CN114997003A - Multi-model fusion tunnel construction risk prediction method, system, device and medium - Google Patents
Multi-model fusion tunnel construction risk prediction method, system, device and medium Download PDFInfo
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Abstract
The invention provides a method, a system, a device and a medium for predicting the risk of tunnel construction with multi-model fusion, wherein the method mainly comprises the following steps: acquiring survey information and basic information of the target tunnel, and constructing a design model of the target tunnel according to the survey information and the basic information; acquiring construction data of a target tunnel, and constructing an actual construction model of the target tunnel according to the construction data; fusing model data of the design model to obtain first fusion data, fusing the actual construction model to obtain second fusion data, and performing superposition analysis according to the first fusion data and the second fusion data to obtain a tunnel deformation deviation value; performing risk prediction according to the deformation deviation value, and performing visual display on a risk prediction result; the method can analyze and process the data uniformly, saves manpower and material resources, obviously improves the management efficiency of tunnel engineering, ensures the safety of tunnel construction, and can be widely applied to the technical field of tunnel engineering.
Description
Technical Field
The invention relates to the technical field of tunnel engineering, in particular to a method, a system, a device and a medium for predicting multi-model fused tunnel construction risks.
Background
Aiming at the problems of high construction difficulty and multiple risk factors of tunnel engineering, the traditional tunnel monitoring and risk early warning means can not meet the requirements of current comprehensive management and comprehensive analysis. In recent years, three-dimensional models based on various modeling means and information management means brought by the three-dimensional models have been widely applied to various subdivided fields of engineering construction. And in the construction industry, key technologies such as artificial intelligence, multi-technology fusion and the like are continuously explored for iteration, if the 3D laser scanning measurement technology is widely used in tunnel construction, the new technologies can obtain measurement and monitoring data with wider range and higher precision and resolution, the aims of quickly identifying risks, predicting risks in time, displaying risks in images and effectively controlling risks are achieved, and the construction efficiency and the engineering quality are improved.
In the tunnel construction environment with poor working conditions such as darkness, closure, humidity and the like, when the traditional tunnel monitoring method such as an inverted ruler, a convergence gauge, a total station and the like is adopted to carry out tunnel construction monitoring, the defects of long working time, high cost, poor environmental adaptability, low efficiency, low precision and the like exist; the traditional single-point monitoring can not meet the requirements of tunnel engineering construction, particularly the integral deformation monitoring of unfavorable geological tunnels such as soft rock, deep soft soil and the like, and can not meet the requirements of the current tunnel engineering construction; and the traditional security risk monitoring means can not meet the requirements of current comprehensive management and comprehensive analysis.
Three-dimensional modeling, informatization and platform management means brought by the three-dimensional modeling, a 3D laser scanning measurement technology and the like are applied to multiple aspects of engineering construction, so that the conversion from extensive management to fine management in the construction industry is effectively promoted, the operation and management efficiency of tunnel engineering is integrally improved, and greater social benefits are created while economic benefits are improved. However, the tunnel construction process in the related technical solution is as follows: planning, surveying, designing, constructing, detecting, completing, checking and accepting and then managing. Data information data of each stage are mainly in each department, if a reconnaissance unit has engineering geological model data, a design unit has tunnel design data and a BIM model, a construction unit has tunnel construction monitoring data and the like, all departments form an information isolated island, and various data information is not integrated.
Disclosure of Invention
In view of this, to at least partially solve one of the above technical problems or disadvantages, an embodiment of the present invention provides a method for predicting a risk of tunnel construction based on multi-model fusion, so as to implement informatization monitoring and security risk management of tunnel construction; embodiments also provide a system, an apparatus, and a storage medium capable of implementing this method.
On one hand, the technical scheme of the application provides a multi-model fusion tunnel construction risk prediction method, which comprises the following steps:
acquiring survey information and basic information of a target tunnel, and constructing a design model of the target tunnel according to the survey information and the basic information, wherein the design model comprises a tunnel three-dimensional model, a three-dimensional real scene model, a geological three-dimensional model and a finite element three-dimensional analysis model;
acquiring construction data of the target tunnel, and constructing an actual construction model of the target tunnel according to the construction data, wherein the actual construction model comprises a point cloud data three-dimensional model and a BIM three-dimensional tunnel actual model;
fusing the model data of the design model to obtain first fusion data, fusing the actual construction model to obtain second fusion data, and performing superposition analysis according to the first fusion data and the second fusion data to obtain a tunnel deformation deviation value;
and predicting the risk according to the deformation deviation value, and visually displaying the risk prediction result.
