Bridge prestress fine detection and evaluation method, device and equipment

Abstract

The invention relates to the technical field of bridge engineering, and provides a bridge prestress fine detection and evaluation method, device and equipment, and the method comprises the steps: carrying out the fine partitioning of a bridge, and determining a key detection region; the key detection area comprises a maximum stress area, a severe corrosion environment area, an area with relatively large man-made interference and an area without full construction; carrying out comprehensive detection on the prestressed steel strand in the key detection area, and determining a detection result; the comprehensive detection comprises detection of prestress degree, corrosion degree, anchor head anchoring efficiency, grouting compactness and grouting material corrosion capacity; determining evaluation variables according to the detection result, wherein the evaluation variables comprise the prestress safety coefficient, the reliability and the remaining service life; and determining an evaluation result according to the evaluation variable, wherein the evaluation result comprises determining that the bridge does not need to be reinforced, needs to be reinforced and needs to be reconstructed. The problem that in the prior art, the bridge reinforcement possibility is difficult to reasonably judge is solved, and whether the prestressed bridge has the reinforcement condition or not is scientifically judged.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge engineering, and in particular to a method, device and equipment for fine detection and evaluation of prestressed bridges. Background Art

[0002] Bridge engineering is an important part of national infrastructure. Prestressed concrete bridges are widely used in various transportation projects due to their advantages such as high bearing capacity, large span, and good seismic performance. At present, prestressed bridges and concrete bridges dominate the bridges in service in my country, accounting for more than 65% and more than 95% respectively. With the continuous growth of transportation volume and the increase in large and heavy-loaded vehicles, the bearing capacity of these bridge structures is facing severe challenges. Due to environmental factors, material aging, construction quality and other issues, prestress loss, steel corrosion, concrete cracking and other defects may occur, affecting the safety and durability of the bridge. Therefore, how to scientifically evaluate the possibility of strengthening prestressed bridges is of great significance for extending the service life of bridges and saving a lot of funds for renovation and renovation.

[0003] Existing technologies for assessing the reinforcement of long-span prestressed concrete bridges rely primarily on empirical judgment and lack a systematic testing and assessment method. This makes it difficult to accurately assess key factors such as the degree of steel strand corrosion and prestress loss, making it difficult to make a reasonable assessment of the potential for bridge reinforcement.

[0004] In addition, there are currently some invention patents to solve the problem of scientifically determining the possibility of reinforcement of long-span bridges using steel strands, threaded steel bars, and thick steel bars. For example: Patent CN106284098A discloses a method for controlling the pre-camber value of pre-tensioned reinforced concrete bridge slabs. This method replaces some pre-tensioned prestressed steel strands with post-tensioned ones. During construction of the post-tensioned prestressed strands, the load is gradually increased in stages, and the pre-camber value of the bridge slab is measured until the required value is achieved. However, this method still suffers from insufficient accuracy and controllability of the pre-camber value.

[0005] Patent CN106638332A discloses a concrete bridge reinforcement design method based on reinforcement stress test results. This method uses a testing system to measure the stress of steel strands and calculate the calculated area of ​​reinforcement material. This calculated area is accurate and reliable, making it suitable for designing flexural reinforcement for cracked and damaged prestressed concrete bridges. However, this method still suffers from the limited accuracy and stability of the reinforcement and surface strain gauges.

[0006] Therefore, it is urgent to establish a complete inspection and evaluation system to determine whether the bridge is suitable for reinforcement. Summary of the Invention

[0007] The present invention provides a method, device and equipment for fine detection and evaluation of prestressed bridges, which solves the problem in the prior art of difficulty in making reasonable judgments on the possibility of bridge reinforcement and realizes scientific judgment on whether a prestressed bridge meets the conditions for reinforcement.

[0008] The present invention provides a bridge prestressing fine detection and evaluation method, comprising the following steps: Carry out detailed zoning of the bridge and determine key inspection areas; the key inspection areas include areas with the greatest stress, areas with severe corrosion environment, areas with significant human interference, and areas with incomplete construction; Conduct a comprehensive inspection of the prestressed steel strands in the key inspection area to determine the inspection results; the comprehensive inspection includes inspections of prestressing intensity, corrosion degree, anchor head anchoring efficiency, grouting density, and corrosion resistance of grouting materials; Determining evaluation variables based on the test results, wherein the evaluation variables include prestress safety factor, reliability, and remaining service life; An evaluation result is determined according to the evaluation variables, wherein the evaluation result includes determining whether the bridge does not need reinforcement, needs reinforcement, or needs reconstruction.

[0009] According to a method for fine detection and evaluation of prestressed steel in bridges provided by the present invention, a comprehensive inspection of the prestressed steel strands in the key inspection area is carried out, specifically including: using non-destructive testing technology to test the prestressing intensity and the degree of corrosion; the non-destructive testing technology includes a stress wave method and a magnetic memory test method; using an anchoring system integrity detection method to detect the anchoring efficiency and grouting density of the anchor head; the anchoring system integrity detection method includes an anchor head pull-out test and ultrasonic testing; using a corrosion performance evaluation method to detect the corrosion ability of the grouting material, and the corrosion performance evaluation method includes an electrochemical test and corrosion product analysis.

[0010] According to a method for fine detection and evaluation of prestressed structures in bridges provided by the present invention, the fine zoning of the bridge specifically includes: obtaining a three-dimensional model of the bridge; dividing the three-dimensional model of the bridge into multiple evaluation units of preset sizes; each evaluation unit includes one or more detection areas; performing multi-source feature extraction based on the evaluation units; constructing a bridge topology map with the evaluation units as nodes and adjacent relationships as edges; inputting the bridge topology map into a trained neural network model to output risk areas; the trained neural network model is obtained by training based on a training set, the training set including the bridge topology map and risk area labels; the risk area labels include areas with the greatest stress, areas with severe corrosion environment, areas with large human interference, and areas with incomplete construction.

