CN114997003B - 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 multi-model fusion tunnel construction risk prediction method, a system, a device and a medium, wherein the method mainly comprises the following steps: acquiring investigation information and basic information of a target tunnel, and constructing a design model of the target tunnel according to the investigation 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 fused data, fusing an actual construction model to obtain second fused data, and performing superposition analysis according to the first fused data and the second fused data to obtain a tunnel deformation deviation value; carrying out risk prediction according to the deformation deviation value, and carrying out visual display on a risk prediction result; the method can uniformly analyze and process the data, saves manpower and material resources, remarkably 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 tunnel construction risks by multi-model fusion.
Background
Aiming at the problems of high construction difficulty and multiple risk factors of tunnel engineering, the conventional tunnel monitoring and risk early warning means cannot meet the current requirements of comprehensive management and comprehensive analysis. In recent years, three-dimensional models based on various modeling means and informationized management means brought by the three-dimensional models have been widely applied to various subdivision fields of engineering construction. The technology is widely used in tunnel construction, and the new technologies can acquire measurement and monitoring data with wider range, higher precision and resolution, thereby realizing the aims of quickly identifying risks, predicting risks in time, displaying risks in images and effectively controlling risks, and improving construction efficiency and engineering quality.
In tunnel construction environments with poor working conditions such as darkness, sealing, humidity and the like, when the traditional tunnel monitoring method such as a reverse hanging ruler, a convergence meter, a total station and the like is adopted for 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 cannot meet the requirements of tunnel engineering, in particular to the integral deformation monitoring of poor geological tunnels such as soft rock, deep soft soil and the like, and the current tunnel engineering construction; and the traditional security risk monitoring means cannot meet the current requirements of comprehensive management and comprehensive analysis.
Three-dimensional modeling, informatization and platform management means brought by the three-dimensional modeling, 3D laser scanning measurement technology and the like are applied to various aspects of engineering construction, so that the conversion of the construction industry from rough management to fine management is effectively promoted, the operation and management efficiency of tunnel engineering is improved on the whole, and greater social benefits are created while the economic benefits are improved. However, in the related technical scheme, the tunnel construction flow is as follows: planning, investigation, design, construction, detection, completion acceptance, and then management and maintenance. The data information data of each stage is mainly in each department, such as a survey unit is provided with engineering geological model data, a design unit is provided with tunnel design data and a BIM model, a construction unit is provided with tunnel construction monitoring data and the like, each department forms an information island, and various data information is not integrated.
Disclosure of Invention
In view of the above, in order to at least partially solve one of the above technical problems or drawbacks, an object of an embodiment of the present invention is to provide a tunnel construction risk prediction method based on multi-model fusion, so as to implement informationized monitoring and security risk management of tunnel construction; embodiments also provide a system, apparatus, and storage medium capable of implementing such a 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 investigation information and basic information of a target tunnel, and constructing a design model of the target tunnel according to the investigation information and the basic information, wherein the design model comprises a tunnel three-dimensional model, a three-dimensional live-action model, a topography 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 fused data, fusing the actual construction model to obtain second fused data, and performing superposition analysis according to the first fused data and the second fused data to obtain a tunnel deformation deviation value;
and carrying out risk prediction according to the deformation deviation value, and carrying out visual display on a risk prediction result.
In a possible embodiment of the present application, the step of fusing the model data of the design model to obtain first fused data, fusing the actual construction model to obtain second fused data, and performing superposition analysis according to the first fused data and the second fused data to obtain a tunnel deformation deviation value includes at least one of the following steps:
determining the target tunnel super-underexcavation deviation;
and determining the deformation geometric deviation of the target tunnel.
