Concrete temperature monitoring optimization method and device based on optical fiber sensor and machine learning, and medium

Abstract

The invention relates to a concrete temperature monitoring optimization method and device based on an optical fiber sensor and machine learning, and a medium. The method comprises the following steps: selecting and setting the optical fiber sensor; performing a simulation experiment of concrete temperature monitoring, and acquiring accurate temperature monitoring data and transmission spectrum data of the current concrete by using an optical fiber sensor; performing moving average algorithm processing on the temperature monitoring data to obtain characteristic temperature data; taking the measurement data of a plurality of peak points of the transmission spectrum as the input of a deep neural network model, taking the characteristic temperature data processed by the moving average algorithm as the output of the deep neural network model, and training the model; and in a normal environment, acquiring transmission spectrum data of the concrete by using an optical fiber sensor, inputting the trained deep neural network model, and outputting a temperature prediction value. Compared with the prior art, the method has the advantages that the economical efficiency and accuracy of concrete temperature monitoring are improved, and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of construction quality monitoring and evaluation, and in particular to a concrete temperature monitoring optimization method, device and medium based on optical fiber sensor and machine learning. Background Art

[0002] With the continuous growth of social economy, the demand for large and high-rise buildings is increasing. These buildings usually use mass concrete as the basic structure. However, during the pouring and hardening process of mass concrete, a large amount of heat is generated by the hydration reaction, which leads to a sharp increase in the temperature difference between the inside and outside of the concrete. This significant temperature difference, combined with the low tensile strength of the concrete material itself, is prone to cause cracks, which in turn affects the integrity of the structure and may have a serious impact on the quality of the entire project, and even bring serious consequences. Therefore, it is of vital importance to effectively monitor the temperature of mass concrete during the pouring process and analyze the changes in its temperature field to guide the prevention and control of cracks and ensure the quality of pouring. The traditional temperature monitoring method mainly relies on point temperature sensors, which require manual regular inspections, have the risk of missed detection, sensor failure problems, and interference with construction during installation. In addition, the amount of data collected by this method is limited and discontinuous, making it difficult to fully reflect the changes in the temperature field inside the concrete. Summary of the invention

[0003] The purpose of the present invention is to overcome the problems of low monitoring and processing efficiency in the above-mentioned prior art, inability to accurately and economically monitor concrete temperature, resulting in excessive temperature difference between the inside and outside of the concrete causing cracks, affecting structural integrity and project quality, and to provide a concrete temperature monitoring optimization method, device and medium based on optical fiber sensors and machine learning.

[0004] The purpose of the present invention can be achieved by the following technical solutions:

[0005] A concrete temperature monitoring optimization method based on optical fiber sensor and machine learning includes the following steps:

[0006] S1. Select and set up the optical fiber sensor;

[0007] S2. Conduct a simulation experiment on concrete temperature monitoring and use optical fiber sensors to obtain accurate temperature monitoring data and transmission spectrum data of the current concrete;

[0008] S3, processing the temperature monitoring data by a moving average algorithm to obtain characteristic temperature data;

[0009] S4, using the measurement data of multiple peak points of the transmission spectrum as the input of the deep neural network model, and using the characteristic temperature data processed by the moving average algorithm as the output of the deep neural network model to train the model;

[0010] S5. Use the optical fiber sensor to obtain the transmission spectrum data of concrete in a normal environment, input it into the trained deep neural network model, and output the temperature prediction value.

[0011] The step S1 specifically includes: selecting a tilted Bragg grating sensor as the optical fiber sensor, determining the grating tilt angle and gauge length of the sensor, as well as the wavelength range, wavelength resolution, and wavelength scanning speed of the transmission spectrum.

[0012] In step S2, the simulation experiment simulates a large-volume concrete structure in a three-dimensional space, and the optical fiber sensors with calibrated positions are arranged in the simulated large-volume concrete structure space.

[0013] In step S3, the temperature monitoring data is processed by a moving average algorithm in the time, space and quality dimensions, and the characteristic temperature data of the sensor at a certain time and a certain spatial point are represented by the arithmetic mean of the original monitoring data of all valid sensors in the time, space and quality dimensions respectively.