[0011] According to a method for fine detection and evaluation of prestressed structures in bridges provided by the present invention, the evaluation variables are determined based on the detection results, specifically including: for each evaluation unit, determining the prestressed safety factor based on the prestressing intensity; determining the reliability based on the anchoring efficiency of the anchor head and the grouting density; determining the remaining service life based on the degree of rust and the corrosion capacity of the grouting material; the evaluation units are obtained by dividing the three-dimensional model of the bridge.

[0012] According to a method for fine detection and evaluation of bridge prestressing provided by the present invention, the calculation formula of the prestressing safety factor is: safety factor = measured prestressing force / actual allowable prestressing force; the Monte Carlo simulation method is used for reliability calculation; the Arrhenius model is used to describe and predict the aging process of steel strands and concrete, and then the remaining service life is estimated.

[0013] According to a bridge prestressed fine detection and evaluation method provided by the present invention, the evaluation result is determined based on the evaluation variables, specifically including: when the prestressed safety factor of all evaluation units is not less than a first preset coefficient, and the reliability is not less than a first preset percentage, and the remaining service life is not less than the first preset life, it is determined that the bridge does not need to be reinforced; when the prestressed safety factor of any evaluation unit is less than a second preset coefficient, or the reliability is less than a second preset percentage, or the remaining service life is less than the second preset life, it is determined that the bridge needs to be demolished and rebuilt; under the condition that the bridge does not need to be demolished and rebuilt, when there is an evaluation unit whose prestressed safety factor is less than the first preset coefficient and not less than the second preset coefficient, or the reliability is less than the first preset percentage and not less than the second preset percentage, or the remaining service life is less than the first preset life and not less than the second preset life, it is determined that the bridge needs to be reinforced.

[0014] A bridge prestressed precise detection and evaluation device, comprising: The zoning module is used to finely zone the bridge and determine key inspection areas; the key inspection areas include areas with the greatest stress, areas with severe corrosion environment, areas with significant human interference, and areas with incomplete construction; A detection module is used to conduct a comprehensive inspection of the prestressed steel strands in the key inspection area and determine the inspection results; the comprehensive inspection includes the inspection of prestressing intensity, corrosion degree, anchor head anchoring efficiency, grouting density and corrosion capacity of grouting material; An evaluation variable determination module is used to determine evaluation variables according to the test results, wherein the evaluation variables include prestressed safety factor, reliability and remaining service life; The reinforcement possibility assessment module is used to determine an assessment result based on the assessment variables, wherein the assessment result includes determining whether the bridge does not need reinforcement, needs reinforcement, or needs reconstruction.

[0015] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-described methods for fine detection and evaluation of bridge prestressing.

[0016] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for fine detection and evaluation of bridge prestressing is implemented as described in any one of the above.

[0017] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-mentioned bridge prestressing fine detection and evaluation methods.

[0018] The present invention provides a method, device and equipment for fine detection and evaluation of prestressed structures in bridges, which include the following beneficial effects: through the step of finely zoning the bridge and determining key detection areas, the precise allocation of detection resources is achieved, the efficiency of traditional uniform detection is improved, and the defect detection rate in high-risk areas is significantly improved; through the step of comprehensive detection of prestressed steel strands in key detection areas, a multi-parameter collaborative detection system can be established to comprehensively evaluate key indicators such as prestressing force and degree of corrosion, thereby improving the scientific nature and credibility of structural status assessment; through the step of determining evaluation variables based on detection results, the construction of a quantitative evaluation model is achieved, and subjective experience judgment is converted into an objective decision-making basis based on safety factor, reliability and remaining service life; through the step of determining evaluation results based on evaluation variables, the maintenance strategy of the bridge can be clearly determined based on the dual threshold mechanism, thereby avoiding excessive maintenance or delayed reconstruction and improving the accuracy of maintenance decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 It is a flow chart of the bridge prestressing fine detection and evaluation method provided by the present invention.

[0021] Figure 2 It is a structural schematic diagram of the bridge prestressing fine detection and evaluation device provided by the present invention.

[0022] Figure 3 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0023] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0024] Bridge construction is a vital component of national infrastructure. Prestressed concrete bridges, in particular, are widely used in various transportation projects due to their high bearing capacity, large spans, and excellent seismic resistance. With the continuous growth of transportation volume and the increase in large and heavy-loaded vehicles, the bearing capacity of these bridge structures is facing severe challenges. Due to environmental factors, material aging, and construction quality issues, prestress loss, steel corrosion, concrete cracking, and other defects may occur, affecting the safety and durability of bridges.

[0025] Scientifically assessing the potential for reinforcement of long-span prestressed concrete bridges is crucial for extending their service life and saving significant renovation costs. Existing technologies rely primarily on empirical assessments and lack systematic testing and evaluation methods. This makes it difficult to accurately assess key factors such as the degree of steel strand corrosion and prestress loss, hindering the ability to make informed decisions about bridge reinforcement.

[0026] Therefore, it is urgent to establish a comprehensive inspection and evaluation system. This system, through comprehensive testing of steel strand prestressing strength, corrosion level, anchoring efficiency, and grouting quality, combined with zoning assessments based on factors such as stress conditions and the corrosive environment, can scientifically calculate the prestressing safety factor, reliability, and remaining service life to determine whether a bridge is suitable for reinforcement. Furthermore, precise inspection and analysis methods should be developed for different bridge types, and corresponding maintenance and reinforcement strategies should be formulated to truly achieve green, low-carbon, and environmentally friendly bridge life extension.

