A single-column pier bridge monitoring method and system suitable for long-term monitoring projects

Abstract

The present invention provides a single-column pier bridge monitoring method and system suitable for long-term monitoring projects, including the following steps: measuring the traffic flow characteristics of the bridge; designing a vehicle load loading model based on the measured traffic flow characteristics; establishing a finite element model and performing finite element analysis based on bridge parameters and the vehicle load loading model; establishing a monitoring system on the bridge and setting an alarm threshold based on the finite element analysis results to achieve monitoring during bridge operation; the method for setting the alarm threshold is as follows: superimposing the quasi-static strain change trend of the structure with the vehicle-induced strain in an unstable state to form a dynamic threshold alarm, and the unstable state includes two types: the bridge structure support is vacated and the structure is stressed to reach the structural bearing capacity. The present invention aims to provide a practical reference for the construction and application of a bridge structure health monitoring system for similar monitoring needs, to achieve long-term effective monitoring of the single-column pier bridge structure, to ensure the normal operation of the bridge, and to avoid the overturning of the bridge.

Description

Technical Field

[0001] The present invention relates to the field of bridge safety monitoring and early warning, and in particular to a single-column pier bridge monitoring method and system suitable for long-term monitoring projects. Background Art

[0002] For urban infrastructure, single-column pier bridges are widely used in the design of viaducts, overpasses, ramp bridges and other bridges due to their advantages of small space occupation, low cost and strong linear adaptability. However, in recent years, urban traffic pressure has increased significantly, and problems such as vehicle load exceeding the limit and irregular driving behavior are common. The risk of overturning of single-column pier bridge structures is extremely high, posing a serious threat to the lives and property of pedestrians and the urban economy. It is necessary to establish a long-term and stable real-time monitoring system for structural anti-overturning capacity suitable for engineering to ensure the normal operation of bridges.

[0003] At present, many provinces and cities across the country have gradually started the investigation of the anti-overturning stability of single-column supported bridges. Based on the finite element model, a detailed analysis of the anti-overturning stability of the bridge under the design limit load was carried out, and the consistency of the anti-overturning stability standard of this type of bridge was further verified by field loading tests. Through accident simulation, the overturning and collapse process of the ramp bridge was deeply discussed. It was found that when calculating the anti-overturning stability coefficient, the second-order effect of beam rotation must be considered, and the anti-overturning capacity of the bridge should be evaluated based on the criterion that the bearing is not empty under overload conditions. Similarly, with the emptying of the bearing as the first limit state of overturning, the analytical limit analysis method of the overturning load of the single-column pier bridge was used to predict the bridge collapse process. The above study gave the limit state of bridge overturning by analyzing the bridge overturning mechanism. However, for bridges in operation, their structural systems are formed, and the random vehicle loads on the bridges have obvious statistical regularities in the context of big data samples. Although the vehicle loads used in the design stage can reflect the common stress characteristics of such bridges, they cannot fully consider the actual traffic flow conditions in the bridge site area. Relying solely on the anti-overturning coefficient threshold specified in the specification may result in excessive structural safety redundancy, resulting in a great waste of structural utilization. At the same time, even if the design requirements of the specifications are met, it is difficult to ensure that the bridge will not overturn during actual operation.

[0004] In order to effectively prevent the occurrence of bridge overturning accidents, it is necessary to actively intervene in possible overturning risk factors, that is, to eliminate the risk factors that may cause bridge overturning from the source by real-time monitoring of the traffic flow in the bridge site area that may cause the instability of the bridge structure, controlling the behavior of passing vehicles that may aggravate the overturning of the bridge. From the perspective of monitoring, by monitoring and collecting data on bridge anti-overturning analysis, a box girder bridge anti-overturning reliability analysis method was proposed, and the feasibility and effectiveness of the bridge structure health monitoring system were verified. On this basis, the temporal and spatial distribution of the actual vehicle queue on the bridge deck was determined by integrating machine vision and weighing monitoring information, and the structural support reaction force was calculated in real time through the derived analytical expression of the main beam support reaction force, realizing a real-time monitoring method for the overturning risk of box girder bridges with predictive ability. The above research provides a new direction for the research on bridge anti-overturning, but its vehicle load monitoring is mostly based on the bridge dynamic weighing system (weigh in motion, abbreviated as WIM), that is, the high-definition camera on the bridge and the sensors embedded in the bridge deck are used to obtain vehicle axle weight, total weight, vehicle speed and other information. However, in the face of the complex vehicle traffic environment on the bridge, the accuracy and durability of the sensor devices in the bridge deck are affected. Due to the limitations of on-site conditions and maintenance costs, the maintenance cycle cannot be too short, resulting in a very low online rate of the long-term WIM monitoring system, incoherent data, low data quality, poor accuracy, and a general error of about 5% to 10%. In addition, based on the actual construction scenario of the structural health monitoring system, the selection of structural response monitoring parameters needs to consider the measurement accuracy and installation feasibility of the on-site monitoring equipment to meet the requirements of the structural health monitoring system for durability, stability, and accuracy. When the bridge enters the operation stage, the bridge structure system is solidified, and the monitoring equipment used to measure parameters such as bearing reaction forces needs to be embedded in the bridge. The installation is difficult and it is difficult to replace the sensor after it is damaged. In addition, due to accuracy issues, the existing deflection monitoring equipment is difficult to meet the monitoring requirements of the deflection changes of small and medium-sized bridges. And because the bridge does not exist in a purely rigid form, the local inclination angles between the beams are not completely consistent, and the installation position of the sensor may cause huge differences in the monitoring values. In engineering practice, the above parameters are not suitable for the long-term monitoring task of the single-column pier bridge structure. To this end, in view of the above-mentioned problems, this application proposes a single-pier bridge monitoring and alarm method to provide long-term and effective monitoring of the single-pillar pier bridge structure. Summary of the invention

[0005] The present invention aims to overcome at least one defect (shortcoming) of the above-mentioned prior art and provides a single-column pier bridge monitoring method and system suitable for long-term monitoring projects, which is used to monitor whether the bridge is likely to overturn and to alarm for possible risks, thereby solving the problem that the single-column pier bridge structure has a high risk of overturning and poses a serious threat to the lives and property of pedestrians and the urban economy.

