CN112084553A - Surveying method for tunnel planning - Google Patents
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Abstract
The invention provides a surveying method for tunnel planning, which comprises the following steps: acquiring historical survey data of a survey area and a specified adjacent area, and creating an initial tunnel planning model according to the historical survey data; acquiring real-time survey data according to a plurality of preset survey points, and inputting a plurality of pre-trained recognition models to acquire corresponding recognition results; wherein the real-time survey data at least comprises outcrop images, well logging data and well drilling data; correcting the initial tunnel planning model according to the identification result, and outputting a tunnel planning scheme; the invention establishes identification aiming at the collected data, reduces the labor cost and improves the accuracy and the efficiency of the survey data processing.
Description
Technical Field
The invention relates to the field of surveying, in particular to a surveying method for tunnel planning.
Background
Tunnel engineering is a geological project, and survey data and actual conditions after tunnel excavation may have large access due to the complexity of rock mass. Geological disasters are key factors for restricting tunnel construction, often cause great blindness in construction due to unclear geological conditions of a tunnel region, and often cause unexpected geological disasters such as water inrush, mud inrush, collapse, rock burst, harmful gas and the like. The various geological disasters induced by excavation have unselectivity, complexity, specificity and paroxysmal property, once the disasters occur, machines and tools are slightly destroyed to submerge a tunnel, and normal construction is forcibly interrupted; and serious casualties are caused, and huge economic losses are generated.
Most of the traditional surveying methods rely on manual arrangement and analysis of surveying data, so that the processing efficiency is low, and the accuracy and the reliability are poor.
Disclosure of Invention
In view of the problems in the prior art, the invention provides a surveying method for tunnel planning, which mainly solves the problems that the traditional surveying method depends on manpower and has low efficiency. .
In order to achieve the above and other objects, the present invention adopts the following technical solutions.
A survey method for tunnel planning, comprising:
acquiring historical survey data of a survey area and a specified adjacent area, and creating an initial tunnel planning model according to the historical survey data;
acquiring real-time survey data according to a plurality of preset survey points, and inputting a plurality of pre-trained recognition models to acquire corresponding recognition results; wherein the real-time survey data at least comprises outcrop images, well logging data and well drilling data;
and correcting the initial tunnel planning model according to the identification result, and outputting a tunnel planning scheme.
Optionally, the historical survey data includes at least: report documents, test records.
Optionally, the step of creating the initial tunnel planning model at least comprises:
acquiring key information in the historical survey data through keyword matching; wherein the key information at least comprises: rock stratum structure, rock-soil physical properties, aquifer distribution and fault distribution;
inputting the key information into a pre-trained prediction model to predict the key information of the survey area;
and acquiring the initial planning model through a preset rule according to the key information of the survey area.
Optionally, the method of training the predictive model comprises at least one of: deep neural networks, recurrent neural networks, artificial neural networks.
Optionally, the preset rule at least includes: the parameter threshold is set according to the underlying design specification.
Optionally, the plurality of identification models includes at least a fault identification model, a karst identification.
Optionally, the step of training the fault recognition model comprises at least:
taking the outcrop images of other built tunnel survey areas as sample images to construct a training sample set;
selecting a specified number of sample images from the training sample set for rock stratum labeling, and constructing a training target set;
and training the fault recognition model by adopting a neural network algorithm according to the training sample set and the training target set.
Optionally, the neural network algorithm comprises at least one of: convolutional neural networks, cyclic neural networks.
Optionally, inputting the outcrop image into the fault recognition model to obtain fault distribution information;
and correcting fault distribution information according to the logging data and/or the drilling data.
As described above, the surveying method for tunnel planning of the present invention has the following advantageous effects.
The method provided by the invention can be used for correcting the initial tunnel planning model by acquiring real-time survey data through the recognition model, fully considering the survey area and the rock stratum change condition nearby, and improving the accuracy of the planning scheme; the problem of subjectivity of manual analysis is reduced, and accurate and objective data reference is provided for tunnel planning while the working efficiency is improved.
Drawings
Fig. 1 is a flow chart of a survey method for tunnel planning in an embodiment of the invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
Referring to fig. 1, the present invention provides a surveying method for tunnel planning, which includes steps S01-S03.
In step S01, historical survey data for the survey area and the designated vicinity is acquired, and an initial tunnel planning model is created from the historical survey data:
in one embodiment, the survey area, which is the area where the tunnel is to be built, may be preselected. Further, historical survey data is acquired for the survey area. Wherein the historical survey data may include seismic monitoring reports, hydrological monitoring reports, and the like. In particular, relevant data for the survey area may be acquired one or five years before the current time, which may be adjusted based on the actual situation. The historical survey data for a survey area is typically not complete and may contain only one or a few of the survey data. Survey data may be acquired for a region proximate to the survey region. The adjacent area can be set as an area within the range of 2km extending from the survey area, and the specific mode of specifying the adjacent area can be adjusted according to actual requirements.
