A method and system for monitoring the distribution of internal prestress of a steel strand

Abstract

The present application relates to the technical field of building detection, and in particular to a steel strand internal prestress distribution monitoring method and system. The steel strand internal prestress distribution monitoring method comprises the following steps: obtaining the wavelength obtained by real-time monitoring of each detection point of the intelligent steel strand. The stress value of the detection point of the intelligent steel strand is calculated by the wavelength obtained by real-time monitoring. The stress value of the detection point of the intelligent steel strand is compared with the stress distribution prediction stress value, and the difference value is calculated. It is judged whether the absolute value of the difference value is greater than a pre-set standard difference value. If yes, it is determined that the building within a preset radius range of the detection point has an abnormal risk, and an alarm is given. If no, no alarm is given. Through this method, each detection point and the surrounding building situation can be monitored in real time, the building risk can be greatly reduced, the timeliness of problem discovery is improved, timely countermeasures are facilitated, and the pertinence of countermeasures is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of building detection, and particularly relates to a steel strand internal prestress distribution monitoring method and system. BACKGROUND

[0002] Steel strand is a kind of steel material composed of multiple steel wires, which is usually used in prestressed concrete structures. Steel strand has various classification methods, such as galvanized steel strand, stainless steel strand according to materials, prestressed steel strand, bridge steel strand according to use, etc. Stress detection of steel strand is a key link to ensure its bearing capacity and structural safety during use, especially in prestressed engineering, bridge cable and other scenarios.

[0003] Common detection methods for steel strand stress detection include direct measurement method, hydraulic sensor method, indirect measurement method including vibration frequency method (dynamic method), magnetoelastic method (magnetic flux method), and nondestructive testing technology (such as ultrasonic method and electromagnetic detection method). Among the existing direct detection methods, the force 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 cannot distinguish the positions of the steel strand in the axial direction, resulting in insufficient monitoring capability. SUMMARY

[0004] To solve the above technical problems, the present application provides a steel strand internal prestress distribution monitoring method for prestress detection of intelligent steel strand and judgment and analysis of whether the stress distribution has errors, so as to facilitate timely repair. The steel strand internal prestress distribution monitoring method can include the following steps:

[0005] S1, obtaining the wavelength λ obtained by real-time monitoring of each detection point of the intelligent steel strand i , wherein i is the number of the detection point in the intelligent steel strand.

[0006] S2, comparing the wavelength λ obtained by real-time monitoring i , calculating the stress value σ of the detection point of the intelligent steel strand i .

[0007] S3, comparing the stress value σ of the detection point of the intelligent steel strand i with the stress distribution predicted stress value σ T , and calculating the difference value Δσ.

[0008] S4, determining whether the absolute value of the difference value Δσ is greater than a pre-set standard difference value Δσ 标 . If yes, it is determined that the building within a preset radius range of the detection point has an abnormal risk, and personnel are arranged to detect it, so as to facilitate timely adjustment. If not, no alarm is given. The absolute value of the difference value Δσ is greater than the pre-set standard difference value Δσ 标There are two cases, the first is that the difference value Δσ is greater than the preset standard difference value Δσ 标 , and the second is that Δσ is less than the negative value -Δσ of the preset standard difference value 标 .

[0009] Preferably, the setting of the detection points can be set according to a preset fixed interval.

[0010] Preferably, the design interval of the detection points can be , wherein L0 is a basic interval, the value of which can be preset by a person; B is an adjustment base; j is the number of a building influence factor, J is the total number of building influence factors, j = 1, 2…J; b j is the coefficient of the building influence factor numbered j;

[0011] Preferably, the building influence factors can include longitudinal distribution factors, transverse span factors and bearing load factors of the building, so the building influence factor coefficients include longitudinal distribution factor coefficients, transverse span factor coefficients and bearing load factor coefficients.

[0012] Preferably, the longitudinal distribution factor coefficient b1 = n-1, wherein n is the number of stories of the building within a preset length range.