[0006] The technical solution adopted by the present invention is a single-column pier bridge monitoring method suitable for long-term monitoring projects, which specifically includes the following steps:

[0007] S1. Measured traffic flow characteristics of bridges;

[0008] S2. Design vehicle load model based on measured traffic flow characteristics;

[0009] S3. Establish a finite element model and perform finite element analysis based on bridge parameters and vehicle load loading model;

[0010] S4. Establish a monitoring system on the bridge and set alarm thresholds based on the finite element analysis results to achieve monitoring during bridge operation; where:

[0011] When the bridge structure support is reached, the alarm threshold is reached to take traffic flow control measures;

[0012] When the bridge structure stress reaches the structural bearing capacity alarm threshold, traffic prohibition control measures are taken;

[0013] The method for setting the alarm threshold in step S4 is as follows:

[0014] The dynamic threshold alarm is formed by superimposing the quasi-static strain change trend of the structure on the vehicle-induced strain in the unstable state. The unstable state includes two types: the bridge structure support is vacant and the structure stress reaches the structure bearing capacity.

[0015] The present invention provides a monitoring method for obtaining the spatiotemporal distribution of vehicle loads on a bridge by using pier strain as the main measurement parameter, low-cost, high-precision electronic toll collection gantry (ETC) and machine vision recognition technology. The method considers the actual traffic load scenario of the bridge, compares and analyzes the stress characteristics of the key monitoring sections of the structure, fully considers the monitoring requirements and restrictions of the bridge, and proposes a monitoring scheme suitable for the operation of the bridge. Based on the monitoring scheme, a large amount of structural response data under various monitoring scenarios is collected, and the difference analysis between the finite element calculation results and the actual measurement results is carried out in combination with the restrictions of the actual driving of the vehicle. Finally, considering the data acquisition characteristics, data change laws and alarm requirements of structural health monitoring, a threshold system of the monitoring system is established. When the system monitors that the bridge structure reaches the alarm threshold, it means that the structural force system has changed to an unstable state, which may cause the danger of bridge overturning. Therefore, setting the alarm threshold can effectively ensure the operation safety of the bridge. The method describes in detail the specific requirements, related problems and solutions when constructing and operating the structural monitoring system, aiming to provide a practical reference for the construction and application of bridge structural health monitoring systems with similar monitoring requirements, and to achieve long-term effective monitoring of single-column pier bridge structures.

[0016] Preferably, the two structural states of the structural support being empty and the structural force reaching the structural bearing capacity are determined by adjusting the lane load partial coefficient through finite element analysis under the most unfavorable arrangement of lane loads specified in the specification. The specific method is:

[0017] For the inner lane, the γ1 times partial factor specified in the specification is applied; γ1 = 1.4;

[0018] For the outer lane, the partial coefficient value γ2 corresponding to the bearing being emptied and the structural force reaching the structural bearing capacity is observed by gradually increasing the partial coefficient; among which 1.4≤γ2≤3.4.

[0019] The above method is used to extract the vehicle eccentric load strain value at the pier bottom under the corresponding state as the alarm limit of the vehicle-induced response. By setting the threshold before the alarm limit, the traffic flow can be restricted immediately when the system issues an alarm, avoiding the risk factors that may trigger the bridge overturning from the source, ensuring early warning and timely intervention of potential dangers of the bridge to the greatest extent, better ensuring the safe operation of the bridge and extending its service life.

[0020] Preferably, the structural quasi-static strain at least includes structural response caused by temperature load, wind load and environmental noise.

[0021] Preferably, the step S1 specifically comprises:

[0022] Obtain traffic monitoring data on the bridge, ignore vehicles with a weight below t, form statistical data, and select the tonnage and number of axles of vehicles with a proportion greater than δ from the statistical data as traffic flow features, where t = 30 tons and δ = 20%.

[0023] Preferably, the vehicle load model in step S2 includes a 4-axle Type 4 truck and a 6-axle Type 6 truck, with mass distribution ranges of 30-40 tons and 40-50 tons respectively, wherein:

[0024] For the four-axle trucks, the ratio of axle weight to vehicle weight is: 1 axle 0.1568, 2 axles 0.2412, 3 axles 0.2788 and 4 axles 0.3232;

[0025] For the six-axle type six trucks, the ratio of axle weight to vehicle weight is: 1 axle 0.1044, 2 axles 0.1474, 3 axles 0.2070, 4 axles 0.1873, 5 axles 0.1710 and 6 axles 0.1828.

[0026] The overturning of a single-column pier bridge is closely related to vehicle load. As one of the main variable loads during bridge operation, eccentric vehicle load is the direct cause of the overturning. Mastering the measured traffic flow characteristics is an important prerequisite for controlling the overturning of bridges. Considering that the vehicle load that causes the overturning of a single-column pier bridge requires a heavier mass and a certain eccentric distance, the four-axle Type 4 trucks and the six-axle Type 6 trucks have the largest proportion of vehicles passing on the bridge. The vehicle weight-vehicle axle number of vehicles passing on the bridge is counted, and the axle weight is used as the input parameter of the vehicle load, which can more truly reflect the impact of vehicles on the bridge.

