Cross-water-area bridge pier pile denudation and undermining synchronous monitoring method and system

Abstract

The invention discloses a water-area-crossing bridge pier pile denudation and undermining synchronous monitoring method. The method comprises the following steps that S1, a sensing heating system pre-buried in bridge pier pile concrete is arranged; s2, using a sensing heating system to perform heating-cooling treatment on each monitoring point at the same time to obtain a heating-cooling time history curve of each monitoring point, calculating a dimensionless parameter zeta, and extracting a linear slope of a zeta-t curve of a cooling section as a thermal response characteristic parameter xi; s3, based on the thermal response characteristic parameter xi of each denudation measuring point, denudation monitoring is carried out; and S4, performing synchronous undermining monitoring based on the thermal response characteristic parameter xi of each undermining measuring point. According to the method, the occurrence positions of denudation and undermining can be effectively and accurately monitored in real time at the same time, and compared with the problems that an underwater robot adopted in the prior art is difficult in positioning maneuvering and high in interference degree, the method is flexible, accurate, convenient to apply, practical, effective and accurate, and has remarkable progress.

Description

Technical Field

[0001] The present invention relates to the technical field of erosion and hollowing, and in particular to a method for synchronously monitoring erosion and hollowing of bridge piers across water areas. Background Art

[0002] As crucial transportation infrastructure, cross-water bridges' core value lies in their "time-space compression effect." This means they break down geographical barriers through physical connections, significantly shorten travel times, and reshape regional economic geography. Therefore, scientifically planned cross-water bridges are strategic infrastructure for promoting regional development.

[0003] Due to the structural limitations of long-span bridges, bridges across waterways often require underwater piers and foundation structures. Water scours directly onto the piers and pile foundations, especially during flood season, when rivers experience increased flow rates. This can lead to surface erosion and pile hollowing.

[0004] Surface erosion leads to a chain reaction of thinning of the protective layer, chloride ion penetration, destruction of the rebar's passive film, rust expansion, new cracks, and accelerated erosion, creating an irreversible vicious cycle. Accurately and promptly monitoring the occurrence and progression of underwater concrete surface erosion is a prerequisite for assessing the operational status of engineering structures and developing repair and reinforcement plans. This is crucial for ensuring the safety of water-related projects and preventing accidents.

[0005] Pile foundations are the very foundation of a bridge. Hollowing out can reduce the bearing capacity of pile foundations, causing piers to tilt, sink, or even collapse. This condition is difficult to detect initially but gradually worsens, potentially leading to catastrophic accidents in the event of a sudden flood or heavy traffic. Repairing or rebuilding bridges is expensive, and traffic disruptions impact the regional economy. In the event of an accident, casualties and property losses are even more difficult to estimate. In summary, research on monitoring technology for surface erosion and pile foundation erosion in underwater sections of cross-water bridges has important engineering application value and practical guidance.

[0006] Currently, bridge scour risk management relies primarily on manual inspections. This method is unreliable and time-consuming, as underwater riverbed erosion is difficult to visually observe. Since the 1980s, a series of advanced underwater non-destructive detection equipment has emerged, including underwater video surveillance systems, ultrasonic instruments, and imaging sonar systems. However, most of these inspection technologies still require divers to conduct underwater operations. The complex and ever-changing underwater environment poses a significant threat to divers' safety. Furthermore, divers have limited operating time, making it difficult to conduct long-term, large-scale inspections. Furthermore, divers' diving depths are generally limited to 50 meters, making deep-water inspections difficult. With the development of artificial intelligence and automation technologies, various underwater robots are gradually replacing divers in underwater operations. Based on machine vision principles, methods using enclosed underwater optical cameras or sonar systems mounted on underwater robots to photograph or scan underwater structures and then analyze the images using deep learning algorithms to identify concrete defects have been widely studied and applied in underwater structures. However, positioning and maneuvering underwater robots in space remain challenging, and they are susceptible to interference from the complex underwater environment, increasing both time and cost. In addition, this type of detection technology has poor timeliness and it is easy to miss the best time to repair the disease. Summary of the Invention

[0007] In order to solve the above problems, the purpose of the present invention is to provide a method and system for synchronous monitoring of erosion and hollowing of bridge piers across water areas. This synchronous monitoring method can effectively, in real time and accurately monitor the occurrence locations of both erosion and hollowing. Compared with the problems of difficult positioning and maneuverability of underwater robots used in the existing technology and the high degree of interference, this method is flexible, accurate, easy to use, and practical, effective and precise, which is a significant improvement.

