AU2021102395A4 - BIM (Building Information Modeling) Parametric Modeling and Augmented Reality Mobile Inspection Method for Pavement Distresses - Google Patents
BIM (Building Information Modeling) Parametric Modeling and Augmented Reality Mobile Inspection Method for Pavement Distresses Download PDFInfo
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- 206010049119 Emotional distress Diseases 0.000 title claims abstract description 376
- 230000009429 distress Effects 0.000 title claims abstract description 376
- 238000000034 method Methods 0.000 title claims abstract description 56
- 238000007689 inspection Methods 0.000 title claims abstract description 46
- 230000003190 augmentative effect Effects 0.000 title claims abstract description 43
- 230000008859 change Effects 0.000 claims abstract description 43
- 238000011161 development Methods 0.000 claims abstract description 43
- 238000004458 analytical method Methods 0.000 claims abstract description 39
- 238000001514 detection method Methods 0.000 claims description 61
- 239000000463 material Substances 0.000 claims description 25
- 238000012423 maintenance Methods 0.000 claims description 19
- 238000005070 sampling Methods 0.000 claims description 19
- 230000000704 physical effect Effects 0.000 claims description 18
- 238000011156 evaluation Methods 0.000 claims description 12
- 230000000007 visual effect Effects 0.000 claims description 10
- 230000005477 standard model Effects 0.000 claims description 8
- 238000004088 simulation Methods 0.000 claims description 7
- 238000004422 calculation algorithm Methods 0.000 claims description 6
- 230000008878 coupling Effects 0.000 claims description 6
- 238000010168 coupling process Methods 0.000 claims description 6
- 238000005859 coupling reaction Methods 0.000 claims description 6
- 238000005516 engineering process Methods 0.000 claims description 5
- 230000008439 repair process Effects 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000013507 mapping Methods 0.000 claims description 3
- 230000003449 preventive effect Effects 0.000 claims description 3
- 230000009467 reduction Effects 0.000 claims description 3
- 238000010276 construction Methods 0.000 abstract description 9
- 238000002474 experimental method Methods 0.000 abstract description 4
- 238000005457 optimization Methods 0.000 abstract description 3
- 238000012795 verification Methods 0.000 abstract description 3
- 238000007794 visualization technique Methods 0.000 abstract description 3
- 239000010426 asphalt Substances 0.000 description 22
- 230000008901 benefit Effects 0.000 description 6
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- 230000007246 mechanism Effects 0.000 description 3
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Abstract
The present invention discloses a BIM (Building Information Modeling) parametric
modeling and augmented reality mobile inspection method for pavement distresses,
and relates to the technical field of traffic informationization and virtual reality, so as to
solve the problems of the lack of a rapid visualization method for typical distresses
and the difficulty in development prediction in the prior art. The BIM parametric
modeling and augmented reality mobile inspection method for the pavement
distresses comprises the following steps: building a BIM initial descriptive model;
solving descriptive model coefficients; obtaining an optimal mathematical descriptive
model; obtaining distress parametric models; and implementing mobile inspection and
linkage alarm of the pavement distresses.
1/3
FIGURES OF THE SPECIFICATION
Semantic description
based BIM Modeling of Reconstruction BIM based Data-driven development Augmented reality
oavementdistresses comarison parametric modeling prediction mobile innention
raendrsyss Paraeteseletio
Semantic analysis of
distresses Parameter selection ime series modeling R real-time marking
and model and difference of distress model
Semantic description based anays construction analysis
BIM Modeling of pavement
distresses
model association Preceding virtual
iisperuonmuma udseuu Parameter fitting nalysis modelpseudo
reconstruction samolina
functionono
Case experiment Dynamic evolution pre ed n p
Dataandmodel and development precedin sample
aa Dtasstyp verifinntion prediction
anomaly machine
analsis ptimizati
on Iterative optimization
Selection of abased feedback f model parameters Verification Change judgment
construction on feedback and linkage alarm
method
Fig. 1
Description
1/3
Semantic description based BIM Modeling of Reconstruction BIM based Data-driven development Augmented reality oavementdistresses comarison parametric modeling prediction mobile innention raendrsyss Paraeteseletio Semantic analysis of distresses Parameter selection ime series modeling R real-time marking and model and difference of distress model Semantic description based anays construction analysis BIM Modeling of pavement distresses model association Preceding virtual iisperuonmuma udseuu Parameter fitting nalysis modelpseudo reconstruction samolina
functionono Case experiment Dynamic evolution pre ed n p Dataandmodel and development precedin sample aa Dtasstyp verifinntion prediction anomaly machine analsis ptimizati on Iterative optimization Selection of abased feedback f model parameters Verification Change judgment construction on feedback and linkage alarm method
Fig. 1
BIM (Building Information Modeling) Parametric Modeling and
Augmented Reality Mobile Inspection Method for Pavement Distresses
The present invention relates to the technical field of traffic
informationization and virtual reality, in particular to a BIM (Building
Information Modeling) parametric modeling and augmented reality mobile
inspection method for pavement distresses.
