CN112084553B - Surveying method for tunnel planning - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000005553 drilling Methods 0.000 claims abstract description 10
- 238000012549 training Methods 0.000 claims description 25
- 238000013528 artificial neural network Methods 0.000 claims description 19
- 239000011435 rock Substances 0.000 claims description 14
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 8
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- 238000004458 analytical method Methods 0.000 description 4
- 238000000605 extraction Methods 0.000 description 3
- 239000003673 groundwater Substances 0.000 description 3
- 238000011176 pooling Methods 0.000 description 3
- 238000009412 basement excavation Methods 0.000 description 2
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- 238000012854 evaluation process Methods 0.000 description 1
- 238000011010 flushing procedure Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000007787 long-term memory Effects 0.000 description 1
- 238000010008 shearing Methods 0.000 description 1
- 230000006403 short-term memory Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
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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 designated 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 includes at least outcrop images, logging data, and 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 acquired data, reduces labor cost and improves the accuracy and efficiency of 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 used as a geological engineering, and due to the complexity of rock mass, the survey data and the actual situation after tunnel excavation can have larger access. Geological disasters are key factors for restricting tunnel construction, often have great blindness due to unclear geological conditions in tunnel areas, and often have unexpected geological disasters such as water burst, mud burst, collapse, rock burst, harmful gas and the like. These various geological disasters induced by excavation have nonselective, complex, special and sudden properties, once the disasters occur, the machines are destroyed by flushing, the tunnel is submerged, and normal construction is forced to be interrupted; heavy weight causes serious casualties, and huge economic loss is generated.
However, most of the traditional surveying methods rely on manual arrangement and analysis of surveying data, and the processing efficiency is low, and the accuracy and 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 scheme.
A survey method for tunnel planning, comprising:
acquiring historical survey data of a survey area and a designated 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 includes at least outcrop images, logging data, and 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 includes:
acquiring key information in the historical survey data through key word matching; wherein, the key information at least comprises: formation structure, geotechnical physical properties, aquifer distribution, fault distribution;
Inputting the key information into a pre-trained prediction model, and predicting the key information of the survey area;
And acquiring the initial planning model according to the key information of the survey area through a preset rule.
Optionally, the method of training the predictive model includes at least one of: deep neural networks, recurrent neural networks, artificial neural networks.
Optionally, the preset rule at least includes: parameter thresholds are set according to the base design specification.
Optionally, the plurality of identification models include at least a fault identification model, a karst identification.
Optionally, the step of training the fault identification model at least includes:
Constructing a training sample set by taking outcrop images of other built tunnel survey areas as sample images;
selecting a specified number of sample images from the training sample set to carry out 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 includes at least one of: convolutional neural network, cyclic neural network.
Optionally, inputting the outcrop image into the fault identification 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.
According to the method, the initial tunnel planning model is corrected by acquiring real-time survey data through the identification model, and the survey area and nearby rock stratum change condition are fully considered, so that the accuracy of a planning scheme can be improved; the method reduces the subjective problem of manual analysis, improves the working efficiency, and provides accurate and objective data reference for tunnel planning.
Drawings
FIG. 1 is a flow chart of a survey method for tunnel planning in an embodiment of the invention.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present invention by way of illustration, and only the components related to the present invention are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
Referring to fig. 1, the present invention provides a surveying method for tunnel planning, comprising steps S01-S03.
In step S01, historical survey data for the survey area and the designated neighboring area is acquired, and an initial tunnel planning model is created from the historical survey data:
In one embodiment, a survey area may be preselected, which is the area of the tunnel to be established. Further, historical survey data for the survey area is acquired. Wherein the historical survey data may include seismic monitoring reports, hydrographic monitoring reports, and the like. In particular, the relevant data for the survey area may be acquired one or five years before the current time, which may be adjusted according to the actual situation. Historical survey data for a survey area is often incomplete and may include only one or a few of the survey data. Survey data for regions adjacent to the survey region may be acquired. The adjacent area can be set as an area within 2km of the survey area, and the specific mode for specifying the adjacent area can be adjusted according to actual requirements.
In an embodiment, the historical survey data may include report documents, test records, and the like, and when the historical survey data is an electronic document, key information in the historical survey data may be obtained directly through text recognition. When the historical survey data is a paper file, the paper file can be converted into an identifiable text file by means of scanning and the like, and then key information is acquired through a text recognition algorithm.