[0008] The present invention is achieved through the following technical solutions:

[0009] A method for synchronously monitoring erosion and hollowing of bridge piers across water areas comprises the following steps:

[0010] S1. Pre-embed at least one set of monitoring tubes vertically in the concrete of the pier / pile foundation, and arrange a monitoring line within each set of monitoring tubes that can slide along the axial direction of the monitoring tube. The monitoring line includes multiple sensor heating units connected in series with equal axial spacing. The monitoring line can be externally heated and real-time temperature data can be collected. Each sensor heating unit corresponds to a concrete monitoring point. Based on the location of the riverbed and the depth of the water area, monitoring points located in the water area are defined as erosion monitoring points, and monitoring points located near the riverbed surface are defined as hollowing monitoring points. Here, the area near the riverbed surface is defined as the sediment-covered area within 20 cm above the riverbed surface and below the riverbed.

[0011] S2. Perform heating-cooling processing on each monitoring point simultaneously to obtain the heating-cooling time history curve of each monitoring point and define the dimensionless characteristic parameter ζ. Where T(t) is the temperature of the heat source at any time, T(t) = T θ +(T0-T θ ).e -kt ; t is time; T θ is the ambient temperature, T0 is the initial temperature of the heat source, and k is the cooling constant;

[0012] Calculate the dimensionless characteristic parameter ζ, perform linear fitting on the linear segment of the time-history curve of the cooling period ζ of each monitoring point, and the slope of the linear segment is the thermal response characteristic parameter ξ of each monitoring point;

[0013] S3. Erosion monitoring: Compare the thermal response characteristic parameter ξ values ​​of each erosion monitoring point. If the thermal response characteristic parameter ξ of a certain erosion monitoring point deviates significantly from the adjacent point, it is marked as an abnormal measuring point. Then extract the thermal response characteristic parameter ξ value of the adjacent normal measuring point of the abnormal measuring point and the concrete cover thickness t c , substitute it into the pre-calibrated mapping relationship t c =f(ξ,q e ), the concrete cover thickness t of the abnormal measuring point is obtained by inversion c Here, adjacent points refer to adjacent erosion monitoring points, such as two adjacent erosion monitoring points above and below;

[0014] S4, synchronous hollowing monitoring: Based on the thermal response characteristic parameter ξ of each monitoring point in S2, the thermal response characteristic parameter ξ of each hollowing monitoring point is obtained, and the S-type ξ-

[0015] Relative elevation function to calculate the hollowing position:

[0016]

[0017] In the formula: y0 is assumed to be the elevation difference between the riverbed surface and the selected measuring point, that is, the riverbed surface elevation; A1, A2, p and y0 are unknown parameters; y0 is the coordinate corresponding to the inflection point of the curve; and y is the position of the hollowing monitoring point.

[0018] The thermal response characteristic parameter ξ is calculated by the following steps: a linear fit is performed on the linear segment of the cooling section ζ-t curve, and the absolute value of the slope is the thermal response characteristic parameter ξ; a dimensionless characteristic parameter ζ is defined as -ktlg(e)=ξt, where e is the base of the natural logarithm.

[0019] Mapping relationship t c =f(ξ,q e ) is obtained through three-layer BP neural network training. The training samples contain different protective layer thicknesses t c , different equivalent convection flow q eThe specific inversion method in S3 is: the ξ value of the abnormal measuring point adjacent to the normal measuring point and the thickness of the concrete cover t c , substitute the pre-calibrated mapping relationship t c =f(ξ,q e ), and obtain the equivalent flow rate q at the normal measuring point e ; Assume that the equivalent flow rate q at the abnormal measuring point e The same as the normal measuring points nearby, the ξ and q of the abnormal measuring point are e Bring in the pre-calibrated mapping relationship t c =f(ξ,q e ), and the concrete cover thickness at the abnormal measuring point is obtained.

[0020] The approximate location of the hollowing can be preliminarily determined in S4: based on the thermal response characteristic parameter ξ of each monitoring point obtained in S2, the thermal response characteristic parameter ξ of each hollowing monitoring point is first obtained, and then based on the up and down ξ mutation characteristics of the riverbed surface, the inflection point position of the S-type ξ-relative elevation function mutation is determined, and the approximate location of the abnormal hollowing can be preliminarily determined.

[0021] The monitoring line is then reconstructed at a distance equal to or less than half the distance between the sensing and heating units. The thermal response characteristic parameter ξ of the reconstructed hollowing monitoring points is then remeasured to accurately locate the hollowing location of the riverbed section. Here, "less than" can be 1 / 3, 1 / 4, and so on, of the distance between the sensing and heating units.

[0022] A synchronous monitoring system for erosion and hollowing of bridge piers across water areas includes a sensing and heating system, an erosion monitoring unit, and a hollowing monitoring unit. The sensing and heating system includes at least one group of monitoring tubes pre-buried in the bridge piers, and a monitoring line that can slide axially along the monitoring tubes is arranged in each group of monitoring tubes. The monitoring line includes a plurality of sensing and heating units connected in series with equal axial spacing. The monitoring line is electrically connected to a power supply, a fiber optic Bragg grating demodulator, and a server arranged outside the bridge piers, and can perform heating control and real-time temperature data acquisition externally. The erosion monitoring unit is used to receive a cooling time curve of each erosion monitoring point obtained from the server, calculate a thermal response characteristic parameter ξ of each erosion monitoring point, determine the existence of an abnormal value of the thermal response characteristic parameter ξ, and inversely calculate the thickness of the concrete protective layer, and output the erosion monitoring result. The hollowing monitoring unit is used to receive a cooling time curve of each erosion monitoring point obtained from the server, calculate the thermal response characteristic parameter ξ and the hollowing monitoring position of each hollowing monitoring point under different flow rates, and output the hollowing monitoring position result.

