CN111583413A - BIM (building information modeling) parametric modeling and augmented reality mobile inspection method for pavement diseases - Google Patents

BIM (building information modeling) parametric modeling and augmented reality mobile inspection method for pavement diseases Download PDF

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CN111583413A
CN111583413A CN202010215115.8A CN202010215115A CN111583413A CN 111583413 A CN111583413 A CN 111583413A CN 202010215115 A CN202010215115 A CN 202010215115A CN 111583413 A CN111583413 A CN 111583413A
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伍朝辉
张洪伟
王亮
李贤统
刘振正
符志强
朱琳
徐萍
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Inner Mongolia Autonomous Region Traffic Construction Engineering Quality Supervision Administration
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Abstract

The invention discloses a BIM (building information modeling) parameterized modeling and augmented reality mobile inspection method for pavement diseases, relates to the technical field of traffic informatization and virtual reality, and aims to solve the problems that a typical disease rapid visualization method is lacked and development and prediction are difficult in the prior art. The BIM parameterized modeling and augmented reality mobile inspection method for the pavement diseases comprises the following steps: and constructing a BIM initial description model, solving the coefficients of the description model, obtaining an optimal mathematical description model, obtaining a disease parameterization model, and realizing the mobile inspection and linkage alarm of the pavement diseases.

Description

BIM (building information modeling) parametric modeling and augmented reality mobile inspection method for pavement diseases
Technical Field
The invention relates to the technical field of traffic informatization and virtual reality, in particular to a BIM (building information modeling) parameterized modeling and augmented reality mobile inspection method for pavement diseases.
Background
The pavement diseases are one of important factors influencing the service performance of the asphalt road, and comprise various types such as cracks, block cracks, transverse cracks, longitudinal cracks, tracks, pushing, pit grooves, flooding, loosening and the like, the diseases are various in types, the geometrical structure difference is large, and the rapid three-dimensional reconstruction of the pavement diseases is difficult to realize by a simple geometrical method. Meanwhile, the method is limited by the current acquisition means, acquisition precision and acquisition frequency of three-dimensional data of the pavement diseases, and the development and prediction of typical diseases lack data support.
Although researchers in different fields carry out extensive research around three-dimensional reconstruction and development prediction of typical diseases of asphalt pavements and achieve better results in the aspects of geometric reconstruction, two-dimensional expression of pavement conditions and the like, some problems to be solved still exist. (1) The method is lack of a rapid visualization method for typical road diseases, detection data and simple two-dimensional coloring are difficult to directly and visually express the damage degree of the road surface, and a rapid construction method for a three-dimensional model of the road diseases with certain accuracy of data acquisition and visual sense is lack. (2) Three-dimensional development and prediction of pavement diseases are difficult, an effective method for realizing dynamic evolution and development prediction of typical diseases based on sampling data is lacked, and construction backtracking of highway maintenance decision and maintenance feedback information is difficult to support effectively.
Disclosure of Invention
The invention aims to provide a BIM (building information modeling) parameterized modeling and augmented reality mobile inspection method for a pavement disease, which is used for solving the problems that a typical disease rapid visualization method is lacked and development and prediction are difficult in the prior art.
In order to achieve the above purpose, the invention provides the following technical scheme:
a BIM (building information modeling) parameterized modeling and augmented reality mobile inspection method for pavement diseases comprises the following steps:
step 101: based on feature analysis and semantic description of typical road diseases, mathematical description is carried out on road disease information, and a BIM initial description model g of typical diseases is constructed0(k0x0,…,kixi…,knxn),n>i>0, wherein k0,…,ki...,knTo describe the model coefficients, x0,...,xi...,xnN is the number of parameters required to describe the function in order to describe the model parameters;
step 102: analyzing the difference between the BIM initial description model and the standard three-dimensional model of the pavement diseases by using an evaluation function to obtain the difference of the pavement diseases; minimizing the difference of the pavement diseases to obtain a description model coefficient
Figure BDA0002424140110000021
Step 103: returning to step 102, model coefficients are described
Figure BDA0002424140110000022
Updating to obtain an optimized pavement disease description model; when the difference of the pavement diseases is smaller than the difference threshold value or the iteration is larger than the preset times, the algorithm converges to obtain the optimal mathematical description model g of the pavement diseasesoptimal(k0x0,…,kixi…,knxn)；
Step 104: according to the optimal mathematical description model goptimal(k0x0,…,kixi…,knxn) Obtaining a BIM parameterized model of a typical disease by using a Dynamo visual programming method; and coupling a BIM parameterized model of typical diseases with a BIM model of a road main body based on the positions of the diseases to obtain the road surface diseases driven by detection dataMoment BIM parameterized model;
step 105A: acquiring pavement disease position information and sampling data sent by mobile terminal equipment, and marking a disease model to a disease position in real time to obtain augmented reality visual comparison of a preorder disease and an actual disease; obtaining a rapid studying and judging result of the development degree of the pavement diseases according to the difference of sampling data at different moments; and obtaining the alarm linkage of the development degree of the pavement diseases according to the quick research and judgment result of the development degree of the pavement diseases.