[0013] Preferably, the transverse span factor coefficient statistical method includes: implanting a planning map into a pre-prepared spatial coordinate system, and then calculating to obtain the transverse span factor coefficient

[0014] , wherein (x1, y1, z1) is the starting point of the smart steel strand in the spatial coordinate system, (x2, y2, z2) is the terminal point of the smart steel strand in the spatial coordinate system, Δx is the increment of the smart steel strand per unit length on the x coordinate axis; Δy is the increment of the smart steel strand per unit length on the y coordinate axis; Δz is the increment of the smart steel strand per unit length on the z coordinate axis; is the cumulative value of the increment of the whole smart steel strand per unit length on the x coordinate axis; is the cumulative value of the increment of the whole smart steel strand per unit length on the y coordinate axis; is the cumulative value of the increment of the whole smart steel strand per unit length on the z coordinate axis.

[0015] Preferably, the unit length can be 1 m.

[0016] Preferably, the stress value of the detection point is , wherein i is the number of the detection point in the smart steel strand, σ i is the monitoring stress value of the detection point numbered i on the smart steel strand, the unit is MPa; λ0 is the wavelength obtained by monitoring in the stress-free state of the smart steel strand, the unit is nm; λi is the wavelength obtained by real-time monitoring of the detection point of the smart steel strand No. i, the unit is nm; E is the elastic modulus of the smart steel strand, and the specific value can be 195 GPa; χ is the wavelength-strain coefficient of the smart steel strand, and the specific value can be obtained by calibrating the smart steel strand through a calibration test, the value is between 0.9-1.3, and the value is generally taken as 1.1.

[0017] Preferably, the predicted stress value , wherein F is the internal force of the smart steel strand; A is the cross-sectional area of the smart steel strand; S is the length of the smart steel strand; S0 is the standard detection length; S1 is the length of the smart steel strand from the current detection point to one end point, and S2 is the length of the smart steel strand from the current detection point to the other end point; and ε is an adjustment coefficient.

[0018] Preferably, the difference value Δσ=σ i -σ T .

[0019] Preferably, the preset radius range can be considered as a spherical range within the preset radius, which is all the buildings and facilities within the spherical range with the current detection point as the center and the distance between the nearest detection points as the radius.

[0020] Preferably, the preset radius range can be all the buildings and facilities covered by the edge surface formed by connecting the nearest detection points in each direction around the current detection point.

[0021] The application also provides a steel strand internal prestress distribution monitoring system for detecting the prestress of the smart steel strand and judging and analyzing whether the stress distribution has errors, so as to facilitate timely repair.

[0022] The fiber grating sensor is used for detecting the wavelength λ i obtained by real-time monitoring of each detection point of the smart steel strand.

[0023] The calculation and analysis module is used for calculating the stress value σ i of the detection point of the smart steel strand through the wavelength λ i obtained by real-time monitoring.

[0024] The judgment and positioning module is used for comparing the stress value σ i of the detection point of the smart steel strand with the predicted stress value σ T of the stress distribution, and calculating the difference value Δσ to determine whether the absolute value of the difference value Δσ is greater than a pre-set standard difference value Δσ 标If yes, it is determined that the building within a preset radius range of the detection point has an abnormal risk, and an alarm is given to inform relevant personnel to detect, so as to adjust in time.

[0025] Preferably, the fiber grating sensor can be a temperature sensor.

[0026] Preferably, the steel strand internal prestress distribution monitoring system can further comprise a space model construction module for constructing a building three-dimensional model according to a current construction progress, the building three-dimensional model comprising information in each building, the intelligent steel strand and the distribution of the detection points.

[0027] The alarm display module is used for marking the position of each detection point in the building three-dimensional model and displaying the abnormal detection points and the preset radius range in the building three-dimensional model, and generally, the position of the monitoring point in the building three-dimensional model can be marked by red flashing, and then the preset radius range can be marked by yellow flashing. Through the construction of the building three-dimensional model and the display of the abnormal detection points on the building three-dimensional model, the abnormal points can be positioned clearly and obviously, so as to analyze the countermeasures in time.

