Intelligent monitoring method for construction quality of power transmission structure foundation in high-water-level environment

Abstract

The invention discloses a power transmission structure foundation construction quality intelligent monitoring method in a high water level environment, which comprises the following steps: S1, installing a manufactured distributed optical fiber sensor on one side of the bottom surface of a power transmission structure or a concrete slab, and connecting an optical fiber interconnection line with an optical fiber signal conditioner; performing batch processing on the acquired spectral distribution to obtain a strain location map; s2, detecting the change of an internal temperature field of a dam body concrete structure through a distributed optical fiber temperature measurement system, monitoring the leakage of the earth and rockfill dam, and evaluating the sensitivity and reuse capability of an optical fiber sensing technology under different crack widths in a quantitative manner; and S3, carrying out underwater construction disease identification and monitoring through an OTDR detector. According to the invention, temperature and strain state information of different positions of a concrete structure in a high-water-level environment can be obtained at the same time, and specific influence factors of large-size complex concrete defects in an optical fiber sensing technology are utilized to analyze the influence degree of the influence factors on optical signal parameter test values at the same time.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission structure foundation construction quality monitoring, and in particular to a power transmission structure foundation construction quality intelligent monitoring method under a high water level environment. Background Art

[0002] OTDR (Optical Time Domain Reflectormetry) is an optical time domain reflectometry technology. OTDR was originally used to evaluate the performance of optical fibers, optical cables, and couplers in the field of optical communications. It is a means of testing optical fiber loss characteristics and pipeline faults. Its working mechanism is that a pulsed laser emits a light pulse to the optical fiber under test. When the light pulse passes through the optical fiber, due to the microscopic inhomogeneity of the refractive index of the optical fiber and the change of the microscopic characteristics of the optical fiber, a part of the light will deviate from the original propagation and scatter into space, forming backscattered light and forward scattered light in the optical fiber. Among them, the backscattered light propagates backward to the beginning of the optical fiber and is sent to the photoelectric detection system through the directional coupler. Since each scattered light propagating backward corresponds to a measuring point on the optical fiber bus, the delay of the scattered light is reflected in the position on the optical fiber bus.

[0003] The optical time domain reflectometry is also called the backscattering method. Its main features are single-ended testing and non-destructive testing. The measurement principle of the backscattering method is similar to that of radar detection of targets, which is to emit narrow pulse light with a certain repetition period and width into the optical fiber under test. When the light is transmitted in the optical fiber, scattering will occur, that is, scattered light will be generated in all directions in the optical fiber. The geometric defects or fracture surfaces of the optical fiber (live connection points and cold connection points) will cause the refractive index to change suddenly, resulting in Fresnel reflection, in which a part of the backscattered light and Fresnel reflected light transmitted backward can be transmitted back to the incident end along the optical fiber. By detecting the backscattering and Fresnel reflected light of the optical fiber, the parameter characteristic distribution of the optical fiber line under test can be obtained according to the amplitude curve of the backlight signal along the time axis.

[0004] With the adjustment of the global energy structure and the rapid development of the power industry, the quality and safety requirements for power infrastructure construction are becoming increasingly stringent. However, there are various difficulties and challenges in the quality inspection of underwater power transmission structures such as bored piles and underground continuous walls during the construction phase: 1) Traditional non-contact monitoring technology cannot monitor in real time and is costly; 2) Traditional contact monitoring technology has high complexity in underwater operations and is easily affected by accuracy; 3) Due to poor visibility in the water under high water levels, the accuracy of non-contact monitoring technology is poor; 4) It is difficult to collect data for underwater concrete structure construction quality monitoring, and currently it still relies on manual local measurement points; 5) It is difficult for current monitoring data to accurately, real-timely, and fully reflect the quality of underwater construction. Summary of the invention

[0005] Purpose of the invention: The purpose of the present invention is to provide an intelligent monitoring method for the construction quality of power transmission structure foundations under high water level environments, so as to realize continuous real-time monitoring of the temperature and strain distribution within the entire optical fiber length range, and to realize simultaneous acquisition of temperature and strain state information at different positions of the concrete structure under high water level environments by utilizing the characteristic changes of the optical signal in the optical fiber during the sensing process.