Further, after step 104, the method further comprises:
step 105B: selecting detection data of two adjacent time points of the same disease, and expressing models on two adjacent time points (t, t +1) of the same disease by using the optimal mathematical description model of the pavement disease obtained in step 103: (x)0,…,xi,…,xn)t，(x0,…,xi,…,xn)(t+1)Based on the parameter difference between the two, the three-dimensional dynamic evolution simulation of the pavement diseases is realized by combining the physical attribute of the pavement diseases and the analysis of a material attenuation change model; or the like, or, alternatively,
after step 104, the method further comprises:
step 105C: obtaining an optimal mathematical description model of the same disease on a time sequence according to detection data of at least 3 adjacent times ((t-1), t, (t +1), …) of the same disease; combining the physical attribute of the pavement disease and the analysis result of the material attenuation change model, and performing change function h on the time sequence of the optimal mathematical description model parameter of the same disease on the time sequencet(x0,…,xi,…,xn) And fitting to obtain the three-dimensional development prediction of the diseases in a certain range.
Further, the step 105B includes:
step 105B 1: obtaining an optimal mathematical description model on two adjacent time points (t, t +1) of the same disease according to detection data of two adjacent time points of the same disease: (x)0,…,xi,…,xn)t，(x0,…,xi,…,xn)(t+1)；
Step 105B 2: combining the physical attributes of the pavement diseases and the analysis results of the material attenuation change model, and obtaining three-dimensional dynamic evolution simulation of the pavement diseases according to the parameter difference between mathematical description models at two adjacent time points (t, t +1) of the same disease; or the like, or, alternatively,
the step 105C includes:
step 105C 1: selecting detection data and a standard model of at least 3 adjacent times ((t +1), t, (t +1) and …) of the same disease aiming at each type of typical disease;
step 105C 2: obtaining disease description model parameters (x) of the same disease in time series (t-1), t, (t +1) and … by using the optimization model in the step 1030,…,xi,…,xn)(t-1),(x0,…,xi,…,xn)t，(x0,…,xi,…,xn)(t+1),…；
Step 105C 3: fitting the disease description model parameters (x) of the same disease on the time sequence by combining the physical attribute of the pavement disease and the analysis of the material attenuation change model0,…,xi,…,xn) Function of variation h over a time seriest(x0,…,xi,…,xn)；
Step 105C 4: based on the variation function obtained by fitting, the parameter value (x) of the next time interval is obtained by outward interpolation0,…,xi,…,xn)(t+2)And the three-dimensional development prediction of typical pavement diseases is realized in a certain range.
Further, after step 104, the method further comprises:
step 105D: and (4) comparing the prediction result of the step 105C with the actual detection data, and combining the physical attribute of the road surface disease and the analysis of the material attenuation change model to obtain an auxiliary decision suggestion for road maintenance.
Further, the step 105D includes:
step 105D 1: comparing data of a road surface disease prediction result at the (t +2) moment obtained by predicting the disease model in the step 105C at the time sequence (t-1), t, (t +1) and … with a disease detection result obtained by detecting at the actual (t +2) moment, and obtaining a parameter difference between the prediction result and the actual detection result;
step 105D 2: combining the physical attribute of the road surface disease and the analysis result of the material attenuation change model, and obtaining an auxiliary decision suggestion of road maintenance according to the parameter difference between the prediction result and the actual detection result;
step 105D 3: and determining that the change of the disease detection result obtained by actual detection is larger than the predicted change obtained by prediction of the disease model, and obtaining corresponding pre-maintenance or minor repair treatment according to the parameter difference between the predicted result and the actual detection result.
Further, the step 101 includes:
step 101A: cutting out basic geometric elements aiming at each typical pavement disease;
step 101B: for regular shapes, the basic geometric elements can be subjected to dimension reduction or conversion to other domains for feature analysis;
step 101C: obtaining a typical disease initial description model g according to a mathematical expression function of basic geometric elements0(k0x0,…,kixi…,knxn),n>i>0, wherein k0,…,ki…,knTo describe the model coefficients, x0,…,xi…,xnN is the number of parameters required to describe the function in order to describe the model parameters;
step 101D: analyzing a general disease combined expression mode and a disease continuous expression rule based on the drawing position and the basic elements of the disease;
step 101E: and (3) performing relation analysis and relation mapping calculation on basic parameters and detection data required by drawing by combining the relation between the disease model expression and the drawing to obtain data and a method for the disease model rapid drawing method driven by the detection data.
Further, the step 103 includes:
step 103A: to describe the model coefficient
Figure BDA0002424140110000051
Updating to obtain optimizedA model; taking the optimized model as a current description model gj(k0x0,…,kixi…,knxn)；
Step 103B: obtaining updated f from the optimized modelevaluation(gj,gstandard) Judging whether the difference between the current model and the standard model is less than a certain threshold value or iteration exceeds a certain number of times j>N，
Step 103C: if no, continuing to execute the coefficient calculation of the model described in the step 102, and then returning to the step 103A;
step 103D: if yes, the algorithm converges and outputs an optimal mathematical description model g for describing the diseasesoptimal(k0x0,…,kixi…,knxn)。
Further, the step 104 includes:
step 104A: combining the limiting conditions of basic geometric elements to express general diseases, and utilizing a modeling tool Dynamo to describe a model g according to the optimized mathematicsoptimal(k0x0,…,kixi…,knxn) Building a BIM parameterized model of typical diseases;
step 104B: and calibrating the constructed disease model to the corresponding position of the BIM model of the road main body, and coupling the three-dimensional geometric model based on the position of the detection point and the BIM model of the road main body to realize BIM parameterized modeling of the road surface diseases driven by the detection data.