[0023] The measuring tube is also half-covered with a thermal insulation layer to enhance the sensitivity of the thermal response to environmental changes.

[0024] It includes an erosion early warning unit, which receives the results of the erosion monitoring unit and broadcasts the erosion monitoring results as an early warning.

[0025] It includes a hollowing-out early warning unit, which receives the results of the hollowing-out monitoring unit and issues an early warning report on the hollowing-out monitoring results.

[0026] The advantages of the present invention are: (1) fusion of homogeneous and heterogeneous data

[0027] This system utilizes a collaborative architecture combining an integrated sensor heating unit and an intelligent measurement host to simultaneously identify erosion depth and pile hollowing damage through thermal-mechanical coupling. This solution effectively avoids the phase shift issue associated with multi-device data fusion, improves data analysis and processing efficiency, and reduces system complexity and maintenance costs.

[0028] (2) Measurement point space reconstruction

[0029] The innovative use of a sliding contact interface enables controlled displacement of the sensor heating unit. By optimizing the measurement path, dynamic optimization of spatial sampling density can be achieved within a single deployment, increasing measurement point coverage and reducing the risk of missed measurements.

[0030] (3) Full lifecycle maintainable design

[0031] The quick-install interface design enables quick plug-in and replacement of fault sensing circuits. The system function recovery rate can reach 100%, meeting the reliability requirements of long-term monitoring of the project.

[0032] The monitoring circuit in the present invention is freely movable within the monitoring pipe and can be replaced at any time. For example, it can be connected to the integrated circuit via a sliding contact interface to achieve real-time movement. The sensing and heating unit can refer to the sensing and heating unit described in ZL2022211283201, a crack monitoring system for water-related concrete projects.

[0033] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0034] In terms of surface erosion monitoring, the present invention utilizes the heat transfer characteristics of a point heat source to establish a mapping relationship between the thickness of the concrete protective layer and the thermal response characteristic parameters and the equivalent flow rate. Through multi-point thermal excitation and data analysis, the erosion of the concrete surface can be accurately identified, and the error between the predicted value and the actual value in the experiment is small, which proves the feasibility and reliability of the method. In addition, hollowing monitoring can be carried out simultaneously through clever settings. In pile foundation hollowing monitoring, an S-type function is constructed based on the heat transfer model of the heat source in the pile body-water-riverbed multiphase medium to determine the position of the riverbed surface, and then calculate the pile foundation hollowing depth. Similarly, the positioning accuracy is high under different scouring flow rates and fitting schemes, which provides an effective means for pile foundation hollowing monitoring and realizes accurate monitoring of the surface erosion condition and hollowing depth of concrete bridge piers. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0036] Figure 1 The heat transfer pattern diagram of a point heat source in concrete;

[0037] Figure 2 Schematic diagram of the heat transfer model of a point heat source in the pile-water-riverbed multiphase medium;

[0038] Figure 3 This is a schematic diagram of the monitoring system scheme;

[0039] Figure 4 This is a schematic diagram of the hollowing monitoring process;

[0040] Figure 5 Schematic diagram of the specimen;

[0041] Figure 6 This is a diagram of the surface erosion monitoring test device;

[0042] Figure 7 When the flow rate is 10000mL.min -1 ζ time course curve of cooling stage;

[0043] Figure 8 ξ follows t c And the changing pattern of q;

[0044] Figure 9 Comparison chart between model prediction value and target value;

[0045] Figure 10 This is the local concave specimen diagram;

[0046] Figure 11 This is a diagram of the hollowing monitoring test device;

[0047] Figure 12 When the flow rate is 6000mL.min -1 ζ time history curve of specimen II during the cooling stage;

[0048] Figure 13(a) shows the variation pattern of ξ near the riverbed surface of specimen I;

[0049] Figure 13(b) shows the variation pattern of ξ near the riverbed surface of specimen II;

[0050] Figure 13(c) shows the variation pattern of ξ near the riverbed surface of specimen III;

[0051] In the figure, 1-monitoring tube, 2-sensing heating unit, 3-monitoring line, 4-server, 5-fiber Bragg grating demodulator, 6-power supply, 7-insulation layer, 8-bridge pier, 9-water pump. DETAILED DESCRIPTION

[0052] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with examples and drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0053] Example 1

[0054] 1 Principle of surface erosion monitoring

[0055] like Figure 1 As shown in the figure, a point heat source is embedded in an underwater concrete structure. Before the point heat source is applied, the concrete in that area is assumed to be at the same temperature as the surrounding environment. At this point, there is no temperature difference, and therefore no heat transfer. When the point heat source is applied, its temperature rises. Due to the temperature difference, the released heat is first transferred to the surrounding medium through heat conduction. When the heat reaches the concrete surface, a temperature difference is created between the concrete surface and the surrounding fluid, and the heat is transferred through convection.