[0014] The moving average calculation formulas for the time, space, and quality dimensions are as follows:

[0015]

[0016] Where t is the moment, s is the spatial point, o is the effective sensor in the monitored object, Δ(t) is the time interval containing the moment, Ω(s) is the spatial unit containing the spatial point, N(o) is all the temperature sensors deployed in the monitored object, n(t), n(s), n(o) are the number of collected temperature values ​​in the time interval, spatial unit and monitored object, respectively, T i (t), T j (s), T k (o) are the corresponding original temperature measurements, T(t), T(s), and T(o) are the characteristic temperature data of the moment, the spatial point, and the monitored object, respectively.

[0017] The input of the deep neural network model is the wavelengths of multiple peak points of the transmission spectrum.

[0018] The training of the deep neural network model specifically includes the following steps:

[0019] Collect the transmission spectrum wavelength data recorded by the tilted Bragg grating sensor under different temperature and strain conditions;

[0020] A specific number of wavelength peak points are selected as model input and the corresponding characteristic temperature data as model output. The data set is divided into training set, validation set and test set in proportion. The model is trained with the training set and validated with the validation set. The model with the best performance on the test set is selected as the optimal model to complete the model training.

[0021] The deep neural network model uses the LM algorithm to minimize the loss function to implement model training.

[0022] A concrete temperature monitoring and optimization device based on optical fiber sensor and machine learning includes a memory, a processor, and a program stored in the memory. When the processor executes the program, the method as described above is implemented.

[0023] A storage medium stores a program, which implements the method described above when executed.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] The present invention transfers the deep neural network model in the field of machine learning and applies it to the field of construction engineering, optimizes the existing concrete temperature monitoring method, improves the efficiency of concrete health status monitoring, can accurately and economically monitor the internal temperature of concrete in real time, and improves the accuracy of concrete health status detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a flow chart of the method of the present invention;

[0027] Figure 2 It is a measurement schematic diagram of the TFBG sensor of the present invention;

[0028] Figure 3 It is a schematic diagram of the arrangement of optical fiber sensors for the simulation experiment of the present invention;

[0029] Figure 4 Schematic diagram of the neuron structure of a deep neural network;

[0030] Figure 5 Schematic diagram of the process of data processing and model training of the deep neural network model of the present invention. DETAILED DESCRIPTION

[0031] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0032] This embodiment provides a concrete temperature monitoring optimization method based on optical fiber sensor and machine learning. Figure 1 As shown, the following steps are included:

[0033] S1. Select and set up the fiber optic sensor.

[0034] Fiber optic sensors, with their advantages of high precision, good safety, anti-interference ability, stability and durability, can provide a variety of information such as temperature, strain, displacement, settlement, tilt and acceleration. Monitoring systems developed based on fiber optic sensing technology have been widely used in various application scenarios in the field of civil engineering. Currently, the widely used fiber optic sensors include Bragg gratings, long-period fiber gratings, Fabry-Perot (FP) and fiber structures based on multi-mode interference (MMI). Among them, Bragg grating (FBG) sensors are widely used for temperature and strain measurement. Fiber Bragg gratings are made by exposing the core of a single-mode optical fiber to strong ultraviolet light with a periodic pattern laterally, and a small amount of light will be reflected at each spatial periodic refractive index change. When the grating period is about half the wavelength of the incident light, all the reflected light is coherently combined into a large reflection with a specific wavelength. This is called the Bragg condition, and the wavelength at which the incident light is reflected is called the Bragg wavelength, which varies with the axial strain and temperature of the sensor. Tilted Fiber Bragg Gate temperature sensors (TFBG) sensors, which are formed by slightly tilting the grating of a Fiber Bragg Grating (FBG) sensor, are sensors that can measure temperature and strain simultaneously without the need for additional sensors.

[0035] This embodiment selects a tilted Bragg grating sensor as the optical fiber sensor, determines the grating tilt angle and gauge length of the sensor, as well as the wavelength range, wavelength resolution, and wavelength scanning speed of the transmission spectrum.

[0036] Specifically, the following case can be used as a reference: A TFBG sensor has a tilt angle of 4° and a gauge length of 6 mm. The schematic diagram of its measurement system is as follows: Figure 2 . The transmission spectrum of the TFBG sensor in the wavelength range of 1510-1560nm was measured using a tunable laser and a power meter. The wavelength resolution was set to 1pm. The wavelength scanning speed was set to 10nm / s. By placing an optical circulator between the light source and the sensor, the transmitted light and the reflected light were acquired simultaneously. An optical isolator was placed between the sensor and the power meter to prevent the light from being reflected backwards due to reflection at the connection point between the optical fiber and the power meter. The light source emits incident light, which is divided into transmitted light and reflected light by the optical circulator. The transmitted light passes through the Bragg grating sensor (under different temperature conditions) and is received by the power meter to obtain different transmission spectra under different temperature conditions.