Further, the step 105A includes:
step 105a 1: the method comprises the steps of utilizing terminal equipment to identify the position of a disease, combining technologies such as GPS positioning, mobile phone positioning and image matching, marking a pavement disease model obtained by reconstructing last detection data to the current disease position, and realizing augmented reality visual comparison of a preorder disease and an actual disease;
step 105a 2: according to the key image and data of the current disease acquired and uploaded by the mobile terminal, quickly comparing key parameters and a predicted development result of a preorder three-dimensional disease model with key sampling data of the current actual disease to obtain a comparison difference of preorders, current and predicted sampling data;
step 105a 3: according to the comparison difference of the preorder, the current and the predicted sampling data, obtaining a rapid research and judgment result of the development degree of the pavement diseases;
step 105a 4: and (4) rapidly studying and judging results according to the development degree of the pavement diseases, and obtaining corresponding acquisition, alarm and other linkage operations to realize the augmented reality mobile inspection of the pavement diseases.
Further, the expression describing the model coefficient is as follows:
Figure BDA0002424140110000061
and/or the presence of a gas in the gas,
the disease three-dimensional model evaluation function fevaluationComprises the following steps:
fevaluation＝α1|Dgeometric|+α2|Dphysical|+α3|Dmaterial|
fevaluationthe method is used for measuring the difference between the reconstructed three-dimensional disease model and the real disease; dgeometricThe difference of the geometrical characteristics between the reconstructed three-dimensional disease model and the real disease; dphysicalDifference of physical change characteristics between the reconstructed model and the real disease; dmaterialα as the main material attenuation characteristic difference between the reconstructed model and the real disease1As a first difference weight, α2As a second difference weight, α3As a third difference weight, α1，α2，α3Are all greater than or equal to 0.
Compared with the prior art, the BIM parameterized modeling and augmented reality mobile inspection method for the pavement diseases has the following beneficial effects:
(1) the BIM parametric modeling method for typical road surface diseases is broken through, and three-dimensional rapid visualization of different road surface diseases is realized. The BIM has natural advantages for visual expression and maintenance aid decision-making of pavement diseases due to the digitalization, parameterization and visualization characteristics, and at present, pavement disease modeling and application research based on BIM is still in a blank state at home and abroad. The invention breaks through a BIM parametric modeling method of typical road diseases, realizes the quick visualization of different road disease models, forms a BIM full parametric model library of the typical diseases, and has better demonstration and popularization values in the traffic industry.
(2) And forming data-driven three-dimensional dynamic evolution and development prediction of the pavement diseases, and providing an intuitive model and an analysis tool for road maintenance. The BIM modeling based on semantic description and the three-dimensional reconstruction method based on detection data have advantages in constructing the pavement diseases in a certain state, but because of lack of support of continuous change rules and physical evolution models, effective simulation and prediction of three-dimensional dynamic changes and development conditions of the diseases are difficult to perform. The method breaks through the three-dimensional dynamic evolution simulation of the pavement diseases driven by the detection data based on the BIM parameterized characteristic, combines a dynamic evolution physical model, a pavement service performance decay model, a probability statistic model and the like, realizes the three-dimensional development prediction of typical diseases driven by the pavement detection data based on the detection data change characteristic analysis over the years, and provides an intuitive model and an analysis tool for road maintenance.
(3) The method for mobile inspection of the road surface diseases with augmented reality is formed, and rapid study and judgment of the road surface diseases and linkage alarm are realized. The method is characterized in that a preorder model, key parameters and a development prediction result of the pavement diseases are enhanced to the current disease position by using a mobile phone or augmented reality glasses, the augmented reality mobile inspection is realized, the rapid study and judgment of the disease development conditions are realized by comparing the preorder, the current and the prediction key parameters, corresponding alarm or other operations are triggered, and the augmented reality rapid mobile inspection of the asphalt pavement diseases is realized.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a technical route schematic diagram of a BIM (building information modeling) parameterized modeling and augmented reality mobile inspection method for a pavement disease in the embodiment of the invention;
FIG. 2 is a flow chart of a BIM parameterized modeling and augmented reality mobile inspection method for a pavement disease in the embodiment of the invention;
fig. 3 is a block diagram of a flow of obtaining an optimal mathematical description model of a road surface defect in an embodiment of the present invention.
Detailed Description
In order to facilitate clear description of technical solutions of the embodiments of the present invention, in the embodiments of the present invention, terms such as "first" and "second" are used to distinguish the same items or similar items having substantially the same functions and actions. For example, the first threshold and the second threshold are only used for distinguishing different thresholds, and the sequence order of the thresholds is not limited. Those skilled in the art will appreciate that the terms "first," "second," etc. do not denote any order or quantity, nor do the terms "first," "second," etc. denote any order or importance.
It is to be understood that the terms "exemplary" or "such as" are used herein to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "e.g.," is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion.
In the present invention, "at least one" means one or more, "a plurality" means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone, wherein A and B can be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of the singular or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, a and b combination, a and c combination, b and c combination, or a, b and c combination, wherein a, b and c can be single or multiple.
The pavement diseases described in the invention refer to: cracking, block cracking, transverse cracking, longitudinal cracking, rutting, pushing, pit slot, flooding, loosening, repairing, sinking, cuddling and other asphalt surface layer structural damage and surface layer performance attenuation diseases. According to the description and classification of asphalt pavement diseases in newly published 'road technical condition assessment standard JTG 5210-2018' and 'road asphalt pavement maintenance design specification JTG 5421-2018', the types of the diseases such as unstable roadbed structure, damaged base layer structure, damaged asphalt surface layer structure and performance attenuation of asphalt surface layer can be classified according to different positions of the diseases, and the classification of main diseases is shown in Table 1.
