Method and system for monitoring prestress distribution in steel strand body

Abstract

The invention relates to the technical field of building detection, in particular to a method and system for monitoring prestress distribution in a steel strand body. The method for monitoring the internal prestress distribution of the steel strand comprises the following steps of: obtaining wavelengths obtained by real-time monitoring of each detection point of the intelligent steel strand; and calculating the stress value of the intelligent steel strand detection point through the wavelength obtained through real-time monitoring. And comparing the stress value of the intelligent steel strand detection point with the stress distribution prediction stress value, and calculating to obtain a difference value. Whether the absolute value of the difference value is larger than a preset standard difference value or not is judged, if yes, it is judged that the building within a preset radius range of the detection point has an abnormal risk, and an alarm is given; and if not, not giving an alarm. Through the method, real-time monitoring can be performed on each detection point and surrounding building conditions in a targeted manner, so that the building risk can be greatly reduced, the timeliness of problem discovery is improved, timely countermeasures are facilitated, and the countermeasure pertinence is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of building detection, and particularly relates to a method and system for monitoring the in - strand prestress distribution of steel strands. Background Art

[0002] A steel strand is a steel product formed by stranding multiple steel wires and is usually used in prestressed concrete structures. There are various classification methods for steel strands. For example, classified by material, there are galvanized steel strands and stainless steel strands; classified by use, there are prestressed steel strands, steel strands for bridges, and so on. The stress detection of steel strands is a key link to ensure their load - bearing capacity and structural safety during use, especially in scenarios such as prestressed projects and bridge cables.

[0003] Common detection methods for steel strand stress detection include the direct measurement method (hydraulic sensor method), and the indirect measurement methods include the vibration frequency method (dynamic method), the magneto - elastic method (magnetic flux method), and non - destructive testing techniques (such as ultrasonic method and electromagnetic detection method). Among the existing direct detection methods, the force - measuring sensor method is the most widely used, but it cannot perform positioning monitoring on the local area of the building where the steel strand is located, so it is impossible to distinguish each position along the axis of the steel strand, resulting in insufficient monitoring ability. Summary of the Invention

[0004] To solve the above - mentioned technical problems, the present invention provides a method for monitoring the in - strand prestress distribution of steel strands, which is used for prestress detection of intelligent steel strands and for judging and analyzing whether there are errors in their stress distribution, facilitating timely repair. The method for monitoring the in - strand prestress distribution of steel strands may include the following steps: S1. Obtain the wavelength λ i obtained by real - time monitoring of each detection point of the intelligent steel strand, where i is the number of the detection point in the intelligent steel strand.

[0005] S2. Calculate the stress value σ i of the detection point of the intelligent steel strand through the wavelength λ i obtained by real - time monitoring.

[0006] S3. Compare the stress value σ i of the detection point of the intelligent steel strand with the predicted stress value σ T of the stress distribution, and calculate the difference value Δσ.

[0007] S4. Determine whether the absolute value of the difference value Δσ is greater than a preset standard difference value Δσ 标 . If so, it is determined that there is an abnormal risk in the building within a preset radius range of this detection point, and personnel are arranged to detect it for timely adjustment. If not, no alarm is given. When the absolute value of the difference value Δσ is greater than the preset standard difference value Δσ 标, there are two cases. The first case is that the different value Δσ is greater than the preset standard difference value Δσ 标 , and the second case is that Δσ is less than the negative value -Δσ of the preset standard difference value 标 .

[0008] Preferably: The detection points can be set according to a preset fixed spacing.

[0009] Preferably: The designed spacing of the detection points can be , where L0 is the basic spacing, and its value can be preset manually; B is the adjustment base number; j is the building influence factor number, J is the total number of building influence factors, j = 1, 2... J; b j is the building influence factor coefficient numbered j; Preferably: The building influence factors can include the longitudinal distribution factor, the transverse span factor, and the bearing load factor of the building. Therefore, the building influence factor coefficients include the longitudinal distribution factor coefficient, the transverse span factor coefficient, and the bearing load factor coefficient.

[0010] Preferably: The longitudinal distribution factor coefficient b1 = n - 1, where n is the number of building layers within a preset length range.

