step S32, the processor processes the risk level by integrating the risk levels The decision condition for adjusting the elements of the basic fuzzy relation matrix is as follows: If then determine as high risk level: The "fit" element tightens: where, is the "fit" element adjustment factor; "Non-conformance" element relaxation: wherein, is the "non-conformance" element adjustment factor; "uninvolved" elements remain unchanged: ; If then a low risk level is determined and the fuzzy relation matrix elements remain unchanged, i.e. .

8. The method of claim 1, wherein the method is characterized by: The specific steps of step S4 are: S41. Obtain GPS data and retrieve the latitude and longitude coordinates of the current construction location based on the GPS data. Call the digital elevation model database, based on latitude and longitude coordinates Extract the corresponding elevation data and terrain features; By a scene recognition function Outputting the current construction scene ; wherein is a scene recognition function; S42, according to the preset scene and special index corresponding relationship, determine the current applicable special index set, the processor according to the current construction scene , execute index set and operation, get the merged index set : When "across a valley", then ; when "across a river", then ; when "regular", then ; S43、according to the index set determining the number of indexes wherein, is the number of elements of the set . For the index set For newly added special indicators, the processor reads their initial weights from the weight preset library. The processor performs weight normalization processing to obtain the contextualized version of the first weight. Individual indicator weights The processor will handle all of them. Integrate into a scenario-based weight vector .

9. The method of claim 1, wherein the method is characterized by: The specific steps of step S5 are: The processor reads a preset evaluation set scene weighting vector , a risk weighted final evaluation vector and an evaluation set The computer maps the evaluation set V into a numerical vector , performs a collaborative calculation of the scene weighting and the risk evaluation vector, and outputs a comprehensive evaluation score Z; The comprehensive evaluation score Z is compared with a preset threshold value , The comparison is performed, the final evaluation result is determined and output, and the determination logic of the execution result is as follows: if : the evaluation result is "compliant"; if : the evaluation result is "non-compliant"; if : the evaluation result is "not involved".

10. The method according to any one of claims 1 or 9, wherein the method is characterized by, Step S5 also includes: Collecting actual construction quality data ; calculating an evaluation error by a processor: wherein is the evaluation error, is an absolute value function; Pre-set error threshold If the processor adjusts the risk mapping function parameters in S23; If then the risk mapping function parameters remain unchanged; If , the processor stores the optimized risk mapping function parameters to the parameter library.

Images