Evaluation method and device for embankment crack

Abstract

The invention provides a method and a device for evaluating embankment cracks, which can be applied to the technical field of geotechnical engineering. The evaluation method comprises the following steps: acquiring initial to-be-evaluated information corresponding to a crack on the surface of an embankment; the initial to-be-evaluated information is processed on the basis of a decomposition strategy and a preset threshold value, to-be-evaluated information is obtained, the to-be-evaluated information comprises geometric information of the embankment, soil body information and environment information corresponding to the embankment, the decomposition strategy is used for decomposing the initial to-be-evaluated information, and the soil body information is stored in the decomposition strategy; the preset threshold value is used for performing threshold value quantification on the decomposed initial to-be-evaluated information; the soil body information, the geometric information and the environment information are input into an evaluation model, a size evaluation result and a rate evaluation result of the crack are obtained, and the evaluation model comprises a feature evaluation sub-model and a state evaluation sub-model.

Description

Technical Field

[0001] The invention relates to the technical field of geotechnical engineering, and in particular to a method and device for evaluating embankment cracks. Background Art

[0002] Embankment cracks can cause many problems. For example, in terms of road structure, cracks can weaken the overall strength of the roadbed and pavement, resulting in a decrease in the bearing capacity of the pavement and accelerating the damage of the pavement structure. In terms of traffic safety, cracks can reduce the smoothness of the pavement, affecting the comfort and stability of vehicle driving. Cracks provide infiltration channels for rain and snow, aggravating the softening and erosion of the roadbed soil, and increasing the cost and difficulty of road maintenance. Related technologies The prediction and simulation methods for embankment cracks mainly include: simulating the stress distribution and deformation of the embankment under different conditions through the finite element method, predicting the location and expansion trend of the cracks; analyzing and segmenting the crack image based on the deep learning network (such as Transformer), predicting the location and morphology of the cracks in the future time period; and installing sensors (such as strain gauges and displacement meters) on the embankment to monitor the state of the road surface in real time.

[0003] However, numerical simulation methods require the construction of relatively accurate mathematical models, and the accuracy of simulation results depends on the accuracy of input parameters. In the prediction process of traditional deep learning models, there is a lack of appropriate preprocessing for multi-source measurement data, and the measurement data are mostly non-stationary signals, with the loss of key features, resulting in low accuracy of prediction results. The real-time monitoring method of equipment is greatly affected by external environmental factors (such as temperature and humidity) and the equipment cost is relatively high. Summary of the invention

[0004] In view of the above problems, the present invention provides a method, device, equipment, medium and program product for evaluating embankment cracks.

[0005] According to a first aspect of the present invention, a method for evaluating embankment cracks is provided, comprising: obtaining initial information to be evaluated corresponding to cracks on the surface of the embankment; processing the initial information to be evaluated based on a decomposition strategy and a preset threshold to obtain information to be evaluated, wherein the information to be evaluated includes geometric information of the embankment, soil information and environmental information corresponding to the embankment, the decomposition strategy is used to decompose the initial information to be evaluated, and the preset threshold is used to perform threshold quantization on the decomposed initial information to be evaluated; inputting the soil information, geometric information and environmental information into an evaluation model to obtain size evaluation results and rate evaluation results of the cracks, wherein the evaluation model includes a feature evaluation sub-model and a state evaluation sub-model.

[0006] The second aspect of the present invention provides an evaluation device for embankment cracks, including: an information acquisition module, used to obtain initial information to be evaluated corresponding to cracks on the surface of the embankment; an information processing module, used to process the initial information to be evaluated based on a decomposition strategy and a preset threshold value to obtain information to be evaluated, wherein the information to be evaluated includes geometric information of the embankment, soil information and environmental information corresponding to the embankment based on different types, the decomposition strategy is used to decompose the initial information to be evaluated, and the preset threshold value is used to threshold quantize the decomposed initial information to be evaluated; an information input module, used to input soil information, geometric information and environmental information into an evaluation model to obtain size evaluation results and rate evaluation results of the cracks, wherein the evaluation model includes a feature evaluation sub-model and a state evaluation sub-model based on different functions.

[0007] A third aspect of the present invention provides an electronic device, comprising: one or more processors; a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0008] The fourth aspect of the present invention further provides a computer-readable storage medium on which a computer program or instruction is stored, and the steps of the above method are implemented when the above computer program or instruction is executed by a processor.

[0009] The fifth aspect of the present invention also provides a computer program product, including a computer program or instructions, which implement the steps of the above method when executed by a processor.

[0010] According to the embodiments of the present invention, the information to be evaluated can retain the key features of the initial information to be evaluated after being processed by the decomposition strategy and the preset threshold, thereby improving the signal-to-noise ratio and quality of the data. Since the evaluation model integrates the evaluation functions for the features and states of multi-source information and time series information, by extracting the key features of multi-source information to capture long-term trends, the nonlinear time-varying characteristics of embankment cracks are processed, and the size evaluation results and rate evaluation results of the cracks at future times are obtained, thereby improving the rationality and reliability of the crack evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above contents and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

[0012] Figure 1 An application scenario diagram of an embankment crack assessment method, device, equipment, medium, and program product according to an embodiment of the present invention is shown;

[0013] Figure 2 A flow chart showing a method for evaluating embankment cracks according to an embodiment of the present invention is shown;

[0014] Figure 3A An example schematic diagram of an initial evaluation model training process according to an embodiment of the present invention is shown;

[0015] Figure 3B A schematic diagram of the structure of a CNN layer according to an embodiment of the present invention is shown;

[0016] Figure 3C A schematic diagram of the structure of a BiLSTM layer according to an embodiment of the present invention is shown;

[0017] Figure 4A A three-dimensional schematic diagram of widening a longitudinal crack of an embankment according to an embodiment of the present invention is shown;

[0018] Figure 4B A comparison chart of the measured value and the predicted value of the crack width of the widened embankment according to an embodiment of the present invention;

[0019] Figure 5 A structural block diagram of a device for evaluating embankment cracks according to an embodiment of the present invention is shown;

[0020] Figure 6 A block diagram of an electronic device suitable for implementing a method for evaluating embankment cracks according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0021] Below, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of embodiments of the present invention. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of concepts of the present invention.