In a possible embodiment of the present application, the step of determining the target tunnel underrun deviation includes:
extracting according to the first fusion data to obtain a tunnel design section, and dividing the tunnel design section into a first polygonal area and a first arc area;
determining the area of the tunnel design section according to the area of the first polygonal area and the area of the first circular arc area;
extracting according to the second fusion data to obtain a tunnel actual section, and dividing the tunnel actual section into a second polygonal area and a second circular arc area;
determining the actual cross-section area of the tunnel according to the area of the second polygonal area and the area of the second circular arc area;
and determining the target tunnel overexcavation 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 application, the step of determining the deformation geometrical deviation of the target tunnel includes:
constructing a real-point cloud curved surface according to the second fusion data, and determining a first coordinate point in the real-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 application, the step of performing risk prediction according to the deformation deviation value and visually displaying a risk prediction result includes at least one of the following steps:
determining that the target tunnel has over-excavation according to the over-under-excavation deviation of the target tunnel, and performing tunnel slurry filling backfill;
and determining that the target tunnel is underexcavated according to the target tunnel overexcavation deviation, and performing tunnel chiseling processing.
In a possible embodiment of the present application, the step of predicting risk according to the deformation deviation value and visually displaying a risk prediction result further includes:
and determining a risk early warning level according to the deformation geometric deviation and a preset deformation threshold value, and obtaining the risk prediction result according to the risk early warning level. .
In a possible embodiment of the present application, the step of obtaining construction data of the target tunnel and constructing an 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 fusion tunnel construction risk prediction system, which comprises:
the system comprises a design model generation unit, a target tunnel detection unit and a target tunnel detection unit, wherein the design model generation unit is used for acquiring investigation information and basic information of a target tunnel and constructing a design model of the target tunnel according to the investigation information and the basic information, and the design model comprises a tunnel three-dimensional model, a three-dimensional live-action model, a topography geological three-dimensional model and a finite element three-dimensional analysis model;
the actual construction model generation 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 fused data, fusing the actual construction model to obtain second fused data, and performing superposition analysis according to the first fused data and the second fused data to obtain tunnel deformation deviation values;
and the risk prediction unit is used for performing risk prediction according to the deformation deviation value and visually displaying a risk prediction result.
On the other hand, the technical scheme of the application also provides a tunnel construction risk prediction device fused by multiple models, and the equipment comprises:
at least one processor;
at least one memory for storing at least one program;
the at least one program, when executed by the at least one processor, causes the at least one processor to perform the multimodal fusion tunneling risk prediction method of any of the first aspects.
In another aspect, the present application further provides a storage medium, in which a processor-executable program is stored, where the processor-executable program is used for executing the method for predicting tunnel construction risk of multimodal fusion according to any one of the first aspects when executed by a processor.
Advantages and benefits of the 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, wherein the method uniformly analyzes and processes data, so that manpower and material resources are saved; the model data can be combined, the connection of each part is enhanced, and the information island problem 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 tunnel engineering management efficiency and ensure the tunnel construction safety.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a block diagram of a multi-model fusion tunnel construction risk prediction system according to an embodiment of the present application;
fig. 2 is a flowchart of steps of a multi-model fusion tunnel construction risk prediction method provided in an embodiment of the present application;
FIG. 3 is a schematic diagram of monitoring a 3D scanner according to an embodiment of the present application;
FIG. 4 is a schematic diagram of the measurement principle of the 3D scanner in the embodiment of the application;
fig. 5 is a schematic diagram of calculation principle of deformation deviation values of a tunnel curved surface in an embodiment of the application.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention. The step numbers in the following embodiments are set for convenience of illustration only, and the order between the steps is not limited in any way, and the execution order of the steps in the embodiments may be adaptively adjusted according to the understanding of those skilled in the 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 topography geological 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 above-mentioned multi-model fusion tunnel construction risk prediction system, the technical scheme of the 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 investigation information and basic information of a target tunnel, and constructing a design model of the target tunnel according to the investigation information and the basic information;
the design model comprises, but is not limited to, a tunnel three-dimensional model, a three-dimensional live-action model, a topography geological three-dimensional model and a finite element three-dimensional analysis model, and in the embodiment, firstly, basic information such as topography geological information, material information, engineering management information, a design central axis, a design section diagram and the like of a tunnel is obtained by an investigation person, a designer and a constructor through an input module; and transmitting to a superposition analysis module. Then, the embodiment obtains information such as a tunnel design central axis, a design section diagram and the like through a 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 real model of the surrounding environment of the tunnel through the unmanned aerial vehicle three-dimensional model module, and transmitting the three-dimensional real model to the superposition analysis module. Further, a survey report and drilling data are obtained through a terrain geological three-dimensional model module, a terrain geological three-dimensional model is built, and the terrain geological three-dimensional model is transmitted to a superposition analysis module. In addition, the embodiment also obtains investigation report and drilling data through a terrain geological three-dimensional model module, establishes a terrain geological three-dimensional model and transmits the terrain geological three-dimensional model to a superposition analysis module; and simulating the tunnel construction excavation through the finite element three-dimensional analysis model module to obtain the simulation data of tunnel excavation mechanics and deformation, and transmitting the simulation data to the superposition analysis module. The generated three-dimensional model of the tunnel, the three-dimensional live-action model, the three-dimensional model of the topography and geology and the three-dimensional analysis model of the finite element are used together as the design model of the target tunnel in the embodiment.