In a feasible embodiment of the scheme of the application, the step of fusing the model data of the design model to obtain first fusion data, fusing the actual construction model to obtain second fusion data, and performing superposition analysis according to the first fusion data and the second fusion data to obtain the tunnel deformation deviation value includes at least one of the following steps:
determining the over-under-excavation deviation of the target tunnel;
and determining the deformation geometric deviation of the target tunnel.
In a possible embodiment of the present disclosure, the step of determining the target tunnel overbreak-undermining deviation includes:
extracting to obtain a tunnel design section according to the first fusion data, and dividing the tunnel design section into a first polygonal area and a first circular arc area;
determining the design section area of the tunnel according to the area of the first polygonal area and the area of the first arc area;
extracting to obtain a tunnel actual section according to the second fusion data, and dividing the tunnel actual section into a second polygonal area and a second circular arc area;
determining the actual cross-sectional area of the tunnel according to the area of the second polygonal area and the area of the second arc area;
and determining the target tunnel overbreak and underexcavation deviation according to the difference value between the designed section area of the tunnel and the actual section area of the tunnel.
In a possible embodiment of the present disclosure, the step of determining the deformed geometric deviation of the target tunnel includes:
constructing an actual measurement point cloud curved surface according to the second fusion data, and determining a first coordinate point in the actual measurement point cloud curved surface;
constructing a design model curved surface according to the first fusion data, and determining at least three second coordinate points in the design model curved surface;
determining a first target plane according to the second coordinate point, and determining a third coordinate point corresponding to the first coordinate point in the first target plane;
and determining the deformation geometric deviation according to the distance between the first coordinate point and the third coordinate point.
In a possible embodiment of the present disclosure, the step of predicting the risk according to the deformation deviation value and visually displaying the risk prediction result includes at least one of the following steps:
determining that the target tunnel is over-excavated according to the over-under-excavation deviation of the target tunnel, and performing slurry supplementing and backfilling on the tunnel;
and determining that the target tunnel is underexcavated according to the target tunnel overbreak and underexcavation deviation, and performing tunnel chiseling treatment.
In a feasible embodiment of the application, the step of predicting the risk according to the deformation deviation value and visually displaying the risk prediction result further includes:
and determining a risk early warning grade according to the deformation geometric deviation and a preset deformation threshold value, and obtaining the risk prediction result according to the risk early warning grade. .
In a feasible embodiment of the present application, the step of obtaining the construction data of the target tunnel and constructing the actual construction model of the target tunnel according to the construction data includes:
acquiring measurement data through a 3D laser scanner and/or a total station, and performing coordinate conversion on the measurement data to obtain first intermediate data;
and carrying out point cloud data processing on the first intermediate data to obtain the point cloud data three-dimensional model.
On the other hand, the technical scheme of the application also provides a multi-model fused tunnel construction risk prediction system, which comprises:
the system comprises a design model generating unit, a target tunnel analysis unit and a target tunnel analysis unit, wherein the design model generating unit is used for acquiring survey information and basic information of a target tunnel and constructing a design model of the target tunnel according to the survey information and the basic information, and the design model comprises a tunnel three-dimensional model, a three-dimensional live-action model, a geological three-dimensional model and a finite element three-dimensional analysis model;
the actual construction model generation unit is used for acquiring the construction data of the target tunnel and constructing an actual construction model of the target tunnel according to the construction data, wherein the actual construction model comprises a point cloud data three-dimensional model and a BIM three-dimensional tunnel actual model;
the deviation value calculation unit is used for fusing the model data of the design model to obtain first fusion data, fusing the actual construction model to obtain second fusion data, and performing superposition analysis according to the first fusion data and the second fusion data to obtain a tunnel deformation deviation value;
and the risk prediction unit is used for predicting the risk according to the deformation deviation value and visually displaying the risk prediction result.
On the other hand, this application technical scheme still provides many models of tunnel construction risk prediction device that fuses, and this equipment includes:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, the at least one program causes the at least one processor to execute the multi-model fused tunnel construction risk prediction method according to any one of the first aspect.