[0006] Technical solution: A method for intelligently monitoring the construction quality of power transmission structure foundations under high water level environment, which uses distributed optical fiber sensors to send real-time monitoring data to a remote monitoring system, applies machine learning and artificial intelligence algorithms to intelligently analyze the monitoring data, and immediately triggers an early warning when the monitoring data exceeds the preset safety range and displays it through a visual interface; including the following steps:

[0007] S1, installing the prepared distributed optical fiber sensor on one side of the power transmission structure or the bottom surface of the concrete slab, and connecting the optical fiber interconnection line to the optical fiber signal conditioner; batch processing the collected spectral distribution to obtain the strain position map;

[0008] S2, using a distributed optical fiber temperature measurement system to detect changes in the temperature field inside the dam concrete structure and monitor leakage in the earth-rock dam, quantitatively evaluating the sensitivity and multiplexing capability of optical fiber sensing technology under different crack widths;

[0009] S3, identification and monitoring of underwater construction defects through OTDR detectors.

[0010] Furthermore, the production of the distributed optical fiber sensor is mainly divided into the following steps:

[0011] S11, the adjustable optical fiber cable and the connector are tightly fixed in the chamfered steel shell to form a mirror head;

[0012] S12, making a slight angle cut at the end of the optical fiber probe, then winding a wire around the optical fiber, and finally fixing the optical fiber in the outer sleeve to complete the production of the optical fiber probe;

[0013] S13, connecting the optical fiber connectors with an optical fiber interconnection line.

[0014] Further, in step S1, after passing through the optical fiber signal conditioner, a modulated signal A′ is obtained. c , the expression is as follows:

[0015] A′ c =A c ·(1+m·signal(t))

[0016] Among them, A c is the carrier amplitude, m is the modulation index, and signal(t) is the time-dependent modulating signal.

[0017] Further, in step S2, through theoretical analysis and simulation experiments, a neural network model is used to qualitatively and quantitatively analyze the multiplexing performance of the optical fiber microbend crack sensor under different crack spacing, angles and widths of the concrete dam, and an optimization formula for predicting the multiplexing capacity is obtained;

[0018] Through model tests and field tests, the applicability, stability, accuracy, advantages and disadvantages, and applicable conditions of the Brillouin method and Raman method in leakage monitoring are compared and analyzed to determine the optimal location range for optical fiber laying.

[0019] Further, in fiber optic sensing technology, the main key parameters include effective sensing distance, spatial resolution, upper limit of frequency response and signal-to-noise ratio;

[0020] The calculation expression of effective sensing distance L is:

[0021]

[0022] Among them, P SBS is the optical power causing the SBS effect, L SBS is the maximum sensing distance affected by the SBS effect, g B is the Brillouin gain coefficient, A eff is the effective area of ​​the fiber core, L c Refers to the coupling length or characteristic length of the optical fiber, P in Indicates the optical power input into the optical fiber;

[0023] The spatial resolution is half the pulse width;

[0024] Frequency response f max The upper limit of is calculated as:

[0025]

[0026] Furthermore, finite element software was used to establish a dam seepage model to simulate the on-site conditions, analyze the seepage field and temperature field cloud maps, and determine the optimal location range for optical fiber laying.

[0027] Compared with the prior art, the present invention has the following significant effects:

[0028] 1. The present invention adopts distributed optical fiber sensing technology to realize continuous real-time monitoring of temperature and strain distribution within the entire optical fiber length range. By utilizing the characteristic changes of the optical signal in the optical fiber during the sensing process, the temperature and strain state information at different positions of the concrete structure under a high water level environment can be simultaneously acquired;

[0029] 2. The present invention proposes a method for monitoring defects in large-volume complex concrete using optical fiber sensing technology. Through analysis, the unique influencing factors of defects in large-volume complex concrete using optical fiber sensing technology are found, and the degree of influence of these influencing factors on the test values ​​of optical signal parameters is analyzed and discussed. The types of defects that may exist in large-volume complex concrete are predicted, and corresponding specific monitoring methods and judgment bases are proposed. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 (a) is a physical diagram of the test scheme proposed by the present invention, and (b) is a schematic diagram of the force-bearing mode;

[0031] Figure 2 is a flow chart of the present invention;

[0032] Figure 3 It is a schematic diagram of cracking of the bottom surface of the concrete beam in the present invention;