S200, acquiring construction data of a 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. In the embodiment, the embodiment uses a BIM three-dimensional tunnel actual model module to process data of point clouds acquired by early field operation, then the point clouds are imported into Revit software to build a BIM three-dimensional tunnel actual model, and three-dimensional axes, geometric dimensions, structural deformation and the like of the tunnel are acquired and transmitted to a superposition analysis module.
In some possible implementations, 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 example 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 an embodiment, through a three-dimensional laser scanning point cloud data model module, measurement data obtained by equipment such as a 3D laser scanner and a total station are collected on site, coordinate conversion and point cloud data processing are carried out on the collected data, a tunnel point cloud data three-dimensional model is obtained, and finally the tunnel point cloud data three-dimensional model is transmitted to a superposition analysis module.
More specifically, as shown in fig. 3, a schematic diagram of tunnel monitoring technology of the 3D scanner is shown; the ranging beam rotates along the instrument's longitudinal and lateral axis, which serves as the Z-axis and Y-axis of the station coordinate system. As shown in fig. 4, the measurement principle of the 3D scanner in the present embodiment is that the station coordinates (X p ,Y p ,Z p ) The calculation formula of (2) is as follows:
further, an error formula in the point position of the target point P is obtained:
wherein m is Xp 、m Yp 、m Zp 、m Ssp 、m θ 、m α Respectively X p 、Y p 、Z P 、S SP Errors in θ, α, S SP θ, α are in order the slant (distance of the instrument to the target point P), vertical angle and horizontal angle.
S300, fusing model data of a design model to obtain first fused data, fusing an actual construction model to obtain second fused data, and performing superposition analysis according to the first fused data and the second fused data to obtain a tunnel deformation deviation value;
in the embodiment, the superposition analysis module is used for fusing the model data and calculating the deviation of the actual model of the tunnel and the design model of the tunnel to obtain data information such as tunnel super-undermining, section deformation (such as flatness, roundness and midline deviation), earth and stone quantity and the like.
S400, carrying out risk prediction according to the deformation deviation value, and carrying out visual display on a risk prediction result;
in the embodiment, through automatic output, analysis results such as each three-dimensional model, a tunnel design three-dimensional model, a tunnel actual three-dimensional model comparison image, tunnel super-undermining, tunnel section deformation and the like are displayed in a unified platform, monitoring visualization is realized, and construction risk assessment reports and solutions of all time periods are output.
In some possible embodiments, in the embodiment method, the step S300 of fusing model data of the design model to obtain first fused data, fusing an actual construction model to obtain second fused data, and performing superposition analysis according to the first fused data and the second fused data to obtain a tunnel deformation deviation value includes at least one of steps S301 or S302:
s301, determining the target tunnel super-undermining deviation;
s302, determining deformation geometric deviation of the target tunnel.