[0022] The terms used herein are only for describing specific embodiments and are not intended to limit the present invention. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0023] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0024] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0025] In related technologies, numerical simulation methods require the construction of relatively accurate mathematical models, and the accuracy of simulation results depends on the accuracy of input parameters. In the prediction process of traditional deep learning models, there is a lack of appropriate preprocessing for multi-source measurement data, and the measurement data are mostly non-stationary signals, and there is a situation where key features are lost, resulting in low accuracy of prediction results. The real-time monitoring method of equipment is greatly affected by external environmental factors (such as temperature and humidity) and the equipment cost is relatively high.

[0026] In view of this, the information to be evaluated can retain the key features of the initial information to be evaluated after decomposition strategy and preset threshold processing, thereby improving the signal-to-noise ratio and quality of the data. Since the evaluation model integrates the evaluation functions for the features and states of multi-source information and time series information, by extracting the key features of multi-source information to capture long-term trends, the nonlinear time-varying characteristics of embankment cracks are processed, and then the size evaluation results and rate evaluation results of the cracks at future times are obtained, thereby improving the rationality and reliability of crack evaluation.

[0027] An embodiment of the present invention provides an evaluation method for embankment cracks, comprising: obtaining initial information to be evaluated corresponding to cracks on the surface of the embankment; processing the initial information to be evaluated based on a decomposition strategy and a preset threshold to obtain information to be evaluated, wherein the information to be evaluated includes geometric information of the embankment, soil information and environmental information corresponding to the embankment, the decomposition strategy is used to decompose the initial information to be evaluated, and the preset threshold is used to perform threshold quantization on the decomposed initial information to be evaluated; inputting the soil information, geometric information and environmental information into an evaluation model to obtain a size evaluation result and a rate evaluation result of the cracks, wherein the evaluation model includes a feature evaluation sub-model and a state evaluation sub-model.

[0028] Figure 1 A diagram showing an application scenario of a method, device, equipment, medium and program product for evaluating embankment cracks according to an embodiment of the present invention is shown.

[0029] like Figure 1As shown, the application scenario 100 according to this embodiment may include a terminal device 101, a network 102, a server 103, and a data acquisition device 104. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0030] The data acquisition device 104 can be used to collect geometric information, soil information and environmental information of the embankment. It includes but is not limited to surveying drones, temperature sensors, humidity sensors and soil pressure sensors. For example, the embankment can be photographed by drones equipped with high-precision cameras, and the geometric information and three-dimensional model of the embankment can be generated using photogrammetry technology. For example, temperature sensors and humidity sensors can be used to monitor the soil temperature information, humidity information and time change information in real time. For example, soil pressure sensors can be used to monitor the total pressure at the interface between the embankment and the soil to evaluate the stress state and stability of the soil.

[0031] The data collection device 104 can send the collected information about the embankment to the server 103. The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages.

[0032] The terminal device 101 may be any electronic device having a display screen and supporting web browsing, including but not limited to a smart phone, a tablet computer, a laptop computer, a desktop computer, and the like.

[0033] The server 103 may be a server that provides various services. The server 103 may process the received relevant information of the embankment (such as soil information, geometric information, and environmental information), obtain the processing results of the cracks on the embankment surface, and send the processing results to the terminal device 101.

[0034] It should be noted that the method for evaluating embankment cracks provided in the embodiment of the present invention can generally be executed by the server 103. Accordingly, the device for evaluating embankment cracks provided in the embodiment of the present invention can generally be set in the server 103. The method for evaluating embankment cracks provided in the embodiment of the present invention can also be executed by a server or server cluster that is different from the server 103 and can communicate with the terminal device 101 and / or the server 103. Accordingly, the device for evaluating embankment cracks provided in the embodiment of the present invention can also be set in a server or server cluster that is different from the server 103 and can communicate with the terminal device 101 and / or the server 103.

[0035] It should be understood that Figure 1The number of terminal devices, networks, data collection devices and servers in the embodiment is only for illustration. Any number of terminal devices, data collection devices, networks and servers may be provided according to the implementation requirements.

[0036] Figure 2 A flow chart of a method for evaluating embankment cracks according to an embodiment of the present invention is shown.

[0037] like Figure 2 As shown, the method for evaluating embankment cracks in this embodiment includes operations S210 to S230.

[0038] In operation S210, initial information to be evaluated corresponding to cracks on the surface of the embankment is obtained.

[0039] In an embodiment of the present invention, the embankment may be an embankment that has been completed and is in service, including at least one of an existing embankment and a newly built embankment. The initial information to be evaluated may be used to evaluate the development trend of existing cracks on the embankment surface and multi-source information of status information, and may also be used to evaluate the probability of cracks appearing on the embankment surface in the future. The method and device for obtaining the initial information to be evaluated are based on the standard of satisfying the comprehensiveness and accuracy of information acquisition, and are not limited here.

[0040] In operation S220, the initial information to be evaluated is processed based on the decomposition strategy and the preset threshold to obtain the information to be evaluated, wherein the information to be evaluated includes geometric information of the embankment, soil information and environmental information corresponding to the embankment, the decomposition strategy is used to decompose the initial information to be evaluated, and the preset threshold is used to perform threshold quantization on the decomposed initial information to be evaluated.

[0041] In an embodiment of the present invention, the decomposition strategy can be used to decompose the acquired initial information to be evaluated to obtain a method for decomposing coefficients of multiple dimensions. The preset threshold can represent the target threshold selected for quantizing the decomposed initial information to be evaluated to remove signal noise. The geometric information can include width information and height information of different dimensions of the embankment. The soil information can include state information and mechanical information of the embankment soil. The environmental information can include external environmental information and settlement deformation information corresponding to the embankment.

[0042] For example, the geometric information, soil information and environmental information of the embankment are decomposed and processed to obtain the multi-dimensional width information and height information of the embankment, the state information and mechanical information of the land, the external environment information corresponding to the embankment and the settlement deformation information.

[0043] In operation S230, soil information, geometric information, and environmental information are input into an evaluation model to obtain a size evaluation result and a rate evaluation result of the crack, wherein the evaluation model includes a feature evaluation sub-model and a state evaluation sub-model.

[0044] In an embodiment of the present invention, the feature evaluation submodel can be used to extract key spatial relationship information of multi-source information, which is time series information; the state evaluation submodel can be used to process the extracted time series information, obtain the long-term dependency between the time series information, and thus obtain the size evaluation result of the crack in the future time period and the rate evaluation result of the crack development. The size evaluation result may include the width, length and depth information of the crack.