[0033] Figure 4 Schematic diagram of various levels of loading strain of the optical cable in the present invention;

[0034] Figure 5 It is a schematic diagram of the strains of the optical cable at each level in the present invention;

[0035] Figure 6 This is a schematic diagram of data at various levels of the fourth crack of the optical cable 1 in the present invention;

[0036] Figure 7 These are actual pictures of cracks in the present invention, (a) is a schematic diagram of a 0.10 mm crack, (b) is a 0.22 mm crack, and (c) is a 0.34 mm crack. DETAILED DESCRIPTION

[0037] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0038] In order to realize intelligent monitoring of construction quality, the following key points are mainly included when carrying out foundation construction of power transmission structure in high water level environment:

[0039] G1) Sensor deployment: Use high-precision water level sensors, displacement sensors and stress sensors to monitor water level changes and foundation settlement, displacement and stress in real time;

[0040] G2) Remote monitoring system: Establish a remote monitoring platform based on the Internet of Things (IoT) to achieve real-time data transmission and analysis, ensuring that construction personnel can remotely understand the real-time status of the construction foundation;

[0041] G3) Big Data Analysis: The collected data is analyzed through big data technology to identify abnormal patterns and predict potential construction risks, such as uneven settlement or structural stability issues;

[0042] G4) Artificial Intelligence Algorithms: Apply machine learning and artificial intelligence algorithms to conduct intelligent analysis of monitoring data to improve the accuracy of problem identification and the timeliness of early warning;

[0043] G5) Environmental adaptability technology: Ensure that the monitoring equipment can work stably in harsh underwater or humid environments, using waterproof and anti-corrosion materials and technologies;

[0044] G6) Real-time warning mechanism: Once the monitored data exceeds the preset safety range, the warning will be triggered immediately, responding quickly to reduce security risks;

[0045] G7) Visualization interface: Provides an intuitive monitoring data visualization interface to help engineers and managers quickly understand the construction status and make decisions;

[0046] Through these intelligent monitoring methods, the quality control and safety management of transmission structure foundation construction under high water level environment can be effectively improved. The algorithm uses random forest method for calculation and constructs multiple decision trees for prediction and classification. The core steps of random forest include: data preprocessing, decision tree construction and prediction. The mathematical model formula of random forest is as follows:

[0047]

[0048] in, is the predicted output value of input x, which is an estimate of the true value y. t (x) is the prediction function of the decision tree input x, and a prediction result f is obtained based on the input x. t (x), T represents the set of all decision trees in the random forest, and t is the element index in the set T, which is used to identify different decision trees.

[0049] like Figure 2 The flowchart of the intelligent monitoring method for the construction quality of the power transmission structure foundation under the high water level environment of the present invention is shown, which mainly includes the following steps:

[0050] Step 1: fabrication and installation of distributed optical fiber acoustic sensors;

[0051] It is mainly divided into the following steps:

[0052] Step 11, making the mirror head: When making the mirror head, you need to use an adjustable optical fiber cable and connector to ensure the correct direction of the optical path. Finally, the adjustable optical fiber cable and connector are tightly fixed in the chamfered steel shell to make the mirror head.

[0053] Step 12, making the fiber optic probe: First, make a small angle cut at the end of the fiber optic probe to produce scattered light, then wrap the wire around the optical fiber to enhance its longitudinal protection, and finally fix the optical fiber in the outer sleeve.

[0054] Step 13, making optical fiber interconnection cables: Connect the optical fiber connectors with optical fiber interconnection cables. Note that the optical fiber in the optical fiber interconnection cables should be in good condition and the optical connection quality should meet the requirements.

[0055] The installation and use of distributed optical fiber sensors: install the prepared optical fiber sensors on one side of the transmission structure or the bottom surface of the concrete slab, and connect the optical fiber interconnection line to the optical fiber signal conditioner (OTDR). Select appropriate parameters on the OTDR for one-time operation and data processing. During the experiment, it is necessary to observe the spectral distribution at regular intervals, collect the corresponding data, and obtain the strain position diagram after batch processing, so as to obtain the deformation information of the transmission structure or the concrete slab. Specifically in mathematical expression, if simple amplitude modulation is considered, the modulated signal A′ c The expression is as follows:

[0056] A′ c =A c ·(1+m·signal(t)) (2)

[0057] Among them, A c is the carrier amplitude, m is the modulation index, and signal(t) is the time-dependent modulating signal.