In particular, in the implementation process, the tunnel super-undermining calculation in the embodiment is performed by comparing the tunnel design cross-sectional areas S T And the actual cross-sectional area S of the tunnel A Is of a size of (a) and (b). The method for detecting the deformation geometric deviation of the three-dimensional curved surface of the tunnel adopts a minimum distance projection algorithm (MDP) to find out the corresponding point of the point on the actual measurement curved surface of the point cloud on the design reference curved surface.
Further, in the embodiment, the step S301 of determining the target tunnel undermining deviation may include steps S3011-S3015:
s3011, extracting according to first fusion data to obtain a tunnel design section, and dividing the tunnel design section into a first polygonal area and a first arc area;
s3012, determining the area of the tunnel design section according to the area of the first polygonal area and the area of the first circular arc area;
s3013, extracting the 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 circular arc area;
s3015, determining the target tunnel overexcitation deviation according to the difference value of the tunnel design cross-section area and the tunnel actual cross-section area.
In particular, in the embodiment, a tunnel design section of the target tunnel is constructed from model data obtained by fusing a plurality of design models, the tunnel design section may be set as a closed region composed of a plurality of polygons and arcs, and vertices constituting a triangular region in the section are set as P (X 0 ,Y 0 )，P(X 1 ,Y 1 ) P (X) 2 ,Y 2 ) The triangular area is thus constituted:
S 1 ＝(x 1 -x 0 )(y 2 -y 0 )-(x 2 -x 0 )(y 1 -y 0 )
from a point P on the section 0 Retrieving arbitrary point P 1 And adjacent points thereof, thereby calculating the triangle area S (P 0 ，P i-1 ，P i ) S (P) 0 ，P i ，P i+1 ) Area S of polygonal region p The method comprises the following steps:
then searching the area with the arc shape of the section edge, and calculating the corresponding arc area S i The method comprises the following steps:
S i ＝R 2 [A/2-cos(A/2)sin(A/2)]
arc-shaped area S c The method comprises the following steps:
tunnel cross-sectional area S T The method comprises the following steps:
S T ＝S c +S P
similarly, calculating the actual tunnel cross-section area S by using a polygonal area calculation method A The super underexcavated area S is:
S＝S A -S T
finally, if S is more than 0, the area is the super-digging area; otherwise, S is less than 0, and the area is the undermined area.
In some possible implementations, the step S302 of determining the deformation geometry deviation of the target tunnel by the example method may include steps S3021 to S3024:
s3021, constructing a real-point cloud curved surface according to second fusion data, and determining a first coordinate point in the real-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 corresponding to the first coordinate point in the first target plane:
s3024, determining deformation geometric deviation according to the distance between the first coordinate point and the third coordinate point.
As shown in fig. 5, in a specific implementation process, a point q (x q ，y q ，z q ) Then, three points are selected on the curved surface diagram of the tunnel design model
And the distance from the three points to the midpoint q of the tunnel real point Yun Qumian satisfies q 1 ＜q 2 ＜q 3 The method comprises the steps of carrying out a first treatment on the surface of the Further, the point q on the cloud surface of the tunnel real-point corresponds to the point q' (x) on the tunnel design surface q′ ，y q′ ，z q′ ) The three-dimensional coordinates of (2) satisfy the following calculation formula:
three points p corresponding to the tunnel design curved surface 1 、p 2 And p 3 Parameters (a, b, c, d) of the determined plane:
wherein, the liquid crystal display device comprises a liquid crystal display device,
and calculating the coordinate of a point q 'for the intersecting vector of the central axis of the tunnel, wherein the point q' is the most probable corresponding point on the curved surface of the reference tunnel, and the distance between the point q and the point q on the actual measurement model of the tunnel is shortest.
Distance |qq' | from point q on the tunnel real-point cloud to the tunnel design model:
in some possible implementations, the step S300 of performing risk prediction according to the deformation deviation value and visually displaying the risk prediction result may include steps S301 to S302:
s301, determining that the target tunnel has over-excavation according to the over-under-excavation deviation of the target tunnel, and performing tunnel slurry filling backfill;
s302, determining that the target tunnel is under-excavated according to the target tunnel over-excavation deviation, and performing tunnel chiseling processing.