Specifically, the entity, the attribute and the relation in the text can be marked, and then the entity-relation-attribute or the entity-relation-entity is converted into the feature vector through feature extraction. Where the entity may be a location, a composition of rock and earth, a depth of borehole, etc., the attribute may be a value such as 5 meters, liters per second, etc., and the relationship may be "greater than", "less than", "yes", etc.
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 in cosine distance, normal form 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 feature clustering algorithms such as Kmeans and the like, and key information corresponding to historical survey data is obtained according to each piece of clustering information. The key information may include, among other things, formation structure, geotechnical physical properties, aquifer distribution, fault distribution, and the like.
In one embodiment, the critical information may be input into a pre-trained predictive model by which the critical information for the survey area is predicted. If the adjacent area has groundwater distribution, the groundwater distribution in the survey area can be predicted according to the groundwater flow direction of the adjacent area, the position of the rock layer and the like. The prediction model can be trained by one of a deep neural network, a long-term and short-term memory neural network, a cyclic neural network or an artificial neural network. The key information in the historical survey data of other built tunnel adjacent areas can be collected as a training sample, and model training is carried out by taking the distribution of built tunnel rock layers and the like as a target set to obtain a prediction model.
In one embodiment, the initial planning model may be obtained according to predetermined rules based on key information of the predicted survey area. Wherein the preset rules may include setting parameter thresholds according to a base design specification. The basic design specifications may include "municipal engineering survey Specification" CJJ56-94, highway tunnel design Specification "JTG D70-2004, geotechnical engineering survey Specification" GB50021-2001, etc. If the parameters such as the allowable value of the foundation bearing capacity, the shearing force, the water inflow and the like can be set according to the basic design specification, and the tunnel planning path conforming to the basic design parameters is selected.
In step S02, real-time survey data is obtained according to a plurality of preset survey points, and a plurality of pre-trained recognition models are input to obtain corresponding recognition results; wherein the real-time survey data includes at least outcrop images, logging data, drilling data:
In one embodiment, a plurality of survey points may be provided in a survey area, with survey data acquisition being performed for each survey point. The acquired real-time survey data may include outcrop images, logging data, drilling data, geophysical prospecting data, and the like.
In one embodiment, when real-time survey data is acquired by the acquisition device, the device location, such as GPS, may be used to correlate location, time, etc. information with the acquired data information. If the time and position information can be embedded into the outcrop image in a watermark coding mode, the time and position information is fed back to a corresponding recognition model along with the image to carry out image recognition.
In an embodiment, the plurality of identification models includes at least a fault identification module, a karst identification module, and the like. Taking a fault identification model as an example, dew images of other built tunnel survey areas can be used as sample images to construct a training sample set, and a specified number of sample images are selected from the training sample set to mark a rock stratum structure, such as associated dislocation, scratch and the like. And taking the marked sample as a training target set, performing model training through a neural network algorithm, and outputting a fault prediction result. The neural network algorithm can adopt one of a convolutional neural network and a cyclic neural network. Taking convolutional neural network as an example, a LeNet-5 network can be adopted, wherein the LeNet-5 network is 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 subjected to feature extraction through a convolution kernel of a convolution layer C1 in the network, and the convolution kernel size can be set to 3*3 and the compensation is 1. And the feature extraction is followed by downsampling by the pooling layer S2, so that the data dimension is reduced. Deep features of the formation corresponding to the outcrop image, such as formation color distribution, can be extracted through the multi-layer convolution layer. And inputting the extracted features into a full-connection layer for feature recognition, and constructing a radial basis function through the similarity between the training sample features and the target sample features to obtain an output result. Wherein, the radial basis function can be expressed as:
Where M j represents the j-th feature of the target sample and x i represents the i-th feature of the training sample.
And acquiring fault distribution information of the survey area according to the fault identification model, and further correcting fault distribution according to the acquired logging data and/or drilling data. If the faults corresponding to the outcrop rock stratum are shifted or not can be judged according to the samples (such as a rock core and the like) obtained by drilling, the rock stratum integrity is judged according to the physical property parameters (such as resistivity and the like) of the rock stratum obtained by logging data, and then the identification result is corrected.
In one embodiment, when karst identification is performed, karst rock strata can be identified according to the acquired outcrop image, karst distribution is judged, and then information such as a karst cavity, an aquifer, static water reserves and water pressure is acquired by combining logging data, so that 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, fault distribution, karst distribution, aquifer distribution and the like can be obtained by identifying measured data of a survey area, and then partial positions of tunnels in an initial tunnel planning model are adjusted so as to effectively avoid high-risk positions, such as positions where water inflow may exceed a standard, sections where fault influence zone distribution is more complex and the like.