[0037] S2. Conduct a simulation experiment on concrete temperature monitoring and use optical fiber sensors to obtain accurate temperature monitoring data and transmission spectrum data of the current concrete.

[0038] The simulation experiment simulates a large-volume concrete structure in a three-dimensional space, and deploys the calibrated optical fiber sensors in the simulated large-volume concrete structure space. Specifically, the following case can be used as a reference: a large-volume concrete structure is simulated in a 4m×1.5m×0.8m three-dimensional space. The sensor deployment model is as follows: Figure 3 As shown, there are 3 layers in total, and each layer is wired in a folded manner.

[0039] S3. Process the temperature monitoring data using a moving average algorithm to obtain characteristic temperature data.

[0040] Moving average (MA) is a commonly used technical means of data analysis. The basic idea is that the object being averaged changes dynamically over time. Traditional moving average is widely used in time series data analysis, industrial filtering, stock prediction, etc. According to the size of the average window, the moving average can be divided into short-term, medium-term and long-term moving average. According to different average calculation methods, the moving average can be divided into simple moving average, weighted moving average and other types. Its general calculation formula can be expressed as:

[0041] FMA=(f(x)+f(x)+f(x)+…+

[0042] f(xN)) / N.

[0043] Among them: F MA is the moving average value of variable x, N is the size of the moving average window, and f(x) is the average calculation function.

[0044] The moving average method was originally used to analyze stock price fluctuations by decomposing price fluctuations into a combination of primary trends, secondary trends, and daily fluctuations. In the field of monitoring data analysis, this method can be understood as identifying components such as true values, random errors, and systematic errors in the data. Random errors are ubiquitous and have a high frequency. They are usually formed by random fluctuations caused by uncertain factors in the measurement process. They have the characteristics of offsetting each other and generally follow a normal distribution. Systematic errors are usually caused by improper instrument installation or abnormal monitoring systems, and are manifested as regular deviations.

[0045] Moving average technology performs well in filtering high-frequency and periodic errors in data. By extending the traditional time series moving average method to the spatial and mass dimensions, a multidimensional moving average analysis method can be developed, which is particularly suitable for real-time temperature monitoring data of concrete. This multidimensional moving average analysis method can not only eliminate the implicit high-frequency random errors in a large amount of real-time temperature monitoring data, but also reduce the systematic errors in the data by dynamically identifying and removing abnormal temperature sensors.

[0046] The temperature monitoring data is processed by the moving average algorithm in the time, space and quality dimensions, and the characteristic temperature data of the sensor at a certain moment and a certain spatial point are represented by the arithmetic mean of the original monitoring data of all valid sensors in the time, space and quality dimensions respectively.

[0047] The moving average calculation formulas for time, space, and quality dimensions are as follows:

[0048]

[0049] Where t is the moment, s is the spatial point, o is the effective sensor in the monitored object, Δ(t) is the time interval containing the moment, Ω(s) is the spatial unit containing the spatial point, N(o) is all the temperature sensors deployed in the monitored object, n(t), n(s), n(o) are the number of collected temperature values ​​in the time interval, spatial unit and monitored object, respectively, T i (t), T j (s), T k (o) are the corresponding original temperature measurements, T(t), T(s), and T(o) are the characteristic temperature data of the moment, the spatial point, and the monitored object, respectively.

[0050] In one embodiment, the average moving algorithm specifically includes the following steps:

[0051] S301, the sensor collects raw data;

[0052] S302, the collected data is transmitted online in real time to the original database for storage;

[0053] S303, processing the temperature monitoring data of the original database using time, space, and quality dimension average moving code programs respectively;

[0054] S304, the processed data is stored in a feature database;

[0055] S305, extracting the characteristic temperature of a certain time point, space point, or monitoring object from the characteristic database for use in the model output of step S4.

[0056] S4. Use the measurement data of multiple peak points of the transmission spectrum as the input of the deep neural network model, and use the characteristic temperature data processed by the moving average algorithm as the output of the deep neural network model to train the model.

[0057] Deep Neural Networks (DNN) is a neural network model composed of multiple hidden layers. Each hidden layer consists of multiple neurons, which transmit information and calculate through weights and activation functions.