[0027] Preferably, the bridge parameters in step S3 are as follows: the bridge is a continuous box girder bridge, with double-column support piers at both ends, a number of single-column consolidation piers distributed between the two double-column support piers, a fixed constraint is used at the bottom of the abutment, a rigid simulation in an elastic connection is used between the main beam of the consolidation pier and the pier column, and the double bearings at the support pier use elastic connections to simulate rubber bearings, the unit types are all beam units, and the calculated loads include structural gravity, live loads, concrete shrinkage creep, temperature effects, and foundation settlement. Collecting relevant parameters can provide strong data support for subsequent data analysis and verification, making the health monitoring of the bridge structure more accurate.

[0028] Further preferably, the finite element analysis in step S3 is specifically:

[0029] It is assumed that the bridge does not undergo plastic deformation before the bridge structure supports are emptied. The bridge structure response caused by vehicle loads is calculated using the superposition principle. The vehicle weight and vehicle spatial position are used as control variables to compare the variation of the transverse bridge pier strain and analyze the most unfavorable cross-sectional position of the structure.

[0030] The vehicle spatial position is specifically a vehicle loading position that is changed in the transverse direction of the bridge along the pier top section of each single-column pier, and the transverse loading position of the bridge is calculated based on the distance between the vehicle axis and the central axis of the bridge.

[0031] The most unfavorable cross-sectional position is specifically:

[0032] For real-time monitoring, the top section of the single-column pier is selected as the main monitoring section;

[0033] For long-term monitoring, the pier bottom section dominated by compressive stress is selected.

[0034] The loading mass does not cause the position change of the maximum tensile and compressive sections of the bridge, and the maximum compressive stress section of the structure is always at the top of the loaded pier. The maximum tensile stress section of the structure is related to the eccentric distance, which is manifested as when the eccentric distance is small, the top of the non-loaded pier is the main tensile section, and when the eccentric distance is greater than a certain threshold, the top of the loaded pier is the main tensile section. The tensile strain at the bottom of the pier caused by the vehicle load is extremely small, but it will significantly increase the compressive strain at the bottom of the loaded pier. It can be seen that in order to ensure that the structural health monitoring system can grasp the most unfavorable position of the structure in real time, the top section of the single-column pier should be selected as the main monitoring section. However, since the single-column pier bridge is a large eccentric structure, obvious deformation will occur when the structure is damaged, and the cracking of the concrete makes the stress and strain present a complex nonlinear relationship. Therefore, in order to meet the needs of the structural health monitoring system to monitor the structural state during operation for a long time, the pier bottom section dominated by compressive stress should also be selected.

[0035] Further preferably, the step S3 also includes a verification process of finite element analysis and measured data, and the specific process is as follows:

[0036] S31 collects the number of vehicles passing on the bridge when the center of gravity of the vehicle is just at the cross section of the top beam of the load-bearing pier and divides the loading condition;

[0037] S32 analyzes the working conditions of vehicles passing on the bridge;

[0038] S33 extracts the vehicle-induced response components of the pier bottom compressive strain at the time when the main working conditions occur, and forms the measured strain distribution;

[0039] S34 respectively applies the finite element simulation results of the compressive strain in the eccentric load direction of the pier bottom when the vehicle eccentricity increases uniformly;

[0040] S35 determines whether the extreme value of the vehicle-induced compressive strain under the maximum eccentricity of the finite element simulation is within the 90% to 95% percentile value of the measured strain.

[0041] Due to the high redundancy of the large base of monitoring data collected, the vehicle weight measurement and vehicle identification errors of the vehicle load monitoring system are amplified, and the low probability of inaccurate vehicle load conditions are frequently extracted. In the application context of the above-mentioned structural health monitoring system, it can be found that if only a small number of measured conditions are used to determine the state of the structure during operation, data discreteness may occur. At the same time, using the probability statistics of a large number of conditions as the reference condition of the alarm value may also cause the monitoring system to alarm frequently, reducing the efficiency and credibility of the alarm. Therefore, the above steps can improve the efficiency and confidence of the alarm, making the monitoring of the bridge more accurate.

[0042] Preferably, the present invention includes a monitoring system for implementing the above-mentioned single-column pier bridge monitoring method applicable to long-term monitoring projects, specifically comprising:

[0043] Multiple strain sensors for monitoring the compressive strain at the bottom of bridge piers.

[0044] Machine vision recognition technology used to identify vehicle types and obtain the spatial position of passing vehicles.

[0045] Electronic toll collection gantry for obtaining vehicle loads,

[0046] A main control system that monitors the possibility of a bridge capsizing based on information collected by strain sensors, machine vision recognition technology and electronic toll collection gantries, combined with set alarm thresholds, and issues alarms for possible risks.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] This solution discloses a structural health monitoring technology route suitable for long-term monitoring projects, including traffic feature extraction, key section comparison, structural response mapping, and vehicle-induced overturning warning, forming a technical framework for the construction of a structural health monitoring system and bridge overturning alarm. The main technical effects are as follows:

[0049] 1. The loading mass of the vehicle load will not cause the position of the maximum tensile and compressive sections of the bridge to change, but the eccentric distance will affect the position of the maximum tensile section. Since concrete is prone to cracks when it is tensile, the stress-strain relationship is complex and nonlinear, and the position of the maximum tensile section is affected by the eccentric distance. Therefore, the vehicle eccentric load side section at the bottom of the pier, where the stress-strain relationship is always linear, the maximum stress section is fixed, and the compressive stress is dominant, is selected as the main monitoring section.

[0050] 2. By comparing the measured vehicle-induced strain and the finite element simulation results, it is found that the vehicle-induced strain simulated by the finite element under the maximum eccentricity can contain 90% to 95% of the measured vehicle-induced strain results, but there are some abnormal results under the massive measured working conditions. This shows that in a complex bridge operation environment, the system error will be significantly amplified, and the low-probability abnormal identification results will still appear frequently. When applying the structural health monitoring system simulation, attention should be paid to the statistical characteristics under multiple working conditions, rather than the calculation results of a single working condition. Using the strain envelope calculated under the most unfavorable eccentric load condition of a single vehicle as the warning value will cause the monitoring system to alarm frequently, reducing the efficiency and credibility of the alarm.