On the other hand, the present technical solution also provides a storage medium, in which a processor-executable program is stored, and when the processor-executable program is executed by a processor, the processor-executable program is configured to perform the multi-model fused tunnel construction risk prediction method according to any one of the first aspect.
Advantages and benefits of the present invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention:
the technical scheme of the application provides a multi-model fusion tunnel construction monitoring system and a risk prediction method thereof, and the method uniformly analyzes and processes data, so that manpower and material resources are saved; the model data can be combined, the relation of each part is enhanced, and the problem of information isolated island is solved; compared with the prior art, the method of the technical scheme can realize model fusion, data visualization, deformation analysis and risk prediction, remarkably improve the management efficiency of tunnel engineering, and guarantee the safety of tunnel construction.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a block diagram of a multi-model fused tunnel construction risk prediction system according to an embodiment of the present disclosure;
fig. 2 is a flowchart illustrating steps of a method for predicting a risk in tunnel construction based on multi-model fusion according to an embodiment of the present disclosure;
FIG. 3 is a schematic view of a 3D scanner monitoring in an embodiment of the present application;
FIG. 4 is a schematic diagram of a measurement principle of a 3D scanner in an embodiment of the present application;
fig. 5 is a schematic diagram illustrating a principle of calculating a deformation deviation value of a tunnel curved surface in an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.
For the technical solutions pointed out in the background art, in a first aspect, as shown in fig. 1, the technical solution of the present application provides a multi-model fusion tunnel construction risk prediction system, which mainly includes: the system comprises an input module 1, a tunnel design model module 2, an unmanned aerial vehicle three-dimensional model module 3, a topographic geology three-dimensional model module 4, a finite element three-dimensional analysis model module 5, a three-dimensional laser scanning point cloud data model module 6, a BIM three-dimensional tunnel actual model module 7, a superposition analysis module 8, a risk intelligent prediction module 9 and an automatic output 10.
Based on the aforementioned multi-model fusion tunnel construction risk prediction system, the technical solution of the present application provides a multi-model fusion tunnel construction risk prediction method, as shown in fig. 2, the method may include steps S100-S400:
s100, acquiring survey information and basic information of the target tunnel, and constructing a design model of the target tunnel according to the survey information and the basic information;
specifically, in the embodiment, firstly, basic information such as topographic and geological information, material information, engineering management information, design central axis, design profile and the like of the tunnel is acquired by a surveyor, a designer and a constructor through an input module; and transmitting the data to the superposition analysis module. Then, the embodiment obtains information such as a tunnel design central axis, a design section diagram and the like through the tunnel design three-dimensional model module, establishes a tunnel three-dimensional model and transmits the tunnel three-dimensional model to the superposition analysis module. And acquiring a three-dimensional live-action model of the surrounding environment of the tunnel through the unmanned aerial vehicle three-dimensional model module, and transmitting the three-dimensional live-action model to the superposition analysis module. Furthermore, a three-dimensional model module of the topography geology is used for obtaining a survey report and drilling data, establishing the three-dimensional model of the topography geology and transmitting the three-dimensional model to the superposition analysis module. In addition, the embodiment also obtains a survey report and drilling data through a topographic and geological three-dimensional model module, establishes a topographic and geological three-dimensional model and transmits the topographic and geological three-dimensional model to the superposition analysis module; and performing tunnel construction simulation excavation through the finite element three-dimensional analysis model module to obtain tunnel excavation mechanics and deformation simulation data, and transmitting the data to the superposition analysis module. The generated three-dimensional model of the tunnel, the three-dimensional live-action model, the geological three-dimensional model and the finite element three-dimensional analysis model are used as the design model of the target tunnel in the embodiment.
S200, acquiring construction data of the target tunnel, and constructing an actual construction model of the target tunnel according to the construction data;
the actual construction model comprises a point cloud data three-dimensional model and a BIM three-dimensional tunnel actual model. Specifically, in the embodiment, the point cloud acquired in the early-stage field is introduced into Revit software to establish the BIM three-dimensional tunnel actual model after data processing through a BIM three-dimensional tunnel actual model module, the three-dimensional axis, the geometric dimension, the structural deformation and the like of the tunnel are acquired, and the acquired point cloud is transmitted to the superposition analysis module.