[0045] For example, soil information, geometric information, and environmental information are input into the feature assessment sub-model, and the key spatial relationship information of the time series is output. The key spatial relationship information is then input into the state assessment sub-model as input information to obtain the size assessment results and rate assessment results of the cracks.

[0046] According to the embodiments of the present invention, the information to be evaluated can retain the key features of the initial information to be evaluated after being processed by the decomposition strategy and the preset threshold, thereby improving the signal-to-noise ratio and quality of the data. Since the evaluation model integrates the evaluation functions for the features and states of multi-source information and time series information, by extracting the key features of multi-source information to capture long-term trends, the nonlinear time-varying characteristics of embankment cracks are processed, and the size evaluation results and rate evaluation results of the cracks at future times are obtained, thereby improving the rationality and reliability of the crack evaluation.

[0047] It can be understood that how to determine the size evaluation result and rate evaluation result of the crack has been described above, and how to obtain the information to be evaluated will be described below.

[0048] According to an embodiment of the present invention, the decomposition strategy includes a decomposition function and a decomposition dimension;

[0049] The initial information to be evaluated is processed based on a decomposition strategy and a preset threshold to obtain the information to be evaluated, including: decomposing the initial information to be evaluated using a decomposition function and a decomposition dimension to obtain decomposition coefficients of multiple frequencies corresponding to the initial information to be evaluated; threshold quantizing the decomposition coefficients of multiple frequencies based on a preset threshold to obtain processing coefficients of multiple frequencies; and fusing the processing coefficients of multiple frequencies based on an inverse transformation function to obtain the information to be evaluated.

[0050] In an embodiment of the present invention, the decomposition function can be used to decompose multi-source information to be evaluated to obtain functions of different scale features. The decomposition dimension is a decomposition dimension determined based on the decomposition function and actual needs, and the decomposition dimension is proportional to the frequency resolution of the signal. The decomposition coefficient can be a high-frequency wavelet coefficient and / or a low-frequency wavelet coefficient obtained by the decomposition function.

[0051] In an embodiment of the present invention, the preset threshold value may be a threshold value set during the quantization operation of the high-frequency wavelet coefficient or the low-frequency wavelet coefficient. The coefficients less than the preset threshold value may be set to zero or other forms of quantization operations may be performed to remove or reduce the noise component and retain the important features in the signal. The processing coefficient may be a processing result obtained after the threshold quantization processing. The inverse transformation function may be to gradually combine the low-frequency coefficients and the high-frequency coefficients through the reconstruction process of the coefficients subjected to multi-level decomposition, and gradually restore the approximate information corresponding to the original information to obtain the complete information to be evaluated.

[0052] In the related art, there is a lack of comprehensive analysis of basic information of different scales of embankment cracks at both macro and micro levels, resulting in poor adaptability of the analysis process and low accuracy of the analysis results. The present application decomposes and reconstructs the acquired information to be evaluated through a decomposition function to obtain the information to be evaluated with the noise component removed or suppressed.

[0053] For example, in the case of symmetrical cracks that are prone to occur during embankment widening, the Symlets wavelet and decomposition scale (determined according to actual needs, generally 3-5 layers) can be selected to perform wavelet decomposition on the preprocessed soil information and decompose the signal into sub-band signals of different frequency bands. By better maintaining the symmetry characteristics of the cracks during the decomposition process, it is helpful to more accurately analyze the shape and distribution of the cracks.

[0054] For example, after the initial information to be evaluated is decomposed at multiple levels and processing coefficients of multiple frequencies are obtained, the processing coefficients can be reconstructed in a corresponding multi-level manner through an inverse transformation function, and the processed low-frequency coefficients and high-frequency coefficients can be gradually combined to restore an approximate version of the original signal step by step until a complete reconstructed signal is obtained.

[0055] According to the embodiments of the present invention, the decomposition function is used to remove noise while retaining the effective information of the crack, making the crack features more prominent, improving the robustness of the detection algorithm to noise, and being able to stably detect cracks in complex environments. By comprehensively analyzing the characteristics and change laws of cracks at different scales, a richer information basis is provided for the evaluation and prediction of cracks. For example, by analyzing the geometric characteristics of cracks at different scales, the development trend and potential risks of cracks can be more accurately judged.

[0056] According to an embodiment of the present invention, the method further includes: determining a decomposition function and a decomposition dimension according to at least one of a length feature and a frequency feature of the initial information to be evaluated.

[0057] In an embodiment of the present invention, the decomposition dimension may be determined by the signal length and high and low frequencies corresponding to the acquired information to be evaluated. The specific dimension may be determined according to actual conditions or requirements and is not limited here.

[0058] For example, in the processing of embankment crack information, if the focus is on the overall trend of the cracks, such as the direction and general distribution of the cracks, a lower decomposition scale (such as 1-2 layers) can be selected. On the contrary, if the detailed characteristics of the cracks, such as the microscopic texture and small branches of the cracks, are analyzed, a higher decomposition scale (such as 4-6 layers) can be selected.

[0059] According to an embodiment of the present invention, soil information, geometric information and environmental information are input into an evaluation model to obtain size evaluation results and rate evaluation results of cracks, including: inputting soil information, geometric information and environmental information into a feature evaluation sub-model to output feature evaluation results; inputting feature evaluation results into a state evaluation sub-model to output size evaluation results and rate evaluation results.

[0060] In an embodiment of the present invention, the feature evaluation submodel may be a deep learning model including multiple network layers for extracting multi-source initial information to be evaluated. The state evaluation submodel may be an evaluation model based on a gating algorithm and a fusion strategy, including long short-term memory network modules in different directions.

[0061] For example, soil information, geometric information, and environmental information are input into a trained convolutional neural network model to extract key spatial relationship information of multi-element data and obtain feature evaluation results of the time series. The feature evaluation results are then input into a trained bidirectional long short-term memory network to simultaneously process the forward information and backward information in the time series information to determine the context information from front to back and from back to front in the sequence respectively. The context information from front to back and the context information from back to front are then fused at each time step to obtain the final output representation, i.e., the size evaluation results and rate evaluation results of the cracks.