[0027] The existing technology has the following disadvantages: 1. The lack of a systematic inspection and assessment method makes it difficult to accurately assess key factors such as the degree of steel strand corrosion and prestress loss, making it impossible to make reasonable judgments on the possibility of bridge reinforcement. 2. The accuracy and controllability of the pre-camber value are insufficient; 3. The accuracy and stability of tendon strain gauges and surface strain gauges are insufficient; 4. Reliance on empirical judgment and a lack of zoning assessment of factors such as stress conditions and corrosive environments make it impossible to scientifically calculate the prestressing safety factor, reliability, and remaining service life; 5. There is a lack of accurate detection and analysis methods for different bridge types.

[0028] The existing technology lacks a systematic detection and assessment method, making it difficult to accurately assess key factors such as the degree of steel strand corrosion and prestress loss, making it difficult to make reasonable judgments about the potential for bridge reinforcement. Therefore, to address this issue, the present invention provides a precise detection and assessment method for bridge prestressing.

[0029] The following combination Figure 1-Figure 3 The embodiments of the present invention are described in detail.

[0030] Figure 1 This is a flow chart of the bridge prestressing fine detection and evaluation method provided by the present invention, such as Figure 1 As shown, the method includes the following steps: S110. Carry out detailed zoning of the bridge and determine key inspection areas; key inspection areas include areas with the greatest stress, areas with severe corrosion environment, areas with significant human interference, and areas with incomplete construction.

[0031] Specifically, the purpose of the present invention is to overcome the shortcomings of the prior art and provide a bridge prestressing fine detection and evaluation method, comprising: Step 1: Conduct a detailed zoning assessment of the bridge, including the areas with the greatest stress, areas with severe corrosion environment, and areas with greater human interference as key inspection areas. Areas with incomplete construction also need to be inspected.

[0032] According to a bridge prestressed fine detection and evaluation method provided by the present invention, the bridge is finely zoned, specifically including: obtaining a three-dimensional model of the bridge; dividing the three-dimensional bridge model into multiple evaluation units of preset sizes; each evaluation unit includes one or more detection areas; performing multi-source feature extraction based on the evaluation units; constructing a bridge topology map with the evaluation units as nodes and adjacent relationships as edges; inputting the bridge topology map into a trained neural network model to output risk areas; the trained neural network model is obtained by training based on a training set, the training set including the bridge topology map and risk area labels; the risk area labels include areas with the greatest stress, areas with severe corrosion environment, areas with large human interference, and areas with incomplete construction.

[0033] Step 1 specifically includes: Step 101: Divide the three-dimensional bridge model into multiple evaluation units, each unit including one or more detection areas; Step 102: Classify the inspection areas using the trained neural network model to determine multiple key inspection areas; or determine the areas with the greatest stress, areas with severe corrosion, areas with significant human interference, and areas with incomplete construction based on the design documents and on-site surveys; Step 103: Perform non-destructive testing on the incomplete construction areas to find defects such as cavities and cracks.

[0034] The three-dimensional bridge model can be constructed by scanning the target bridge in three dimensions using a laser scanner or photogrammetry technology to obtain point cloud data (accuracy ≤ 5mm). BIM software can then be used to convert the point cloud data into a parametric three-dimensional model. A graph neural network (GNN) can be used as a neural network model, and the trained risk area labels can be cross-labeled by multiple experienced engineers. Before model training or use, the three-dimensional model is discretized into evaluation units (for example, each evaluation unit is 0.5m×0.5m×0.2m). Features are extracted for each unit, including mechanical characteristics (such as maximum principal stress), environmental characteristics (such as annual wet hours × chloride ion diffusion coefficient), and structural characteristics (distance to the nearest expansion joint / rebar cover thickness). The evaluation units are then used as nodes, and edges are established between units with shared surfaces to generate a topological map.

[0035] S120. Carry out comprehensive inspection on prestressed steel strands in key inspection areas to determine the inspection results; comprehensive inspection includes inspection of prestressing strength, degree of corrosion, anchor head anchoring efficiency, grouting density and corrosion capacity of grouting materials.

[0036] Step 2: Conduct a comprehensive inspection of the prestressed steel strands in the key inspection area, including prestressing strength, corrosion degree, anchor head anchoring efficiency, grouting density and grouting material corrosion capacity.

[0037] According to a detailed inspection and evaluation method for prestressed bridges provided by the present invention, a comprehensive inspection of prestressed steel strands in key inspection areas is carried out, specifically including: using non-destructive testing technology to test the prestressing intensity and degree of corrosion; non-destructive testing technologies include stress wave method and magnetic memory test method; using anchor system integrity detection method to detect anchor head anchoring efficiency and grouting density; anchor system integrity detection method includes anchor head pull-out test and ultrasonic detection; using corrosion performance evaluation method to detect the corrosion ability of grouting material, and corrosion performance evaluation method includes electrochemical test and corrosion product analysis.

[0038] Step 2 specifically includes: Step 201: Using non-destructive testing techniques such as stress wave method and magnetic memory test method to test the prestressing strength and corrosion degree of the steel strand; Step 202: Using methods such as anchor head pull-out test and ultrasonic testing to detect the anchor head anchoring efficiency and grouting density; Step 203: Use electrochemical testing, corrosion product analysis and other methods to detect the corrosion ability of the grouting material.

[0039] Step 3: Determine whether the detection and evaluation of all areas are completed. If not, return to step 1. If so, execute step 4.

[0040] S130. Determine evaluation variables based on the test results, where the evaluation variables include prestressed load safety factor, reliability, and remaining service life.