In some possible embodiments, the step S200 of obtaining the construction data of the target tunnel and constructing the actual construction model of the target tunnel according to the construction data in the embodiment method may include steps S210 to S220:
s210, acquiring measurement data through a 3D laser scanner and/or a total station, and performing coordinate conversion on the measurement data to obtain first intermediate data;
s220, performing point cloud data processing on the first intermediate data to obtain a point cloud data three-dimensional model;
in the embodiment, the measurement data obtained by the 3D laser scanner, the total station and other devices are collected on site through the three-dimensional laser scanning point cloud data model module, coordinate conversion and point cloud data processing are performed on the collected data, a tunnel point cloud data three-dimensional model is obtained, and the tunnel point cloud data three-dimensional model is finally transmitted to the superposition analysis module.
More specifically, as shown in fig. 3, it is a schematic diagram of monitoring technology of a tunnel of a 3D scanner; the ranging beam rotates along the vertical and horizontal axes of the instrument, which serve as the Z-axis and Y-axis of the coordinate system of the station. As shown in fig. 4, the station coordinates (X) of the target point P in the point cloud data are the measurement principle of the 3D scanner in this embodiment p ,Y p ,Z p ) The calculation formula of (a) is as follows:
further, an error formula in the point location of the target point P is obtained:
wherein m is Xp 、m Yp 、m Zp 、m Ssp 、m θ 、m α Are each X p 、Y p 、Z P 、S SP Middle error of theta, alpha, S SP Theta, alpha are in turn the slope (the distance of the instrument to the target point P), the vertical angle and the horizontal angle.
S300, fusing model data of the design model to obtain first fusion data, fusing the actual construction model to obtain second fusion data, and performing superposition analysis according to the first fusion data and the second fusion data to obtain a tunnel deformation deviation value;
specifically, in the embodiment, the superposition analysis module is used for fusing the model data, and calculating the deviation amount of the actual tunnel model and the designed tunnel model to obtain data information such as tunnel overbreak, section deformation (such as flatness, roundness and centerline deviation), earth and stone square amount and the like.
S400, risk prediction is carried out according to the deformation deviation value, and the risk prediction result is displayed in a visual mode;
specifically, in the embodiment, analysis results such as comparison images of the three-dimensional models, the tunnel design three-dimensional model and the actual three-dimensional model of the tunnel, tunnel overbreak, tunnel section deformation and the like are displayed in a unified platform through automatic output, the visualization of monitoring is realized, and a construction risk assessment report and a solution in each future time period are output.
In some feasible embodiments, in the embodiment method, the step S300 of fusing model data of the design model to obtain first fusion data, fusing the actual construction model to obtain second fusion data, and performing superposition analysis according to the first fusion data and the second fusion data to obtain the tunnel deformation deviation value includes at least one of the steps S301 or S302:
s301, determining the over-under-excavation deviation of the target tunnel;
s302, determining the deformation geometric deviation of the target tunnel.
Specifically, in the implementation process, the tunnel overbreak and underexcavation in the embodiment are calculated by comparing the section area S of the design tunnel T And actual cross-sectional area S of tunnel A The size of (2). The deformation geometric deviation detection method of the tunnel three-dimensional curved surface adopts a minimum distance projection algorithm (MDP) to find out corresponding points of points on a point cloud actual measurement curved surface on a design reference curved surface.
Further, in the step S301 of determining the overbreak deviation of the target tunnel, the step S3011-S3015 may include:
s3011, extracting a tunnel design section according to the first fusion data, and dividing the tunnel design section into a first polygonal area and a first circular arc area;
s3012, determining the area of the designed section of the tunnel according to the area of the first polygonal area and the area of the first arc area;
s3013, extracting an actual section of the tunnel according to the second fusion data, and dividing the actual section of the tunnel into a second polygonal area and a second circular arc area;
s3014, determining the actual cross-section area of the tunnel according to the area of the second polygonal area and the area of the second arc area;
s3015, determining the target tunnel overbreak and underbreak deviation according to the difference value of the designed section area of the tunnel and the actual section area of the tunnel.