[0051] 3. To ensure the long-term normal operation of the bridge, the alarm limit in the long-term online evaluation method of the structure should be based on the unstable state when the structural force system is converted. Considering that vehicle load is the root cause of bridge overturning and the relative-coupling characteristics of structural monitoring data, the vehicle-induced strain in the unstable state can be superimposed on the quasi-static strain change trend of the structure to form a dynamic threshold alarm system. Setting the alarm threshold can effectively ensure the operational safety of the bridge and avoid bridge overturning. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a structural diagram of the bridge vehicle load monitoring system of the present invention.

[0053] Figure 2 This is a working flow chart of the cross-section structure health monitoring system of the present invention.

[0054] Figure 3 It is the on-site working condition diagram of the viaduct of the present invention.

[0055] Figure 4 It is a three-dimensional columnar statistical chart of vehicle model-vehicle axle number of the present invention.

[0056] Figure 5 It is a three-dimensional columnar statistical chart of vehicle weight-vehicle axle number of the present invention.

[0057] FIG. 6( a ) is a representative vehicle type T159 of the traffic flow on the bridge of the present invention.

[0058] FIG. 6( b ) shows a representative vehicle type T115 of the traffic flow on the bridge of the present invention.

[0059] Figure 7 It is a schematic diagram of the method for indicating the loading position on a bridge according to the present invention.

[0060] Figure 8 It is a schematic diagram of the finite element calculation model and measuring point numbering of the single-column pier bridge of the present invention.

[0061] Fig. 9 This is a schematic diagram of the strain change of the measuring point in the loaded tensile zone at the top of pier 41# of the present invention.

[0062] Fig.10 It is a schematic diagram of the strain change of the measuring point in the loaded tensile zone of the top of pier 40# of the present invention.

[0063] Fig.11 This is a schematic diagram of the strain change of the measuring point in the loaded compression zone at the top of pier 41# of the present invention.

[0064] Fig.12 It is a schematic diagram of the strain change of the measuring point in the loaded compression zone of the top of 40# pier of the present invention.

[0065] Fig.13 It is a schematic diagram of the arrangement of system measuring points of the present invention.

[0066] Fig.14 It is a schematic diagram of the vehicle-induced response identification method of the present invention.

[0067] Fig.15 It is a schematic diagram of the vehicle-mounted rollover threshold design of the present invention. DETAILED DESCRIPTION

[0068] The drawings of the present invention are only for illustrative purposes and should not be construed as limiting the present invention. In order to better illustrate the following embodiments, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product; it is understandable to those skilled in the art that some well-known structures and their descriptions in the drawings may be omitted.

[0069] Example 1

[0070] A single-column pier bridge monitoring method suitable for long-term monitoring projects is used to monitor whether the bridge is likely to overturn and to alarm for possible risks, solving the problem that the single-column pier bridge structure has a high risk of overturning and poses a serious threat to the lives and property of pedestrians and the urban economy.

[0071] Preferably, the basic framework of the structural health monitoring system is built first:

[0072] The structural health monitoring system includes a vehicle load monitoring system, an overturning analysis system, and an alarm and current limiting system. Since the reform of the toll road system, ETC gantries have become popular, and their quality inspection accuracy can reach 0.5%, which is much higher than the WIM system. The dense ETC gantries provide a new idea for vehicle load monitoring of road network-level bridge groups. Figure 1 This is a working diagram of the bridge vehicle load monitoring system that combines on-site high-definition cameras and toll station weighing data. By deploying high-definition cameras on the roadside and combining machine vision recognition technology, key information such as license plates (identity information), lanes (lateral distribution positions), and vehicle travel times (which can be used for vehicle spatiotemporal distribution inversion) of vehicles passing on the bridge can be obtained. Using the unique deterministic identification of vehicle information, the vehicle positions and vehicle loads under the same traffic flow can be matched to form data fusion of multi-dimensional vehicle information.

[0073] The overturning analysis system separates the influencing components of quasi-static effects such as temperature and issues an alarm based on the allowable value of the bridge under vehicle load conditions. For the structural health monitoring system, the structural alarm value is not just a structural alarm when the bridge overturns, but also an alarm should be issued when unstable conditions that may cause structural instability are found during bridge operation, so as to determine dangerous traffic flow and control it in combination with the alarm current limiting system. Figure 2 For the overall workflow of the structural health monitoring system, the vehicle load monitoring system is used to locate the temporal and spatial distribution of vehicle loads, the overturning analysis system is used to determine the degree of danger of the passing traffic flow, and the alarm and current limiting system is used to make real-time online judgments on the overturning risk. The judgment results are fed back to the front end for decision-making, forming a closed-loop control.

[0074] Preferably, a vehicle load loading model is constructed:

[0075] In this example, a viaduct in Guangdong Province is selected as the research object for detailed description. The bridge has four spans, with a span combination of 4×16.75m. The cross section of the upper structure is a single-box two-chamber ordinary reinforced concrete equal-section continuous box beam. The bridge deck width is 11.6 meters. The double-column support pier and the single-column consolidation pier are as follows. Figure 3 shown.