[0011] Preferably: The statistical method of the transverse span factor coefficient includes: implanting the planning map into a pre-made space coordinate system, and then calculating the obtained transverse span factor coefficient , where (x1, y1, z1) is the starting point of the intelligent steel strand in the space coordinate system, (x2, y2, z2) is the end point of the intelligent steel strand in the space coordinate system, Δx is the increment per unit length of the intelligent steel strand on the x coordinate axis; Δy is the increment per unit length of the intelligent steel strand on the y coordinate axis; Δz is the increment per unit length of the intelligent steel strand on the z coordinate axis; is the cumulative value of the increment per unit length of the entire intelligent steel strand on the x coordinate axis; is the cumulative value of the increment per unit length of the entire intelligent steel strand on the y coordinate axis; is the cumulative value of the increment per unit length of the entire intelligent steel strand on the z coordinate axis.

[0012] Preferably: The unit length can be 1m.

[0013] Preferably: The stress value of the detection point , where i is the number of the detection point in the intelligent steel strand, σ i is the monitored stress value of the detection point numbered i on the intelligent steel strand, with the unit of MPa; λ0 is the wavelength monitored under the stress-free state of the intelligent steel strand, with the unit of nm; λ iλ is the wavelength obtained by real-time monitoring of the detection point with the intelligent strand number i, with the unit of nm; E is the elastic modulus of the intelligent strand, and its specific value can be 195 GPa; χ is the wavelength-strain coefficient of the intelligent strand, and its specific value can be obtained through a calibration test on the intelligent strand, with the value ranging between 0.9 and 1.3, and generally taking 1.1.

[0014] Preferably: the predicted stress value , where F is the internal force of the intelligent strand; A is the cross-sectional area of the intelligent strand; S is the length of the intelligent strand; S0 is the standard detection length; S1 is the length of the intelligent strand from the current detection point to one end, and S2 is the length of the intelligent strand from the current detection point to the other end; ε is the adjustment coefficient.

[0015] Preferably: the difference value Δσ = σ i -σ T .

[0016] Preferably: the preset radius range can be considered that the range within the preset radius is a spherical range, and its range is all building facilities within the spherical range centered on the current detection point with the distance to the nearest detection point as the radius.

[0017] Preferably: the preset radius range can be all building facilities covered by the curved surface formed by connecting the nearest detection points in each direction around the current detection point as the edge surface.

[0018] The present invention also proposes a prestress distribution monitoring system in the strand for detecting the prestress of the intelligent strand and judging and analyzing whether there is an error in its stress distribution for timely repair. The prestress distribution monitoring system in the strand includes: Fiber Bragg grating sensors for detecting the wavelength λ obtained by real-time monitoring of each detection point of the intelligent strand i , where i is the number of the detection point in one intelligent strand.

[0019] Calculation and analysis module for calculating the stress value σ of the detection point of the intelligent strand through the wavelength λ obtained by real-time monitoring i . i

[0020] Judgment and positioning module for comparing the stress value σ of the detection point of the intelligent strand i with the predicted stress value σ of the stress distribution T , calculating the difference value Δσ, and determining whether the absolute value of the difference value Δσ is greater than a preset standard difference value Δσ 标, if so, it is determined that there is an abnormal risk for the buildings within a preset radius of the detection point, and an alarm is given to notify relevant personnel to conduct inspections for timely adjustment. If not, no alarm is given.

[0021] Preferably, the fiber Bragg grating sensor can be a temperature sensor.

[0022] Preferably, the in - strand prestress distribution monitoring system of the steel strand can further include a spatial model construction module for constructing a three - dimensional building model according to the current construction progress. The three - dimensional building model includes information within each building, intelligent steel strands, and the distribution of detection points.

[0023] The alarm display module is used to mark the positions of each detection point in the three - dimensional building model and display the abnormal detection points and the preset radius range in the three - dimensional building model. Generally, the position of the monitoring point in the three - dimensional building model can be flashed in red, and then the preset radius range can be flashed in yellow. By constructing the three - dimensional building model and displaying the abnormal detection points on it, the abnormal points can be clearly located, which is convenient for timely analysis of countermeasures.