[0028] The technical effects and advantages of the present application are as follows: through the method, each detection point and the surrounding building situation can be monitored in real time, when stress abnormality occurs, the abnormality can be found in the first time, then the detection point is checked, the building risk can be greatly reduced, the problem finding timeliness is improved, the countermeasures are facilitated and the countermeasures are improved in pertinence, so that the engineering progress can be maximized, and the loss can be reduced. The stress value of the intelligent steel strand detection point obtained by the method is suitable for the stress performance of the intelligent steel strand, the detection is convenient and fast, each detection point of the intelligent steel strand can be read and calculated in real time, so that the intelligent steel strand can be evaluated in real time, the detection performance is strong, the calculation is accurate, the stress situation of the intelligent steel strand can be understood in time, so that the real situation of the building can be reflected, the building can be monitored, especially during construction, the influence of construction on the building can be understood in time, and the construction can be adjusted in time. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 A flow chart of a steel strand internal prestress distribution monitoring method is provided for the present application.

[0030] Figure 2 A structure block diagram of a steel strand internal prestress distribution monitoring system is provided for the present application. DETAILED DESCRIPTION

[0031] The application is further described in detail below with reference to the accompanying drawings and specific embodiments. The embodiments of the application are given for illustrative and descriptive purposes only and are not intended to be exhaustive or to limit the application to the forms disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art. Embodiments are chosen and described in order to best explain the principles of the application and its practical application to thereby enable others skilled in the art to best utilize the application in various embodiments and with various modifications as are suited to the particular use contemplated.

[0032] Embodiment 1

[0033] Reference Figure 1 In this embodiment, a steel strand internal prestress distribution monitoring method is proposed for prestress detection of intelligent steel strands and for judging and analyzing whether the stress distribution has errors, so as to facilitate timely repair. The steel strand internal prestress distribution monitoring method can include the following steps:

[0034] S1, obtaining 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 a intelligent steel strand. The intelligent steel strand can be a fiber grating steel strand, the fiber grating can be a CFRP-OFBG intelligent tendon, and the fiber grating steel strand is mainly composed of a CFRP-OFBG intelligent tendon, an external strand tendon, an external monitoring optical fiber, and an end packaging. The external monitoring optical fiber can be connected to the CFRP-OFBG intelligent tendon signal through the end packaging after construction is completed. During detection, the external monitoring optical fiber can be connected to a detection device. The specific detection process is not described here. Taking the CFRP-OFBG intelligent tendon as an example, during the production of the fiber grating steel strand, the CFRP-OFBG intelligent tendon can be sent into a mold cavity that can be pressurized and heated in parallel. After passing through the high-temperature and high-pressure mold cavity, the CFRP-OFBG intelligent tendon is coupled at the center of the external strand tendon twisted together. The grating sensor cooperates with the FRP to complete the strain measurement function. In this way, the FRP material produced is embedded with a fiber grating sensor with sensing properties. With the anchoring and torsion effect of the end under the stress state of the steel strand, the CFRP-OFBG intelligent tendon is naturally wrapped, achieving the effect of cooperative deformation of the CFRP-OFBG intelligent tendon and the six outer wires of the ordinary steel strand. The intelligent steel strand has geometric, mechanical, and sensing properties that meet the long-term monitoring needs of actual engineering. Its nominal diameter is consistent with that of other steel strands. In this way, we can set fiber grating sensors for each detection point, so as to obtain the real-time monitoring wavelength λ of each detection point iThe setting of the detection points can be set according to a preset fixed interval, and of course can be set according to the complexity of the actual building. The fixed interval can be a basic interval L0, which is generally between 50m-200m, and of course other numerical settings are not excluded. For buildings with high complexity or frequent detection requirements, the setting of the detection points can be increased according to the actual situation. The detection point design interval can be wherein L0 is a basic interval, the value of which can be set artificially in advance, and the interval can be set according to the actual situation, generally between 50m-200m. B is an adjustment base, the value of which is greater than 1, generally between 1-5, and is obtained 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. j is the building influence factor coefficient numbered j. In general, the building influence factor can include the longitudinal distribution factor of the building, the transverse span factor, and the bearing load factor, so the building influence factor coefficient includes 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 stories distributed in the axial direction of the intelligent steel strand. The greater the number of stories, the greater the value of the longitudinal distribution factor coefficient. Specifically, it can be b1=n-1, wherein n is the number of stories of the building within a preset length range, and the preset length can be 100 meters, and of course other numerical settings are not excluded. For example, a section of elevated bridge deck uses intelligent steel strand, and 100 meters is assembled by 5 bridge sections, so b1=5-1=4. Of course, this is only a simple example and is not necessarily universal, and other cases are not described here. The transverse span factor is the span distance in the vertical axis of the intelligent steel strand. Here, the span in each direction needs to be calculated. If the intelligent steel strand is distributed in a straight line, the value is 0 in this case. Of course, when there is a transverse span, the planning map needs to be counted according to the planning map. The statistical method can be to implant the planning map into the spatial coordinate system, and then calculate the transverse span factor coefficient