TABLE 1 asphalt pavement disease classification chart
Figure BDA0002424140110000081
Figure BDA0002424140110000091
The invention provides a BIM (building information modeling) parameterized modeling and augmented reality mobile inspection method for asphalt pavement diseases, and aims to realize BIM-based pavement disease modeling, dynamic evolution, three-dimensional development prediction, augmented reality mobile inspection and the like. The method breaks through key technologies such as BIM parametric modeling and data-driven dynamic evolution of typical asphalt pavement diseases, and realizes rapid visualization of asphalt pavement diseases driven by detection data. The evolution mechanism research of asphalt pavement diseases is deepened, data and technical support are provided for asphalt pavement service performance decay analysis, road digital asset management and auxiliary maintenance decision, and the level of highway maintenance technology and investment benefit are improved in an auxiliary mode. The BIM technology has natural advantages in the aspects of digitization and visualization of engineering information, multi-dimensional information integration and the like, traditional two-dimensional disease marking information is expressed in a more visual three-dimensional form, and the engineering information in the design and construction stages transmitted by the BIM model is combined, so that more visual pavement disease prediction and highway asset management are realized, and an auxiliary decision support basis is provided for a manager.
Fig. 1 shows a technical route schematic diagram of a BIM (building information modeling) parameterized modeling and augmented reality mobile inspection method for a road surface disease provided by the embodiment of the invention. As shown in fig. 1, the technical route of the method is as follows:
the BIM modeling method for the pavement diseases based on semantic description provides an initial model base, a standard data set and a model base for experimental comparison are constructed based on three-dimensional reconstruction of detection data, and an evaluation function of a model reconstruction result is defined by comprehensively considering geometric, physical and material attributes; realizing BIM-based parameterized modeling through function selection, parameter fitting, case experiments, model verification and optimization iteration; analyzing the model parameters and the detection data change characteristics along with time on the standard data set, and realizing dynamic evolution and development prediction of a data-driven three-dimensional disease model through time sequence modeling, difference analysis and physical attribute model association; and the disease model, parameters and key information are augmented and realistically marked to the position of the road surface disease in real time by utilizing peripherals such as a mobile phone, augmented reality glasses and the like, and the rapid study and judgment and linkage alarm of the disease change state are realized by comparing the preorder, the current disease sampling and the predicted disease sampling.
Fig. 2 shows a structural block diagram of a BIM parameterized modeling and augmented reality mobile inspection method for a road surface defect provided by the embodiment of the invention. As shown in fig. 2, the method comprises the steps of:
step 099: selecting detection data of typical road diseases, and performing high-precision three-dimensional reconstruction on the typical road diseases by using the detection data to construct a standard model base of the typical road diseases;
aiming at each type of diseases of the pavement, steps 099A-099C are carried out:
step 099A: selecting a disease sample with typical significance, and collecting the disease sample data by using a pavement disease detection tool and method; it should be noted that the road surface damage detection tool and the road surface damage detection method are well known to those skilled in the art, and therefore, detailed descriptions thereof are omitted.
Step 099B: reconstructing a three-dimensional model of the disease sample based on the detection data, and establishing an incidence relation between the detection data of the disease sample and the three-dimensional reconstruction model;
step 099C: organizing the disease acquisition data, the reconstructed model and the incidence relation thereof to form a typical disease standard model base of the asphalt pavement; each sample in the standard model library comprises: disease semantic description, detection data and a three-dimensional model;
the detection data may include pictures, laser scanning point cloud data, manual measurement data, and the like.
Step 100: defining an evaluation function f of a disease three-dimensional model by combining geometric characteristics, physical characteristics and material decay characteristics of the diseaseevaluation(gj,gstandard),N>j is more than or equal to 0(N is the maximum iteration number allowed by the invention) and is used for measuring the constructed disease three-dimensional model gjWith the standard model gstandardThe difference between them;
wherein f isevaluationThe method is an evaluation function of a disease three-dimensional model and is used for measuring the difference between a reconstructed three-dimensional disease model and a real disease, and specifically comprises the following steps:
fevaluation＝α1|Dgeometric|+α2|Dphysical|+α3|Dmaterial|，
wherein f isevaluationThe method is used for measuring the difference between the reconstructed three-dimensional disease model and the real disease;
Dgeometricthe difference of the geometrical characteristics between the reconstructed three-dimensional disease model and the real disease; specific definition can be carried out according to the geometric characteristics of the diseases;
Dphysicalthe difference of physical change characteristics between the reconstructed model and the real diseases can be specifically defined according to physical factors such as environment, stress, vehicle running load and the like which are suffered by the diseases as integral objects;
Dmaterialthe main material attenuation characteristic difference between the reconstructed model and the real diseases can be specifically defined according to the material attribute and the decay rule of the main material of the asphalt pavement;
α1as a first difference weight, α2As a second difference weight, α3Is the third differenceIso-weight, α1，α2，α3Are all greater than or equal to 0;
when α1＝1，α2＝α3When the value is 0, the disease three-dimensional model evaluation function fevaluationDegenerates to an evaluation function that only considers geometric differences between models.