[0024] The technical effects and advantages of the present invention: Through this method, the real - time monitoring of each detection point and the surrounding building conditions can be carried out targeted. When stress anomalies occur, they can be discovered in the first time, and then fixed - point inspections can be carried out, which can greatly reduce the building risks, improve the timeliness of problem discovery, facilitate timely countermeasures and improve the pertinence of countermeasures, thereby maximizing the acceleration of the project progress and reducing losses. The stress values of the intelligent steel strand detection points calculated by this method adapt to the stress performance of the intelligent steel strand, and the detection is convenient and fast. The stress values of each detection point of the intelligent steel strand can be read and calculated in real time, so as to conduct a real - time evaluation of the intelligent steel strand. The detection performance is strong and the calculation is accurate, which is convenient for timely understanding the stress conditions of the intelligent steel strand, thus reflecting the real situation of the building and facilitating the monitoring of the building. Especially during the construction period, it is possible to timely understand the impact of construction on the building and facilitate timely adjustment of construction. Brief Description of the Drawings

[0025] Figure 1 It is a flowchart of a method for monitoring the in - strand prestress distribution of a steel strand proposed by the present invention.

[0026] Figure 2 It is a structural block diagram of a system for monitoring the in - strand prestress distribution of a steel strand proposed by the present invention. Detailed Embodiments

[0027] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments of the present invention are given for the purpose of illustration and description, and are not exhaustive or limit the present invention to the disclosed form. Many modifications and variations are obvious to those of ordinary skill in the art. The embodiments are selected and described to better illustrate the principles and practical applications of the present invention, and to enable those of ordinary skill in the art to understand the present invention and thus design various embodiments with various modifications suitable for specific purposes.