[0056] When heat is transferred inside an object by heat conduction, the resistance encountered is the thermal resistance. When the heat flow passes through a flat plate with a constant cross-sectional area, the thermal resistance is:

[0057]

[0058] Where: L is the thickness of the plate, A is the cross-sectional area of ​​the plate perpendicular to the direction of heat flow, and k is the thermal conductivity coefficient of the plate material.

[0059] Thickness of the covering layer of the point heat source t c The smaller the value, the smaller the thermal resistance of the heat released by the point heat source to the outer surface of the concrete, and the less time it takes for the heat to be transferred to the concrete surface. Since the convection heat transfer efficiency in water is much higher than the heat conduction efficiency in concrete, when the concrete is eroded and the protective layer becomes thinner (i.e. Figure 1 t in c When the heat source is heated to a certain temperature and then cooled, the heat transfer efficiency of the point heat source will increase. c Reducing it will make the cooling rate faster. Therefore, the thickness of the concrete cover can be indirectly obtained through the cooling law of the point heat source in the concrete, thereby monitoring the erosion condition of the concrete surface.

[0060] 2 Principle of Pile Foundation Hollowing Monitoring

[0061] like Figure 2 As shown, there are two point heat sources A and B in the pile body. The heat released by them is first transferred in the pile body by heat conduction. When the heat is transferred to the surface of the pile body, the water around the pile body will transfer the heat by convection heat transfer. Figure 2 In the example, heat source A is located above the riverbed, where the pile surface at the corresponding location is in direct contact with the water and has a large convection coefficient. Heat source B, on the other hand, is located below the riverbed, and the seepage generated by the river scouring in the riverbed produces a significantly weaker convection effect on the pile surface at the corresponding location of heat source B than at heat source A. This is the characteristic of the sudden change in the thermal response eigenvalues ​​above and below the riverbed interface.

[0062] Newton's law of cooling is an empirical law that describes the rate of heat exchange between an object and its surroundings. Its core idea is that the rate of temperature change of an object is proportional to the temperature difference between the object and the surroundings. It can be expressed mathematically as:

[0063]

[0064] Where: T(t) is the temperature of the heat source at any time; t is time; T θ is the ambient temperature; k is the cooling constant, which is related to factors such as the object material, surface properties, and convection conditions. The larger k is, the faster the cooling rate.

[0065] The solution of the differential equation shown in formula (1) is:

[0066] T(t)=T θ +(T0-T θ ).e -kt (3)

[0067] Where: T0 is the initial temperature of the heat source.

[0068] Define the dimensionless parameter ζ:

[0069]

[0070] From formula (3) and formula (4), we can get:

[0071] ζ=-ktlg(e)=ξt (5)

[0072] In the formula: ξ = -klg(e), which is similar to the cooling constant k and is also a coefficient that reflects the speed of cooling.

[0073] When the temperature variation pattern of the heat source during the cooling phase is measured, ξ can be calculated using equations (4) and (5). Taking the riverbed surface as the interface, the absolute value of ξ for the heat source above it is significantly higher than that below it. Based on the mutation characteristics of ξ, the riverbed interface can be determined, thereby identifying the hollowing depth.

[0074] Synchronous monitoring system for erosion and hollowing:

[0075] Concrete surface erosion and pile foundation hollowing monitoring program Figure 3As shown, it includes a sensing and heating system, an erosion monitoring unit and a hollowing monitoring unit. The sensing and heating system includes at least one group of monitoring tubes 1 pre-buried in the bridge pier 8. A monitoring line 3 that can slide axially along the monitoring tube 1 is arranged in each group of monitoring tubes 1. The monitoring line 3 includes a plurality of sensing and heating units 2 connected in series with equal axial spacing. The monitoring line 3 is connected to a power supply 6, a fiber optic Bragg grating demodulator 5 and a server 4 arranged outside the bridge pier 8, and can perform heating control and real-time temperature data acquisition from the outside. The erosion monitoring unit is used to receive the cooling time curve of each erosion monitoring point obtained from the server 4, calculate the thermal response characteristic parameter ξ of each erosion monitoring point, determine the existence of abnormal values ​​of the thermal response characteristic parameter ξ, and inversely calculate the thickness of the concrete protective layer, and output the erosion monitoring result. The hollowing monitoring unit is used to receive the cooling time curve of each erosion monitoring point obtained from the server 4, calculate the thermal response characteristic parameter ξ and the hollowing monitoring position of each hollowing monitoring point, and output the hollowing monitoring position result. This solution requires that when pouring concrete, a monitoring tube-insulation layer composite assembly be pre-buried at a certain thickness from the concrete surface. For reinforced concrete structures, the monitoring tube 1 can be arranged between the longitudinal steel bars. The concrete surface erosion and pile foundation hollowing monitoring system consists of a temperature measurement system and an electric heating system. In order to facilitate the series connection and miniaturization of the two sensing and heating units, the temperature measurement can adopt a fiber optic Bragg grating sensing system, and the point heat source is simulated by a ceramic heating tube. The fiber optic Bragg grating temperature sensor and the ceramic heating tube are integrated into one, which is called the sensing and heating unit 2. The sensing and heating units 2 are connected in series at a certain distance (equal distance) to form a monitoring line 3. The insulation layer 7 arranged on the inside of the monitoring tube 1 forms an insulating barrier, and uses the thermal resistance effect to constrain the inner radial heat flow, so that the heat released by the point heat source is concentrated and transferred outward, thereby increasing the sensitivity of the thermal response special diagnostic parameters to the external environment of the pile body. The monitoring tube 1 is also half-covered with an insulation layer 7.