Specifically, in the embodiment, a tunnel design cross section of the target tunnel is constructed from model data obtained by fusing a plurality of design models, the tunnel design cross section can be set to be a closed region formed by a plurality of polygons and arcs, and the vertex forming a triangular region in the cross section is set to be P (X) 0 ,Y 0 )，P(X 1 ,Y 1 ) And P (X) 2 ,Y 2 ) The area of the triangle thus formed is:
S 1 ＝(x 1 -x 0 )(y 2 -y 0 )-(x 2 -x 0 )(y 1 -y 0 )
from a point P on the cross section 0 Retrieving an arbitrary point P 1 And its adjacent point, thereby calculating the area S (P) of triangle formed by three points 0 ，P i-1 ，P i ) And S (P) 0 ，P i ，P i+1 ) Then area S of the polygonal area p Comprises the following steps:
then, searching the area with the shape of the sideline of the cross section being the arc, and calculating the corresponding arc area S i Comprises the following steps:
S i ＝R 2 [A/2-cos(A/2)sin(A/2)]
the area S of the arc region c Comprises the following steps:
cross-sectional area S of tunnel T Comprises the following steps:
S T ＝S c +S P
similarly, the actual tunnel section area S is calculated by using a polygon area calculation method A Then the overbreak area S is:
S＝S A -S T
finally, if S is larger than 0, the area is the overexcavation area; otherwise, if S is less than 0, the area is the underexcavated area.
In some possible embodiments, the step S302 of the embodiment method determining the deformation geometry deviation of the target tunnel may include steps S3021 to S3024:
s3021, constructing an actual measurement point cloud curved surface according to the second fusion data, and determining a first coordinate point in the actual measurement point cloud curved surface;
s3022, constructing a design model curved surface according to the first fusion data, and determining at least three second coordinate points in the design model curved surface;
s3023, determining a first target plane according to the second coordinate point, and determining a third coordinate point of the first coordinate point in the first target plane;
and S3024, determining the geometric deviation of the deformation according to the distance between the first coordinate point and the third coordinate point.
As shown in fig. 5, in the implementation process, a point q (x) is selected from the tunnel measured point cloud curved surface map q ，y q ，z q ) Then, three points p are selected on the curved surface diagram of the tunnel design model 1 (x p1 ，y p1 ，z p1 )、p 2 (x p2 ，y p2 ，z p2 )、p 3 (x p3 ，y p3 ，z p3 ) And the distance between the three points and the midpoint q of the tunnel actual measurement point cloud curved surface satisfies q 1 ＜q 2 ＜q 3 (ii) a Further, a point q on the tunnel measured point cloud curved surface corresponds to a point q' (x) on the tunnel design curved surface q′ ，y q′ ，z q′ ) Satisfies the following calculation formula:
corresponding to three points p on the curved surface of the tunnel design 1 、p 2 And p 3 Parameters (a, b, c, d) of the determined plane:
wherein the content of the first and second substances,
and calculating the coordinate of a point q ' for the intersection vector of the central axis of the tunnel, wherein the distance between the point q ' and the point q on the tunnel actual measurement model is the shortest, and the point q ' is the most possible corresponding point on the reference tunnel curved surface.
Distance | qq' | from point q on the tunnel measured point cloud to the tunnel design model:
in some possible embodiments, the step S300 of performing risk prediction according to the deformation deviation value and visually displaying a risk prediction result by the example method may include steps S301 to S302:
s301, determining that the target tunnel is over-excavated according to the over-under-excavation deviation of the target tunnel, and performing slurry supplementing and backfilling on the tunnel;
and S302, determining that the target tunnel is under-excavated according to the over-under-excavation deviation of the target tunnel, and performing tunnel chiseling treatment.
Specifically, in the implementation process, the embodiment method strictly controls the tunnel overbreak and underbreak: for the overbreak, the slurry supplement and backfill of the tunnel are carried out in time; and for underexcavation, performing tunnel chiseling treatment in time. And (4) controlling the tunnel overbreak and underbreak in real time by comparing the three-dimensional model designed by the tunnel with the actual three-dimensional model image of the tunnel.
In some possible embodiments, the step S300 of performing risk prediction according to the deformation deviation value and visually displaying a risk prediction result in the embodiment method may further include the step S303:
s303, determining a risk early warning grade according to the deformation geometric deviation and a preset deformation threshold, and obtaining a risk prediction result according to the risk early warning grade;
specifically, in the implementation process, the intelligent risk prediction in the embodiment adopts three-level early warning management, and the deformation deviation measured value | qq' | of the tunnel and the allowable deformation U are compared n And comparing to determine the prediction management level:
prediction management level I level: | qq'. Lily<U n And/3, normal construction;
prediction management level II: u shape n /3≤|qq′|≤2U n 3, enhancing monitoring;
prediction management level III: | qq'. The purple>2U n And/3, enhancing the monitoring and adopting corresponding engineering measures.