[0062] According to an embodiment of the present invention, by processing the information to be evaluated through an evaluation model that integrates a feature evaluation submodel and a state evaluation submodel, it is possible to extract multidimensional key feature information from multi-source data using the feature evaluation submodel, capture the spatial distribution, morphology, position and other characteristics of cracks, and the mutual influence between different positions, and provide rich spatial details for prediction. For processing time series data, the evolution law and trend of embankment cracks in the time dimension can be captured, and the front-to-back dependencies in the time series can be integrated. The fused evaluation model can organically integrate spatial features and temporal features, and can more comprehensively and accurately model the development process of embankment cracks, thereby improving the prediction accuracy, more accurately predicting the expansion direction, speed and degree of cracks, and providing a reliable basis for taking effective maintenance measures in a timely manner.

[0063] According to an embodiment of the present invention, a feature evaluation submodel includes an extraction layer, a conversion layer and an update layer; soil information, geometric information and environmental information are input into the feature evaluation submodel, and a feature evaluation result is output, including: inputting soil information, geometric information and environmental information into the extraction layer for feature extraction to obtain initial feature information; inputting the initial feature information into the conversion layer for nonlinear conversion to obtain intermediate feature information; inputting the intermediate feature information into the update layer for dimensionality reduction and feature enhancement processing to obtain multidimensional feature information; and converting the multidimensional feature information to obtain a feature evaluation result.

[0064] In an embodiment of the present invention, the extraction layer can be used to extract features from multidimensional information to be evaluated to obtain initial feature information; the conversion layer can be used to transform the extracted initial feature information by introducing nonlinear factors to fit more complex relationships in the data to obtain intermediate feature information; and then the intermediate feature information obtained is reduced in dimension, compressed, or enhanced using the updated layer height to retain key feature information to obtain key multidimensional feature information.

[0065] In an embodiment of the present invention, considering the problem that the information format of the multidimensional feature information obtained by using the update layer cannot be processed by the state evaluation sub-model, the multidimensional feature information can be subjected to dimensionality reduction transformation to obtain the reduced-dimensional feature evaluation result.

[0066] Take the feature evaluation sub-model as a convolutional neural network and the state evaluation sub-model as a bidirectional long short-term memory network as an example. The feature information output by the feature evaluation sub-model is multi-dimensional information with a spatial structure, such as height, width, and number of channels. However, the input information received by the state evaluation sub-model is in the form of a sequence, that is, a one-dimensional vector sequence. In order to input the features extracted by the convolutional neural network into the bidirectional long short-term memory network for subsequent time series modeling, the high-dimensional features can be converted into a sequence format suitable for the input of the bidirectional long short-term memory network through a flattening layer.

[0067] According to an embodiment of the present invention, by converting the multidimensional feature information, the input vector of each time step contains the comprehensive features of the information to be evaluated at that position, so that the state assessment sub-model can better capture the front-end dependencies in the time series information, thereby improving the accuracy of the model in predicting the development of embankment cracks.

[0068] According to an embodiment of the present invention, multidimensional feature information is converted to obtain a feature evaluation result, including: reducing the dimension of the multidimensional feature information based on a time step or a feature dimension to obtain a plurality of reduced-dimensional features; and fusing the plurality of reduced-dimensional features to obtain a feature evaluation result.

[0069] In the embodiment of the present invention, the dimensionality reduction process may represent the process of flattening multi-dimensional feature information to obtain dimensionality reduction features. The feature fusion process may represent the process of further integrating and transforming the dimensionality reduction features after the flattening process to extract higher-level feature representations.

[0070] For example, receiving multidimensional feature information output from a feature evaluation submodel, the feature information has a certain height, width, and number of channels. For example, assuming that the feature information output by the feature evaluation submodel has a size of [height, width, number of channels] = [h, w, c]; the feature vector at each position can be flattened, that is, the three-dimensional feature information can be converted into a two-dimensional matrix. For example, the feature information can be flattened in the height and width dimensions to obtain a matrix with a shape of [h×w, c], where each row represents a feature vector at a position. Or, as needed, flattening can be performed in the channel number dimension to obtain a vector sequence with a shape of [h×w×c, 1].

[0071] After flattening, the feature vectors can be further integrated and transformed using a fully connected layer. The fully connected layer maps each feature vector to a new feature space by learning weight matrices and bias terms to extract higher-level feature representations. The output dimension of the fully connected layer can be set according to the input requirements of the bidirectional long short-term memory network, for example, it can be set to the input dimension of the bidirectional long short-term memory network at each time step.

[0072] An embodiment of the present invention inputs a feature evaluation result into a state evaluation sub-model, and outputs a size evaluation result and a rate evaluation result, including: processing the feature evaluation result based on a forward propagation strategy to obtain first state information corresponding to multiple moments, and processing the feature evaluation information based on a backward propagation strategy to obtain second state information corresponding to multiple moments; splicing the first state information and the second state information to obtain target state information; and performing pooling processing on the target state information to obtain a size evaluation result and a rate evaluation result.

[0073] In an embodiment of the present invention, the first state information may represent state information obtained by sequentially processing the feature evaluation results backward from the first time step of multiple time steps using the forward propagation layer in the state evaluation submodel. The second state information may represent state information obtained by sequentially processing the feature evaluation results forward from the last time step of multiple time steps using the backward propagation layer in the state evaluation submodel. The target state information may be fused state information obtained by concatenating or adding the first state information and the second state information. Pooling processing may represent global pooling processing of the fused target state information.

[0074] For example, the forward LSTM network in the bidirectional LSTM network can start from the first time step of the sequence and process the features of each time step backwards in sequence; the backward LSTM network can start from the last time step of the sequence and process the features of each time step forward in sequence. Through bidirectional processing, the state assessment sub-model can capture the front-to-back dependencies and evolution trends of cracks in the time dimension. Thus, the hidden states of each time step are output through the bidirectional LSTM network, and these hidden states integrate the temporal information of the cracks in both the front and back directions of the time step.

[0075] Furthermore, the temporal features output by the bidirectional long short-term memory network can be fused with the spatial features extracted by the convolutional neural network. The two features can be combined by splicing, addition, etc. to form the final target state information, and the target state information can be globally pooled to obtain the scale assessment results and rate assessment results of the cracks.

[0076] In an embodiment of the present invention, the embankment includes an existing embankment and a newly built embankment, the geometric information includes first geometric information of the existing embankment and second geometric information of the newly built embankment; the soil information includes density information, water content information, compression modulus information, internal friction angle and cohesion information; the environmental information includes temperature information, humidity information, rainfall information and settlement deformation information.