In one embodiment, the historical survey data may include report documents, test records, and the like, and when the historical survey data is an electronic document, the key information in the historical survey data may be obtained directly by text recognition. When the historical survey data is a paper file, the paper file can be converted into a recognizable text file in a scanning mode and the like, and then key information is obtained through a text recognition algorithm.
Specifically, entities, attributes and relationships in the text can be labeled, and then the entities-relationship-attributes or the entities-relationship-entities are converted into feature vectors through feature extraction. The entities can be sites, rock and soil components, drilling depths and the like, the attributes can be numerical values such as 5 meters, liters per second and the like, and the relationships can be greater than, less than, yes and the like.
And matching entity information in the extracted features through keywords, keywords or key phrases, for example, calculating the similarity between the features corresponding to the keywords and the extracted features by adopting cosine distance, paradigm distance, Euclidean distance and other modes, and acquiring corresponding feature information when the similarity reaches a set threshold value. Further, feature clustering can be performed through a feature clustering algorithm such as Kmeans, and key information corresponding to historical survey data is obtained according to each clustering information. The key information may include, among other things, formation structure, geophysical properties, aquifer distribution, fault distribution, etc.
In one embodiment, the critical information may be input into a pre-trained predictive model from which the critical information for the survey area is predicted. If the adjacent area has underground water distribution, the distribution of the underground water in the survey area can be predicted according to the information of the underground water flow direction, the rock stratum position and the like of the adjacent area. The prediction model can adopt one of a deep neural network, a long-short term memory neural network, a cyclic neural network or an artificial neural network for model pre-training. Key information in historical survey data of other completed tunnel adjacent areas can be collected to be used as training samples, and model training is carried out by taking completed tunnel rock stratum distribution and the like as a target set to obtain a prediction model.
In one embodiment, the initial planning model may be obtained according to preset rules based on predicted key information for the survey area. Wherein presetting the rules may include setting parameter thresholds according to the underlying design specifications. The basic design specifications can include municipal engineering survey specifications CJJ56-94, road and tunnel design specifications JTG D70-2004, geotechnical engineering survey specifications GB50021-2001 and the like. Parameters such as allowable value of bearing capacity of foundation, shearing force, water inflow and the like can be set according to basic design specifications, and a tunnel planning path which meets the basic design parameters is selected.
In step S02, acquiring real-time survey data according to a plurality of preset survey points, and inputting a plurality of pre-trained recognition models to acquire corresponding recognition results; wherein the real-time survey data at least comprises outcrop images, well logging data and well drilling data:
in one embodiment, a plurality of survey points may be located in a survey area, with survey data acquisition being performed for each survey point. The collected real-time survey data may include outcrop images, well log data, well drilling data, geophysical data, and the like.
In one embodiment, when real-time survey data is collected by the collection device, the location, time, etc. information may be correlated with the collected data information by locating the device location via GPS, etc. If the time and position information can be embedded into the outcrop image in a watermark coding mode, the outcrop image and the image are fed back to a corresponding recognition model for image recognition.
In an embodiment, the plurality of recognition models includes at least a fault recognition module, a karst recognition module, and the like. Taking the fault recognition model as an example, an outcrop image of other built tunnel survey areas can be used as a sample image to construct a training sample set, and a specified number of sample images are selected from the training sample set to label the rock stratum structure, such as accompanying dislocation, scratch and the like. And taking the marked sample as a training target set, carrying out model training through a neural network algorithm, and outputting a fault prediction result. The neural network algorithm may adopt one of a convolutional neural network and a cyclic neural network. Taking convolutional neural networks as an example, LeNet-5 networks can be adopted, and the LeNet-5 networks are totally divided into 8 layers of networks, namely an input layer, a convolutional layer C1, a pooling layer S2, a convolutional layer C2, a pooling layer S4, a convolutional layer C5, a full-connection layer and an output layer. The outcrop image can be characterized by the convolution kernel of convolution layer C1 in the network, which can be set to 3 x 3 with a compensation of 1. And performing down-sampling through a pooling layer S2 after feature extraction to reduce data dimensionality. Deep features of the rock stratum corresponding to the outcrop image, such as rock stratum color distribution and the like, can be extracted through the multilayer convolution layer. And inputting the extracted features into a full-connection layer for feature recognition, and constructing a radial basis function through the similarity of the features of the training sample and the features of the target sample to obtain an output result. Wherein the radial basis function can be expressed as:
wherein M isjJ-th feature, x, representing a target sampleiRepresenting the ith feature of the training sample.