[0041] According to a detailed detection and evaluation method for prestressed bridges provided by the present invention, evaluation variables are determined based on the detection results, specifically including: for each evaluation unit, the prestressed safety factor is determined based on the prestressing intensity; the reliability is determined based on the anchoring efficiency of the anchor head and the grouting density; the remaining service life is determined based on the degree of rust and the corrosion capacity of the grouting material; and the evaluation units are obtained by dividing the three-dimensional model of the bridge.

[0042] Step 4: Based on the inspection and evaluation results, calculate the prestressed steel safety factor, reliability and remaining service life to determine whether the bridge is suitable for reinforcement.

[0043] Specifically, step 4 includes: Step 401: For each evaluation unit, determine the prestress safety factor according to the prestressing intensity; determine the reliability according to the anchoring efficiency and grouting density of the anchor head; Step 402: Based on the designed service life and material aging, estimate the remaining service life of each assessment unit according to the degree of corrosion and the corrosion capacity of the grouting material; Step 403: Conduct a comprehensive assessment of the entire bridge to determine whether reinforcement conditions are met. If so, formulate a reinforcement plan.

[0044] According to a detailed detection and evaluation method for bridge prestressing provided by the present invention, the formula for calculating the prestressing safety factor is: safety factor = measured prestressing force / actual allowable prestressing force; the Monte Carlo simulation method is used for reliability calculation; and the Arrhenius model is used to describe and predict the aging process of steel strands and concrete, thereby estimating the remaining service life.

[0045] Specifically, 1. The calculation formula of prestress safety factor is as follows: Where λ is the prestress safety factor and P is the prestressing force (MPa).

[0046] 2. Monte Carlo reliability analysis (1) Limit state function Limit state function Defined as: in, The resistance R depends on the anchoring efficiency η and the grouting density ρ, and obeys the normal distribution ; Represents the dead load effect, which obeys the normal distribution ; Represents the live load effect, which obeys the normal distribution .

[0047] (2) Failure probability calculation Failure probability Defined as The probability of . It is a normal distribution, and its probability density function is: Therefore, the failure probability for: in, is the cumulative distribution function of the standard normal distribution, represents the mean value of the limit state function Z; represents the standard deviation of the limit state function Z.

[0048] In actual simulation, a large number of 、 and The sample value of Values ​​and statistics Number of times , thus obtaining an estimate of the failure probability: Where, represents an estimate of the probability of failure; is the number of times Z is less than 0; is the total number of simulations.

[0049] (3) Reliability index Reliability Index Defined as the inverse function of the standard normal distribution The probability of failure The value at: because is a monotonically increasing function, so and According to GB 50153, when When , the target reliability is considered to be met.

[0050] In practical applications, an estimate of the failure probability can be obtained through simulation , and then calculate the corresponding reliability index : And check whether it meets .

[0051] 3. Arrhenius model life prediction (1) Calculation of the corrosion rate of steel strands Corrosion rate Given the formula: f(C corr ); Where A is the intrinsic corrosion rate of the steel strand under standard conditions, is the activation energy of the steel strand (kJ / mol), is the gas constant, 8.314 J / (mol·K), is the ambient temperature (K), and needs to be converted from °C to K: ;f(C corr ) is the corrosion capacity of the grouting material.

[0052] Alternatively, the corrosion rate can be determined based on the corrosion degree δ, and the relationship between the two is: in, is the initial corrosion degree, and t is the time.

[0053] (2) Calculation of concrete corrosion rate Given the formula: in: is the activation energy of concrete (kJ / mol), and B is the adjustment factor.

[0054] (3) Estimation of remaining useful life Given the formula: in: is the current material damage degree (measured value), is the allowable damage threshold (standard value, usually 0.8), It is the comprehensive corrosion rate (the weighted average of the steel strand corrosion rate and the concrete corrosion rate).

[0055] S140. Determine an evaluation result based on the evaluation variables. The evaluation result includes determining whether the bridge does not need reinforcement, needs reinforcement, or needs reconstruction.

[0056] According to a bridge prestressing fine detection and evaluation method provided by the present invention, the evaluation result is determined according to the evaluation variables, specifically including: when the prestressing safety factor of all evaluation units is not less than a first preset coefficient, and the reliability is not less than a first preset percentage, and the remaining service life is not less than the first preset life, it is determined that the bridge does not need to be reinforced; when the prestressing safety factor of any evaluation unit is less than a second preset coefficient, or the reliability is less than a second preset percentage, or the remaining service life is less than the second preset life, it is determined that the bridge needs to be demolished and rebuilt; under the condition that the bridge does not need to be demolished and rebuilt, when there is an evaluation unit whose prestressing safety factor is less than the first preset coefficient and not less than the second preset coefficient, or the reliability is less than the first preset percentage and not less than the second preset percentage, or the remaining service life is less than the first preset life and not less than the second preset life, it is determined that the bridge needs to be reinforced.

[0057] Specifically, the comprehensive reinforcement judgment formula can be used to determine whether a bridge meets the reinforcement conditions: (1) If or or years, the judgment result is "needs to be demolished and rebuilt".

[0058] (2) Excluding the need for demolition and reconstruction, if there is or or years, the judgment result is "preventive reinforcement is recommended".

[0059] (3) In other cases, the judgment result is "no reinforcement required". Next, the judgment can be made based on these conditions: (4) Demolition and reconstruction is required: If The value of is less than 0.8, or The value of is less than 3.2, or If the value is less than 15 years, then you should select "Needs to demolish and rebuild".