In the implementation process, the embodiment method strictly controls the tunnel super-underexcavation: for the super-excavation, the slurry filling and backfilling of the tunnel are timely carried out; and (5) for underexcavation, timely performing tunnel chiseling treatment. And comparing the tunnel design three-dimensional model with the tunnel actual three-dimensional model image, and controlling the tunnel super-underexcavation in real time.
In some possible implementations, the step S300 of performing risk prediction according to the deformation deviation value and visually displaying the risk prediction result in the example method may further include step S303:
s303, determining a risk early warning level according to the deformation geometric deviation and a preset deformation threshold value, and obtaining a risk prediction result according to the risk early warning level:
in the implementation process, the risk intelligent prediction in the embodiment adopts three-level early warning management, and the actual measurement value |qq' | of tunnel deformation deviation and the allowable deformation U are adopted n And comparing and determining a prediction management level:
predictive management level I level: i qq' | < U n 3, normally constructing;
predictive management level II: u (U) n /3≤|qq′|≤2U n 3, enhancing monitoring;
predictive management level III: i qq' | > 2U n And 3, enhancing monitoring, and adopting corresponding engineering measures.
The embodiment method in the technical scheme of the application is fully described as follows:
the technical scheme of the application provides a multi-model fusion tunnel construction monitoring system and a risk prediction method thereof, wherein 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 terrain and 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 images and the like; the method comprises the steps of obtaining information such as a tunnel design central axis and a design section diagram through a tunnel design model module, establishing a tunnel design model, obtaining data such as the surrounding inspection and the aerial survey of the tunnel through an unmanned aerial vehicle three-dimensional aerial survey model module, and establishing an unmanned aerial vehicle three-dimensional aerial survey model; acquiring bad geologic body information through a terrain geological three-dimensional model module, and performing real-time dynamic simulation; simulating tunnel excavation through a finite element three-dimensional analysis model module; acquiring measurement data obtained by equipment such as a 3D laser scanner, a total station and the like through a laser scanning three-dimensional point cloud model module, and carrying out coordinate conversion and processing on site acquisition data point clouds to obtain a tunnel point cloud three-dimensional model; the method comprises the steps of importing point clouds acquired by early field operation into Revit software through a BIM three-dimensional tunnel actual model after data processing to establish the BIM three-dimensional tunnel actual model, and acquiring three-dimensional axes, geometric dimensions, structural deformation and the like of a tunnel; the superposition analysis module is used for fusing the model data and calculating the deviation amount of the actual model of the tunnel and the design model of the tunnel to obtain data information such as tunnel super-underexcavation, section deformation, flatness, earth and stone amount, roundness, midline deviation and the like; and obtaining 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 fusion tunnel construction risk prediction system, which comprises:
the system comprises a design model generating unit, a target tunnel detection unit and a target tunnel detection unit, wherein the design model generating unit is used for acquiring investigation information and basic information of a target tunnel and constructing a design model of the target tunnel according to the investigation information and the basic information, and the design model comprises a tunnel three-dimensional model, a three-dimensional live-action model, a topography geological three-dimensional model and a finite element three-dimensional analysis model;
the actual construction model generation 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 fused data, fusing the actual construction model to obtain second fused data, and performing superposition analysis according to the first fused data and the second fused data to obtain a tunnel deformation deviation value;
and the risk prediction unit is used for performing risk prediction according to the deformation deviation value and visually displaying a risk prediction result.
On the other hand, the technical scheme of the application also provides a tunnel construction risk prediction device with multiple model fusion; it comprises the following steps:
at least one processor; at least one memory for storing at least one program; the at least one program, when executed by the at least one processor, causes the at least one processor to perform the multimodal fusion tunnel construction risk prediction method as in 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 to realize the multi-model fusion tunnel construction risk prediction method in the first aspect.