[0035] wherein (x1, y1, z1) is the starting point of the intelligent steel strand in the spatial coordinate system, (x2, y2, z2) is the terminal point of the intelligent steel strand in the spatial coordinate system, Δx is the unit length increment of the intelligent steel strand on the x coordinate axis, and the unit length can be 1m, and of course other numerical settings are not excluded. Δy is the unit length increment of the intelligent steel strand on the y coordinate axis. Δz is the unit length increment of the intelligent steel strand on the z coordinate axis. is the cumulative value of the unit length increment of the entire intelligent steel strand on the x coordinate axis, is the cumulative value of the unit length increment of the entire intelligent steel strand on the y coordinate axis, The is the cumulative value of the increment of the whole intelligent steel strand on the unit length on the z coordinate axis. Through this method, the distribution change of the intelligent steel strand in the non-axial direction can be calculated and obtained, the axial change can be excluded, the non-axial distribution can be accumulated and calculated, various changes can be fully counted, the increase and decrease offsetting situation can be avoided, so as to facilitate the calculation of the distribution of the non-axial stress, the larger the non-axial stress distribution, the greater the load on the intelligent steel strand, and the more accurate the detection data density can be obtained by the distribution detection point, so as to facilitate the timely observation of the data of the intelligent steel strand. The bearing load factor is the weight and frequency of the building, and the purpose and use frequency of the building need to be fully considered, which can be valued according to experience and planning, and the value is generally 0-10, which will not be described in detail here.

[0036] S2, the wavelength λ obtained by real-time monitoring i The stress value σ of the detection point of the intelligent steel strand is calculated i . Wherein the stress value σ of the detection point , wherein i is the number of the detection point in the intelligent steel strand, σ i is the monitoring stress value of the detection point numbered i on the intelligent steel strand, unit MPa. λ0 is the wavelength obtained by monitoring in the stress-free state of the intelligent steel strand, unit nm; λ iis the wavelength obtained by real-time monitoring of the detection point of the smart steel strand numbered i, the unit is nm; E is the elastic modulus of the smart steel strand, and the specific value can be 195 GPa; χ is the wavelength-strain coefficient of the smart steel strand, and the specific value can be obtained by calibrating the smart steel strand, the value is between 0.9-1.3, and the value is generally taken as 1.1, of course, other values are not excluded, and the specific value is not described here. The stress value of the smart steel strand detection point obtained by this method is suitable for the stress performance of the smart steel strand, convenient and fast to detect, and can read and calculate each detection point of the smart steel strand in real time, so as to evaluate the smart steel strand in real time, has strong detection performance, and the calculation is accurate, which is convenient for timely understanding the stress condition of the smart steel strand, so as to reflect the real condition of the building, and the building is monitored, especially during construction, which is convenient for timely understanding the influence of construction on the building, and timely adjusting the construction. In the embodiment, an airport tower building is taken as an example, ordinary slow-setting steel strands in 5 beams are replaced by smart steel strands, and the smart steel strands are numbered as 1#, 2#, 3#, 4# and 5# monitoring beams. The smart steel strand is used for accurate measurement of the effective force of each position in the steel strand. The smart steel strand increases the embedded grating sensor on the basis of the ordinary slow-setting steel strand, so that the signal test and optical cable protection are increased during the construction process, and the other implementation processes are consistent with the ordinary slow-setting steel strand. One 5-detection-point smart steel strand is installed in each of monitoring beams 1# and 2#, and one 9-detection-point smart steel strand is installed in each of monitoring beams 3# to 5#. The interval of each detection point is a fixed value. In the field tensioning, one end is tensioned in stages to 100%, and then the other end is tensioned to 100% in the tensioning mode. The detailed monitoring data are shown in Tables 1 and 2.