[0060] (5) Preventive reinforcement is recommended: If demolition and reconstruction are not necessary, has a value between 0.8 and 1.0 (inclusive of 0.8 but exclusive of 1.0), or has a value between 3.2 and 4.0 (inclusive of 3.2 but exclusive of 4.0), or If the value is between 15 and 30 years (including 15 years but excluding 30 years), you should select "Preventive reinforcement recommended".

[0061] (6) No reinforcement required: If neither of the above two conditions is met, then “No reinforcement required” should be selected.

[0062] The present invention provides the following embodiments: Example 1: A bridge prestressing fine detection and evaluation method comprises the following steps: Step 1: Conduct a detailed zoning assessment of the bridge, including: Step 101: Based on the design documents and on-site survey, or intelligent prediction using a neural network model, determine the areas with the greatest stress, areas with severe corrosion environments, and areas with significant human interference.

[0063] Step 102: Perform nondestructive testing on the incompletely constructed areas using a combination of impact-rebound testing and infrared thermal imaging to identify defects such as cavities and cracks. The test energy for the combined impact-rebound testing is 107.8 Newton meters.

[0064] Step 103: Divide the bridge into multiple evaluation units, each containing one or more inspection areas. The evaluation units are divided according to the following principles: continuous rigid frame bridges are divided by segments, formation processes, or construction batches, and other bridge types are divided by construction batches.

[0065] This embodiment provides multiple options for categorizing assessment units by segment, formation process, or construction batch for continuous rigid frame bridges, while other bridge types are categorized by construction batch. This flexible categorization adapts to the characteristics of different bridge structures, ensuring a more rational division of assessment units and improving the targeted nature of refined assessments.

[0066] Step 2: Conduct a comprehensive inspection of the prestressed steel strands, including: Step 201: Use nondestructive testing techniques such as stress wave testing and magnetic memory testing to test the prestress and corrosion level of the steel strands. The stress wave testing parameters are: excitation voltage 10-30V, frequency range 20-200kHz; the magnetic memory testing parameters are: magnetizing current 5-20A, magnetizing frequency 0.1-10Hz.

[0067] Step 202: Use methods such as anchor pullout testing and ultrasonic testing to test the anchor head anchoring efficiency and grouting density. The maximum pullout force of the anchor head pullout test is 80% to 120% of the designed tensile force of the steel strand. The ultrasonic testing has a transmitting frequency of 54 kHz and a receiving frequency of 2 MHz.

[0068] Step 203: Electrochemical testing and corrosion product analysis are used to test the corrosion capacity of the grouting material. The scanning potential range of the electrochemical test is -0.8 to 0.8 V, and the scanning rate is 1 mV / s. The corrosion product analysis uses X-ray diffraction and energy dispersive X-ray spectrometry.

[0069] This embodiment simultaneously adopts multiple detection methods such as stress wave method, magnetic memory test method, anchor head pull-out test, ultrasonic detection, electrochemical test and corrosion product analysis, covering the comprehensive detection of prestressing strength, corrosion degree, anchoring efficiency, grouting density and corrosion ability of grouting materials of prestressed steel strands.

[0070] By combining multiple detection methods, the first embodiment can obtain health status data of the bridge more comprehensively, thereby improving the accuracy and reliability of the detection results.

[0071] Step 3: Determine whether the detection and evaluation of all areas are completed. If not, return to step 1. If so, execute step 4.

[0072] Step 4: Based on the test results, calculate the prestress safety factor, reliability, and remaining service life to determine whether the bridge is suitable for reinforcement. Specifically, the following are included: Step 401: For each evaluation unit, calculate the prestress safety factor and reliability based on the test results. The formula for calculating the prestress safety factor is: safety factor = measured prestress strength / actual allowable prestress strength; the reliability calculation uses the Monte Carlo simulation method.

[0073] Step 402: Estimate the remaining service life of each assessment unit based on the design service life and material aging. Use the Arrhenius model to describe and predict the aging process of steel strands and concrete.

[0074] Step 403: Conduct a comprehensive assessment of the entire bridge to determine whether reinforcement is possible. If the prestressed safety factor of any assessment unit is less than 1.2, the reliability is less than 90%, or the remaining service life is less than 5 years, the bridge is deemed unsuitable for reinforcement and requires demolition and reconstruction. Otherwise, reinforcement may be possible, and a reinforcement plan will be developed, or no reinforcement is required.

[0075] This embodiment uses three indicators—prestress safety factor, reliability, and remaining service life—for comprehensive assessment. The criteria are: if the prestress safety factor of any evaluation unit is less than 1.2, the reliability is less than 90%, or the remaining service life is less than 5 years, the bridge is deemed unsuitable for reinforcement and requires demolition and reconstruction. This multi-indicator comprehensive assessment allows a more comprehensive assessment of the potential for bridge reinforcement, avoiding the limitations of a single indicator.

[0076] Example 2: A bridge prestressing fine detection and evaluation method comprises the following steps: Step 1: Conduct a detailed zoning assessment of the bridge, including: Step 101: Based on the design documents and on-site survey, or intelligent prediction using a neural network model, determine the areas with the greatest stress, areas with severe corrosion environments, and areas with significant human interference.

[0077] Step 102: Perform non-destructive testing on the incomplete construction area, using the impact-rebound method to find out the cavity defects, with a test energy of 107.8 Newton meters; and use an infrared thermal imager to find out the crack defects, with a thermal imager resolution of 640×512 pixels.

[0078] Step 103: Divide the continuous rigid frame bridge into evaluation units by segments, and divide other bridge types into evaluation units by construction batches, each unit containing one or more detection areas.

[0079] This embodiment uses only electrochemical testing methods to detect the corrosion capacity of the grouting material, without using corrosion product analysis. This simplifies the corrosion detection process, reduces detection costs and time, while still effectively evaluating the corrosion performance of the grouting material.