[0058] Step 2, accuracy and reliability evaluation method of optical fiber sensing technology;

[0059] The main steps include:

[0060] Step 21, sensitivity and multiplexing capability evaluation: For concrete dam crack monitoring, the sensitivity and multiplexing capability of fiber optic sensing technology based on Rayleigh scattering need to be evaluated. This involves the study of single crack concrete model tests to quantitatively study the sensitivity and multiplexing capability of fiber optic sensing technology at different crack widths.

[0061] Step 22, temperature field change monitoring: Through the distributed optical fiber temperature measurement system, the changes in the temperature field inside the dam concrete structure can be detected quickly and accurately. This system is easy to install and is conducive to the health diagnosis and safe operation of the dam.

[0062] Step 23, research on leakage monitoring technology: A distributed optical fiber temperature measurement system is used to monitor leakage in earth-rock dams. The system has high information density and can be buried in the downstream drainage or filter layer of the impermeable body. It will not be damaged by mechanical construction and shows good characteristics of low construction interference and anti-electromagnetic interference.

[0063] Step 24, theoretical and simulation tests: Through theoretical analysis and simulation tests, the multiplexing performance of the optical fiber micro-bend crack sensor under different crack spacing, angles and widths of the concrete dam is qualitatively and quantitatively analyzed, and an optimization formula for predicting the multiplexing capacity is obtained.

[0064] The optimization formula is: In convolutional neural networks, the optimization of data reuse is often related to the amount of computation and the number of memory accesses. Taking the input data reuse (IR) mode as an example, assume that M K·K·N three-dimensional convolution kernels are used in the convolution, and the size of the input three-dimensional feature map is N·L·N. After convolution, a three-dimensional feature map of R·C·M is obtained.

[0065] Step 25, selection and deployment of distributed fiber optic technology: Study the application of distributed fiber optic technology in leakage monitoring, select appropriate distributed fiber optic technology, the location and method of laying the fiber optic according to the production and installation of distributed fiber optic acoustic sensors in step 1, and also involve a comparison of the characteristics of three distributed temperature sensing technologies based on Rayleigh, Brillouin and Raman scattering principles.

[0066] The installation location is based on the actual situation and can be installed in a suitable location. The method can be direct buried, which is as follows:

[0067] Step 25a1, trench excavation: excavate the trench for laying optical fibers according to the design requirements. The depth of the trench should generally be below the frozen soil layer. The width is determined according to the number of optical fibers and the protection requirements, generally about 0.3 to 0.6 meters. The bottom of the trench should be flat without sharp stones or other hard objects.

[0068] Step 25a2, optical fiber laying and protection: Place the optical fiber in the trench, lay fine sand or soft soil around the optical fiber with a thickness of about 10 to 20 cm as a buffer layer, and then cover it with a waterproof protective plate, such as a plastic plate or a corrugated steel plate, and finally backfill the soil and tamp it. A certain length of optical fiber should be reserved at both ends of the optical fiber to facilitate connection with other lines;

[0069] Step 25a3, marking and warning: Obvious marking stakes should be set up on the ground above the directly buried optical fiber to indicate the direction, depth and other information of the optical fiber. Warning signs should also be set up in some areas where excavation work may be carried out to remind construction workers to pay attention to the existence of optical fiber lines underground.

[0070] Step 26, model test and field test: Through model test and field test, compare and analyze the applicability, stability, accuracy, advantages and disadvantages and applicable conditions of Brillouin method and Raman method in leakage monitoring. These tests help to determine the optimal location range for optical fiber laying.