[0037]

[0038]

[0039] From the above table, the stress values of each detection point can be accurately obtained by the smart steel strand.

[0040] S3, comparing the stress value σ of the smart steel strand detection point with the stress distribution prediction stress value σ i and calculating the difference value Δσ, the stress distribution prediction stress value σ T T The prediction stress value can be calculated according to the average value of the stress values of each detection point under the same normal use condition, but because each construction condition is different, the specific condition is rarely consistent, so it is more appropriate to calculate the prediction stress value ​Wherein F is the internal force of the smart steel strand, which can be considered as the tension applied at its end or its tension, and its specific value can be obtained according to the detection data, which is not described in detail here. A is the cross-sectional area of the smart steel strand, which can be determined according to the parameters of the smart steel strand, which is not described in detail here. S is the length of the smart steel strand, which can be calculated according to the length between the two end anchoring points, which is not described in detail here. S0 is the standard detection length, which can be set by artificial experience, generally 50-100m, of course, other values are not excluded, which is not described in detail here. S1 is the length of the smart steel strand from the current detection point to one end point, and S2 is the length of the smart steel strand from the current detection point to the other end point, both of which can be obtained by measurement, which is not described in detail here. Epsilon is the adjustment coefficient, which is generally 0.8-1.2, and its value can be obtained by comparing the average value obtained by detection with the calculated value, which is not described in detail here. The stress distribution of the smart steel strand under normal conditions can be fitted by this method, which is affected by the length, but the length increases and the downward trend decreases, and the stress distribution of the smart steel strand presents a trend of low in the middle and high at both ends, with the lowest point in the middle. By exponential processing, the stress distribution of the smart steel strand is perfectly fitted, the fitted data is accurate, and the stress distribution of the smart steel strand is very consistent. Based on this, the stress of the detection point can be judged, so that the stress of the detection point can be judged well. The difference value Δσ = σ i -σ T , which is not described in detail here.

[0041] S4, determine whether the absolute value of the difference value Δσ is greater than a pre-set standard difference value Δσ 标 If yes, it is determined that the buildings within a pre-set radius range of the detection point have abnormal risk, and personnel are arranged to detect them to facilitate timely adjustment. The pre-set radius range can be designed according to the situation, and in general it can be considered that the pre-set radius range is a spherical range with the current detection point as the center and the distance between the nearest detection points as the radius. All buildings and facilities within the spherical range are manually checked. Of course, it can also be all buildings and facilities covered by the edge surface formed by connecting the nearest detection points in each direction around the current detection point. By this method, targeted inspection can be carried out to increase the efficiency of inspection. The absolute value of the difference value Δσ is greater than the pre-set standard difference value Δσ 标 There are two cases, the first is that the difference value Δσ is greater than the pre-set standard difference value Δσ 标, this situation belongs to the case of excessive tensile stress, which is dangerous, and generally causes material damage, the intelligent steel strand may enter the plastic deformation stage, or even breakage (especially under high stress + fatigue load). There is also a possibility of accelerated loss of prestress: the stress relaxation rate of steel strand increases with the increase of initial stress, and the long-term performance decreases. Or brittle failure risk, concrete may be crushed due to excessive local compressive stress. For example, the end concrete of a factory building precast beam was crushed due to over tension of the steel strand. The second is the negative value of the standard difference value -Δσ 标 , which indicates that there is a potential risk of insufficient tensile stress, which may cause the following problems: insufficient prestress, concrete cannot effectively resist external load tensile stress, resulting in increased cracking risk and reduced structural durability. Increased deformation, reduced structural stiffness, which may cause excessive deflection (such as beam sagging, floor vibration), affecting normal use. Long-term performance deterioration: concrete cracks accelerate steel corrosion, shortening the service life of the structure (especially in humid or corrosive environments). For example, the prestressed beam of a certain bridge had insufficient tension force during construction, and cracks appeared on the beam bottom after the bridge was opened to traffic, requiring emergency reinforcement. Through this method, each detection point and its surrounding building conditions can be monitored in real time. When stress anomalies occur, they can be detected in the first place, and then checked at specific points, which can greatly reduce building risks, improve problem detection timeliness, facilitate timely countermeasures and improve the relevance of countermeasures, thereby maximizing project progress and reducing losses.