[0076] The sensing and heating unit 2 consists of a temperature sensor and a heating element bonded together. Alternatively, depending on the structural characteristics of the heating element, such as a hollow heating element, the heating element can be placed over the temperature sensor, with the two being detachable or fixed together. This is a prior art device designed to simultaneously monitor temperature and provide heating.

[0077] Surface erosion identification process

[0078] (1) The concrete cover thickness t is established by calibration test as shown in formula (6) c and thermal response characteristic parameter ξ and equivalent flow rate q e The mapping relationship;

[0079] t c =f(ξ,q e ) (6)

[0080] (2) Perform multi-point thermal excitation on each measuring point of the entire monitoring line 3 to obtain the temperature rise and fall time history curve of each measuring point;

[0081] (3) Calculate the dimensionless characteristic parameter ζ according to formula (4), perform linear fitting on the linear segment of the ζ time history curve during the cooling period, and obtain the thermal response characteristic parameter ξ of each measuring point;

[0082] (4) Determine whether there is an abnormal value at each measuring point. Extract the ξ value of the abnormal measuring point adjacent to the normal measuring point and the thickness of the concrete cover t c , bring them into the pre-calibrated statistical model (Equation (6)) to obtain the equivalent flow rate q at the normal measuring point e ;

[0083] (5) Assume that the equivalent flow rate q at the abnormal measuring point e The same as the normal measuring points nearby, the ξ and q of the abnormal measuring point are e Substitute the pre-calibrated statistical model (Equation (6)) to obtain the thickness of the protective layer at the abnormal measuring point.

[0084] 3.5 Hollow Identification Process

[0085] (1) Figure 4 As shown in (a), the initial positions of the measuring points are known, and the distance between the measuring points is d. Based on the distribution characteristics of the ξ values ​​at each measuring point, it can be predicted that the riverbed surface is located between measuring points B and C.

[0086] (2) In order to further determine the exact position of the riverbed, the monitoring line 3 can be dragged to reconstruct the measuring point space. Assuming that all measuring points are moved upward by d / 2, new measuring points A1, B1, C1 and D1 are obtained, as shown in Figure 4 As shown in (b); Generally speaking, during hollowing monitoring, the distance between hollowing monitoring points is large. In order to improve the accuracy of solving the s-function problem, the monitoring points can be spatially reconstructed by moving the monitoring line 3 up and down, and enough hollowing monitoring points can be constructed to perform hollowing monitoring positioning and calculation.

[0087] (3) Test at the new measuring point. If the ξ value of measuring point C1 changes suddenly, it means that the riverbed surface is between measuring points C1 and C; otherwise, the riverbed surface is between measuring points B and C1.

[0088] (4) Figure 4 The measuring point C in (b) is taken as the zero point of relative elevation (positive upwards). Assuming that the height difference between the riverbed surface and the measuring point C is y0, the S-shaped function shown in formula (7) is constructed:

[0089]

[0090] Where: A1, A2, p and y0 are unknown parameters, and y0 is the coordinate corresponding to the inflection point of the curve.

[0091] (5) Under the constraint condition of d / 2≤y0≤d, the ξ value and relative elevation of the measuring point near the riverbed are substituted into formula (7) for curve fitting, and the relative elevation of the inflection point of the S-shaped curve can be obtained, which is the predicted value of the relative elevation of the riverbed.

[0092] Surface erosion monitoring

[0093] 1 specimen

[0094] The test scheme of using stainless steel pipe as monitoring pipe 1 and covering it with random copolymer polypropylene (PolypropyleneRandom) insulation pipe is selected. The design scheme of concrete specimen is as follows: Figure 5 As shown. The monitoring tube 1 has an outer diameter of 20 mm and a wall thickness of 2 mm, and one end is sealed with glass glue. The inner diameter of the insulation tube is 20 mm and the wall thickness is 15 mm. The concrete mix ratio is 1:0.55:1.20:2.50, with 42.5 grade Portland cement as the cementitious material, well-graded river sand as the fine aggregate, and crushed stone with a particle size of 5 mm to 10 mm as the coarse aggregate. A total of 6 different t c Multi-condition tests were carried out on the specimens.