[0028] Embodiment 1 Reference Figure 1 , in this embodiment, a method for monitoring the prestress distribution in steel strands is proposed, which is used for prestress detection of intelligent steel strands and judging and analyzing whether there are errors in their stress distribution, so as to facilitate timely repair. The method for monitoring the prestress distribution in steel strands may include the following steps: S1. Obtain the wavelength λ obtained by real-time monitoring of each detection point of the intelligent steel strand i , where i is the number of the detection point in an intelligent steel strand. The intelligent steel strand can be a fiber Bragg grating steel strand, and the fiber Bragg grating can be a CFRP-OFBG intelligent tendon. The fiber Bragg grating steel strand mainly consists of a CFRP-OFBG intelligent tendon, an external stranded tendon, an external monitoring optical fiber, and an end encapsulation. The external monitoring optical fiber therein can be signal-connected to the CFRP-OFBG intelligent tendon through the end encapsulation after construction is completed, and during detection, it can be connected to the detection device through the external monitoring optical fiber. The specific detection process will not be elaborated here. Taking the CFRP-OFBG intelligent tendon as an example, during the process of processing and producing the fiber Bragg grating steel strand from the intelligent steel strand, the CFRP-OFBG intelligent tendon can be fed parallel into a mold cavity that can be pressurized and heated. After passing through the high-temperature and high-pressure mold cavity, the CFRP-OFBG intelligent tendon is coupled to the center of the twisted external stranded tendon. The grating sensor and the FRP cooperate to deform to complete the function of strain measurement, so that the produced FRP material is embedded with a fiber Bragg grating sensor with sensing characteristics. With the help of the anchoring and torsional effects at the end under the stress state of the steel strand, the CFRP-OFBG intelligent tendon is naturally wrapped, achieving the effect of the CFRP-OFBG intelligent tendon and the 6 outer wires of the ordinary steel strand deforming together. The intelligent steel strand has geometric, mechanical, and sensing properties that meet the long-term monitoring needs of actual projects. Its nominal diameter is the same as that of other steel strands. In this way, we can set the fiber Bragg grating sensors as each detection point, and thus obtain the real-time monitoring wavelength λ of each detection point i。The detection points can be set according to a preset fixed spacing. Of course, they can also be set according to the complexity of the actual building. The fixed spacing can be the basic spacing L0, generally between 50m and 200m. Of course, other numerical settings are not excluded. For buildings with a higher complexity or frequent detection requirements, the number of detection points can be increased according to the actual situation. The designed spacing of the detection points can be , where L0 is the basic spacing, and its value can be set artificially in advance. Its spacing can be set according to the actual situation, generally between 50m and 200m. B is the adjustment base number, and its value is greater than 1, generally between 1 and 5, and is obtained specifically according to construction experience. j is the building influence factor number, and J is the total number of building influence factors, j = 1, 2... J. b j is the building influence factor coefficient of the j-th number. Generally, the building influence factors can include the longitudinal distribution factor, the transverse span factor, and the bearing load factor of the building. Therefore, the building influence factor coefficients include the longitudinal distribution factor coefficient, the transverse span factor coefficient, and the bearing load factor coefficient. The longitudinal distribution factor is related to the number of building layers distributed axially along the intelligent steel strand. The larger the number of layers, the larger the value of the longitudinal distribution factor coefficient. Specifically, it can be b1 = n - 1, where n is the number of building layers within a preset length range. The preset length can specifically be 100 meters. Of course, other numerical settings are not excluded. For example, for a section of elevated bridge deck using intelligent steel strands, which is assembled by 5 bridge sections within 100 meters, then b1 = 5 - 1 = 4. Of course, this is just a simple example and may not be universal. Other situations are not elaborated here. The transverse span factor is the span distance on the vertical axis of the intelligent steel strand. Here, the spans in all directions need to be statistically calculated. If the intelligent steel strand is distributed in a straight line, then in this case, its value is 0. Of course, when there is a transverse span, it needs to be statistically calculated according to the planning diagram. The statistical method can be to implant the planning diagram into the space coordinate system, and then the transverse span factor coefficient can be calculated , where (x1, y1, z1) is the starting point of the intelligent steel strand in the space coordinate system, (x2, y2, z2) is the ending point of the intelligent steel strand in the space coordinate system, Δx is the increment per unit length of the intelligent steel strand on the x coordinate axis, and the unit length can be 1m. Of course, other numerical settings are not excluded. Δy is the increment per unit length of the intelligent steel strand on the y coordinate axis. Δz is the increment per unit length of the intelligent steel strand on the z coordinate axis. is the cumulative value of the increment per unit length of the entire intelligent steel strand on the x coordinate axis, is the cumulative value of the increment per unit length of the entire intelligent steel strand on the y coordinate axis, It is the cumulative value of the increment per unit length of the entire intelligent steel strand on the z-axis. By this method, the distribution change of the intelligent steel strand in the non-axial direction can be calculated, and the axial change can be excluded. For the non-axial distribution, cumulative calculation can be carried out, and various change situations can be fully counted, avoiding the situation of increase and decrease offset, so as to facilitate the calculation of the non-axial stress distribution. The greater the non-axial stress distribution, the greater the load on the intelligent steel strand. Based on this, distribution detection points are set, and the density of detection data can be obtained more accurately, facilitating the timely observation of the data of the intelligent steel strand. The load-bearing factor is the consideration of the weight and frequency borne by the building. The use and usage frequency of the building need to be fully considered, and it can be estimated according to experience and planning. Its value is generally 0-10, and the specific details are not elaborated here.

[0029] S2. The wavelength λ obtained through real-time monitoring i Calculate the stress value σ of the detection point of the intelligent steel strand i . Among them, the stress value of the detection point , where i is the number of the detection point in the intelligent steel strand, and σ i is the monitored stress value of the detection point numbered i on the intelligent steel strand, with the unit of MPa. λ0 is the wavelength obtained by monitoring the intelligent steel strand in the stress-free state, with the unit of nm; λ iis the wavelength obtained from real-time monitoring of the intelligent strand with the number i at the detection point, unit nm; E is the elastic modulus of the intelligent strand, and its specific value can be 195 GPa; χ is the wavelength-strain coefficient of the intelligent strand, and its specific value can be obtained through calibration tests on the intelligent strand, with the value ranging from 0.9 to 1.3. Generally, the value is taken as 1.1. Of course, other numerical settings are not excluded, and the specific values are not elaborated here. The stress value of the detection point of the intelligent strand calculated by this method adapts to the stress performance of the intelligent strand, is convenient and fast to detect, can read and calculate each detection point of the intelligent strand in real time, so as to conduct real-time evaluation on the intelligent strand, has strong detection performance, and accurate calculation, is convenient to timely understand the stress condition of the intelligent strand, so as to reflect the real situation of the building, is convenient to monitor the building, especially during the construction period, is convenient to timely understand the impact of construction on the building, and is convenient to timely adjust the construction. In this embodiment, taking an airport tower building as an example, the ordinary slow-setting strands in 5 beams are replaced with intelligent strands and numbered as 1#, 2#, 3#, 4# and 5# monitoring beams respectively. For the precise measurement of the effective force at each position inside the strand, the intelligent strand adds an embedded grating sensor on the basis of ordinary slow bonding, so signal testing and optical cable protection and other work are added during the construction process, and other implementation processes are the same as those of ordinary slow-bonded strands. One 5-measurement-point intelligent strand is installed in each of the monitoring beams 1# and 2#, and one 9-measurement-point intelligent strand is installed in each of the monitoring beams 3# to 5#, and the distance between each detection point is a fixed value. On-site tensioning adopts the tensioning method of tensioning to 100% in one end in stages and then supplementing and tensioning 100% at the other end. The detailed monitoring data are shown in Tables 1 and 2.