[0042] Embodiment 2

[0043] Reference Figure 2 A steel strand internal prestress distribution monitoring system for detecting the prestress of intelligent steel strands and determining whether the stress distribution has errors to facilitate timely repair, the steel strand internal prestress distribution monitoring system comprising:

[0044] An optical fiber grating sensor for detecting the wavelength λ i obtained by real-time monitoring of each detection point of the intelligent steel strand, wherein i is the number of the detection point in the intelligent steel strand. The optical fiber grating sensor can be a temperature sensor, which can use the anchoring and torsion effect of the end under the stress state of the steel strand, the CFRP-OFBG intelligent bar is naturally wrapped, and the CFRP-OFBG intelligent bar and the six outer wires of the ordinary steel strand are deformed cooperatively. The wavelength is detected by the temperature signal, which is not described in detail here.

[0045] A calculation and analysis module for calculating the stress value σ i of the detection point of the intelligent steel strand by the wavelength λ i obtained by real-time monitoring. The calculation formula can be the stress value σ of the detection point, wherein i is the number of the detection point in the intelligent steel strand, and σi is the monitoring stress value of the detection point numbered i on the intelligent steel strand, unit MPa. λ0is the wavelength obtained by monitoring in the stress-free state of the intelligent steel strand, unit nm; λ i is the wavelength obtained by real-time monitoring of the detection point numbered i of the intelligent steel strand, unit nm; E is the elastic modulus of the intelligent steel strand, which can be 195 GPa; χ is the wavelength-strain coefficient of the intelligent steel strand, which can be obtained by calibration test of the intelligent steel strand.

[0046] The judgment positioning module is used for comparing the stress value σ i of the detection point of the intelligent steel strand with the stress distribution prediction stress value σ T , and calculating the difference value Δσ to determine whether the absolute value of the difference value Δσ is greater than a pre-set standard difference value Δσ 标 . If yes, it is determined that the building within a preset radius range of the detection point has an abnormal risk, and an alarm is given to inform relevant personnel to detect and facilitate timely adjustment.

[0047] The spatial model construction module is used for constructing a building three-dimensional model according to the current construction progress, which includes the information of each building, the intelligent steel strand and the distribution of the detection points. The specific method for constructing the three-dimensional model is prior art, which is not described here.

[0048] The alarm display module is used for marking the position of each detection point in the building three-dimensional model and displaying the abnormal detection point and the preset radius range in the building three-dimensional model. Generally, the position of the monitoring point in the building three-dimensional model can be marked by red flashing, and the preset radius range can be marked by yellow flashing. Of course, this is only a simple example and is not necessarily universal. Other cases are not described here. By constructing the building three-dimensional model and displaying the abnormal detection point thereon, the abnormal point can be positioned clearly and conveniently for timely analysis and countermeasures.

[0049] Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art and related fields without creative labor shall belong to the scope of protection of the present application. The structures, devices and operation methods not specifically described and explained in the present application, such as without special description and limitation, are implemented according to the conventional means in the art.

Claims

1. A method for monitoring prestress distribution in a steel strand, characterized in that: The method for monitoring the prestress distribution in a steel strand comprises the following steps: S1. Obtain the wavelength 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; S2, wavelength obtained by real-time monitoring Calculate the stress value of the intelligent steel strand detection point ; S3, the stress value of the intelligent steel strand detection point Predicting stress values ​​with stress distribution Compare and calculate the difference ; S4. Determine the difference value Is the absolute value greater than a preset standard difference value? If yes, it is determined that there is an abnormal risk in the building within a preset radius of the detection point and an alarm is issued; if not, no alarm is issued; Design spacing of detection points ,in is the basic spacing; B is the adjustment base number; j is the building influencing factor number, J is the total number of building influencing factors, ; is the coefficient of the influencing factor of the building numbered j; The building influencing factor coefficients include the longitudinal distribution factor coefficient, the transverse span factor coefficient and the bearing load factor coefficient; Longitudinal distribution factor coefficient , where n is the number of layers of a building within a preset length range; The calculation method of the horizontal span factor coefficient includes: embedding the planning map into a pre-made spatial coordinate system, and then calculating the horizontal span factor coefficient ,in, It is the starting point of the smart steel strand in the space coordinate system. It is the end point of the smart steel strand in the space coordinate system. is the increment of the smart steel strand per unit length on the x-axis; is the increment of the smart steel strand per unit length on the y-axis; is the increment of the smart steel strand per unit length on the z-axis; It is the cumulative value of the increment of the entire smart steel strand per unit length on the x-axis; It is the cumulative value of the increment of the entire smart steel strand per unit length on the y-axis; It is the cumulative value of the increment of the entire smart steel strand per unit length on the z-axis.