From the above specific implementation process, it can be summarized that, compared with the prior art, the technical solution provided by the present invention has the following advantages or advantages:
1) According to the technical scheme, the data are analyzed and processed uniformly, so that manpower and material resources are saved; the model data can be combined, the connection of all parts is enhanced, and the information island problem is solved.
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 tunnel engineering management efficiency and ensure the tunnel construction safety.
In some 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 flowcharts 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 a larger operation are performed independently.
Furthermore, while the invention is described in the context of functional modules, it should be appreciated that, unless otherwise indicated, one or more of the functions and/or features may be integrated in a single physical device and/or software module or may be implemented in separate physical devices or software modules. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary to an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be apparent to those skilled in the art from consideration of their attributes, functions and internal relationships. Accordingly, one of ordinary skill in the art can implement 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 and are not intended to be limiting upon the scope of the invention, which is to be defined in the appended claims and their full scope of equivalents.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing 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 of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means 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 present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. 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 present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiment of the present invention has been described in detail, the present invention is not limited to the above embodiments, and various equivalent modifications and substitutions can be made by those skilled in the art without departing from the spirit of the present invention, and these equivalent modifications and substitutions are intended to be included in the scope of the present invention as defined in the appended claims.
Claims (8)
1. The tunnel construction risk prediction method based on multi-model fusion is characterized by comprising the following steps of:
acquiring investigation information and basic information of a target tunnel, and constructing a design model of the target tunnel according to the investigation information and the basic information, wherein the design model comprises a tunnel three-dimensional model, a three-dimensional live-action model, a topography geological three-dimensional model and a finite element three-dimensional analysis model; the tunnel three-dimensional model is obtained by establishing a tunnel design central axis and a design section view through a tunnel design three-dimensional model module; the three-dimensional live-action model is obtained by establishing a tunnel surrounding environment obtained through an unmanned aerial vehicle three-dimensional model module; the terrain geological three-dimensional model is obtained by establishing a survey report and drilling data through a terrain geological three-dimensional model module; the finite element three-dimensional analysis model is obtained by establishing tunnel construction simulation excavation, tunnel excavation mechanics and deformation simulation data through a finite element three-dimensional analysis model module;
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 three-dimensional model of the point cloud data is obtained by acquiring measurement data through a 3D laser scanner and/or a total station, performing coordinate conversion on the measurement data to obtain first intermediate data, and performing point cloud data processing on the first intermediate data to obtain the point cloud data; the BIM three-dimensional tunnel actual model is obtained by importing point clouds acquired by early field operation into Revit software after data processing through a BIM three-dimensional tunnel actual model module;
fusing the model data of the design model to obtain first fused data, fusing the actual construction model to obtain second fused data, and performing superposition analysis according to the first fused data and the second fused data to obtain a tunnel deformation deviation value;
the method for obtaining the tunnel deformation deviation value comprises the following steps of:
fusing model data of the design model through a superposition analysis module to obtain first fused data;
fusing the actual construction model with the first fusion data to obtain second fusion data;
the first fusion data and the second fusion data are fused through the superposition analysis module, the deviation value of the actual tunnel model and the deviation value of the design tunnel model are calculated, and the tunnel deformation deviation value is obtained, and the method comprises at least one of the following steps:
determining the target tunnel super-underexcavation deviation;
determining a deformation geometry deviation of the target tunnel;
and carrying out risk prediction according to the deformation deviation value, and carrying out visual display on a risk prediction result.
2. The method for predicting risk of tunnel construction by multi-model fusion according to claim 1, wherein said step of determining said target tunnel underrun deviation comprises:
extracting according to the first fusion data to obtain a tunnel design section, and dividing the tunnel design section into a first polygonal area and a first arc area;
determining the area of the tunnel design section according to the area of the first polygonal area and the area of the first circular arc area;
extracting according to the second fusion data to obtain a tunnel actual section, and dividing the tunnel actual section into a second polygonal area and a second circular arc area;
determining the actual cross-section area of the tunnel according to the area of the second polygonal area and the area of the second circular arc area;
and determining the target tunnel overexcavation deviation according to the difference value between the designed section area of the tunnel and the actual section area of the tunnel.