In an embodiment, the output tunnel planning scheme can be displayed through a visual interaction interface, and related designers can confirm or adjust the scheme according to the display result and experience so as to ensure the feasibility of the scheme.
In summary, according to the surveying method for tunnel planning, the historical data is analyzed through the intelligent algorithm to construct the initial tunnel planning model, 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, the post data arrangement analysis by professionals is not needed, the engineering evaluation period can be effectively shortened, and the efficiency is improved; the model is corrected through the measured data, so that the accuracy of the identification result can be ensured, and the reliability of an output scheme is improved; by predicting the historical data of the adjacent area, the rock stratum condition of the survey area can be known in advance, the survey point in 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 above embodiments are merely illustrative of the principles of the present invention and its effectiveness, and are not intended to limit the invention. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the invention. Accordingly, it is intended that all equivalent modifications and variations of the invention be covered by the claims, which are within the ordinary skill of the art, be within the spirit and scope of the present disclosure.
Claims (8)
1. A survey method for tunnel planning, comprising:
Acquiring historical survey data of a survey area and a designated adjacent area, and creating an initial tunnel planning model according to the historical survey data; the step of creating the initial tunnel planning model comprises at least: acquiring key information in the historical survey data through key word matching; wherein, the key information at least comprises: formation structure, geotechnical physical properties, aquifer distribution, fault distribution; inputting the key information into a pre-trained prediction model, and predicting the key information of the survey area; acquiring the initial tunnel planning model according to the key information of the survey area through a preset rule;
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 includes at least outcrop images, logging data, and drilling data; the plurality of identification models comprise fault identification models and karst identification models;
Correcting the initial tunnel planning model according to the identification result, and outputting a tunnel planning scheme, wherein the method comprises the following steps: acquiring fault distribution information of a survey area according to the fault identification model, and correcting the fault distribution information according to the logging data and/or the drilling data; and when the karst recognition is carried out according to the karst recognition model, recognizing the karst rock stratum according to the outcrop image, judging the karst distribution, acquiring a karst cavity, a water-bearing layer, a static water reserve and water pressure by combining the logging data, and correcting the recognition result.
2. A survey method for tunnel planning according to claim 1 wherein the historical survey data comprises at least: report documents, test records.
3. A survey method for tunnel planning according to claim 1 wherein the method of training the predictive model comprises at least one of: deep neural networks, recurrent neural networks, artificial neural networks.
4. A survey method for tunnel planning according to claim 1, wherein the preset rules comprise at least: parameter thresholds are set according to the base design specification.
5. A survey method for tunnel planning according to claim 1 wherein the plurality of identification models comprises at least a fault identification model, a karst identification.
6. A survey method for tunnel planning according to claim 5 wherein the step of training the fault identification model comprises at least:
Constructing a training sample set by taking outcrop images of other built tunnel survey areas as sample images;
selecting a specified number of sample images from the training sample set to carry out 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.
7. A survey method for tunnel planning according to claim 6 wherein the neural network algorithm comprises at least one of: convolutional neural network, cyclic neural network.
8. The survey method for tunnel planning of claim 5, wherein the outcrop image is input to the fault identification model to acquire fault distribution information;
And correcting fault distribution information according to the logging data and/or the drilling data.
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| CN118113805B (en) * | 2024-04-29 | 2024-06-25 | 山东省国土测绘院 | Geographic information survey calibration method and system based on deep learning |
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| CN104390534A (en) * | 2014-10-11 | 2015-03-04 | 同济大学 | Tunnel smooth surface blasting quality control method |
| CN104881435A (en) * | 2015-05-05 | 2015-09-02 | 中国海洋石油总公司 | Data mining based automatic research flow well logging evaluation expert system |
| US10078337B1 (en) * | 2017-07-14 | 2018-09-18 | Uber Technologies, Inc. | Generation of trip estimates using real-time data and historical data |
| CN110689042A (en) * | 2019-08-20 | 2020-01-14 | 中国矿业大学(北京) | Tunnel leakage grade identification method and device, storage medium and electronic device |
| CN111259517A (en) * | 2020-01-08 | 2020-06-09 | 京工博创（北京）科技有限公司 | Tunnel blasting design method, device and equipment |
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