[0071] The advantages are as follows: Brillouin method: it can realize distributed measurement, monitor leakage along the optical fiber for tens of kilometers, greatly improve the monitoring range, have a certain tolerance for optical fiber bending and loss, and is relatively easy to install, and can adapt to a variety of complex laying environments; Raman method: it has high measurement accuracy and can provide accurate temperature information, which is of great significance for early leakage detection and accurate diagnosis, is not affected by electromagnetic interference, can work normally in a strong electromagnetic environment, and is suitable for leakage monitoring around some special industrial sites and power facilities;

[0072] Applicable conditions include: Brillouin method: suitable for long-distance and large-scale leakage monitoring, which does not require extremely high accuracy, and the monitoring environment is relatively harsh, and the conditions for laying and protecting optical fibers are limited, such as large-scale water conservancy projects in the wild, oil pipelines in mountainous areas, etc. Raman method: suitable for places with high requirements for leakage monitoring accuracy, relatively small monitoring range, and good environmental conditions, which can ensure the normal operation of optical fibers and equipment, such as leakage monitoring of chemical pipelines in cities, basements of buildings, etc.

[0073] Step 27, software simulation and data analysis: Use finite element software to build a dam seepage model to simulate the on-site conditions, analyze the seepage field and temperature field cloud maps, and thus determine the optimal location range for fiber optic laying. At the same time, by comparing the measured data with the theoretical calculation results, the accuracy of the fiber optic sensing technology is evaluated.

[0074] Among them, the optimal position range combines the analysis results of the seepage field and temperature field cloud map to find areas with low seepage velocity, sparse equipotential lines and relatively stable temperature as the optimal position range for laying optical fiber. For example, in the central part of the dam (away from the water-facing and water-retaining surfaces), the seepage velocity is usually low, and if the temperature in this area does not change much, it is more suitable for laying optical fiber. For dams with anti-seepage walls, optical fiber can be laid along the inner side of the anti-seepage wall, where the seepage is blocked by the anti-seepage wall, the seepage velocity is low, and at the same time, it can be protected by the anti-seepage wall to a certain extent, reducing the impact of external factors on the optical fiber.

[0075] Step 3, identification of underwater construction defects of distributed optical fibers;

[0076] In this embodiment, the unique optical time domain reflectometry (OTDR) technology is mainly used to monitor any small changes along the optical fiber, such as strain, temperature change or breakage, which may be related to defects in construction.

[0077] OTDR is a sophisticated optoelectronic integrated technology that uses the backscattering caused by Rayleigh scattering and Fresnel reflection when light is transmitted in optical fiber. It is widely used in the maintenance and construction of optical cable lines. It can measure optical fiber length, optical fiber transmission attenuation, joint attenuation and fault location. When light pulses are transmitted in optical fiber, they will be scattered and reflected due to the properties of the optical fiber itself, connectors, joints, bends or other similar events. Part of the scattering and reflection will return to the OTDR. The returned useful information is measured by the OTDR detector. They are used as time or curve fragments at different positions in the optical fiber. The time from the transmission signal to the return signal is determined, and the speed of light in the glass material is determined to calculate the distance. The following formula explains how OTDR measures distance:

[0078] d=(c×t) / 2(IOR) (3)

[0079] Where c is the speed of light in a vacuum, and t is the total time from when the signal is transmitted to when it is received (round trip). The two values ​​multiplied by two equals the one-way distance. Because light travels slower in glass than in a vacuum, in order to accurately measure the distance, the optical fiber being measured must have a specified refractive index (IOR), which is specified by the optical fiber manufacturer.

[0080] OTDR uses Rayleigh scattering and Fresnel reflection to characterize the characteristics of optical fiber. Rayleigh scattering is caused by the irregular scattering of optical signals along the optical fiber. OTDR measures a portion of the scattered light that returns to the beginning of the optical fiber. These backscattered signals indicate the degree of attenuation (loss / distance) caused by the optical fiber. The resulting trajectory is a downward curve, which shows that the backscattered power is constantly decreasing. This is because both the transmitted and backscattered signals are lost after a certain distance of transmission. After the optical fiber parameters are given, the Rayleigh scattering power can be indicated. If the wavelength is known, it is proportional to the pulse width of the signal: the longer the pulse width, the stronger the backscattering power. The Rayleigh scattering power is also related to the wavelength of the transmitted signal. The shorter the wavelength, the stronger the power. That is to say, the trajectory generated by the 1310nm signal will have a higher Rayleigh backscattering than the trajectory generated by the 1550nm signal.