[0030]

[0031]

[0032] As can be seen from the above table, the stress values of each detection point can be accurately obtained through the intelligent strand.

[0033] S3. Compare the stress value σ of the detection point of the intelligent strand i with the predicted stress value σ of the stress distribution T and calculate to obtain the difference value Δσ. The predicted stress value σ of the stress distribution T can be calculated based on the average value of the stress values of each detection point under the same normal use conditions. However, due to different construction conditions, the situation where they are exactly the same is very rare, so it is more appropriate to calculate. The predicted stress value , where F is the internal force of the intelligent steel strand, which can generally be considered as the tensile force or tension applied at its end, and its specific value can be obtained from the test data, which will not be elaborated here. A is the cross-sectional area of the intelligent steel strand, which can be determined according to the parameters of the intelligent steel strand, and will not be elaborated here. S is the length of the intelligent steel strand, which can be calculated based on the length between the two end anchor points, and will not be elaborated here. S0 is the standard test length, and its value can be set according to manual experience, generally 50 - 100 m. Of course, other values are not excluded, and will not be elaborated here. S1 is the length of the intelligent steel strand from the current test point to one of the endpoints, and S2 is the length of the intelligent steel strand from the current test point to the other endpoint. Both data can be obtained by measurement, and will not be elaborated here. ε is the adjustment coefficient, and its value is generally 0.8 - 1.2, and can be obtained by comparing the average value obtained from the test with the calculated value, which will not be elaborated here. Through this method, the stress distribution of the intelligent steel strand under normal conditions can be fitted. Its value is affected by the length, but as the length increases, it shows a downward trend, and the downward trend gradually decreases. The stress distribution of the intelligent steel strand shows a distribution trend of low in the middle and high at both ends, reaching the lowest point at the middle position. Through exponential processing, the stress distribution in the intelligent steel strand is perfectly fitted, and the fitted data is accurate and highly consistent with the stress distribution of the intelligent steel strand. Based on this, a judgment can be made, and thus the stress at the test point can be well judged. The difference value Δσ = σ i - σ T , which will not be elaborated here.