[0095] .2 Test equipment and procedures

[0096] The test piece is immersed in a water tank. The monitoring line 3 is led out from the monitoring tube 1 and connected to the fiber Bragg grating demodulator 5 and the voltage-stabilized power supply 6. The water pump 9 in the water tank pumps water to flush the surface of the test piece, simulating the flowing water environment of the underwater concrete structure. The water pipe of the water pump 9 is equipped with a valve to adjust the water flow rate to simulate different flow rate environments. The test device is as follows: Figure 6 shown.

[0097] Use the valve to adjust the pump flow rate of water pump 9, adjust the heating voltage of voltage-stabilized power supply 6, set the demodulator sampling frequency, and after the wavelength stabilizes, begin data acquisition. First, conduct a 60-second room temperature test in an unheated state. Then, activate voltage-stabilized power supply 6 to heat the ceramic heating tube in monitoring tube 1. After heating for the predetermined period, shut off voltage-stabilized power supply 6 to allow the tube to cool. Once the temperature stabilizes, the test concludes.

[0098] 3 Test results

[0099] The sensor heating unit 2 was adjusted to the middle of the specimen, and the flow rate q was set to 2000 mL.min in a static water environment. -1 、6000mL.min -1 and 10000mL.min -1 When q is set to 10000mL.min -1 When the temperature is lowered, the measured time course curve of ζ is as follows: Figure 7 It can be seen that when the convection environment of the specimen is consistent, as the thickness of the protective layer decreases, the cooling rate of the point heat source accelerates, and the absolute value of the slope of the linear segment of the ζ time history curve gradually increases.

[0100] The time course curve of the cooling period of 500s to 1200s is taken as a linear segment, and the slope of the linear fitting is taken as the thermal response characteristic parameter ξ. c The changing rules of q are as follows Figure 8 As shown. It can be seen that when the flow rate is constant, the absolute value of ξ changes with t c decreases and increases; when t c When t is constant, the absolute value of ξ increases with the increase of q. c When it is less than 20mm, ξ is proportional to t c The sensitivity of t c The smaller it is, the more sensitive ξ is to q, that is, the more sensitive it is to the convection coefficient of the basin environment.

[0101] Establish a system with ξ and q as input parameters, t c As the output parameter of the three-layer BP neural network, Figure 8 The calibration results shown are used for training the network model, and the t c and ξ, q e The training results are as follows: Figure 9 As shown in the figure, the root mean square error between the predicted value and the target value of the model for the training samples is 0.636mm.

[0102] t c =32mm, a pit with a plane size of 10cm×10cm and a depth of 8mm was chiseled out in the middle of the surface of the test piece. Figure 10 As shown. The sensor heating unit 2 is moved to the three positions A, B, and C in the figure (the distance between the measuring points is 10 cm) to perform heating and cooling tests, and the thermal response characteristic parameters at the three measuring points are obtained. The ξ values ​​of points A and C and the protective layer thickness (32 mm) corresponding to these two measuring points are brought into the above-trained neural network model, and the equivalent flow rates are obtained as 4215 mL.min -1 and 4428 mL.min -1 , taking their average value as the equivalent flow rate at measuring point B. Substituting the ξ value and its equivalent flow rate at measuring point B into the trained neural network model, the predicted value of the protective layer thickness at point B is 22.4 mm. The actual protective layer thickness at this location is 24 mm, with an error of only 1.6 mm.

[0103] Example 2 - Hollowing Monitoring

[0104] 1 specimen

[0105] use Figure 5The specimen design scheme shown in the figure is used to make three specimens (specimen I (t c =19mm), specimen II (t c =25mm) and specimen III (t c =29mm)) carried out multi-condition tests.

[0106] 2 Experimental equipment and process

[0107] The specimen was immersed in a water tank, and the bottom of the water tank was filled with 200mm thick gravel with a particle size of 5mm to 10mm to simulate a riverbed. The monitoring line 3 was led out from the monitoring tube 1 and connected to the fiber optic Bragg grating demodulator 5 and the voltage-stabilized power supply 6 respectively. The water pump 9 in the water tank was used to pump water to flush the riverbed and the surface of the specimen, simulating the erosion environment in which the underwater concrete structure is located. A valve was installed on the water pipe of the water pump 9 to adjust the pumping flow rate to simulate different levels of scouring. The test device is as follows: Figure 11 shown.

[0108] Heating and cooling tests were conducted at five measuring points within a range of 10 cm above and below the riverbed surface, with 5 cm spacing between each point. The measuring points were numbered A, B, C, D, and E from top to bottom, with points A and B located above the riverbed, point C just above the surface, and points D and E below.