[0058] Through multiple layers of nonlinear transformation, deep neural networks can learn more abstract and complex feature representations. Each layer can transform input data into a more advanced representation, thereby better capturing the characteristics and patterns of the data. By continuously stacking hidden layers, the network can gradually learn more abstract features and improve the model's expressiveness.

[0059] A deep neural network consists of an input layer, a hidden layer, and an output layer. The input layer is only responsible for inputting data, has no activation function, and contains neurons equal to the number of features contained in a single instance; the hidden layer is used to extract features and must contain an activation function; the output layer is responsible for outputting the predicted value of the model and can contain an activation function. The number of neurons contained in the output layer is related to the number of label categories.

[0060] Neurons are the most basic units in neural networks. Their structure is as follows: Figure 4 As shown, x is the input, each connection has a weight w, and the middle node is the artificial neuron node; δ is a nonlinear transformation, called an activation function, the purpose of which is to enable artificial neurons to have the ability to represent nonlinear relationships; parameter b is called bias; output is the output of the artificial neuron.

[0061] The activation function is a nonlinear function in a neural network that acts on the input signal of a neuron and converts it into the output of the neuron. The activation function plays a role in introducing nonlinear transformations in neural networks and increases the network's expressive power.

[0062] The loss function is a mathematical function that measures the error between the predicted value and the true value to determine whether the model's prediction results are accurate and to evaluate the model's performance.

[0063] The tilted Bragg grating sensor (TFBG sensor) can sense the wavelength changes in the transmission spectrum and use the wavelengths of multiple peak points of the transmission spectrum as feature values ​​input to machine learning. 70% of the data is used for training, 15% for validation, and 15% for testing.

[0064] like Figure 5 As shown, the training of the deep neural network model specifically includes the following steps:

[0065] S401, collecting transmission spectrum wavelength data recorded by the tilted Bragg grating sensor under different temperature and strain conditions;

[0066] S402, select a specific number of wavelength peak points as model input and the corresponding characteristic temperature data as model output, divide the data set into training set, validation set and test set in the ratio of 70%, 15% and 15%, train the model with the training set, validate the model with the validation set, and select the model with the best performance on the test set as the optimal model to complete the model training.

[0067] This embodiment uses the Levenberg-Marquardt algorithm (LM algorithm) as a solution to reduce the loss function. The LM algorithm uses the gradient descent method and the Gauss-Newton method to reduce the loss function. The gradient descent method is less affected by the position of the minimum value of the loss function, while the Gauss-Newton method is faster and more accurate near the minimum value. The LM algorithm combines the advantages of these two methods.

[0068] The number of layers of the deep neural network is set to 30, and the network code is written using Python and its libraries.

[0069] The root mean square error indicator RMSE is used to evaluate the performance of the model, and its calculation formula is as follows:

[0070]

[0071] Among them, N is the number of predicted samples, observed t Indicates the accurate temperature monitoring value of the tth sample, predicted t Represents the model predicted temperature value for the tth sample.

[0072] S5. Use the optical fiber sensor to obtain the transmission spectrum data of concrete in a normal environment, input it into the trained deep neural network model, and output the temperature prediction value.

[0073] In the specific implementation, taking the pouring and maintenance of large-volume concrete of bridge towers as an example, the selection and parameter setting of TFBG sensors are completed before pouring. The original temperature data is collected in the laboratory through a proportional space simulation experiment, and the moving average algorithm of time, space and quality dimensions is used. At the same time, an appropriate number of peak points are selected from the transmission spectrum data collected by the TFBG sensor system. The temperature data processed by the moving average algorithm is divided into training set, test set and validation set as the input and output of the deep neural network model respectively. After repeated model training and model evaluation, the model with the best performance is obtained. In the concrete pouring process of the actual project, the actual TFBG sensors are arranged according to the arrangement method in the simulation experiment. The real-time transmission spectrum data of concrete is obtained through demodulation and real-time transmission, and the actual temperature monitoring value is predicted using the trained prediction model.

[0074] The present embodiment provides a concrete temperature monitoring and optimization device based on an optical fiber sensor and machine learning, including a memory, a processor, and a program stored in the memory, and the processor implements the method described above when executing the program.