[0076] According to the measured traffic flow characteristics, the overturning of single-column pier bridges is closely related to vehicle loads. As one of the main variable loads during the operation of the bridge, eccentric vehicle loads are the direct cause of the overturning. Mastering the measured traffic flow characteristics is an important prerequisite for controlling the overturning of the bridge. In order to analyze the traffic flow characteristics of the supporting project, a vehicle load monitoring system was deployed at the bridge, and one year of traffic monitoring data on the bridge was obtained. A total of 11.58 million passing vehicles were monitored. Considering that the vehicle load that causes the overturning of the single-column pier bridge requires a heavier mass and a certain eccentric distance, the impact of vehicles with a mass of less than 30 tons on the bridge can be ignored. Figure 4 Ignoring cars under 30 tons, the three-dimensional bar chart of vehicle type and vehicle axle number shows that the 4-axle 4-type truck and the 6-axle 6-type truck have the largest proportion. To further analyze the mass distribution of vehicles passing on the bridge, Figure 5 The weight-number of axles of vehicles passing on the bridge was counted, and it can be seen that the mass of 4-axle vehicles is mainly distributed in the range of 30 to 40 tons, and the mass of 6-axle vehicles is mainly distributed in the range of 40 to 50 tons.

[0077] The vehicle load model is designed based on the measured traffic flow characteristics. In actual engineering, the weight of the vehicle is not evenly distributed on the bridge deck, but is concentrated at the wheels (axles). For super-large bridges, the bridge length is long, and the instantaneous force position of the vehicle will also experience a large range of changes during the driving process on the bridge. In addition, the vehicle travels on the bridge at a certain speed, so that this change can be simulated as a uniformly changing load to a certain extent. In the anti-overturning research on small and medium-sized single-column pier bridges, due to the short length of the bridge and the slow speed of vehicles passing on the bridge, the instantaneous change range of the force position of the vehicle on the bridge is small, and the pressure on a single pier cannot be simplified as a uniformly distributed load. Using axle weight as the input parameter of vehicle load can more realistically reflect the impact of vehicles on the bridge.

[0078] The application of the bridge vehicle load monitoring system shows that the heavy vehicles passing on the bridge are mainly 4-axle Type IV trucks and 6-axle Type VI trucks, and the mass distribution range is 30-40 tons and 40-50 tons respectively. In order to optimize the load selection strategy for vehicles passing on the bridge, it is still necessary to conduct in-depth research on the wheelbase and axle weight of vehicles passing on the bridge. The 7-year traffic data of the Beijing-Hong Kong-Macao Expressway contains more than 5 million sets of truck vehicle load passage records for 1918 valid working days. The normal distribution is used to fit the wheelbase of vehicles of different models, and the estimated model parameters (including mean and standard deviation) of the normal distribution of each truck type are obtained, as shown in Table 1. The Beijing-Hong Kong-Macao Expressway is an important transportation hub in Guangdong Province. The statistical results of traffic and transportation conditions along the highway have a high reference value. Therefore, T115 is selected as the representative vehicle model of 4-axle Type IV trucks, and T159 is selected as the representative vehicle model of 6-axle Type VI trucks. By analyzing the data of tens of millions of vehicles on the Nansha Bridge in the past two years, the KNN model was used to analyze the correlation between the total weight and axle weight of various types of vehicles and to model the correlation, forming a vehicle axle weight prediction method. Table 2 shows the ratio of axle weight to vehicle weight for each axle type of vehicle. Combining Tables 1 and 2, the representative vehicle types and load characteristics on the bridge can be determined, as shown in Figure 6, providing a data basis for subsequent model calculations.

[0079] Table 1 Wheelbase model parameter estimates for each truck type (mean, standard deviation)

[0080]

[0081] Table 2 The ratio of axle weight to vehicle weight of various axle types

[0082]

[0083]

[0084] Preferably, a finite element analysis is established based on bridge parameters and vehicle load loading model;

[0085] In this embodiment, the bridge is a 4×16.75m reinforced concrete continuous box girder bridge, the plane of which is located on a circular curve of R=1500m. Figure 8 The 40#, 41# and 42# piers shown are single-column consolidation piers. Their main beams and pier columns are made of C30 concrete with a density of 2500kg / m 3 , Young's modulus is 30GPa, Poisson's ratio is 0.2. The cap and pile foundation are made of C25 concrete with a density of 2500kg / m 3 , Young's modulus is 28GPa, Poisson's ratio is 0.2. Asphalt concrete is used for bridge deck paving, and its density is 2300kg / m 3 The pier column steel bars are made of HRB335 steel bars with a density of 7850kg / m3 , Young's modulus is 200GPa, and yield strength is 280MPa. Fixed constraints are used at the bottom of the abutment, rigidity simulation in elastic connection is used between the main beam and the pier column of the consolidation pier, elastic connection is used to simulate the rubber bearing at the double bearing at the bearing pier, and the elastic connection stiffness is input into the rubber bearing stiffness. The model has a total of 106 nodes and 94 units, all of which are beam units. The main beam is 1m high, the top width is 11.75m, and the bottom width is 6.75m. The heights of piers 40, 41, and 42 are 7.92m, 7.58m, and 7.25m, respectively, and the cross section adopts a circular section with a diameter of 1.2m. Double bearings are arranged at the bottom of the beam of the double-column bearing pier, and the bearing spacing is 5m. The main calculated loads include structural gravity, live load, concrete shrinkage creep, temperature effect, and foundation settlement. The structural calculation takes into account the influence of the construction method, and the construction stage model is established according to the construction method shown in the completion drawing.

[0086] Preferably, T115 is selected as the representative model of vehicles with 30 to 40 tons, and T159 is selected as the representative model of vehicles with 40 to 50 tons. Before the structure reaches the characteristic state (support is empty), it can be assumed that the structure has not undergone obvious plastic deformation. The structural response of the bridge caused by the vehicle load can be calculated by applying the superposition principle. Therefore, only a single vehicle load is input for each calculation. By comparing the structural responses of each position under different working conditions, a more intuitive understanding of the most unfavorable cross-sectional position of the bridge can be obtained.