[0077] In an embodiment of the present invention, the first geometric information may include the embankment width, height information and roadbed depth of the existing embankment, and the second geometric information may include the embankment width, height information and roadbed depth of the newly built embankment. The environmental information may also include traffic flow information of the embankment.

[0078] For example, within a specific time frame, the settlement and deformation data of the embankment can be accurately measured using measuring equipment. Key locations such as the junction of the existing embankment and the newly built embankment, the embankment slope, and the bottom of the embankment can be measured in a targeted manner. Crack observation instruments can be used to record key information such as the width and length of the cracks in detail, and the ultrasonic method can be used to measure the crack depth. Temperature and humidity sensors, rain gauges and other equipment can be deployed to collect real-time data on environmental factors such as temperature, humidity, and rainfall at the site and surrounding environment, and obtain traffic flow information from relevant databases. Detailed construction drawings can also be collected to obtain construction process-related parameters and physical and mechanical parameters of the roadbed soil.

[0079] In an embodiment of the present invention, an evaluation model is trained by the following operations: obtaining sample initial information to be evaluated corresponding to cracks on the surface of a sample embankment; processing the sample initial information to be evaluated based on a decomposition strategy and a preset threshold to obtain sample information to be evaluated, wherein the sample information to be evaluated includes sample geometry information of the sample embankment, sample soil information, sample crack information, and sample environment information corresponding to the sample embankment based on different types, the decomposition strategy is used to decompose the initial information to be evaluated, and the preset threshold is used to perform threshold quantization on the decomposed initial information to be evaluated; using the sample crack information, sample geometry information, sample soil information, and sample environment information to train the initial evaluation model to obtain an evaluation model that meets preset evaluation conditions.

[0080] In an embodiment of the present invention, the sample initial information to be evaluated is processed based on the decomposition strategy and the preset threshold to obtain the sample information to be evaluated, which is the same as the process of processing the initial information to be evaluated based on the decomposition strategy and the preset threshold to obtain the information to be evaluated in the application phase, and will not be repeated here.

[0081] In the embodiment of the present invention, considering the influence of different data dimensions of multi-source information, the sample information to be evaluated can be normalized to eliminate the influence of multi-source data dimensions and improve the model training efficiency. The Min-Max normalization method is used to scale the eigenvalues ​​to between 0 and 1, as shown in the following formula (1):

[0082] (1);

[0083] Among them, X' can be the normalized data; X can be the original data; X min Can be the minimum value in the data; X max Can be the maximum value in the data.

[0084] For example, the initial evaluation model may include an initial feature evaluation submodel and an initial state evaluation submodel. Taking the example that the initial feature evaluation submodel and the initial state evaluation submodel are respectively a hybrid model of a convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM), the initial feature evaluation submodel and the initial state evaluation submodel may be regarded as the CNN layer and the BiLSTM layer of the initial evaluation model.

[0085] Figure 3A An example schematic diagram of an initial evaluation model training process according to an embodiment of the present invention is shown.

[0086] like Figure 3AAs shown, the sample information to be evaluated can be divided into a training set 31 and a test set 32. The initial evaluation model may include an input layer 301, a CNN layer 302, a flattening layer 303, a BiLSTM layer 304, and an output layer 305. The CNN layer 302 may be composed of a stack of a convolutional layer 3021, an activation function layer 3022, and a maximum pooling layer 3023, and the BiLSTM layer 304 may be composed of a forward LSTM layer 3041, a Dropout layer 3042, a backward LSTM 3043, and a fully connected layer 3044. The CNN layer 302 may be used to extract key spatial relationship information of multi-source data, and the BiLSTM layer 304 may be used to obtain long-term dependency information related to time series.

[0087] The data in the training set 31 are serialized by time step, and the serialized data are input into the input layer 301, where each time step contains all the eigenvalue information; thus, the convolution layer 3021 in the CNN layer 302 is used to perform a convolution operation on the input data, extract the spatial relationship between the widened embankment parameters and the generated cracks, and generate spatial features.

[0088] Figure 3B A schematic structural diagram of a CNN layer according to an embodiment of the present invention is shown.

[0089] like Figure 3B As shown, the convolution layer 3021 performs a convolution operation on the input sample information to be evaluated, which can be set to a 2×2 convolution kernel. The data is processed by window sliding to obtain the spatial relationship between the widened embankment parameters and the generated cracks, and a feature matrix (feature map) is generated. Setting two convolution layers can further combine and refine the key features of the processed data, as shown in the following formula (2):

[0090] (2);

[0091] in,

[0092] A i+m,j+n You can input an element in the matrix, whose position is determined by the current output position (i, j) and the position of the convolution kernel (n, m); f cov Can be an activation function; w m,n can be the weight of the convolution kernel in the mth row and nth column; b m,n can be the bias parameter of the convolution kernel in the mth row and nth column; i, j can be the output feature map position, It can be an element in the output matrix or feature map, located in the i-th row and j-th column, and Σ can be a summation symbol, indicating the weighted summation of the elements in the area covered by the convolution kernel.

[0093] The activation function layer can use the ReLu activation function to activate the extracted spatial features and add nonlinearity; the maximum pooling layer 3023 can perform maximum pooling dimensionality reduction operation on the feature map generated by the convolution layer 3021. The pooling window is set to 2×2, the step size is 2, and the maximum value is taken in the local area of ​​the data to reduce the dimension of the data and enhance the robustness of the model. At the same time, the key features in the data are retained to generate several feature maps; the flattening layer 303 operates in the form of a fully connected layer to integrate the feature vectors generated by the CNN layer 302 to adapt to the input form of the BiLSTM layer 304.

[0094] Figure 3C A schematic structural diagram of a BiLSTM layer according to an embodiment of the present invention is shown.