Step 101: based on feature analysis and semantic description of typical road diseases, mathematical description is carried out on road disease information, and a BIM initial description model g of typical diseases is constructed0(k0x0,...,kixi...,knxn),n>i>0, wherein k0,…,ki…,knTo describe the model coefficients, x0,…,xi…,xnN is the number of parameters required to describe the function in order to describe the model parameters;
it should be noted here that the typical disease BIM initial description model refers to a combination idea, a description function, a boundary definition, a function description, and other definition conditions for expressing such general diseases by using basic geometric elements for a certain disease, and the geometric shape of different diseases has a large difference, and a description function which is convenient for expressing the geometric appearance of each disease and can reflect the further characteristic development and change of each disease needs to be selected. And selecting an applicable description function according to different diseases and variation characteristics thereof, and providing an important research basis for BIM (building information modeling) parametric modeling and development prediction of the pavement diseases.
Specifically, the step 101 includes:
step 101A: cutting out basic geometric elements aiming at each typical pavement disease;
it should be noted here that the basic geometric element refers to a geometric unit capable of expressing clear disease semantics and being easily expressed in combination.
Step 101B: for regular shapes, the basic geometric elements can be subjected to dimension reduction or conversion to other domains for feature analysis;
here, it should be noted that: the dimensionality reduction or transformation of the basic geometric elements into other domains for feature analysis may include, but is not limited to: if the pavement diseases are circular, the pavement diseases can be expressed by using the radius; if the pavement diseases are bowl-shaped pit slots, the dimension can be reduced to 1/4 circular arcs for expression; the pavement diseases are complex models and can be converted into frequency domains for analysis;
step 101C: obtaining a typical disease initial description model g according to a mathematical expression function of basic geometric elements0(k0x0,…,kixi…,knxn),n>i>0, wherein k0,…,ki…,knTo describe the model coefficients, x0,…,xi…,xnN is the number of parameters required to describe the function in order to describe the model parameters;
step 101D: analyzing a general disease combined expression mode and a disease continuous expression rule based on the drawing position and the basic elements of the disease;
step 101E: and (3) performing relation analysis and relation mapping calculation on basic parameters and detection data required by drawing by combining the relation between the disease model expression and the drawing to obtain data and a method for the disease model rapid drawing method driven by the detection data.
Step 102: analyzing the difference between the BIM initial description model and the standard three-dimensional model of the pavement diseases by using an evaluation function to obtain the difference of the pavement diseases; minimizing the difference of the pavement diseases to obtain a description model coefficient
Figure BDA0002424140110000131
The expression describing the model coefficients is:
Figure BDA0002424140110000132
step 103: returning to step 102, model coefficients are described
Figure BDA0002424140110000133
Updating to obtain an optimized pavement disease description model; when the difference of the pavement diseases is smaller than the difference threshold value or the iteration is larger than the preset times, the algorithm converges to obtain the optimal mathematical description model g of the pavement diseasesoptimal(k0x0,…,kixi…,knxn)；
Fig. 3 shows a flow chart of step 103 of the BIM parametric modeling and augmented reality mobile inspection method for a road surface defect provided by the embodiment of the present invention. As shown in fig. 3, obtaining the optimal mathematical description model of the pavement damage includes:
step 103A: to describe the model coefficient
Figure BDA0002424140110000134
Updating to obtain an optimized model; taking the optimized model as a current description model gj(k0x0,…,kixi…,knxn)；
Step 103B: obtaining updated f from the optimized modelevaluation(gj,gstandard) Judging whether the difference between the current model and the standard model is less than a certain threshold value or iteration exceeds a certain number of times j>N，
Step 103C: if "no", continue to step 220 to describe the coefficient calculation of the model, then go back to step 230A;
step 103D: if yes, the algorithm converges and outputs an optimal mathematical description model g for describing the diseasesoptimal(k0x0,…,kixi…,knxn)。
Step 104: according to the optimal mathematical description model goptimal(k0x0,…,kixi…,knxn) Obtaining a BIM parameterized model of a typical disease by using a Dynamo visual programming method; coupling a BIM parameterized model of typical diseases with a BIM model of a road main body based on the positions of the diseases to obtain a BIM parameterized model of the road diseases driven by detection data at each moment;
it should be noted here that the Dynamo visualization programming method for obtaining BIM parameterized modeling of typical diseases specifically includes using the Dynamo visualization programming method to programmatically represent an abstracted function, adding pavement disease parameters extracted from pavement disease detection data to Dynamo, and implementing parameterized modeling of pavement diseases through Dynamo programming.
Specifically, the step 104 includes:
step 104A: combining the limiting conditions of basic geometric elements to express general diseases, and utilizing a modeling tool Dynamo to describe a model g according to the optimized mathematicsoptimal(k0x0,…,kixi…,knxn) Building a BIM parameterized model of typical diseases;
step 104B: and calibrating the constructed disease model to the corresponding position of the BIM model of the road main body, and coupling the three-dimensional geometric model based on the position of the detection point and the BIM model of the road main body to realize BIM parameterized modeling of the road surface diseases driven by the detection data.
Step 105A: acquiring pavement disease position information and sampling data sent by mobile terminal equipment, and marking a disease model to a disease position in real time to obtain augmented reality visual comparison of a preorder disease and an actual disease; obtaining a rapid studying and judging result of the development degree of the pavement diseases according to the difference of sampling data at different moments; and obtaining the alarm linkage of the development degree of the pavement diseases according to the quick research and judgment result of the development degree of the pavement diseases.
It should be noted that the mobile terminal device includes, but is not limited to, a mobile phone or augmented reality glasses.