[0109] The test steps are as follows: 1) Adjust the heating voltage of the voltage-stabilized power supply 6 to 11V and the sampling frequency of the demodulator to 1Hz; 2) Adjust the measuring point to the desired location and adjust the water flow rate (q) of the water pump 9; 3) After the wavelength stabilizes, conduct a 60-second room temperature test in an unheated state; 4) Start the voltage-stabilized power supply 6 and heat the ceramic heating tube in the monitoring tube 1 for 3 minutes, then turn off the voltage-stabilized power supply 6 and allow it to cool. Once the temperature stabilizes, the test for that measuring point is complete; 5) Repeat steps 2) to 4) until all operating conditions for the specimen are complete.

[0110] 5.3 Test results

[0111] When q is set to 6000 mL.min -1 When , the time history curve of the cooling stage measured by specimen II is as follows Figure 12 As shown in the figure, it can be seen that: with the increase of time, the increase rate of the absolute value of ζ gradually decreases and tends to be stable; the curve in the period of 500s to 1200s is basically linear, and the thermal response characteristic parameter ξ can be obtained by linear fitting the data in this period.

[0112] The ξ values ​​of each measuring point of each specimen measured at different pumping flow rates are shown in Table 1, and their spatial distribution characteristics are shown in Figure 13. It can be seen that ξ has an S-shaped distribution near the riverbed surface and a sudden change occurs at measuring point C. The absolute values ​​of ξ at the measuring points above the riverbed surface (measuring points A and B) are significantly higher than those at the measuring points below the riverbed surface (measuring points D and E). This is because the convective heat transfer coefficient of the specimen surface covered by the riverbed is much lower than that of the exposed water portion. When t c When the scouring flow rate is constant, the mutation amplitude of the measuring point ξ near the riverbed decreases with the increase of the scouring flow rate. This is because the greater the scouring flow rate, the stronger the seepage in the riverbed, and the stronger the convective heat transfer effect is generated below the riverbed. When the scouring flow rate is constant, with the increase of t c As the value of ξ decreases, the mutation amplitude of ξ at the measuring point near the riverbed increases.

[0113] Table 1 ξ values ​​of each measuring point of each specimen measured at different pumping flow rates

[0114]

[0115] For the measuring point on the same pile foundation, when the height difference between the measuring point and the riverbed exceeds the heat diffusion range of the point heat source, the ξ value should be the same due to the same heat transfer environment around the measuring point. This feature is consistent with the distribution law of the S-type function. Based on the above characteristics, a virtual measuring point V is constructed at a position 10 cm above point A. A , construct a virtual measuring point V at a position 10 cm below point E E . V A and V E The ξ values ​​of points A and E are taken as ξ, and these two virtual measuring points are added to the curve fitting sample. The four fitting schemes shown in Table 2 are used to fit Equation (7) to the curve, and the coordinates of the curve inflection points of each fitting scheme are shown in Table 2. If the inflection point is used for riverbed surface positioning, the root mean square errors of the positioning of the three specimens under different scour flow rates and fitting schemes are 1.46 cm, 2.29 cm, and 2.12 cm, respectively. It can be seen that the curve inflection points determined by all schemes are lower than the actual riverbed surface position. This is because as the burial depth increases, the seepage and convective heat transfer effects in the riverbed gradually decay, resulting in the change rate of the ξ value of the measuring point below the riverbed surface being lower than that of the measuring point above the riverbed surface. Therefore, the inflection points of the fitted S-shaped curve all have a downward trend.

[0116] Table 2 Inflection point y0 obtained by different fitting schemes

[0117]

[0118] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for synchronous monitoring of erosion and hollowing of bridge piers across water areas, characterized in that: The following steps are involved: S1. Pre-embed at least one set of monitoring tubes vertically in the concrete of the pier / pile foundation. Within each set of monitoring tubes, arrange a monitoring circuit that can slide axially along the monitoring tube. The monitoring circuit includes multiple axially equidistant and serially connected sensor heating units. The monitoring circuit can be externally heated and allows for real-time temperature data collection. Each sensor heating unit corresponds to a concrete monitoring point. Based on the riverbed location and water depth, monitoring points located in the water area are defined as erosion monitoring points, while monitoring points near the riverbed surface are defined as hollowing monitoring points. S2. Perform heating-cooling processing on each monitoring point simultaneously to obtain the heating-cooling time history curve of each monitoring point and define the dimensionless characteristic parameter ζ. Where T(t) is the temperature of the heat source at any time, T(t) = T θ +(T0-T θ ).e -kt ; t is time; T θ is the ambient temperature, T0 is the initial temperature of the heat source, and k is the cooling constant; Calculate the dimensionless characteristic parameter ζ, perform linear fitting on the linear segment of the time-history curve of the cooling period ζ of each monitoring point, and the slope of the linear segment is the thermal response characteristic parameter ξ of each monitoring point; S3. Erosion monitoring: Compare the thermal response characteristic parameter ξ values ​​of each erosion monitoring point. If the thermal response characteristic parameter ξ of a certain erosion monitoring point deviates significantly from the adjacent point, it is marked as an abnormal measuring point. Then extract the thermal response characteristic parameter ξ value of the adjacent normal measuring point of the abnormal measuring point and the concrete cover thickness t c , substitute it into the pre-calibrated mapping relationship t c =f(ξ,q e ), the concrete cover thickness t of the abnormal measuring point is obtained by inversion c ; S4, synchronous hollowing monitoring: Based on the thermal response characteristic parameter ξ of each monitoring point in S2, the thermal response characteristic parameter ξ of each hollowing monitoring point is obtained, and the S-type ξ- Relative elevation function calculates the hollowing position: In the formula: y0 is assumed to be the elevation difference between the riverbed surface and the selected measuring point, that is, the riverbed surface elevation; A1, A2, p and y0 are unknown parameters; y0 is the coordinate corresponding to the inflection point of the curve; and y is the position of the hollowing monitoring point.