[0095] like Figure 3C As shown, the results of the flattening layer 303 can be arranged in chronological order as input 3031, and the forward LSTM layer 3041 and the backward LSTM 3043 are operated to simultaneously learn the forward and reverse information of the crack features in the time dimension, and the forward and reverse results are combined to obtain the output result 3032; it is set to two layers, and a Dropout layer 3042 is added between the two layers, and the dropout rate is set to 0.2 to improve the generalization ability of the model, improve the model performance, prevent overfitting problems, and enhance robustness; the calculation formula of the BiLSTM layer can be shown in the following formulas (3) to (8):

[0096] (3);

[0097] (4);

[0098] (5);

[0099] (6);

[0100] (7);

[0101] (8);

[0102] Among them, x t is the input data; i t 、f t , o t are the values ​​of the input gate, forget gate, and output gate respectively; c t is the unit state; h t is the hidden state; σ is the Sigmoid function; ⊙ is the element-by-element multiplication; w is the weight; and b is the bias parameter.

[0103] like Figure 3AAs shown, the fully connected layer 3044 can be connected to the output layer 305 to be set as a multi-output result, outputting the width, length, depth and development rate of the crack respectively.

[0104] The training set can be used to perform iterative training and hyperparameter optimization on the initial evaluation model; the hyperparameter optimization can adopt the Adam algorithm 308, which combines the advantages of the Adagrad algorithm and the RMSProp algorithm, and dynamically adjusts the learning rate of each parameter based on the first-order moment estimation and the second-order moment estimation of the gradient. The learning rate can be dynamically adjusted according to the gradient history information of each parameter, and a larger learning rate is given to the parameter update with a lower frequency, and a smaller learning rate is given to the parameter update with a higher frequency, so as to converge to the optimal solution faster during the training process.

[0105] For example, the learning rate of the Adam algorithm 308 can be initialized to 0.001, and the first-order moment estimate β 1 = 0.8, second-order moment estimate β 2 =0.999, constant term ε=e- 8 ; Forward propagation, for a given input data, it can be input into the constructed initial evaluation model, and calculated according to the structure and parameters of the neural network, through the input layer 301, CNN layer 302, flattening layer 303, BiLSTM layer 304 and output layer 305 in sequence, to obtain the initial prediction result 306 of the network; the loss function 307 is calculated based on the prediction result and the true value, and the mean square error is used as the loss function to evaluate the neural network; then back propagation is performed, and the gradient of the loss function to each parameter is calculated using the chain rule, as shown in the following formulas (9) to (11):

[0106] (9);

[0107] (10);

[0108] (11);

[0109] Among them, MSE(θ) can be the loss function; n can be the number of samples; y i Can be the true value of the i-th sample; can be the corresponding predicted value; the gradient vector of the loss function with respect to the parameter θ uses the partial derivative of the loss function with respect to the parameter Indicates; m can be the total number of parameters.

[0110] The Adam algorithm 308 can be used to update parameters according to the rules based on the calculated gradient to minimize the loss function; the above operation steps are repeated until all samples are processed, and the prediction accuracy of the model is determined 33. If the prediction accuracy 33 meets the requirements, the model is determined as the evaluation model 30, otherwise the parameters are adjusted to continue optimization; the performance and accuracy of the model are evaluated using the test set 32, and the evaluation parameter is the determination coefficient (R 2 ), the closer the determination coefficient is to 1, the better the model prediction performance is, and the closer it is to 0, the worse the model prediction effect is. 2 The calculation method of is shown in the following formula (12):

[0111] (12);

[0112] Among them, y i It can be the true value of the i-th sample. It can be the corresponding predicted value, that is, the value obtained by predicting each sample point according to the regression model. It can be the average of the observed values, which is used to measure the central location of the data. The residual sum of squares can be represented as the sum of squares of the differences between the model predicted values ​​and the actual observed values, reflecting the error of the model fitting data. The total sum of squared deviations can be expressed as the sum of squares of the differences between the actual observed values ​​and the mean, reflecting the variability of the data itself.

[0113] Figure 4A A three-dimensional schematic diagram of widening a longitudinal crack of an embankment according to an embodiment of the present invention is shown.

[0114] like Figure 4A As shown, a case of widening an embankment in city a is selected for verification. The widened embankment includes an existing embankment 401 and a newly built embankment 402. Multiple longitudinal cracks 404 are generated along the junction 403 of the widened embankment and the newly built embankment.

[0115] Figure 4B It is a comparison chart of the measured value and the predicted value of the crack width of the widened embankment according to an embodiment of the present invention.

[0116] The soil information, geometric information and environmental information of a certain section of the widened embankment in City A are processed in chronological order as the input of the constructed CNN-BiLSTM model. After a series of iterative training and hyperparameter optimization, the evaluation value obtained by using the intermediate evaluation model is obtained. Figure 4B As shown in the figure, the predicted value is compared with the measured value obtained by the actual detection equipment to obtain the comparison result, and the determination coefficient (R 2) is 0.98833, which shows that the prediction results of the trained model can meet the actual prediction accuracy requirements and verify the rationality and feasibility of the crack assessment method.

[0117] Based on the above-mentioned evaluation method for embankment cracks, the present invention also provides an evaluation device for embankment cracks. Figure 5 The device is described in detail.

[0118] Figure 5 A structural block diagram of a device for evaluating embankment cracks according to an embodiment of the present invention is shown.

[0119] like Figure 5 As shown, the embankment crack assessment device of this embodiment includes an information acquisition module 510 , an information processing module 520 and an information input module 530 .

[0120] The information acquisition module 510 is used to acquire initial information to be evaluated corresponding to cracks on the surface of the embankment. In one embodiment, the information acquisition module 510 can be used to perform the operation S210 described above, which will not be described in detail here.

[0121] The information processing module 520 is used to process the initial information to be evaluated based on the decomposition strategy and the preset threshold value to obtain the information to be evaluated, wherein the information to be evaluated includes geometric information of the embankment, soil information, and environmental information corresponding to the embankment based on different types, the decomposition strategy is used to decompose the initial information to be evaluated, and the preset threshold value is used to perform threshold quantization on the decomposed initial information to be evaluated. In one embodiment, the information processing module 520 can be used to perform the operation S220 described above, which will not be repeated here.

[0122] The information input module 530 is used to input soil information, geometric information and environmental information into the evaluation model to obtain the size evaluation result and rate evaluation result of the crack, wherein the evaluation model includes a feature evaluation sub-model and a state evaluation sub-model based on different functions. In one embodiment, the information input module 530 can be used to perform the operation S230 described above, which will not be repeated here.