Specifically, the step 105A includes:
step 105a 1: the method comprises the steps of identifying the position of a disease by using mobile terminal equipment, and marking a pavement disease model obtained by reconstructing the last detection data to the current disease position by combining the technologies of GPS positioning, mobile phone positioning, image matching and the like, so as to realize the augmented reality visual comparison of a preorder disease and an actual disease; it should be noted that: the pavement disease model obtained by the last detection data reconstruction is a pavement disease parameterized model obtained by the last detection data reconstruction;
step 105a 2: according to the key image and data of the current disease acquired and uploaded by the mobile terminal, quickly comparing key parameters and a predicted development result of a preorder three-dimensional disease model with key sampling data of the current actual disease to obtain a comparison difference of preorders, current and predicted sampling data;
step 105a 3: according to the comparison difference of the preorder, the current and the predicted sampling data, obtaining a rapid research and judgment result of the development degree of the pavement diseases;
step 105a 4: and (4) rapidly studying and judging results according to the development degree of the pavement diseases, and obtaining corresponding acquisition, alarm and other linkage operations to realize the augmented reality mobile inspection of the pavement diseases.
Step 105B: selecting detection data of two adjacent time points of the same disease, and expressing models on two adjacent time points (t, t +1) of the same disease by using the optimal mathematical description model of the pavement disease obtained in step 103: (x)0,…,xi,…,xn)t，(x0,…,xi,…,xn)(t+1)And based on the parameter difference between the two, the three-dimensional dynamic evolution simulation of the pavement diseases is realized by combining the physical attributes of the pavement diseases and the analysis of a material attenuation change model.
Specifically, the step 105B includes:
step 105B 1: obtaining mathematical description model parameters on two adjacent time points (t, t +1) of the same disease according to detection data of two adjacent time points of the same disease: (x)0,…,xi,…,xn)t，(x0,…,xi,…,xn)(t+1)；
Step 105B 2: combining the physical attributes of the pavement diseases and the analysis results of the material attenuation change model, and obtaining three-dimensional dynamic evolution simulation of the pavement diseases according to the parameter difference between mathematical description models at two adjacent time points (t, t +1) of the same disease;
step 105C: obtaining a mathematical description model of the same disease on a time sequence according to detection data of at least 3 adjacent times ((t-1), t, (t +1), …) of the same disease; combining the physical attribute of the pavement diseases and the analysis result of the material attenuation change model, and carrying out a change function h on the model parameters on the time sequencet(x0,…,xi,…,xn) And fitting to obtain the three-dimensional development prediction of the diseases in a certain range.
Specifically, the step 105C includes:
step 105C 1: selecting detection data and a standard model of at least 3 adjacent times ((t +1), t, (t +1) · of the same disease aiming at each type of typical disease;
step 105C 2: obtaining the disease description model parameters (x) of the same disease on the time sequence (t-1), t,) t +1), … by using the optimization model of the step 1030,…,xi,…,xn)(t-1),(x0,…,xi,…,xn)t，(x0,…,xi,…,xn)(t+1),…；
Step 105C 3: fitting model parameters (x) by combining the physical attribute of the pavement diseases and the analysis result of the material attenuation change model0,…,xi,…,xn) Function of variation h over a time seriest(x0,…,xi,…,xn)；
Step 105C 4: based on the variation function obtained by fitting, the parameter value (x) of the next time interval is obtained by outward interpolation0,…,xi,…,xn)(t+2)And the three-dimensional development prediction of typical pavement diseases is realized in a certain range.
Step 105D: and (4) comparing the prediction result of the step 105C with the actual detection data, and combining the physical attribute of the road surface disease and the analysis of the material attenuation change model to obtain an auxiliary decision suggestion for road maintenance.
It should be noted that the prediction result of the step 105C is specifically a road surface disease model parameter prediction result; the actual detection data is the detection data of the current road surface diseases at the corresponding moment.
Specifically, the step 105D includes:
step 105D 1: comparing the road surface disease prediction result at the (t +2) moment obtained by predicting the disease model in the step 105C in the time sequence (t-1), t (t + 1).. with the disease detection result obtained by detecting at the actual (t +2) moment, and obtaining the parameter difference between the prediction result and the actual detection result;
step 105D 2: combining the physical attribute of the road surface disease and the analysis result of the material attenuation change model, and obtaining an auxiliary decision suggestion of road maintenance according to the parameter difference between the prediction result and the actual detection result;
step 105D 3: and determining that the change of the disease detection result obtained by actual detection is larger than the predicted change obtained by prediction of the disease model, and obtaining corresponding pre-maintenance or minor repair treatment according to the parameter difference between the predicted result and the actual detection result.
The invention breaks through a BIM-based asphalt pavement typical disease parametric modeling method and realizes the rapid visualization of pavement disease detection data. Three-dimensional modeling of asphalt pavement diseases is usually realized by adopting a method based on images, point cloud or semantic description, and a modeling method based on BIM has natural advantages in the aspect of pavement disease parameterization. At present, BIM modeling and application are mostly carried out around structural objects such as roads, bridges, tunnels, house buildings and the like, and pavement damage modeling and application research based on BIM is not seen.
The method is beneficial to deepening the research on the evolution mechanism of the asphalt pavement diseases and improving the level of the road maintenance technology and the investment benefit. On the basis of analyzing historical detection data of the same road section, development and change processes of typical asphalt pavement diseases such as cracks, tracks, pits, cuddles and the like are visually analyzed, so that the method is beneficial to further mastering the evolution mechanism of the diseases and assisting in improving the maintenance decision level and the investment benefit.