[0087] The following parameters are used as control variables to analyze the most unfavorable cross-sectional position of the structure: (1) Vehicle weight: 30t, 35t, 40t (T115), 45t, and 50t (T159) are selected as the total weight of the input vehicle load, and the vehicle wheelbase and axle weight ratio in Table 1 and Table 2 are used as the specific parameters of the input vehicle load. (2) Vehicle spatial position: The direction along the bridge axis is defined as the Y-axis direction, and the transverse direction is defined as the X-axis direction. The vehicle loading position is changed along the transverse direction of the pier top section of each single-column pier. The transverse loading position is calculated as the distance between the vehicle axis and the bridge centerline, as follows: Figure 7 shown.

[0088] Since the monitoring of single-column pier bridges is mainly aimed at bridge overturning, it is only necessary to compare the variation of the strain in the transverse direction of the pier. It is easy to know that when a single vehicle travels along the Y direction of the bridge, when the vehicle reaches a certain pier, it will cause the maximum vehicle-induced strain in the transverse direction of the pier. In order to better describe the influence of vehicle load and its spatial position on the strain of each cross-section of the structure, the strain measuring points at the top and bottom of the transverse pier of the bridge are numbered. Considering that the bridge is a one-way two-lane bridge and the vehicle load is only on one side, in the measuring point numbering, the measuring point on the side loaded with vehicle load is named as a small number, and the other side is named as a large number. The specific numbering format is as follows: Figure 8 shown.

[0089] Since the bridge is a symmetrical structure along the longitudinal direction of the bridge, only the eccentric load conditions of the vehicle at the top section of the 40# and 41# piers are considered. The strain changes at the measuring points of each pier column caused by the vehicle load in the X direction eccentric distance of 0.2m to 4m are calculated at intervals of 0.2m. A total of 160 groups of loading conditions are included, and the loading details are shown in Table 3.

[0090] Fig. 9 This is the relationship between the tensile strain and eccentricity of the measuring point in the tension zone when the 41# pier is loaded. It can be seen from the figure that when the eccentricity is less than 0.6 meters, the P7 measuring point at the top of the non-loaded pier is the main tensile section. When the eccentricity is 0.6 to 4 meters, the P9 measuring point at the top of the loaded pier is the main tensile section. The loading mass will not affect the cross-sectional position of the main tensile section, but will only affect the peak value of the tensile strain of the tensile section. Fig.10 The relationship between the tensile strain and eccentricity of the measuring point in the tension zone when the 40# pier is loaded is shown in Figure 2. It can be seen that in addition to the critical eccentricity of the most unfavorable section transformation caused by vehicle load becoming 0.8m, Fig. 9 The conclusions are basically the same, that is, when the eccentricity distance is less than the critical one, the most unfavorable section is at the top of the non-loaded pier (P9 measuring point), and when the eccentricity distance is greater than the critical one, the most unfavorable section is at the top of the loaded pier (P7 measuring point). Fig. 9 and Fig.10 It can be seen that the tensile strain at the bottom of the bridge pier caused by vehicle load is extremely small, or even no tensile strain occurs.

[0091] Fig.11 Figure 2 is the relationship between the compressive strain and eccentricity of the measuring point in the compression zone when the 41# pier is loaded. It can be seen from the figure that the maximum compressive strain always appears at the P3 measuring point, that is, the top of the loaded pier in the direction of vehicle load loading. The top of the non-loaded pier and the bottom of the loaded pier also show obvious compressive strain changes. The loading mass has little effect on the strain distribution of each compressed section. Fig.12 The same conclusion can be drawn. Fig.11 and Fig.12 It can be seen that the vehicle load will only cause a significant change in the compressive strain at the bottom of the loaded pier, and will hardly affect the compressive strain at the bottom of the adjacent pier.

[0092] In summary, the loading mass will not cause the position change of the maximum tension and compression sections of the bridge, and the maximum compressive stress section of the structure is always at the top of the loaded pier. The maximum tensile stress section of the structure is related to the eccentric distance, which is manifested as when the eccentricity is small, the top of the non-loaded pier is the main tensile section, and when the eccentricity is greater than a certain threshold, the top of the loaded pier is the main tensile section. The tensile strain at the bottom of the pier caused by the vehicle load is extremely small, but it will significantly increase the compressive strain at the bottom of the loaded pier. It can be seen that in order to ensure that the structural health monitoring system can grasp the most unfavorable position of the structure in real time, the top section of the single-column pier should be selected as the main monitoring section. However, since the single-column pier bridge is a large eccentric structure, obvious deformation will occur when the structure is damaged, and the cracking of the concrete makes the stress and strain present a complex nonlinear relationship. Therefore, in order to meet the needs of the structural health monitoring system to monitor the structural properties during operation for a long time, the pier bottom section dominated by compressive stress should also be selected. It should be noted that Figures 9 to 12 The calculated tensile and compressive strains are the stress changes of the structure under the action of vehicle loads alone.

[0093] Table 3 Loading condition details

[0094]

[0095] Preferably, a monitoring system is established on the bridge and an alarm threshold is set according to the finite element analysis results to realize the monitoring of bridge operation;

[0096] In this embodiment, considering the limitations of the on-site environment of the bridge site and the requirements for the economic efficiency of system construction, fiber grating strain sensors are only arranged in the transverse direction of the bridge bottom to detect the changes in the pressure strain at the bottom of the pier. In addition, there are multiple high-definition cameras with embedded machine vision recognition algorithms on the road side of the bridge, which can obtain the spatial position of passing vehicles in real time, and combined with the ETC gantry monitoring system, it is used to obtain the refined spatial position of vehicle loads. The monitoring points of the bridge are arranged as follows: Fig.13 shown.