[0123] According to the embodiment of the present invention, through the information acquisition module 510, the information processing module 520 and the information input module 530 in the evaluation device for embankment cracks, the information to be evaluated after being processed by the decomposition strategy and the preset threshold can retain the key features of the initial information to be evaluated, thereby improving the signal-to-noise ratio and quality of the data. Since the evaluation model integrates the evaluation functions for the features and states of multi-source information and time series information, by extracting the key features of multi-source information to capture long-term trends, the nonlinear time-varying characteristics of embankment cracks are processed, and then the size evaluation results and rate evaluation results of the cracks at the future time are obtained, thereby improving the rationality and reliability of the crack evaluation.

[0124] According to an embodiment of the present invention, the decomposition strategy includes a decomposition function and a decomposition dimension; the information processing module 520 includes: an information processing module 520, a quantization submodule and a coefficient fusion submodule. The information decomposition submodule is used to decompose the initial information to be evaluated using the decomposition function and the decomposition dimension to obtain decomposition coefficients of multiple frequencies corresponding to the initial information to be evaluated; the quantization submodule is used to perform threshold quantization on the decomposition coefficients of multiple frequencies based on a preset threshold to obtain processing coefficients of multiple frequencies; the coefficient fusion submodule is used to fuse the processing coefficients of multiple frequencies based on an inverse transformation function to obtain the information to be evaluated.

[0125] According to an embodiment of the present invention, the device further includes: a dimension determination module, configured to determine a decomposition function and a decomposition dimension according to at least one of a length feature and a frequency feature of the initial information to be evaluated.

[0126] According to an embodiment of the present invention, the information input module 530 includes: a result output submodule and a result input submodule. The result output submodule is used to input soil information, geometric information and environmental information into the feature evaluation submodel and output the feature evaluation result; the result input submodule is used to input the feature evaluation result into the state evaluation submodel and output the size evaluation result and the rate evaluation result.

[0127] According to an embodiment of the present invention, the feature evaluation sub-model includes an extraction layer, a conversion layer and an update layer; the result output sub-module includes: an extraction unit, a conversion unit, an enhancement unit and an information conversion unit. The extraction unit is used to input soil information, geometric information and environmental information into the extraction layer for feature extraction to obtain initial feature information; the conversion unit is used to input the initial feature information into the conversion layer for nonlinear conversion to obtain intermediate feature information; the enhancement unit is used to input the intermediate feature information into the update layer for dimensionality reduction and feature enhancement processing to obtain multi-dimensional feature information; the information conversion unit is used to convert the multi-dimensional feature information to obtain feature evaluation results.

[0128] According to an embodiment of the present invention, the information conversion unit includes: a dimensionality reduction subunit and a fusion subunit. The dimensionality reduction subunit is used to perform dimensionality reduction processing on multi-dimensional feature information based on time steps or feature dimensions to obtain multiple dimensionality reduction features; the fusion subunit is used to perform feature fusion on multiple dimensionality reduction features to obtain feature evaluation results.

[0129] According to an embodiment of the present invention, the result input submodule includes: a result processing unit, a splicing unit and a pooling processing unit. The result processing unit is used to process the feature evaluation result based on the forward propagation strategy to obtain the first state information corresponding to multiple moments, and to process the feature evaluation information based on the backward propagation strategy to obtain the second state information corresponding to multiple moments; the splicing unit is used to splice the first state information and the second state information to obtain the target state information; the pooling processing unit is used to perform pooling processing on the target state information to obtain the size evaluation result and the rate evaluation result.

[0130] According to an embodiment of the present invention, the embankment includes an existing embankment and a newly built embankment, the geometric information includes first geometric information of the existing embankment and second geometric information of the newly built embankment; the soil information includes density information, water content information, compression modulus information, internal friction angle and cohesion information; the environmental information includes temperature information, humidity information, rainfall information and settlement deformation information.

[0131] According to an embodiment of the present invention, the evaluation model is trained by the following operations: obtaining sample initial information to be evaluated corresponding to cracks on the surface of the sample embankment; processing the sample initial information to be evaluated based on a decomposition strategy and a preset threshold to obtain sample information to be evaluated, wherein the sample information to be evaluated includes sample geometry information of the sample embankment, sample soil information, sample crack information and sample environmental information corresponding to the sample embankment based on different types, the decomposition strategy is used to decompose the initial information to be evaluated, and the preset threshold is used to perform threshold quantization on the decomposed initial information to be evaluated; using the sample crack information, sample geometry information, sample soil information and sample environmental information to train the initial evaluation model to obtain an evaluation model that meets the preset evaluation conditions.

[0132] According to an embodiment of the present invention, any multiple modules among the information acquisition module 510, the information processing module 520 and the information input module 530 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present invention, at least one of the information acquisition module 510, the information processing module 520 and the information input module 530 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in any appropriate combination of any of them. Alternatively, at least one of the information acquisition module 510, the information processing module 520 and the information input module 530 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be executed.

[0133] Figure 6 A block diagram of an electronic device suitable for implementing a method for evaluating embankment cracks according to an embodiment of the present invention is shown.

[0134] like Figure 6 As shown, the electronic device according to an embodiment of the present invention includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage part 608 to a random access memory (RAM) 603. The processor 601 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include an onboard memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0135] In RAM 603, various programs and data required for the operation of the electronic device are stored. The processor 601, ROM 602 and RAM 603 are connected to each other via a bus 604. The processor 601 performs various operations of the method flow according to the embodiment of the present invention by executing the programs in ROM 602 and / or RAM 603. It should be noted that the program can also be stored in one or more memories other than ROM 602 and RAM 603. The processor 601 can also perform various operations of the method flow according to the embodiment of the present invention by executing the programs stored in the one or more memories.

[0136] According to an embodiment of the present invention, the electronic device may further include an input / output (I / O) interface 605, which is also connected to the bus 604. The electronic device may further include one or more of the following components connected to the input / output (I / O) interface 605: an input portion 606 including a keyboard, a mouse, etc.; an output portion 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 608 including a hard disk, etc.; and a communication portion 609 including a network interface card such as a LAN card, a modem, etc. The communication portion 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the input / output (I / O) interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed into the storage portion 608 as needed.

[0137] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiment; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present invention is implemented.

[0138] According to an embodiment of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present invention, the computer-readable storage medium may include the ROM 602 and / or RAM 603 described above and / or one or more memories other than ROM 602 and RAM 603.

[0139] The embodiment of the present invention also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the embankment crack assessment method provided by the embodiment of the present invention.