[0075] In a preferred embodiment, the device comprises:

[0076] Data acquisition module: select and set up optical fiber sensors; conduct simulation experiments on concrete temperature monitoring, and use optical fiber sensors to obtain accurate temperature monitoring data and transmission spectrum data of the current concrete;

[0077] Temperature data processing module: Process the temperature monitoring data using a moving average algorithm to obtain characteristic temperature data;

[0078] Model training module: The measured data of multiple peak points of the transmission spectrum are used as the input of the deep neural network model, and the characteristic temperature data processed by the moving average algorithm are used as the output of the deep neural network model to train the model;

[0079] Temperature monitoring module: Use optical fiber sensors to obtain the transmission spectrum data of concrete in a normal environment, input the trained deep neural network model, and output the temperature prediction value.

[0080] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0081] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0082] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning, or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A concrete temperature monitoring optimization method based on optical fiber sensor and machine learning, characterized in that: The following steps are involved: S1. Select and set up the optical fiber sensor; S2. Conduct a simulation experiment on concrete temperature monitoring and use optical fiber sensors to obtain accurate temperature monitoring data and transmission spectrum data of the current concrete; S3, processing the temperature monitoring data by a moving average algorithm to obtain characteristic temperature data; S4, using the measurement data of multiple peak points of the transmission spectrum as the input of the deep neural network model, and using the characteristic temperature data processed by the moving average algorithm as the output of the deep neural network model to train the model; S5. Use the optical fiber sensor to obtain the transmission spectrum data of concrete in a normal environment, input it into the trained deep neural network model, and output the temperature prediction value.

2. According to claim 1, a concrete temperature monitoring optimization method based on optical fiber sensor and machine learning is characterized in that: The step S1 specifically includes: selecting a tilted Bragg grating sensor as the optical fiber sensor, determining the grating tilt angle and gauge length of the sensor, as well as the wavelength range, wavelength resolution, and wavelength scanning speed of the transmission spectrum.

3. The concrete temperature monitoring optimization method based on optical fiber sensor and machine learning according to claim 1 is characterized in that: In step S2, the simulation experiment simulates a large-volume concrete structure in a three-dimensional space, and the optical fiber sensors with calibrated positions are arranged in the simulated large-volume concrete structure space.

4. The method for optimizing concrete temperature monitoring based on optical fiber sensor and machine learning according to claim 1 is characterized in that: In step S3, the temperature monitoring data is processed by a moving average algorithm in the time, space and quality dimensions, and the characteristic temperature data of the sensor at a certain time and a certain spatial point are represented by the arithmetic mean of the original monitoring data of all valid sensors in the time, space and quality dimensions respectively.

5. The concrete temperature monitoring optimization method based on optical fiber sensor and machine learning according to claim 4 is characterized in that: The moving average calculation formulas for the time, space, and quality dimensions are as follows: Where t is the moment, s is the spatial point, o is the effective sensor in the monitored object, Δ(t) is the time interval containing the moment, Ω(s) is the spatial unit containing the spatial point, N(o) is all the temperature sensors deployed in the monitored object, n(t), n(s), n(o) are the number of collected temperature values ​​in the time interval, spatial unit and monitored object, respectively, T i (t), T j (s), T k (o) are the corresponding original temperature measurements, T(t), T(s), and T(o) are the characteristic temperature data of the moment, the spatial point, and the monitored object, respectively.

6. The method for optimizing concrete temperature monitoring based on optical fiber sensor and machine learning according to claim 1 is characterized in that: The input of the deep neural network model is the wavelengths of multiple peak points of the transmission spectrum.

7. The method for optimizing concrete temperature monitoring based on optical fiber sensor and machine learning according to claim 1 is characterized in that: The training of the deep neural network model is specifically The following steps are involved: Collect the transmission spectrum wavelength data recorded by the tilted Bragg grating sensor under different temperature and strain conditions; A specific number of wavelength peak points are selected as model input and the corresponding characteristic temperature data as model output. The data set is divided into training set, validation set and test set in proportion. The model is trained with the training set and validated with the validation set. The model with the best performance on the test set is selected as the optimal model to complete the model training.

8. The method for optimizing concrete temperature monitoring based on optical fiber sensor and machine learning according to claim 1 is characterized in that: The deep neural network model uses the LM algorithm to minimize the loss function to implement model training.

9. A concrete temperature monitoring and optimization device based on optical fiber sensor and machine learning, comprising a memory, a processor, and a program stored in the memory, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 8 is implemented.

10. A storage medium having a program stored thereon, characterized in that: When the program is executed, the method according to any one of claims 1 to 8 is implemented.

Images