[0080] Step 2: Conduct a comprehensive inspection of the prestressed steel strands, including: Step 201: Use the stress wave method to test the prestress of the steel strand, with the test parameters being: excitation voltage 15V, frequency range 50-150kHz; use the magnetic memory test method to test the corrosion degree of the steel strand, with the test parameters being: magnetization current 10A, magnetization frequency 1Hz.

[0081] Step 202: An anchor head pull-out test is used to detect the anchoring efficiency of the anchor head, with the maximum pull-out force being 90% of the designed tension of the steel strand; an ultrasonic detection method is used to detect the grouting density, with a transmitting frequency of 54 kHz and a receiving frequency of 2 MHz.

[0082] Step 203: Use an electrochemical test method to detect the corrosion ability of the grouting material, with a scanning potential range of -0.6~0.6V and a scanning rate of 1mV / s.

[0083] This example optimizes the parameters of the stress wave and magnetic memory testing methods. The stress wave method uses an excitation voltage of 15V and a frequency range of 50-150kHz; the magnetic memory method uses a magnetizing current of 10A and a magnetizing frequency of 1Hz. These optimized parameters improve detection accuracy and efficiency, particularly in testing steel strand prestress and corrosion levels, more accurately reflecting the actual condition of the bridge.

[0084] Step 3: Determine whether the detection and evaluation of all areas are completed. If not, return to step 1. If so, execute step 4.

[0085] Step 4: Based on the test results, calculate the prestress safety factor, reliability, and remaining service life to determine whether the bridge is suitable for reinforcement. Specifically, the following are included: Step 401: For each evaluation unit, calculate the prestress safety factor based on the ratio of the measured prestress strength to the actual allowable prestress strength; and calculate the reliability using the Monte Carlo simulation method with a simulation number of 100,000 times.

[0086] Step 402: Use the Arrhenius model to describe the steel strand corrosion process and concrete aging process, and estimate the remaining service life of each assessment unit.

[0087] Step 403: Conduct a comprehensive assessment of the entire bridge. If the prestressed safety factor of any assessment unit is less than 1.2, or the reliability is less than 90%, or the remaining service life is less than 5 years, the bridge is determined to be unsuitable for reinforcement and needs to be demolished and rebuilt. Otherwise, the bridge may be suitable for reinforcement, and a reinforcement plan may be formulated, or no reinforcement is required.

[0088] In the present embodiment, the number of Monte Carlo simulations in the reliability calculation is specified to be 100,000. The specified number of simulations ensures the repeatability and accuracy of the reliability calculation and improves the credibility of the evaluation results.

[0089] Example 3: A bridge prestressing fine detection and evaluation method comprises the following steps: Step 1: Conduct a detailed zoning assessment of the bridge, including: Step 101: Based on the design documents and on-site survey, or intelligent prediction using a neural network model, determine the areas with the greatest stress, areas with severe corrosion environments, and areas with significant human interference.

[0090] Step 102: Perform non-destructive testing on the incompletely constructed areas, using an infrared thermal imager with a resolution of 1024×768 pixels to find crack defects.

[0091] Step 103: Divide the continuous rigid frame bridge into evaluation units according to the formation process, and divide other bridge types into evaluation units according to the construction batches, each unit including one or more detection areas.

[0092] In this embodiment, an infrared thermal imager with a resolution of 1024×768 pixels is used in non-destructive testing of incomplete construction areas. A high-resolution infrared thermal imager can more clearly identify defects such as cracks, thereby improving the accuracy and reliability of the test.

[0093] Step 2: Conduct a comprehensive inspection of the prestressed steel strands, including: Step 201: Use the stress wave method to test the prestress of the steel strand, with the test parameters being: excitation voltage 20V, frequency range 80-180kHz; use the magnetic memory test method to test the degree of corrosion of the steel strand, with the test parameters being: magnetization current 15A, magnetization frequency 5Hz.

[0094] Step 202: An anchor head pull-out test is performed to detect the anchoring efficiency of the anchor head, with the maximum pull-out force being 100% of the designed tension of the steel strand; an ultrasonic testing method is used to detect the grouting density, with a transmitting frequency of 54 kHz and a receiving frequency of 2 MHz.

[0095] Step 203: Use a corrosion product analysis method to detect the corrosion ability of the grouting material, and use X-ray diffraction and energy dispersive X-ray spectrometer to perform qualitative and quantitative analysis on the corrosion products.

[0096] This embodiment further optimizes the parameters of the stress wave and magnetic memory testing methods. The stress wave method uses an excitation voltage of 20V and a frequency range of 80-180kHz; the magnetic memory method uses a magnetizing current of 15A and a magnetizing frequency of 5Hz. These more precise parameter settings further enhance detection sensitivity and accuracy, particularly in corrosion and prestress testing, enabling more accurate capture of subtle changes.

[0097] Step 3: Determine whether the detection and evaluation of all areas are completed. If not, return to step 1. If so, execute step 4.

[0098] Step 4: Based on the test results, calculate the prestress safety factor, reliability, and remaining service life to determine whether the bridge is suitable for reinforcement. Specifically, the following are included: Step 401: For each evaluation unit, calculate the prestress safety factor based on the ratio of the measured prestress strength to the actual allowable prestress strength; and calculate the reliability using the Monte Carlo simulation method with a simulation number of 200,000 times.

[0099] Step 402: Use the Arrhenius model to describe the steel strand corrosion process and concrete aging process, and estimate the remaining service life of each assessment unit in combination with the design service life.