[0140] The computer program executes the above functions defined in the system / device of the embodiment of the present invention when it is executed by the processor 601. According to the embodiment of the present invention, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0141] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 609, and / or installed from a removable medium 611. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0142] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the processor 601, the above functions defined in the system of the embodiment of the present invention are performed. According to the embodiment of the present invention, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0143] According to an embodiment of the present invention, the program code for executing the computer program provided by the embodiment of the present invention can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0144] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0145] It will be appreciated by those skilled in the art that the features described in the various embodiments of the present invention may be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present invention. In particular, without departing from the spirit and teachings of the present invention, the features described in the various embodiments of the present invention may be combined and / or combined in various ways. All of these combinations and / or combinations fall within the scope of the present invention.

[0146] The embodiments of the present invention are described above. However, these embodiments are only for the purpose of illustration, and are not intended to limit the scope of the present invention. Although each embodiment is described above, it does not mean that the measures in each embodiment cannot be used in combination advantageously. Without departing from the scope of the present invention, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

Claims

1. A method for evaluating embankment cracks, characterized in that: The method comprises: Obtaining initial information to be evaluated corresponding to cracks on the embankment surface; The initial information to be evaluated is processed based on a decomposition strategy and a preset threshold value to obtain information to be evaluated, wherein the information to be evaluated includes geometric information of the embankment, soil information, and environmental information corresponding to the embankment, the decomposition strategy is used to decompose the initial information to be evaluated, and the preset threshold value is used to perform threshold quantization on the decomposed initial information to be evaluated; The soil information, the geometric information and the environmental information are input into an evaluation model to obtain a size evaluation result and a rate evaluation result of the crack, wherein the evaluation model includes a feature evaluation sub-model and a state evaluation sub-model.

2. The method according to claim 1, characterized in that The decomposition strategy includes a decomposition function and a decomposition dimension; The initial information to be evaluated is processed based on the decomposition strategy and the preset threshold to obtain the information to be evaluated, including: Decomposing the initial information to be evaluated by using the decomposition function and the decomposition dimension to obtain decomposition coefficients of multiple frequencies corresponding to the initial information to be evaluated; Performing threshold quantization on the decomposition coefficients of the multiple frequencies based on the preset threshold to obtain processing coefficients of the multiple frequencies; The processing coefficients of the multiple frequencies are fused based on an inverse transformation function to obtain the information to be evaluated.

3. The method according to claim 2, characterized in that The method further comprises: The decomposition function and the decomposition dimension are determined according to at least one of a length feature and a frequency feature of the initial information to be evaluated.

4. The method according to claim 1, characterized in that Inputting the soil information, the geometric information and the environmental information into an evaluation model to obtain a size evaluation result and a rate evaluation result of the crack includes: Inputting the soil information, the geometric information and the environmental information into the feature evaluation sub-model, and outputting a feature evaluation result; The feature evaluation result is input into the state evaluation sub-model, and the size evaluation result and the speed evaluation result are output.

5. The method according to claim 4, characterized in that The feature evaluation sub-model includes an extraction layer, a conversion layer and an update layer; Inputting the soil information, the geometric information and the environmental information into the feature evaluation sub-model and outputting feature evaluation results includes: Inputting the soil information, the geometric information and the environmental information into the extraction layer to perform feature extraction to obtain initial feature information; Inputting the initial feature information into the conversion layer for nonlinear conversion to obtain intermediate feature information; Inputting the intermediate feature information into the update layer for dimensionality reduction and feature enhancement processing to obtain multi-dimensional feature information; The multi-dimensional feature information is converted to obtain the feature evaluation result.

6. The method according to claim 5, characterized in that The multi-dimensional feature information is converted to obtain the feature evaluation result, including: Performing dimensionality reduction processing on the multi-dimensional feature information based on a time step or a feature dimension to obtain a plurality of dimensionality reduction features; The multiple dimension reduction features are subjected to feature fusion to obtain the feature evaluation result.

7. The method according to claim 4, characterized in that Inputting the feature evaluation result into the state evaluation sub-model, and outputting the size evaluation result and the rate evaluation result, comprises: Processing the feature evaluation results based on a forward propagation strategy to obtain first state information corresponding to a plurality of moments, and processing the feature evaluation information based on a backward propagation strategy to obtain second state information corresponding to the plurality of moments; concatenating the first state information and the second state information to obtain target state information; Pooling is performed on the target state information to obtain the size evaluation result and the rate evaluation result.

8. The method according to claim 1, characterized in that The embankment includes an existing embankment and a newly built embankment, the geometric information includes first geometric information of the existing embankment and second geometric information of the newly built embankment; the soil information includes density information, water content information, compression modulus information, internal friction angle and cohesion information; the environmental information includes temperature information, humidity information, rainfall information and settlement deformation information.

9. The method according to any one of claims 1 to 8, characterized in that The evaluation model is trained by the following operations: Obtaining initial sample information to be evaluated corresponding to cracks on the surface of the sample embankment; The sample initial information to be evaluated is processed based on a decomposition strategy and a preset threshold to obtain sample information to be evaluated, wherein the sample information to be evaluated includes sample geometry information of a sample embankment, sample soil information, sample crack information, and sample environment information corresponding to the sample embankment based on different types, the decomposition strategy is used to decompose the initial information to be evaluated, and the preset threshold is used to perform threshold quantization on the decomposed initial information to be evaluated; The sample crack information, the sample geometry information, the sample soil information and the sample environment information are used to train an initial evaluation model to obtain an evaluation model that meets preset evaluation conditions.

10. An evaluation device for embankment cracks, characterized in that: The device comprises: An information acquisition module, used to acquire initial information to be evaluated corresponding to cracks on the surface of the embankment; An information processing module, used for processing the initial information to be evaluated based on a decomposition strategy and a preset threshold value to obtain information to be evaluated, wherein the information to be evaluated includes geometric information of the embankment, soil information and environmental information corresponding to the embankment based on different types, the decomposition strategy is used to decompose the initial information to be evaluated, and the preset threshold value is used to perform threshold quantization on the decomposed initial information to be evaluated; The information input module is used to input the soil information, the geometric information and the environmental information into the evaluation model to obtain the size evaluation result and the rate evaluation result of the crack, wherein the evaluation model includes a feature evaluation sub-model and a state evaluation sub-model based on different functions.

Images