[0081] Step 4, concrete crack identification and monitoring using distributed optical fiber sensing technology;

[0082] Assume that the cross-sectional dimensions of the concrete beam specimen are 120mm×160mm, 1800mm long, the concrete strength grade is C30, the steel bar is HRB335 grade, the bottom tension bar is 12mm, the frame bar is 8mm, the stirrup is φ6@100, and the protective layer thickness is 25mm. Two 1m long, 0.9mm high-transmission tight-sheathed optical cables are symmetrically arranged on the tensile surface at the bottom of the beam, respectively recorded as optical cable 1 and optical cable 2. The distance between the two optical cables is 3cm. They are glued with epoxy resin and connected to the OFDR (Optical Frequency Domain Reflectometry) strain demodulator. The force mode is four-point bending, and the pure bending section is 600mm long. The test plan is as follows Figure 1 As shown in (a) and (b), OFDR was used to monitor the strain of the optical cable before the test and recorded as the initial data. After the distribution beam and jack were set up, it was recorded as the first-level load. Then, each load level increased by 2kN until the concrete beam cracked and failed. OFDR was used to monitor the strain value of the optical cable under each load level, and the crack width was recorded by a crack width gauge under each load level. At the end of the test, a total of 10 cracks were generated on the tensile surface of the concrete beam, of which four were larger cracks such as Figure 3 As shown in Figure 2, the strain data of the two optical cables under various levels of load are as follows: Figure 4 , Figure 5 As shown in the figure, both optical cable 1 and optical cable 2 have 10 strain peaks, and the peak positions almost coincide with the actual crack positions. After the distribution beam is placed on the small beam, that is, under the first level of load, the strain data has peaks as shown in Figure 5 As shown in the figure, the positions of 0.63m and other positions on the strain data of the second optical cable are where cracks occur. The crack width integrated after deducting the beam strain from the data at this time is about 0.002mm, which is invisible to the naked eye. After loading the third level, visible cracks appear at the corners. When loading the fourth level, the crack width reaches about 0.5mm, indicating that OFDR distributed optical fiber sensing technology can monitor the existence of cracks before the cracks are visible to the naked eye, and can be used for early warning before the cracks occur. The calculated widths of 10 cracks are calculated by the last level strain of the second optical cable, and the comparison with the actual width is shown in Table 1:

[0083] Table 1 Comparison of calculated and actual widths of 110 cracks

[0084] Crack number 1 2 3 4 5 6 7 8 9 10 Measurement 0.178 0.202 0.277 0.303 0.166 0.199 0.179 0.288 0.238 0.151 Actual value 0.18 0.20 0.28 0.31 0.16 0.20 0.18 0.28 0.24 0.16 Absolute error -0002 0.002 -0.003 0.007 0.006 -0.001 -0.001 0.008 -0.002 -0.009 Relative error -1.111 1.000 -1.071 -2.258 3.750 <![CDATA[-0 . 500]]> -0.556 2.857 -0.833 -5.625

[0085] From Table 1, we can see that the maximum error is 5.63% and the average error is 1.96%. By analyzing the development law of the same crack, we find that the expansion of the crack under each level of load is roughly the same. For example, the width of the fourth crack of the optical cable under each level of load changes. Figure 6The crack increases by about 0.025 mm at each level of loading, and the crack width reaches 0.10 mm, 0.22 mm, and 0.32 mm at the 7th, 11th, and 15th levels of loading, which is basically consistent with the results of the crack observation instrument, as shown in Figure 7 As shown in (a), (b), and (c), in the optical fiber sensing technology, the calculation formulas of several key parameters in the present invention include:

[0086] Step 41, calculation of effective sensing distance (L), expressed as:

[0087]

[0088] Among them, P SBS is the optical power that causes the SBS effect (Stimulated Brillouin Scattering), L SBS is the maximum sensing distance affected by the SBS effect, g B is the Brillouin gain coefficient, A eff is the effective area of ​​the fiber core, L c Refers to the coupling length or characteristic length of the optical fiber, P in Indicates the optical power input into the optical fiber.

[0089] Step 42, the spatial resolution (Δz) is usually related to the excitation light pulse width and is expressed as half of the pulse width, but in actual applications it will be affected by the comprehensive influence of system components.

[0090] Step 43, the upper limit of the frequency response (fmax), is calculated as follows:

[0091]

[0092] This ensures that there is no aliasing between the pulses.