2. A method for monitoring prestress distribution in a steel strand according to claim 1, characterized in that: The stress value of the detection point , where i is the number of the detection point in the intelligent steel strand, is the monitored stress value of the detection point numbered i on the intelligent strand; It is the wavelength obtained by monitoring the intelligent steel strand in the stress-free state; The wavelength obtained by real-time monitoring of the intelligent steel strand numbered i detection point; E is the elastic modulus of the smart strand; is the wavelength-gauge factor of the smart steel strand.

3. A method for monitoring prestress distribution in 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. A method for monitoring prestress distribution in a steel strand according to claim 2, characterized in that: The wavelength-gauge factor of the intelligent steel strand The value is between 0.9-1.

3.

5. A method for monitoring prestress distribution in a steel strand according to claim 2, characterized in that: The wavelength-gauge factor of the intelligent steel strand The value is 1.

1.

6. A method for monitoring prestress distribution in a steel strand according to claim 1, characterized in that: The difference value .

7. A method for monitoring prestress distribution in a steel strand according to claim 1, characterized in that: The preset radius range is a spherical range within the preset radius, and its range is all buildings and facilities within the spherical range with the current detection point as the center and the distance from the nearest detection point as the radius.

8. The method for monitoring prestress distribution in a steel strand according to claim 1, characterized in that: The preset radius range is all buildings and facilities covered by the curved surface formed by connecting the nearest detection points in all directions around the current detection point as the center.

9. A system for monitoring the prestress distribution in a steel strand, characterized in that: The steel strand body prestress distribution monitoring system includes: Fiber Bragg grating sensor, which is used to detect the wavelength obtained by real-time monitoring of each detection point of the intelligent steel strand , where i is the number of the detection point in an intelligent steel strand; Computational analysis module, which is used to obtain the wavelength through real-time monitoring Calculate the stress value of the intelligent steel strand detection point ; The judgment and positioning module is used to determine the stress value of the intelligent steel strand detection point Predicting stress values ​​with stress distribution Compare and calculate the difference , determine the difference value Is the absolute value greater than a preset standard difference value? If yes, it is determined that there is an abnormal risk in the building within a preset radius of the detection point and an alarm is issued. If not, no alarm is issued; Design spacing of detection points ,in is the basic spacing; B is the adjustment base number; j is the building influencing factor number, J is the total number of building influencing factors, ; is the coefficient of the influencing factor of the building numbered j; The building influencing factor coefficients include the longitudinal distribution factor coefficient, the transverse span factor coefficient and the bearing load factor coefficient; Longitudinal distribution factor coefficient , where n is the number of layers of a building within a preset length range; The calculation method of the horizontal span factor coefficient includes: embedding the planning map into a pre-made spatial coordinate system, and then calculating the horizontal span factor coefficient ,in, It is the starting point of the smart steel strand in the space coordinate system. It is the end point of the smart steel strand in the space coordinate system. is the increment of the smart steel strand per unit length on the x-axis; is the increment of the smart steel strand per unit length on the y-axis; is the increment of the smart steel strand per unit length on the z-axis; It is the cumulative value of the increment of the entire smart steel strand per unit length on the x-axis; It is the cumulative value of the increment of the entire smart steel strand per unit length on the y-axis; It is the cumulative value of the increment of the entire smart steel strand per unit length on the z-axis.

10. A system for monitoring prestress distribution in a steel strand according to claim 9, characterized in that: The steel strand body prestress distribution monitoring system also includes a space model building module and an alarm display module; The spatial model building module is used to build a three-dimensional building model based on the current construction progress; The alarm display module is used to calibrate the position of each detection point in the building three-dimensional model, and display the abnormal detection points and preset radius range in the building three-dimensional model.

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