The embodiment method in the technical scheme of the application is completely described as follows by combining the attached drawings of the specification:
the technical scheme of the application provides a tunnel construction monitoring system with multi-model fusion and a risk prediction method thereof, and the method comprises the following steps: the system comprises an input module, a tunnel design model, an unmanned aerial vehicle three-dimensional aerial survey model module, a topographic geological three-dimensional model module, a finite element three-dimensional analysis module, a three-dimensional laser scanning point cloud model module, a BIM three-dimensional tunnel actual model module, a superposition analysis module and a risk prediction module; inputting tunnel engineering information, topographic and geological information, design data information and the like through an input module to obtain basic information such as tunnel engineering management information, topographic and geological information, material information, design central axis, design section diagram and the like; information such as a tunnel design central axis, a design section diagram and the like is obtained through a tunnel design model module, a tunnel design model is established, data such as tunnel periphery patrol flight, aerial survey and the like are obtained through an unmanned aerial vehicle three-dimensional aerial survey model module, and an unmanned aerial vehicle three-dimensional aerial survey model is established; acquiring information of bad geological bodies through a topographic and geological three-dimensional model module, and dynamically simulating in real time; simulating tunnel excavation through a finite element three-dimensional analysis model module; acquiring measurement data obtained by a 3D laser scanner, a total station and other equipment through a laser scanning three-dimensional point cloud model module, and performing coordinate conversion and processing on the point cloud of the on-site acquired data to obtain a tunnel point cloud three-dimensional model; through a BIM three-dimensional tunnel actual model, point clouds acquired by field work in the early stage are led into Revit software after data processing to establish the BIM three-dimensional tunnel actual model, and a three-dimensional axis, a geometric dimension, structural deformation and the like of the tunnel are obtained; through a superposition analysis module, fusing the model data, and calculating deviation of a tunnel actual model and a tunnel design model to obtain data information of tunnel overbreak, section deformation, flatness, earth and stone volume, roundness, center line deviation and the like; and acquiring a tunnel construction risk grade through a risk prediction module, and guiding tunnel construction.
On the other hand, the technical scheme of the application also provides a multi-model fused tunnel construction risk prediction system, which comprises:
the design model generation unit is used for acquiring survey information and basic information of the target tunnel and constructing a design model of the target tunnel according to the survey information and the basic information, wherein the design model comprises a tunnel three-dimensional model, a three-dimensional live-action model, a geological three-dimensional model and a finite element three-dimensional analysis model;
the actual construction model generating unit is used for acquiring construction data of the target tunnel and constructing an actual construction model of the target tunnel according to the construction data, wherein the actual construction model comprises a point cloud data three-dimensional model and a BIM three-dimensional tunnel actual model;
the deviation value calculation unit is used for fusing the model data of the design model to obtain first fusion data, fusing the actual construction model to obtain second fusion data, and performing superposition analysis according to the first fusion data and the second fusion data to obtain a tunnel deformation deviation value;
and the risk prediction unit is used for predicting the risk according to the deformation deviation value and visually displaying the risk prediction result.
On the other hand, the technical scheme of the application also provides a multi-model fused tunnel construction risk prediction device; it includes:
at least one processor; at least one memory for storing at least one program; when the at least one program is executed by the at least one processor, the at least one processor is caused to execute the multi-model fusion tunnel construction risk prediction method according to the first aspect.
The embodiment of the invention also provides a storage medium, which stores a corresponding execution program, and the program is executed by a processor, so that the multi-model fusion tunnel construction risk prediction method in the first aspect is realized.
From the above specific implementation process, it can be concluded that the technical solution provided by the present invention has the following advantages or advantages compared to the prior art:
1) according to the technical scheme, data are analyzed and processed uniformly, so that manpower and material resources are saved; the method can combine all model data, enhance the contact of all parts and solve the problem of information isolated island.
2) Compared with the prior art, the method and the device can realize model fusion, data visualization, deformation analysis and risk prediction, remarkably improve the management efficiency of tunnel engineering and guarantee the safety of tunnel construction.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flow charts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.
Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the functions and/or features may be integrated in a single physical device and/or software module, or one or more of the functions and/or features may be implemented in a separate physical device or software module. It will also be understood that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer, given the nature, function, and internal relationship of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the invention, which is defined by the appended claims and their full scope of equivalents.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. The method for predicting the tunnel construction risk through multi-model fusion is characterized by comprising the following steps of:
acquiring survey information and basic information of a target tunnel, and constructing a design model of the target tunnel according to the survey information and the basic information, wherein the design model comprises a tunnel three-dimensional model, a three-dimensional real scene model, a geological three-dimensional model and a finite element three-dimensional analysis model;
acquiring construction data of the target tunnel, and constructing an actual construction model of the target tunnel according to the construction data, wherein the actual construction model comprises a point cloud data three-dimensional model and a BIM three-dimensional tunnel actual model;
fusing the model data of the design model to obtain first fusion data, fusing the actual construction model to obtain second fusion data, and performing superposition analysis according to the first fusion data and the second fusion data to obtain a tunnel deformation deviation value;
and predicting the risk according to the deformation deviation value, and visually displaying the risk prediction result.
2. The method for predicting the risk of the multi-model fused tunnel construction according to claim 1, wherein the step of fusing the model data of the design model to obtain first fused data, the step of fusing the actual construction model to obtain second fused data, and the step of performing the superposition analysis according to the first fused data and the second fused data to obtain the tunnel deformation deviation value comprises at least one of the following steps:
determining the over-under-excavation deviation of the target tunnel;
and determining the deformation geometric deviation of the target tunnel.
3. The method of predicting risk in multi-model fused tunnel construction according to claim 2, wherein the step of determining the target tunnel overbreak deviation comprises:
extracting to obtain a tunnel design section according to the first fusion data, and dividing the tunnel design section into a first polygonal area and a first circular arc area;
determining the design section area of the tunnel according to the area of the first polygonal area and the area of the first arc area;
extracting to obtain a tunnel actual section according to the second fusion data, and dividing the tunnel actual section into a second polygonal area and a second circular arc area;
determining the actual cross-sectional area of the tunnel according to the area of the second polygonal area and the area of the second arc area;
and determining the target tunnel overbreak and underexcavation deviation according to the difference value between the designed section area of the tunnel and the actual section area of the tunnel.
4. The method for predicting risk in multi-model fused tunnel construction according to claim 2, wherein the step of determining the geometric deviation of the target tunnel deformation comprises:
constructing an actual measurement point cloud curved surface according to the second fusion data, and determining a first coordinate point in the actual measurement point cloud curved surface;
constructing a design model curved surface according to the first fusion data, and determining at least three second coordinate points in the design model curved surface;
determining a first target plane according to the second coordinate point, and determining a third coordinate point corresponding to the first coordinate point in the first target plane;
and determining the deformation geometric deviation according to the distance between the first coordinate point and the third coordinate point.
5. The method for predicting the risk of the multi-model fused tunnel construction according to claim 3, wherein the step of predicting the risk according to the deformation deviation value and visually displaying the risk prediction result comprises at least one of the following steps:
determining that the target tunnel is over-excavated according to the over-and-under-excavation deviation of the target tunnel, and performing slurry supplement and backfill on the tunnel;
and determining that the target tunnel is underexcavated according to the target tunnel overbreak and underexcavation deviation, and performing tunnel chiseling treatment.
6. The method for predicting the risk of the multi-model fused tunnel construction according to claim 4, wherein the step of predicting the risk according to the deformation deviation value and visually displaying the risk prediction result further comprises:
and determining a risk early warning grade according to the deformation geometric deviation and a preset deformation threshold value, and obtaining the risk prediction result according to the risk early warning grade.
7. The method for predicting the risk of the tunnel construction through multi-model fusion according to claim 1, wherein the step of obtaining the construction data of the target tunnel and constructing the actual construction model of the target tunnel according to the construction data comprises the following steps:
acquiring measurement data through a 3D laser scanner and/or a total station, and performing coordinate conversion on the measurement data to obtain first intermediate data;
and carrying out point cloud data processing on the first intermediate data to obtain the point cloud data three-dimensional model.