[0097] Compared with the vehicle-induced response monitoring needs, the acquisition frequency of the structural health monitoring system is extremely high, which can capture the spatial distribution position of vehicle loads and the structural response in each instantaneous state. The loading conditions are divided according to the number of heavy vehicles passing on the bridge when the center of gravity of the heavy vehicle is just at the cross-section of the top beam of the load-bearing pier, namely single-vehicle condition, double-vehicle condition, and multi-vehicle condition. Using the vehicle load monitoring data of the bridge, a total of 24,985 sets of vehicle traffic conditions were obtained, of which there were 24,645 single-vehicle conditions with only one heavy vehicle driving on the bridge, 340 conditions with two heavy vehicles driving on the bridge at the same time, and no situation where more than two heavy vehicles were driving on the bridge at the same time was monitored. This should be due to the short span of the bridge and the limitations of vehicle distance, vehicle speed and lateral position when vehicles pass, so the number of heavy vehicles on the bridge at the same time in the instantaneous state is relatively small.

[0098] The high-frequency strain change data collected by the structural health monitoring system of the bridge site are used to extract the vehicle-induced response components of the pier bottom compressive strain when the single-vehicle condition occurs, such as Fig.14 As shown in the figure, when the vehicle reaches the load-bearing pier, an obvious local peak will appear at the bottom of the load-bearing pier. By obtaining the specific time of the single-vehicle working condition, the peak value of the vehicle-induced strain at the bottom of the pier under the single-vehicle working condition can be searched out respectively. Due to the limitation of the bridge traffic lane, the maximum eccentricity of the normal vehicle passing through the bridge during the operation period is about 4m. Therefore, the simulation results of the compressive strain in the eccentric load direction of the vehicle at the bottom of the pier are respectively applied when the vehicle eccentricity is 1m, 2m, 3m, and 4m. According to the results of 24645 sets of measured working conditions, the extreme value of the vehicle-induced compressive strain under the maximum eccentricity of the finite element simulation is between the 90% and 95% percentile values ​​of the measured strain. Although the finite element calculation results are generally close to the distribution position of the measured data set, there are still many discrete points. This is because the vehicle weight measurement and vehicle identification errors in the vehicle load monitoring system are amplified under the background of highly redundantly collected large-base monitoring data, and the low-probability vehicle load inaccurate working conditions are also frequently extracted. In the application context of the above-mentioned structural health monitoring system, it can be found that if only a small number of measured working conditions are used to determine the state of the structure during operation, data discreteness may occur. At the same time, using the probability statistical values ​​of a large number of working conditions as the reference conditions for the alarm value may also cause the monitoring system to alarm frequently, reducing the efficiency and credibility of the alarm.

[0099] Preferably, unlike the bridge design concept based on empirical anti-overturning safety factor evaluation, the alarm of the bridge structure health monitoring system is a strategy for potential risk identification and rapid emergency response. That is, when the system issues an alarm, although the structure has a potential overturning risk, it still has a certain safety redundancy, providing sufficient time for vehicle restriction decisions. Therefore, when setting the system alarm threshold, not only the ultimate damage state of the bridge structure should be considered, but also the unstable state that causes the transformation of the structural force system should be taken into account. By setting the threshold before the unstable state, the traffic flow can be restricted immediately when the system issues an alarm, avoiding the risk factors that may trigger the overturning of the bridge from the source, ensuring early warning and timely intervention of potential dangers of the bridge to the greatest extent, better ensuring the safe operation of the bridge and extending its service life.

[0100] In this embodiment, the above finite element model is applied to adjust the lane load partial coefficient under the most unfavorable arrangement of lane loads specified in the specification to determine the two structural states of structural support emptying and structural force reaching the structural bearing capacity. For the inner lane, the 1.4 times partial coefficient specified in the specification is applied; for the outer lane, the partial coefficient values ​​corresponding to the support emptying and reaching the ultimate bearing capacity of the pier column are observed by gradually increasing the partial coefficient, as shown in Table 4. It can be seen that when the vehicle load partial coefficient reaches 1.6, the structure is about to reach the support emptying state, and when the vehicle load partial coefficient reaches 3, the axial force of the pier column is about to exceed the ultimate bearing capacity of the structure. At this time, ξ is 0.51, which is a large eccentric structural damage state. The vehicle eccentric load strain value at the bottom of the pier under the corresponding state is extracted as the alarm limit of the vehicle-induced response.

[0101] Table 4 Structural health monitoring alarm status selection

[0102]

[0103] Before embedding the vehicle-induced strain of the structural characteristic state simulated by finite element simulation into the structural health monitoring system, it is necessary to analyze the characteristics of the on-site monitoring data first. The monitoring system is usually installed after the bridge is built, and the initial state of the monitoring data is set to the structural response when the equipment is installed. The monitoring data obtained is the relative structural response, and the strain monitoring results include not only the influence of vehicle loads, but also different environmental factors such as temperature loads, wind loads, and even environmental noise. When setting the alarm threshold of the monitoring system, it is necessary to consider the impact of the above factors to ensure the accuracy of the monitoring system. After calculation, the vehicle eccentric load strain values ​​of the pier bottom extracted from the two structural states of structural support de-stressing and structural stress reaching the structural bearing capacity are shown in Table 5. Substituting it into the measured strain to form dynamic yellow alarm and red alarm dynamic curves, as shown in Fig.15 As shown in the figure, when the yellow alarm sounds, it indicates that a traffic condition has occurred that causes the bearing on one side of the bridge to become emptied. At this time, the on-site situation when the bearing became emptied should be traced back, and the driving behavior of vehicles on the bridge should be adjusted by means of vehicle weight and lane restrictions to prevent overturning incidents.

[0104] Table 5 Alarm limits for vehicle-induced responses

[0105]

[0106] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the technical solution of the present invention, and are not intended to limit the specific implementation methods of the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the claims of the present invention shall be included in the protection scope of the claims of the present invention.