Pavement distresses, as one of the important factors affecting the service
performance of asphalt highways, include cracks, block fault, transverse
cracks, longitudinal cracks, ruts, translation, pits, flushing asphalt, raveling
and the like. However, due to wide varieties and large differences in
geometric structures, it is difficult to implement rapid 3D reconstruction of the
distresses by simple geometric methods. Also, by limitations of current
collection methods, accuracy and frequency of 3D data of the distresses, data
are not sufficient to support the development prediction of typical distresses.
Although researchers in different fields have made extensive research on
3D reconstruction and development prediction of typical asphalt pavement
distresses, and have gained considerable achievements in geometric
reconstruction and 2D expression of pavement conditions, there are still some
urgent issues to be resolved: (1) a rapid visualization method is lacking for
typical pavement distresses, and while detection data and simple 2D coloring
can hardly express the degree of pavement damage directly and intuitively, a
3D model construction method, which has certain data accuracy and visual reality sense, is demanded; (2) 3D development prediction of the distresses is difficult, and due to the lack of an effective method for dynamic evolution and development prediction of typical distresses based on sampling data, it is difficult to effectively support highway maintenance decisions and construction backtracking of maintenance feedback information.
The present invention aims to provide a BIM (Building Information
Modeling) parametric modeling and augmented reality mobile inspection
method for pavement distresses, so as to solve the problems of the lack of a
rapid visualization method for typical distresses and the difficulty in
development prediction in the prior art.
To achieve the objective, the present invention provides the following
technical scheme:
A BIM parametric modeling and augmented reality mobile inspection
method for pavement distresses, comprises the following steps:
step 101, based on characteristic analysis and semantic description of
typical pavement distresses, mathematically describing pavement distress
information, and constructing a BIM initial descriptive model go (koxo, . . , kixi, . . ,
knxn) of the typical distresses, n>i>0, where, ko, . . , ki, . . , kn are coefficients of
the descriptive model, xo, ..., Xi ..., n are parameters of the descriptive model,
and n is the number of parameters demanded by a descriptive function;
step 102, analyzing the difference between the BIM initial descriptive
model of each type of pavement distress and a standard 3D model of the
pavement distresses by using an evaluation function to obtain the difference
of the pavement distresses; and minimizing the difference of the pavement distresses to obtain coefficients (ko, -i- --- n ) of the descriptive model; step 103, returning to the step 102 and updating the coefficients
(ko -- ki, --- kn ) of the descriptive model to obtain an optimized descriptive
model of the pavement distresses; and when the difference of the pavement
distresses is lower than a difference threshold or iteration is larger than a
preset number of times, performing algorithm convergence to obtain an
optimal mathematical descriptive model goptimai (koxo, ..., kixi, ..., knxn) of the
pavement distresses;
step 104, obtaining a BIM parametric model of typical distresses by using
a Dynamo visual programming method, according to the optimal mathematical
descriptive model goptima (koxo, ..., kixi, ..., knx); and based on distress
locations, coupling the BIM parametric model of the typical distresses with a
BIM model of a highway main body to obtain a detection data driven BIM
parametric model of the pavement distresses at each moment;
step 105A, obtaining pavement distress location information and
sampling data sent by mobile terminal equipment, and marking the distress
models to distress locations in real time to obtain augmented reality visual
comparison between the preceding distress and actual distresses; obtaining
rapid judgment results of the development degree of the pavement distresses
according to the difference of sampling data at different moments; and
obtaining alarm linkage of the development degree of the pavement
distresses according to rapid judgment results of the development degree;
Further, after step 104, the method also comprises the following steps:
step 105B, selecting detection data of the same distress at two adjacent
times, expressing models (xo, ..., Xi ..., Xn)t and (xo, ..., Xi ..., Xn)(t+1) of the same distress at two adjacent time points (t, t+1) by using the optimal mathematical descriptive model of the pavement distresses obtained in step 103, and based on the parameter difference between the two models, implementing 3D dynamic evolution simulation of the pavement distresses by combining the physical properties of pavement distresses and analysis of a material decay change model; after step 104, the method further comprises the following steps: step 105C, obtaining an optimal mathematical descriptive model of the same distress in time series according to detection data of the same distress at least three adjacent time points ((t-1), t, (t+1), . . ); and fitting the change function ht(xo, ..., Xi ..., Xn) of parameters of the optimal mathematical descriptive model of the same distress in time series by combining the physical properties of the pavement distresses and analysis results of the material decay change model, so as to obtain 3D development prediction of the distress within a certain range.