8. The multi-model fused tunnel construction risk prediction system is characterized by comprising:
the system comprises a design model generating unit, a target tunnel analysis unit and a target tunnel analysis unit, wherein the design model generating unit is used for acquiring survey information and basic information of a target tunnel and constructing a design model of the target tunnel according to the survey information and the basic information, and the design model comprises a tunnel three-dimensional model, a three-dimensional live-action model, a geological three-dimensional model and a finite element three-dimensional analysis model;
the actual construction model generating unit is used for acquiring construction data of the target tunnel and constructing an actual construction model of the target tunnel according to the construction data, wherein the actual construction model comprises a point cloud data three-dimensional model and a BIM three-dimensional tunnel actual model;
the deviation value calculation unit is used for fusing the model data of the design model to obtain first fusion data, fusing the actual construction model to obtain second fusion data, and performing superposition analysis according to the first fusion data and the second fusion data to obtain a tunnel deformation deviation value;
and the risk prediction unit is used for predicting the risk according to the deformation deviation value and visually displaying the risk prediction result.
9. Many models fuse's tunnel construction risk prediction device, its characterized in that includes:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to perform the method for multi-model fused tunnel construction risk prediction according to any one of claims 1-7.
10. A storage medium having stored therein a processor-executable program, wherein the processor-executable program, when executed by a processor, is configured to execute the multi-model fused tunnel construction risk prediction method according to any one of claims 1-7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116108397A (en) * | 2022-12-22 | 2023-05-12 | 福建亿榕信息技术有限公司 | Electric power field operation violation identification method integrating multi-mode data analysis |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106327579A (en) * | 2016-08-12 | 2017-01-11 | 浙江科技学院 | Method for realizing tunnel blasting quality digitalization based on BIM (Building Information Modeling) and multi-dimensional imaging fusion technologis |
| CN207281624U (en) * | 2017-05-10 | 2018-04-27 | 广东交通职业技术学院 | A kind of monitoring system of freeway tunnel |
| US20180349817A1 (en) * | 2017-06-01 | 2018-12-06 | Autodesk, Inc. | Architecture, engineering and construction (aec) risk analysis system and method |
| CN109598714A (en) * | 2018-12-03 | 2019-04-09 | 中南大学 | A kind of Tunnel Overbreak & Underbreak detection method based on 3-dimensional reconstruction and grid surface |
| CN110827406A (en) * | 2019-10-25 | 2020-02-21 | 徐佳 | Method for realizing rapid three-dimensional reconstruction of large water network tunnel |
| CN111023966A (en) * | 2019-11-28 | 2020-04-17 | 中铁十八局集团第五工程有限公司 | Tunnel measurement and control method based on combination of three-dimensional laser scanner and BIM |
| CN113914407A (en) * | 2021-10-09 | 2022-01-11 | 重庆科技学院 | Excavator excavation tunnel accurate control system based on BIM and machine vision |
-
2022
- 2022-05-25 CN CN202210575218.4A patent/CN114997003B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106327579A (en) * | 2016-08-12 | 2017-01-11 | 浙江科技学院 | Method for realizing tunnel blasting quality digitalization based on BIM (Building Information Modeling) and multi-dimensional imaging fusion technologis |
| CN207281624U (en) * | 2017-05-10 | 2018-04-27 | 广东交通职业技术学院 | A kind of monitoring system of freeway tunnel |
| US20180349817A1 (en) * | 2017-06-01 | 2018-12-06 | Autodesk, Inc. | Architecture, engineering and construction (aec) risk analysis system and method |
| CN109598714A (en) * | 2018-12-03 | 2019-04-09 | 中南大学 | A kind of Tunnel Overbreak & Underbreak detection method based on 3-dimensional reconstruction and grid surface |
| CN110827406A (en) * | 2019-10-25 | 2020-02-21 | 徐佳 | Method for realizing rapid three-dimensional reconstruction of large water network tunnel |
| CN111023966A (en) * | 2019-11-28 | 2020-04-17 | 中铁十八局集团第五工程有限公司 | Tunnel measurement and control method based on combination of three-dimensional laser scanner and BIM |
| CN113914407A (en) * | 2021-10-09 | 2022-01-11 | 重庆科技学院 | Excavator excavation tunnel accurate control system based on BIM and machine vision |
Non-Patent Citations (1)
| Title |
|---|
| 阳军生;张宇;祝志恒;傅金阳;谢壮;王立川;: "基于图像三维重建的隧道超欠挖检测方法研究" * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116108397A (en) * | 2022-12-22 | 2023-05-12 | 福建亿榕信息技术有限公司 | Electric power field operation violation identification method integrating multi-mode data analysis |
| CN116108397B (en) * | 2022-12-22 | 2024-01-09 | 福建亿榕信息技术有限公司 | Electric power field operation violation identification method integrating multi-mode data analysis |
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