[0034] S4. Determine whether the absolute value of the difference value Δσ is greater than a preset standard difference value Δσ 标 . If so, it is determined that there is an abnormal risk for the buildings within a preset radius range of the test point, and personnel are arranged to conduct inspections on them for timely adjustment. The preset radius range can be designed according to the situation. Generally, it can be considered that the preset radius range is a spherical range, and the range includes all building facilities within the spherical range centered on the current test point with the distance to the nearest test point as the radius, and manual inspections are carried out on them. Of course, it can also be all building facilities covered by the curved surface formed by connecting the nearest test points in each direction around the current test point as the edge surface. Through this method, targeted inspections can be carried out, improving the inspection efficiency. When the absolute value of the difference value Δσ is greater than the preset standard difference value Δσ 标 , there are two situations. The first is that the difference value Δσ is greater than the preset standard difference value Δσ 标, this situation belongs to excessive tensile stress, which is somewhat dangerous. Generally, it is material damage. The intelligent steel strand may enter the plastic deformation stage and even fracture (especially under high stress + fatigue load). There may also be an accelerated loss of prestress: the stress relaxation rate of the steel strand accelerates with the increase of the initial stress, and the long-term performance deteriorates. Or there is a risk of brittle failure. The concrete may be crushed due to excessive local compressive stress. For example, the end concrete of a precast beam in a factory building was crushed due to over-tensioning of the steel strand. The second case is that Δσ is less than the negative value -Δσ of the preset standard difference value 标 , indicating the potential risk of too small tensile stress, which may cause the following problems: insufficient prestress, the concrete cannot effectively resist the tensile stress of external loads, resulting in an increased risk of cracking and reduced structural durability. Increased deformation, decreased structural stiffness, which may cause excessive deflection (such as beam sagging, floor vibration), affecting normal use. Deterioration of long-term performance: concrete cracks accelerate the corrosion of steel bars, shortening the structural life (especially in humid or corrosive environments). For example, due to insufficient tension of the steel strand during the construction of a prestressed beam of a bridge, obvious cracks appeared at the bottom of the beam after the bridge was opened to traffic, and emergency reinforcement was required. Through this method, each detection point and the surrounding building conditions can be monitored in real time. When stress anomalies occur, they can be discovered immediately, and then fixed-point inspections can be carried out, which can greatly reduce the building risk, improve the timeliness of problem discovery, facilitate timely countermeasures and improve the pertinence of countermeasures, thereby maximizing the project progress and reducing losses.

[0035] Example 2 Reference Figure 2 , a monitoring system for the prestress distribution in steel strands, which is used to detect the prestress of intelligent steel strands and judge and analyze whether there are errors in their stress distribution for timely repair. The monitoring system for the prestress distribution in steel strands includes: Fiber Bragg grating sensors, which are used to detect and obtain the wavelength λ of each detection point of the intelligent steel strand obtained by real-time monitoring i , where i is the number of the detection point in an intelligent steel strand. The fiber Bragg grating sensor can be a temperature sensor, which can utilize the anchoring and torsional effects at the end under the stress state of the steel strand, and the CFRP-OFBG intelligent bar is naturally wrapped, achieving the effect of the 6 outer wires of the CFRP-OFBG intelligent bar and the ordinary steel strand deforming together. The wavelength is detected through the temperature signal, which will not be elaborated here specifically.

[0036] Calculation and analysis module, which is used to obtain the stress value σ of the detection point of the intelligent steel strand by calculating through the wavelength λ obtained by real-time monitoring i i . Its calculation formula can be the stress value of the detection point , where i is the number of the detection point in the intelligent steel strand, σ i ​is the monitored stress value of the detection point numbered i on the intelligent steel strand, with the unit of MPa. λ0 is the wavelength monitored under the stress-free state of the intelligent steel strand, with the unit of nm; λ i is the wavelength obtained from the real-time monitoring of the detection point numbered i on the intelligent steel strand, with the unit of nm; E is the elastic modulus of the intelligent steel strand, and its specific value can be 195 GPa; χ is the wavelength-strain coefficient of the intelligent steel strand, and its specific value can be obtained through a calibration test on the intelligent steel strand.

[0037] The judgment and positioning module is used to compare the stress value σ of the detection point on the intelligent steel strand i with the predicted stress value σ of the stress distribution T to calculate the difference value Δσ, and determine whether the absolute value of the difference value Δσ is greater than a preset standard difference value Δσ 标 . If so, it is determined that there is an abnormal risk for the buildings within a preset radius of this detection point, and an alarm is issued to notify relevant personnel for detection to facilitate timely adjustment.

[0038] The space model construction module is used to construct a three-dimensional building model according to the current construction progress. The three-dimensional building model includes information about each building, intelligent steel strands, and the distribution of detection points. The specific method for constructing the three-dimensional model is a prior art and will not be elaborated here.

[0039] The alarm display module is used to mark the positions of each detection point in the three-dimensional building model, and display the abnormal detection points and the preset radius range in the three-dimensional building model. Generally, the position of the monitored point in the three-dimensional building model can be flashed in red, and then the preset radius range can be flashed in yellow. Of course, this is just a simple example and is not necessarily universal. Other situations will not be elaborated here. By constructing a three-dimensional building model and displaying the abnormal detection points on it, the abnormal points can be clearly located to facilitate timely analysis of countermeasures.