3. The method for predicting risk of tunnel construction by multi-model fusion according to claim 1, wherein said step of determining deformation geometrical deviation of said target tunnel comprises:
constructing a real-point cloud curved surface according to the second fusion data, and determining a first coordinate point in the real-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.
4. The method for predicting risk of multi-model fusion tunnel construction according to claim 2, wherein the step of predicting 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 has over-excavation according to the over-under-excavation deviation of the target tunnel, and performing tunnel slurry filling backfill;
and determining that the target tunnel is underexcavated according to the target tunnel overexcavation deviation, and performing tunnel chiseling processing.
5. A multi-model fusion tunnel construction risk prediction method according to claim 3, wherein the step of performing risk prediction according to the deformation deviation value and visually displaying a risk prediction result further comprises:
and determining a risk early warning level according to the deformation geometric deviation and a preset deformation threshold value, and obtaining the risk prediction result according to the risk early warning level.
6. A multi-model fused tunnel construction risk prediction system, the system comprising:
the system comprises a design model generation unit, a target tunnel detection unit and a target tunnel detection unit, wherein the design model generation unit is used for acquiring investigation information and basic information of a target tunnel and constructing a design model of the target tunnel according to the investigation information and the basic information, and the design model comprises a tunnel three-dimensional model, a three-dimensional live-action model, a topography geological three-dimensional model and a finite element three-dimensional analysis model; the tunnel three-dimensional model is obtained by establishing a tunnel design central axis and a design section view through a tunnel design three-dimensional model module; the three-dimensional live-action model is obtained by establishing a tunnel surrounding environment obtained through an unmanned aerial vehicle three-dimensional model module; the terrain geological three-dimensional model is obtained by establishing a survey report and drilling data through a terrain geological three-dimensional model module; the finite element three-dimensional analysis model is obtained by establishing tunnel construction simulation excavation, tunnel excavation mechanics and deformation simulation data through a finite element three-dimensional analysis model module;
the actual construction model generation 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 three-dimensional model of the point cloud data is obtained by acquiring measurement data through a 3D laser scanner and/or a total station, performing coordinate conversion on the measurement data to obtain first intermediate data, and performing point cloud data processing on the first intermediate data to obtain the point cloud data; the BIM three-dimensional tunnel actual model is obtained by importing point clouds acquired by early field operation into Revit software after data processing through a BIM three-dimensional tunnel actual model module;
the deviation value calculation unit is used for fusing the model data of the design model to obtain first fused data, fusing the actual construction model to obtain second fused data, and performing superposition analysis according to the first fused data and the second fused data to obtain tunnel deformation deviation values;
the deviation value calculating unit is configured to fuse model data of the design model to obtain first fused data, fuse the actual construction model to obtain second fused data, and perform superposition analysis according to the first fused data and the second fused data to obtain a tunnel deformation deviation value, and includes:
fusing model data of the design model through a superposition analysis module to obtain first fused data;
fusing the actual construction model with the first fusion data to obtain second fusion data;
the first fusion data and the second fusion data are fused through the superposition analysis module, the deviation value of the actual tunnel model and the deviation value of the design tunnel model are calculated, and the tunnel deformation deviation value is obtained, and the method comprises at least one of the following steps:
determining the target tunnel super-underexcavation deviation;
determining a deformation geometry deviation of the target tunnel;
and the risk prediction unit is used for performing risk prediction according to the deformation deviation value and visually displaying a risk prediction result.
7. The utility model provides a tunnel construction risk prediction device that many models fuse which characterized in that includes:
at least one processor;
at least one memory for storing at least one program;
the at least one program, when executed by the at least one processor, causes the at least one processor to run the multimodal fusion tunneling risk prediction method of any of claims 1-5.
8. A storage medium having stored therein a processor-executable program, which when executed by a processor is for running the multimodal fusion tunnel construction risk prediction method of any of claims 1-5.
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