Claims

1. A single-column pier bridge monitoring method suitable for long-term monitoring projects is used to monitor whether the bridge may overturn and alarm the possible risks, specifically including the following steps: S1. Measured traffic flow characteristics of bridges; S2. Design vehicle load model based on measured traffic flow characteristics; S3. Establish a finite element model and perform finite element analysis based on bridge parameters and vehicle load loading model; S4. Establish a monitoring system on the bridge and set alarm thresholds based on the finite element analysis results to achieve monitoring during bridge operation; where: When the bridge structure support is reached, the alarm threshold is reached to take traffic flow control measures; When the bridge structure stress reaches the structural bearing capacity alarm threshold, traffic prohibition control measures are taken; It is characterized in that The method for setting the alarm threshold in step S4 is as follows: The dynamic threshold alarm is formed by superimposing the quasi-static strain change trend of the structure by the vehicle-induced strain under the unstable state. The unstable state includes two types: the bridge structure support is empty and the structure stress reaches the structural bearing capacity; The bridge parameters in step S3 are specifically as follows: the bridge is a continuous box girder bridge with double-column support piers at both ends, a number of single-column consolidation piers distributed between the two double-column support piers, a fixed constraint is adopted at the bottom of the pedestal, a rigid simulation in an elastic connection is adopted between the main beam and the pier column of the consolidation pier, an elastic connection is adopted for the double bearings at the support piers to simulate rubber bearings, the unit types are all beam units, and the calculated loads include structural gravity, live load, concrete shrinkage creep, temperature effects and foundation settlement.

2. A single-pillar bridge monitoring method suitable for long-term monitoring projects according to claim 1, characterized in that: The two structural states of structural support being empty and structural force reaching the structural bearing capacity are determined by adjusting the lane load partial coefficient through finite element analysis under the most unfavorable arrangement of lane loads specified in the specification. The specific method is: For the inner lane, the γ1 times partial factor specified in the specification is applied; γ1 = 1.4; For the outer lane, the partial coefficient value corresponding to the bearing being emptied and the structural force reaching the structural bearing capacity is observed by gradually increasing the partial coefficient; among them, 1.4≤γ2≤3.

4.

3. A single-pillar bridge monitoring method suitable for long-term monitoring projects according to claim 1, characterized in that: The quasi-static strain of the structure includes the structural response caused by temperature load, wind load and environmental noise.

4. A method for monitoring a single-pillar bridge suitable for long-term monitoring projects according to any one of claims 1 to 3, characterized in that: The step S1 is specifically as follows: Obtain traffic monitoring data on the bridge, ignore vehicles with a weight below t, form statistical data, and select the tonnage and number of axles of vehicles with a proportion greater than δ from the statistical data as traffic flow features, where t = 30 tons and δ = 20%.

5. A method for monitoring a single-pillar bridge suitable for long-term monitoring projects according to claim 4, characterized in that: The vehicle load model in step S2 includes a 4-axle Type 4 truck and a 6-axle Type 6 truck, with mass distribution ranges of 30-40 tons and 40-50 tons respectively, wherein: For the four-axle trucks, the ratio of axle weight to vehicle weight is: 1 axle 0.1568, 2 axles 0.2412, 3 axles 0.2788 and 4 axles 0.3232; For the six-axle type six trucks, the ratio of axle weight to vehicle weight is: 1 axle 0.1044, 2 axles 0.1474, 3 axles 0.2070, 4 axles 0.1873, 5 axles 0.1710 and 6 axles 0.1828.

6. A method for monitoring a single-pillar bridge suitable for long-term monitoring projects according to claim 5, characterized in that: The finite element analysis in step S3 is specifically: It is assumed that the bridge does not undergo plastic deformation before the bridge structure supports are emptied. The bridge structure response caused by vehicle loads is calculated using the superposition principle. The vehicle weight and vehicle spatial position are used as control variables to compare the variation of the transverse bridge pier strain and analyze the most unfavorable cross-sectional position of the structure. The vehicle spatial position is specifically the vehicle loading position changed in the transverse direction of the bridge along the cross section of the pier top of each single-column pier, and the transverse loading position of the bridge is calculated by the distance between the vehicle axis and the central axis of the bridge; The most unfavorable cross-sectional position is specifically: For real-time monitoring, the top section of the single-column pier is selected as the main monitoring section; For long-term monitoring, the pier bottom section dominated by compressive stress is selected.

7. A method for monitoring a single-pillar bridge suitable for long-term monitoring projects according to claim 6, characterized in that: The step S3 also includes a verification process of finite element analysis and measured data, and the specific process is as follows: S31 collects the number of vehicles passing on the bridge when the center of gravity of the vehicle is just at the cross section of the top beam of the load-bearing pier and divides the loading condition; S32 analyzes the working conditions of vehicles passing on the bridge; S33 extracts the vehicle-induced response components of the pier bottom compressive strain at the time when the main working conditions occur, and forms the measured strain distribution; S34 respectively applies the finite element simulation results of the compressive strain in the eccentric load direction of the pier bottom when the vehicle eccentricity increases uniformly; S35 determines whether the extreme value of the vehicle-induced compressive strain under the maximum eccentricity of the finite element simulation is within the 90% to 95% percentile value of the measured strain.

8. A monitoring system for implementing the monitoring method of a single-pillar pier bridge applicable to a long-term monitoring project as claimed in any one of claims 1 to 7, characterized in that: include: Multiple strain sensors for monitoring the compressive strain at the bottom of bridge piers. Machine vision recognition technology used to identify vehicle types and obtain the spatial position of passing vehicles. Electronic toll collection gantry for capturing vehicle loads A main control system that monitors the possibility of a bridge capsizing based on information collected by strain sensors, machine vision recognition technology and electronic toll collection gantries, combined with set alarm thresholds, and issues alarms for possible risks.

Images