[0040] Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art and related fields without creative efforts shall fall within the protection scope of the present invention. The structures, devices, and operation methods not specifically described and explained in the present invention shall be implemented according to the conventional means in the art without special instructions and limitations.

Claims

1. A method for monitoring the in - strand prestress distribution of steel strands, characterized in that, The method for monitoring the internal prestress distribution of the steel strand includes the following steps: S1. Obtain the wavelength λ obtained by real-time monitoring of each detection point of the intelligent steel strand i , where i is the number of the detection point in the intelligent steel strand; S2. Wavelength λ obtained through real-time monitoring i Calculate the stress value σ of the intelligent strand detection point i ; S3. Compare the stress value σ of the intelligent strand detection point i with the predicted stress value σ of the stress distribution T to obtain a difference value Δσ through calculation; S4. Determine whether the absolute value of the difference value Δσ is greater than a preset standard difference value Δσ 标 , if yes, determine that there is an abnormal risk for the buildings within a preset radius of the detection point and give an alarm; if no, do not give an alarm.

2. The method for monitoring the in - strand prestress distribution of a steel strand according to claim 1, wherein, The stress value of the detection point , where i is the number of the detection point in the intelligent steel strand, and σ i is the monitored stress value of the detection point numbered i on the intelligent steel strand; λ0 is the wavelength obtained by monitoring the intelligent steel strand in the stress-free state; λ i is the wavelength obtained from the real-time monitoring of the detection point with the intelligent strand number i E is the elastic modulus of the intelligent steel strand; χ is the wavelength-strain coefficient of the intelligent steel strand.

3. The method for monitoring the in - strand prestress distribution of a steel strand according to claim 2, characterized in that, The specific value of the elastic modulus E of the intelligent steel strand is 195 GPa.

4. The method for monitoring the in - strand prestress distribution of a steel strand according to claim 2, characterized in that, The value of the wavelength-strain coefficient χ of the intelligent steel strand is between 0.9 and 1.

3.

5. The method for monitoring the in - strand prestress distribution of a steel strand according to claim 2, characterized in that, The value of the wavelength-strain coefficient χ of the intelligent steel strand is 1.

1.

6. The method for monitoring the in - strand prestress distribution of a steel strand according to claim 1, characterized in that, The difference value Δσ = σ i - σ T .

7. The method for monitoring the in - strand prestress distribution of a steel strand according to claim 1, characterized in that, The preset radius range is a spherical range within the preset radius. It is all building facilities within the spherical range centered on the current detection point with the distance to the nearest detection point as the radius.

8. The method for monitoring the in - strand prestress distribution of a steel strand according to claim 1, wherein, The preset radius range is all building facilities covered by the curved surface formed by connecting the nearest detection points in each direction around the current detection point as the edge surface.

9. A monitoring system for the in - body prestress distribution of a steel strand, characterized in that, The system for monitoring the internal prestress distribution of the steel strand includes: An optical fiber grating sensor is used to detect the wavelength λ obtained by real-time monitoring of each detection point of the intelligent steel strand i , where i is the number of the detection point in an intelligent steel strand; A calculation and analysis module, which is used to obtain the wavelength λ through real-time monitoring i Calculate the stress value σ of the intelligent strand detection point i ; A judgment and positioning module, which is used to obtain the stress value σ of the intelligent strand detection point i and compare it with the stress distribution predicted stress value σ T to calculate the obtained difference value Δσ, and determine whether the absolute value of the difference value Δσ is greater than a preset standard difference value Δσ 标 . If so, it is determined that there is an abnormal risk in the building within a preset radius range of the detection point and an alarm is issued. If not, no alarm is issued.

10. The in - strand prestress distribution monitoring system for steel strands according to claim 9, characterized in that, The system for monitoring the internal prestress distribution of the steel strand further includes a spatial model construction module and an alarm display module; The spatial model construction module is used to construct a three-dimensional building model according to the current construction progress; The alarm display module is used to mark the positions of each detection point in the three-dimensional building model and display the abnormal detection points and the preset radius range in the three-dimensional building model.

Images