The method is beneficial to realizing more visual pavement diseases and highway asset management and assisting in improving the scientificity of maintenance decision. The BIM model has advantages in the aspects of digitization and visualization of engineering information and multi-dimensional information integration, expresses traditional two-dimensional disease marking information in a more intuitive three-dimensional form, combines engineering information in the design and construction stages transmitted by the BIM model, realizes more intuitive pavement disease prediction and highway asset management, and provides an auxiliary decision support basis for managers.
Parts of the invention not described in detail are well known to the person skilled in the art.
While the invention has been described in connection with various embodiments, other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a review of the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the word "a" or "an" does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.
While the invention has been described in conjunction with specific features and embodiments thereof, it will be evident that various modifications and combinations can be made thereto without departing from the spirit and scope of the invention. Accordingly, the specification and figures are merely exemplary of the invention as defined in the appended claims and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the invention. It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (10)

1. A BIM (building information modeling) parameterized modeling and augmented reality mobile inspection method for pavement diseases is characterized by comprising the following steps of:
step 101: based on feature analysis and semantic description of typical road diseases, mathematical description is carried out on road disease information, and a BIM initial description model g of typical diseases is constructed0(k0x0,...,kixi...,knxn),n>i>0, wherein k0,...,ki...,knTo describe the model coefficients, x0,...,xi...,xnN is the number of parameters required to describe the function in order to describe the model parameters;
step 102: analyzing the difference between the BIM initial description model and the standard three-dimensional model of the pavement diseases by using an evaluation function to obtain the difference of the pavement diseases; minimizing the difference of the pavement diseases to obtain a description model coefficient
Figure FDA0002424140100000011
Step 103: returning to step 102, model coefficients are described
Figure FDA0002424140100000012
Updating to obtain an optimized pavement disease description model; when the difference of the pavement diseases is smaller than the difference threshold value or the iteration is larger than the preset times, the algorithm converges to obtain the optimal mathematical description model g of the pavement diseasesoptimal(k0x0,...,kixi…,knxn)；
Step 104: according to the optimal mathematical description model goptimal(k0x0,…,kixi…,knxn) Obtaining a BIM parameterized model of a typical disease by using a Dynamo visual programming method; coupling a BIM parameterized model of typical diseases with a BIM model of a road main body based on the positions of the diseases to obtain a BIM parameterized model of the road diseases driven by detection data at each moment;
step 105A: acquiring pavement disease position information and sampling data sent by mobile terminal equipment, and marking a disease model to a disease position in real time to obtain augmented reality visual comparison of a preorder disease and an actual disease; obtaining a rapid studying and judging result of the development degree of the pavement diseases according to the difference of sampling data at different moments; and obtaining the alarm linkage of the development degree of the pavement diseases according to the quick research and judgment result of the development degree of the pavement diseases.
2. The BIM parametric modeling and augmented reality mobile inspection method for pavement diseases according to claim 1, wherein after step 104, the method further comprises:
step 105B: selecting detection data of two adjacent time points of the same disease, and expressing models on two adjacent time points (t, t +1) of the same disease by using the optimal mathematical description model of the pavement disease obtained in step 103: (x)0,…,xi,…,xn)t，(x0,…,xi,…,xn)(t+1)Based on the parameter difference between the two, the three-dimensional dynamic evolution simulation of the pavement diseases is realized by combining the physical attribute of the pavement diseases and the analysis of a material attenuation change model; or the like, or, alternatively,
after step 104, the method further comprises:
step 105C: obtaining an optimal mathematical description model of the same disease on a time sequence according to detection data of at least 3 adjacent times ((t-1), t, (t +1), …) of the same disease; combining the physical attribute of the pavement diseases and the analysis result of the material attenuation change model, and carrying out a change function h on the model parameters on the time sequencet(x0,…,xi,…,xn) And fitting to obtain the three-dimensional development prediction of the diseases in a certain range.
3. The BIM parametric modeling and augmented reality mobile inspection method for pavement diseases according to claim 2, wherein the step 105B comprises:
step 105B 1: obtaining a mathematical description model on two adjacent time points (t, t +1) of the same disease according to detection data of two adjacent time points of the same disease: (x)0,…,xi,…,xn)t，(x0,…,xi,…,xn)(t+1)；
Step 105B 2: combining the physical attributes of the pavement diseases and the analysis results of the material attenuation change model, and obtaining three-dimensional dynamic evolution simulation of the pavement diseases according to the parameter difference between mathematical description models at two adjacent time points (t, t +1) of the same disease; or the like, or, alternatively,
the step 105C includes:
step 105C 1: selecting detection data and a standard model of at least 3 adjacent times ((t +1), t, (t +1) and …) of the same disease aiming at each type of typical disease;
step 105C 2: obtaining disease description model parameters (x) of the same disease in time series (t-1), t, (t +1) and … by using the optimization model in the step 1030,…,xi,…,xn)(t-1),(x0,…,xi,…,xn)t，(x0,…,xi,…,xn)(t+1),…；
Step 105C 3: fitting model parameters (x) by combining the physical attribute of the pavement diseases and the analysis of the material attenuation change model0,…,xi,…,xn) Function of variation h over a time seriest(x0,…,xi,…,xn)；
Step 105C 4: based on the variation function obtained by fitting, the parameter value (x) of the next time interval is obtained by outward interpolation0,…,xi,…,xn)(t+2)And the three-dimensional development prediction of typical pavement diseases is realized in a certain range.