2. The synchronous monitoring method according to claim 1, characterized in that: The thermal response characteristic parameter ξ is calculated by the following steps: linear fitting is performed on the linear segment of the ζ-t curve of the cooling section, and the absolute value of the slope is the thermal response characteristic parameter ξ; The dimensionless characteristic parameter ζ is defined as: -ktlg(e) = ξt, where e is the base of the natural logarithm.

3. The synchronous monitoring method according to claim 1, characterized in that: Mapping relationship t c =f(ξ,q e ) is obtained through three-layer BP neural network training. The training samples contain different protective layer thicknesses t c , different equivalent convection flow q e The value of ξ below.

4. The synchronous monitoring method according to claim 1, characterized in that: The specific inversion method in S3 is: the ξ value of the abnormal measuring point adjacent to the normal measuring point and the thickness of the concrete cover t c , substitute the pre-calibrated mapping relationship t c =f(ξ,q e ), and obtain the equivalent flow rate q at the normal measuring point e ; Assume that the equivalent flow rate q at the abnormal measuring point e The same as the normal measuring points nearby, the ξ and q of the abnormal measuring point are e Bring in the pre-calibrated mapping relationship t c =f(ξ,q e ), and the concrete cover thickness at the abnormal measuring point is obtained.

5. The synchronous monitoring method according to claim 1, characterized in that: The approximate location of the hollowing can be preliminarily determined in S4: based on the thermal response characteristic parameter ξ of each monitoring point obtained in S2, the thermal response characteristic parameter ξ of each hollowing monitoring point is first obtained, and then based on the up and down ξ mutation characteristics of the riverbed surface, the inflection point position of the S-type ξ-relative elevation function mutation is determined, and the approximate location of the abnormal hollowing can be preliminarily determined.

6. The synchronous monitoring method according to claim 5, characterized in that: The measuring points are reconstructed at a distance of 1 / 2 or less of the spacing between the overall slip sensing and heating units of the monitoring line, and the thermal response characteristic parameters ξ of the reconstructed hollowing monitoring points are remeasured to accurately locate the hollowing position of the riverbed section.

7. A synchronous monitoring system for bridge pier erosion and hollowing across waters used in the synchronous monitoring method according to any one of claims 1 to 6, characterized in that: It includes a sensing and heating system, an erosion monitoring unit and a hollowing monitoring unit. The sensing and heating system includes at least one group of monitoring tubes embedded in the bridge piers. A monitoring line that can slide along the axial direction of the monitoring tubes is arranged in each group of monitoring tubes. The monitoring line includes multiple sensing and heating units connected in series with equal axial spacing. The monitoring line is connected to a power supply, a fiber optic Bragg grating demodulator and a server arranged outside the bridge piers. Heating control and real-time temperature data collection can be performed externally. The erosion monitoring unit is used to receive the cooling time curve of each erosion monitoring point obtained from the server, calculate the thermal response characteristic parameter ξ of each erosion monitoring point, determine the existence of abnormal values ​​of the thermal response characteristic parameter ξ, and inversely calculate the thickness of the concrete protective layer, and output the erosion monitoring results. The hollowing monitoring unit is used to receive the cooling time curve of each erosion monitoring point obtained from the server, calculate the thermal response characteristic parameter ξ and the hollowing monitoring position of each hollowing monitoring point, and output the hollowing monitoring position result.

8. The synchronous monitoring system according to claim 7, characterized in that: The monitoring tube is also half-covered with a thermal insulation layer.

9. The synchronous monitoring system according to claim 7, characterized in that: It also includes an erosion early warning unit, which receives the results of the erosion monitoring unit and broadcasts the erosion monitoring results as an early warning.

10. The synchronous monitoring system according to claim 7, characterized in that: It also includes a hollowing-out early warning unit, which receives the results of the hollowing-out monitoring unit and issues an early warning report on the hollowing-out monitoring results.

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