[0100] Step 403: Conduct a comprehensive assessment of the entire bridge. If the prestressed safety factor of any assessment unit is less than 1.2, or the reliability is less than 95%, or the remaining service life is less than 5 years, the bridge is determined to be unsuitable for reinforcement and needs to be demolished and rebuilt. Otherwise, the bridge may be suitable for reinforcement, and a reinforcement plan may be formulated, or no reinforcement is required.

[0101] This embodiment strengthens the reliability requirements in the judgment criteria. The criteria are: if the prestressed safety factor of any evaluation unit is less than 1.2, the reliability is less than 95%, or the remaining service life is less than 5 years, the bridge is deemed unsuitable for reinforcement and should be demolished and rebuilt. These stricter criteria ensure the safety and reliability of bridges after reinforcement and are suitable for bridge projects with higher safety requirements.

[0102] The beneficial effects of each embodiment are as follows: Example 1: Through a comprehensive combination of detection methods and flexible division of evaluation units, a comprehensive assessment of the health status of a bridge is achieved, which is suitable for bridge projects that require high-precision and multi-dimensional detection.

[0103] Example 2: Through optimized detection parameters and simplified corrosion detection methods, the detection efficiency and accuracy are improved, and it is suitable for bridge projects with high requirements on detection cost and time.

[0104] Example 3: Through high-precision detection parameters, high-resolution non-destructive testing and strict judgment standards, higher requirements for bridge safety are achieved, and it is suitable for bridge projects with extremely high safety requirements.

[0105] Compared with the existing technology, the present invention provides a method for fine detection and evaluation of bridge prestressing, which has the following beneficial effects: 1. A systematic inspection and evaluation system has been established. Through comprehensive testing of steel strand prestressing strength, corrosion level, anchoring efficiency, and grouting quality, combined with zoning assessments based on factors such as stress conditions and corrosion environment, the system can scientifically calculate the prestressing safety factor, reliability, and remaining service life, thereby accurately determining whether a bridge is suitable for reinforcement. 2. Developed precise inspection and analysis methods for different bridge types, improving the accuracy and reliability of inspection data and facilitating the development of scientific and rational repair and reinforcement strategies; 3. A repair and reinforcement strategy based on a detailed assessment adopted a localized reinforcement approach, repairing only damaged areas. This avoided large-scale demolition and reconstruction, achieved a green, low-carbon, and environmentally friendly life extension, and saved significant funds. 4. Through the inspection and evaluation of prestressed steel strands, problems such as prestress loss and corrosion can be effectively discovered and resolved, extending the service life of the bridge and improving structural safety and reliability; 5. The method of the present invention is simple to operate, low in cost, and highly applicable, and can be widely used in reinforcement assessment and maintenance decision-making for various prestressed concrete bridges.

[0106] The following describes the bridge prestressing fine detection and evaluation device provided by the present invention. The bridge prestressing fine detection and evaluation device described below and the bridge prestressing fine detection and evaluation method described above can refer to each other.

[0107] like Figure 2 The present invention provides a bridge prestressed precise detection and evaluation device, comprising: The zoning module 210 is used to finely zone the bridge and determine key inspection areas; the key inspection areas include areas with the greatest stress, areas with severe corrosion environment, areas with significant human interference, and areas with incomplete construction; The inspection module 220 is used to conduct a comprehensive inspection of the prestressed steel strands in the key inspection area and determine the inspection results; the comprehensive inspection includes the inspection of the prestressing strength, corrosion degree, anchor head anchoring efficiency, grouting density and corrosion resistance of the grouting material; An evaluation variable determination module 230 is used to determine evaluation variables based on the test results, where the evaluation variables include prestress safety factor, reliability, and remaining service life; The reinforcement possibility assessment module 240 is used to determine an assessment result based on the assessment variables. The assessment result includes determining whether the bridge does not need reinforcement, needs reinforcement, or needs reconstruction.

[0108] Figure 3 An example of a physical structure diagram of an electronic device is shown below. Figure 3 As shown, the electronic device may include: a processor 310, a communications interface 320, a memory 330, and a communications bus 340. The processor 310, the communications interface 320, and the memory 330 communicate with each other via the communications bus 340. The processor 310 may invoke logic instructions in the memory 330 to execute a bridge prestressing fine inspection and evaluation method. The method includes: finely zoning the bridge to determine key inspection areas; the key inspection areas include areas with the greatest stress, areas with severe corrosion environments, areas with significant human interference, and areas with incomplete construction; conducting a comprehensive inspection of the prestressing steel strands in the key inspection areas to determine the inspection results; the comprehensive inspection includes testing of prestressing force, corrosion level, anchor head anchoring efficiency, grouting density, and grouting material corrosion resistance; determining evaluation variables based on the inspection results, including prestressing safety factor, reliability, and remaining service life; and determining an evaluation result based on the evaluation variables, including determining whether the bridge requires reinforcement, requires reinforcement, or requires reconstruction.

[0109] Furthermore, the logic instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0110] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the bridge prestressing fine detection and evaluation method provided by the above methods, which includes: finely zoning the bridge to determine key detection areas; key detection areas include areas with the greatest stress, areas with severe corrosion environment, areas with large human interference and areas with incomplete construction; comprehensive detection of prestressed steel strands in key detection areas to determine the detection results; comprehensive detection includes detection of prestressing force, degree of corrosion, anchor head anchoring efficiency, grouting density and corrosion ability of grouting material; determining evaluation variables based on the detection results, the evaluation variables include prestressing safety factor, reliability and remaining service life; determining evaluation results based on the evaluation variables, the evaluation results include determining whether the bridge does not need reinforcement, needs reinforcement and needs reconstruction.