4. The BIM parametric modeling and augmented reality mobile inspection method for road surface diseases according to any one of claims 1-3, wherein after step 104, the method further comprises:
step 105D: and (4) comparing the prediction result of the step 105C with the actual detection data, and combining the physical attribute of the road surface disease and the analysis of the material attenuation change model to obtain an auxiliary decision suggestion for road maintenance.
5. The BIM parametric modeling and augmented reality mobile inspection method for pavement diseases according to claim 4, wherein the step 105D comprises:
step 105D 1: comparing data of a road surface disease prediction result at the (t +2) moment obtained by predicting the disease model in the step 105C at the time sequence (t-1), t, (t +1) and … with a disease detection result obtained by detecting at the actual (t +2) moment, and obtaining a parameter difference between the prediction result and the actual detection result;
step 105D 2: combining the physical attribute of the road surface disease and the analysis result of the material attenuation change model, and obtaining an auxiliary decision suggestion of road maintenance according to the parameter difference between the prediction result and the actual detection result;
step 105D 3: and determining that the change of the disease detection result obtained by actual detection is larger than the predicted change obtained by prediction of the disease model, and obtaining corresponding pre-maintenance or minor repair treatment according to the parameter difference between the predicted result and the actual detection result.
6. The BIM parametric modeling and augmented reality mobile inspection method for pavement diseases according to claim 1, wherein the step 101 comprises:
step 101A: cutting out basic geometric elements aiming at each typical pavement disease;
step 101B: for regular shapes, the basic geometric elements can be subjected to dimension reduction or conversion to other domains for feature analysis;
step 101C: obtaining a typical disease initial description model g according to a mathematical expression function of basic geometric elements0(k0x0,…,kixi…,knxn),n>i>0, wherein k0,…,ki…,knTo describe the model coefficients, x0,…,xi…,xnN is the number of parameters required to describe the function in order to describe the model parameters;
step 101D: analyzing a general disease combined expression mode and a disease continuous expression rule based on the drawing position and the basic elements of the disease;
step 101E: and (3) performing relation analysis and relation mapping calculation on basic parameters and detection data required by drawing by combining the relation between the disease model expression and the drawing to obtain data and a method for the disease model rapid drawing method driven by the detection data.
7. The BIM parametric modeling and augmented reality mobile inspection method for pavement diseases according to claim 1, wherein the step 103 comprises:
step 103A: to describe the model coefficient
Figure FDA0002424140100000041
Updating to obtain an optimized model; taking the optimized model as a current description model gj(k0x0,…,kixi…,knxn)；
Step 103B: obtaining updated f from the optimized modelevaluation(gj,gstandard) Judging whether the difference between the current model and the standard model is less than a certain threshold value or iteration exceeds a certain number of times j>N；
Step 103C: if no, continuing to execute the coefficient calculation of the model described in the step 102, and then returning to the step 103A;
step 103D: if yes, the algorithm converges and outputs an optimal mathematical description model g for describing the diseasesoptimal(k0x0,…,kixi…,knxn)。
8. The BIM parametric modeling and augmented reality mobile inspection method for pavement diseases according to claim 1, wherein the step 104 comprises:
step 104A: combining the limiting conditions of basic geometric elements to express general diseases, and utilizing a modeling tool Dynamo to describe a model g according to the optimized mathematicsoptimal(k0x0,…,kixi…,knxn) Building a BIM parameterized model of typical diseases;
step 104B: and calibrating the constructed disease model to the corresponding position of the BIM model of the road main body, and coupling the three-dimensional geometric model based on the position of the detection point and the BIM model of the road main body to realize BIM parameterized modeling of the road surface diseases driven by the detection data.
9. The BIM parametric modeling and augmented reality mobile inspection method for pavement diseases according to claim 1, wherein the step 105A comprises:
step 105a 1: recognizing the position of a disease by using terminal equipment, and marking a pavement disease model obtained by reconstructing the last detection data to the current disease position by combining GPS positioning, mobile phone positioning and image matching technologies to realize augmented reality visual comparison of a preorder disease and an actual disease;
step 105a 2: according to the key image and data of the current disease acquired and uploaded by the mobile terminal, quickly comparing key parameters and a predicted development result of a preorder three-dimensional disease model with key sampling data of the current actual disease to obtain a comparison difference of preorders, current and predicted sampling data;
step 105a 3: according to the comparison difference of the preorder, the current and the predicted sampling data, obtaining a rapid research and judgment result of the development degree of the pavement diseases;
step 105a 4: and (4) rapidly studying and judging results according to the development degree of the pavement diseases, obtaining corresponding acquisition and alarm linkage operation, and realizing the augmented reality mobile inspection of the pavement diseases.
10. The BIM parameterized modeling and augmented reality mobile inspection method for the road surface diseases according to any one of claims 1 to 3, wherein the expression for describing the model coefficients is as follows:
Figure FDA0002424140100000051
and/or the presence of a gas in the gas,
the disease three-dimensional model evaluation function fevaluationComprises the following steps:
fevaluation＝α1|Dgeometric|+α2|Dphysical|+α3|Dmaterial|
fevaluationthe method is used for measuring the difference between the reconstructed three-dimensional disease model and the real disease; dgeometricThe difference of the geometrical characteristics between the reconstructed three-dimensional disease model and the real disease; dphysicalDifference of physical change characteristics between the reconstructed model and the real disease; dmaterialα as the main material attenuation characteristic difference between the reconstructed model and the real disease1As a first difference weight, α2Is the second difference weight，α3As a third difference weight, α1，α2，α3Are all greater than or equal to 0.
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