And acquiring the fault distribution information of the surveying area according to the fault identification model, and further correcting the fault distribution according to the acquired logging data and/or drilling data. For example, whether the fault corresponding to the outcrop rock stratum has displacement or not can be judged according to a sample (such as a rock core and the like) obtained by drilling, the integrity of the rock stratum is judged according to physical property parameters (such as resistivity and the like) of the rock stratum obtained by logging data, and then the recognition result is corrected.
In one embodiment, when identifying the karst, the karst rock stratum can be identified according to the collected outcrop images, the distribution of the karst is judged, information such as a karst cavity, a water-bearing stratum, static water reserve and water pressure is further acquired by combining logging data, and the identification result is corrected.
In step S03, the initial road planning model is corrected according to the recognition result, and a tunnel planning scheme is output:
in an embodiment, by identifying measured data of a survey area, fault distribution, karst distribution, aquifer distribution and the like can be obtained, and then part of the position of a tunnel in an initial tunnel planning model is adjusted, so that high-risk positions, such as positions where water inflow may exceed standards, sections where fault influence zones are distributed more complexly, and the like are effectively avoided.
In one embodiment, the output tunnel planning scheme can be displayed through a visual interactive interface, and relevant designers can confirm or adjust the scheme according to the display result and experience so as to ensure the feasibility of the scheme.
In conclusion, according to the survey method for tunnel planning, the initial tunnel planning model is constructed by analyzing the historical data through the intelligent algorithm, so that the manual analysis of the historical data containing a large amount of redundant information is avoided, the labor cost is reduced, and the efficiency is improved; the real-time survey data training model is trained, the survey result can be obtained only by inputting the collected data into the recognition model, the recognition speed is high, and the later data arrangement and analysis by professionals are not needed, so that the project evaluation period can be effectively shortened, and the efficiency is improved; the model is corrected through the actually measured data, so that the accuracy of the recognition result can be ensured, and the reliability of the output scheme is improved; through the prediction of the historical data of the adjacent area, the rock stratum condition of the surveyed area can be known in advance, the survey point in the real time can be determined, and the evaluation process is further simplified. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (9)
1. A survey method for tunnel planning, comprising:
acquiring historical survey data of a survey area and a specified adjacent area, and creating an initial tunnel planning model according to the historical survey data;
acquiring real-time survey data according to a plurality of preset survey points, and inputting a plurality of pre-trained recognition models to acquire corresponding recognition results; wherein the real-time survey data at least comprises outcrop images, well logging data and well drilling data;
and correcting the initial tunnel planning model according to the identification result, and outputting a tunnel planning scheme.
2. A surveying method for tunnel planning according to claim 1, characterized in that the historical survey data comprises at least: report documents, test records.
3. A surveying method for tunnel planning according to claim 1, characterized in that the step of creating the initial tunnel planning model comprises at least:
acquiring key information in the historical survey data through keyword matching; wherein the key information at least comprises: rock stratum structure, rock-soil physical properties, aquifer distribution and fault distribution;
inputting the key information into a pre-trained prediction model to predict the key information of the survey area;
and acquiring the initial planning model through a preset rule according to the key information of the survey area.
4. A survey method for tunnel planning according to claim 3, characterized in that the method of training the predictive model comprises at least one of: deep neural networks, recurrent neural networks, artificial neural networks.
5. A surveying method for tunnel planning according to claim 3, characterized in that the preset rules comprise at least: the parameter threshold is set according to the underlying design specification.
6. A surveying method for tunnel planning according to claim 1, characterized in that the plurality of identification models comprises at least a fault identification model, a karst identification.
7. A surveying method for tunnel planning according to claim 6, characterized in that the step of training the fault recognition model comprises at least:
taking the outcrop images of other built tunnel survey areas as sample images to construct a training sample set;
selecting a specified number of sample images from the training sample set for rock stratum labeling, and constructing a training target set;
and training the fault recognition model by adopting a neural network algorithm according to the training sample set and the training target set.
8. A survey method for tunnel planning according to claim 7, characterized in that the neural network algorithm comprises at least one of: convolutional neural networks, cyclic neural networks.
9. The surveying method for tunnel planning according to claim 6, wherein the outcrop image is input to the fault recognition model, and fault distribution information is acquired;
and correcting fault distribution information according to the logging data and/or the drilling data.
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Cited By (1)
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| CN115688251A (en) * | 2022-12-19 | 2023-02-03 | 山东大学 | Earthquake multi-occurrence-zone tunnel risk decision method and system based on deep learning |
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Effective date of registration: 20240430 Address after: No.69, Yanghe village, Jiangbei District, Chongqing Applicant after: Chongqing Design Group Co.,Ltd. Country or region after: China Address before: No. 69, Yangheyi Village, Jiangbei District, Chongqing, 404100 Applicant before: Chongqing Municipal Design and Research Institute Co.,Ltd. Country or region before: China |