[0093] Step 44, the signal-to-noise ratio (SNR) is very important in optical fiber sensing, and its expression takes into account the inherent loss and attenuation of signal transmission. According to formula (4), in general, the SNR is related to the loss of the optical fiber, the background noise and the signal strength.

[0094] The monitoring results show that OFDR can locate and monitor cracks in concrete structures through its strain curve. The peak position of the strain curve is the crack position. OFDR can monitor the development of cracks. The crack width can be obtained by integrating the strain peak data at the crack, with a maximum error of 5.63%, which meets engineering requirements. OFDR can monitor the emergence of tiny cracks and identify and warn when the structural crack reaches 0.002mm.

[0095] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent monitoring method for the construction quality of power transmission structure foundations under high water level environment, which uses distributed optical fiber sensors to send real-time monitoring data to a remote monitoring system, applies machine learning and artificial intelligence algorithms to intelligently analyze the monitoring data, and immediately triggers an early warning and displays it through a visual interface when the monitoring data exceeds a preset safety range; it is characterized by: The following steps are involved: S1, installing the prepared distributed optical fiber sensor on one side of the power transmission structure or the bottom surface of the concrete slab, and connecting the optical fiber interconnection line to the optical fiber signal conditioner; batch processing the collected spectral distribution to obtain the strain position map; S2, using a distributed optical fiber temperature measurement system to detect changes in the temperature field inside the dam concrete structure and monitor leakage in the earth-rock dam, quantitatively evaluating the sensitivity and multiplexing capability of optical fiber sensing technology under different crack widths; S3, identification and monitoring of underwater construction defects through OTDR detectors.

2. According to the method for intelligent monitoring of construction quality of power transmission structure foundation under high water level environment in claim 1, it is characterized in that: The production of the distributed optical fiber sensor is mainly divided into the following steps: S11, the adjustable optical fiber cable and the connector are tightly fixed in the chamfered steel shell to form a mirror head; S12, making a slight angle cut at the end of the optical fiber probe, then winding a wire around the optical fiber, and finally fixing the optical fiber in the outer sleeve to complete the production of the optical fiber probe; S13, connecting the optical fiber connectors with an optical fiber interconnection line.

3. According to the method for intelligent monitoring of construction quality of power transmission structure foundation under high water level environment in claim 1, it is characterized in that: In step S1, after passing through the optical fiber signal conditioner, the modulated signal A′ is obtained. c , the expression is as follows: A' c = A c ·(1+m·signal(t)) Among them, A c is the carrier amplitude, m is the modulation index, and signal(t) is the time-dependent modulating signal.

4. According to the method for intelligent monitoring of construction quality of power transmission structure foundation under high water level environment in claim 1, it is characterized in that: In step S2, through theoretical analysis and simulation experiments, a neural network model is used to qualitatively and quantitatively analyze the multiplexing performance of the optical fiber microbend crack sensor under different crack spacing, angles and widths of the concrete dam, and an optimization formula for predicting the multiplexing capacity is obtained; Through model tests and field tests, the applicability, stability, accuracy, advantages and disadvantages, and applicable conditions of the Brillouin method and Raman method in leakage monitoring are compared and analyzed to determine the optimal location range for optical fiber laying.

5. According to the method for intelligent monitoring of construction quality of power transmission structure foundation under high water level environment of claim 4, it is characterized in that: In fiber optic sensing technology, the main key parameters include effective sensing distance, spatial resolution, upper limit of frequency response and signal-to-noise ratio; The calculation expression of effective sensing distance L is: Among them, P SBS is the optical power causing the SBS effect, L SBS is the maximum sensing distance affected by the SBS effect, g B is the Brillouin gain coefficient, A eff is the effective area of ​​the fiber core, L c Refers to the coupling length or characteristic length of the optical fiber, P in Indicates the optical power input into the optical fiber; The spatial resolution is half the pulse width; Frequency response f max The upper limit of is calculated as:

6. According to the method for intelligent monitoring of construction quality of power transmission structure foundation under high water level environment in claim 4, it is characterized in that: Finite element software was used to establish a dam seepage model to simulate the on-site situation, and the seepage field and temperature field cloud maps were analyzed. Based on the analysis results of the seepage field and temperature field cloud maps, areas with low seepage velocity, sparse equipotential lines and relatively stable temperature were found as the optimal location range for optical fiber laying.

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