[0111] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the bridge prestressing fine detection and evaluation method provided by the above-mentioned methods, the method comprising: finely zoning the bridge to determine key detection areas; the key detection areas include areas with the greatest stress, areas with severe corrosion environment, areas with large human interference and areas with incomplete construction; comprehensive detection of the prestressed steel strands in the key detection areas to determine the detection results; the comprehensive detection includes detection of prestressing force, degree of rust, anchor head anchoring efficiency, grouting density and corrosion ability of grouting material; determining evaluation variables based on the detection results, the evaluation variables including prestressing safety factor, reliability and remaining service life; determining evaluation results based on the evaluation variables, the evaluation results including determining whether the bridge does not need reinforcement, needs reinforcement and needs reconstruction.

[0112] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units. That is, they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0113] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods of each embodiment or certain portions of the embodiments.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A bridge prestressed precise detection and evaluation method, characterized in that: include: Carry out detailed zoning of the bridge and determine key inspection areas; the key inspection areas include areas with the greatest stress, areas with severe corrosion environment, areas with significant human interference, and areas with incomplete construction; Conduct a comprehensive inspection of the prestressed steel strands in the key inspection area to determine the inspection results; the comprehensive inspection includes inspections of prestressing intensity, corrosion degree, anchor head anchoring efficiency, grouting density, and corrosion resistance of grouting materials; Determining evaluation variables based on the test results, wherein the evaluation variables include prestress safety factor, reliability, and remaining service life; An evaluation result is determined according to the evaluation variables, wherein the evaluation result includes determining whether the bridge does not need reinforcement, needs reinforcement, or needs reconstruction.

2. The bridge prestressing fine detection and evaluation method according to claim 1 is characterized in that: The comprehensive inspection of the prestressed steel strands in the key inspection area specifically includes: Using non-destructive testing technology to test the prestressing strength and corrosion degree; the non-destructive testing technology includes stress wave method and magnetic memory test method; Anchoring system integrity testing methods are used to test the anchoring efficiency and grouting density of the anchor head; the anchoring system integrity testing methods include anchor head pull-out test and ultrasonic testing; The corrosion performance evaluation method is used to detect the corrosion ability of the grouting material, and the corrosion performance evaluation method includes electrochemical testing and corrosion product analysis.

3. The bridge prestressing fine detection and evaluation method according to claim 1 is characterized in that: The detailed zoning of bridges specifically includes: Obtain a three-dimensional model of the bridge; Dividing the three-dimensional bridge model into a plurality of evaluation units of preset sizes; each evaluation unit includes one or more detection areas; Performing multi-source feature extraction based on the evaluation unit; Constructing a bridge topology graph with the evaluation units as nodes and adjacent relationships as edges; The bridge topology map is input into a trained neural network model to output the risk area; the trained neural network model is trained based on a training set, which includes the bridge topology map and risk area labels; the risk area labels include areas with the greatest stress, areas with severe corrosion environment, areas with significant human interference, and areas with incomplete construction.

4. The bridge prestressing fine detection and evaluation method according to claim 2 is characterized in that: Determining the evaluation variables according to the test results specifically includes: For each assessment unit, the prestress safety factor is determined based on the prestressing intensity; the reliability is determined based on the anchoring efficiency and grouting density; Determine the remaining service life based on the degree of rust and the corrosion capacity of the grouting material; The evaluation unit is obtained by dividing the three-dimensional model of the bridge.

5. The bridge prestressing fine detection and evaluation method according to claim 1 is characterized in that: The calculation formula of the prestress safety factor is: safety factor = measured prestress strength / actual allowable prestress strength; The Monte Carlo simulation method is used for reliability calculation; The Arrhenius model is used to describe and predict the aging process of steel strands and concrete, and then the remaining service life is estimated.

6. The bridge prestressing fine detection and evaluation method according to claim 4 is characterized in that: Determining the evaluation result according to the evaluation variable specifically includes: When the prestressed safety factor of all evaluation units is not less than the first preset factor, the reliability is not less than the first preset percentage, and the remaining service life is not less than the first preset life, it is determined that the bridge does not need reinforcement; When the prestressed safety factor of any evaluation unit is less than the second preset factor, or the reliability is less than the second preset percentage, or the remaining service life is less than the second preset years, it is determined that the bridge needs to be demolished and rebuilt; Excluding the need to demolish and rebuild the bridge, when the prestressed safety factor of an evaluation unit is less than the first preset factor and not less than the second preset factor, or the reliability is less than the first preset percentage and not less than the second preset percentage, or the remaining service life is less than the first preset period and not less than the second preset period, then it is determined that the bridge needs to be reinforced.

7. A bridge prestressed precise detection and evaluation device, characterized in that: include: The zoning module is used to finely zone the bridge and determine key inspection areas; the key inspection areas include areas with the greatest stress, areas with severe corrosion environment, areas with significant human interference, and areas with incomplete construction; A detection module is used to conduct a comprehensive inspection of the prestressed steel strands in the key inspection area and determine the inspection results; the comprehensive inspection includes the inspection of prestressing intensity, corrosion degree, anchor head anchoring efficiency, grouting density and corrosion capacity of grouting material; An evaluation variable determination module is used to determine evaluation variables according to the test results, wherein the evaluation variables include prestressed safety factor, reliability and remaining service life; The reinforcement possibility assessment module is used to determine an assessment result based on the assessment variables, wherein the assessment result includes determining whether the bridge does not need reinforcement, needs reinforcement, or needs reconstruction.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the bridge prestressing fine detection and evaluation method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the bridge prestressing fine detection and evaluation method according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the bridge prestressing fine detection and evaluation method according to any one of claims 1 to 6 is implemented.

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