Further, the step 105B comprises the following steps:
step 105B1, obtaining optimal mathematical descriptive models (xo,
Xi ... , n)t and (xo, ... , Xi ... , n)(t+) of the same distress at two adjacent time
points (t, t+1) according to the detection data of the same distress at two
adjacent time points;
and step 105B2, obtaining 3D dynamic evolution simulation of the
pavement distresses according to the parameter difference between the
mathematical descriptive models of the same distress at two adjacent time
points (t, t+1), by combining the physical properties of the pavement
distresses and analysis of the material decay change model; or, step 105C comprises the following steps: step 105C1, for each type of typical distress, selecting detection data and a standard model of the same distress at least three adjacent time points
((t+1), t, (t+1), . . );
step 105C2, obtaining distress descriptive model parameters (xo, . . , xi . .
, Xn)(t-1), (XO, ... , Xi ... , Xn)t, (xo,..., Xi ... , Xn)(t+1), ... of the same distress in time
series (t-1), t, (t+1), . . by adopting the optimized model in step 103;
step 105C3, fitting the change function ht(xo, ..., Xi ..., Xn) of the distress
descriptive model parameters (xo, ..., Xi ..., n) of the same distress in time
series by combining the physical properties of the pavement distresses and
analysis of the material decay change model;
and step 105C4, based on the fitted change function, obtaining
parameter values (xo, . . , Xi ..., Xn)(t+2) at the next time interval by outward
interpolation, and implementing 3D development prediction of the typical
pavement distresses within a certain range.
Further, after step 104, the method also comprises the following steps:
step 105D, comparing the prediction result of the step 105C with actual
detection data, and obtaining aided decision suggestions for highway
maintenance by combining the physical properties of the pavement distresses
and analysis of the material decay change model.
Further, the step 105D comprises the following steps:
step 105D1, comparing data of the pavement distress prediction result at
the moment (t+2), predicted by the distress models in time series (t-1), t,
(t+1), . . in the step 105C, with data of the distress detection result, actually
detected at the moment (t+2), to obtain the parameter difference between the prediction result and the actual detection result; step 105D2, obtaining the aided decision suggestions for highway maintenance according to the parameter difference between the prediction result and the actual detection result, by combining the physical properties of the pavement distresses and the analysis results of the material decay change model; and step 105D3, determining that the distress detection result change obtained by actual detection is larger than the prediction change obtained by the distress model, and obtaining corresponding preventive maintenance or minor repair treatment according to the parameter difference between the prediction result and the actual detection result.
Further, the step 101 comprises the following steps:
step 101A, for each typical pavement distress, segmenting primitive
geometric elements;
step 101B, for regular shapes, performing dimension reduction on
primitive geometric elements or performing conversion to other domains for
characteristic analysis;
step 101C, obtaining the initial descriptive model go(koxo, . . , kixi, . . , knxn)
of the typical distresses according to the mathematical expression function of
the primitive geometric elements, n>i>0 where, ko, . . , ki . . , kn are
coefficients of the descriptive model, xo, ..., Xi ..., Xn are parameters of the
descriptive model, and n is the number of parameters demanded by the
descriptive function;
step 101D, analyzing the common distress joint expression mode and
distress continuous expression rule based on the drawing location and the distress primitive elements; and step 101E, performing relationship analysis and relationship mapping calculation on primitive parameters and detection data demanded for drawing by combining the relationship between distress model expression and drawing, so as to obtain data and method for detection data driven rapid distress model drawing.
Further, the step 103 comprises the following steps:
step 103A, updating the coefficients k0 = p i = ki> -... .. n = kn of
the descriptive model to obtain an optimized model; and taking the optimized
model as the current descriptive model gj(koxo, ... , kixi, ... , knxn);
step 103B, obtaining an updated evaluation function fevaluation(gi, gstandard)
according to the optimized model, and judging whether the difference
between the current model and the standard model is smaller than a certain
threshold F or iteration exceeds a certain number of times j>N,
step 103C, if no, continuing to calculate the coefficients of the descriptive
model at step 102, and then returning to step 103A;
and step 103D, if yes, performing algorithm convergence and outputting
the optimal mathematical descriptive model goptimai(koxo, ..., kixi, ..., knxn)
describing the type of distress.
Further, the step 104 comprises the following steps:
step 104A, expressing limiting conditions of general distresses by
combining primitive geometric elements, and constructing a BIM parametric
model of the typical distresses by using the modeling tool Dynamo according
to the optimized mathematical descriptive model goptima(koxo, ... , kixi, ..., knxn);
and step 104B, marking the constructed distress model to the corresponding location of the BIM model of the highway main body, and performing 3D geometric model coupling based on locations of detection points and the BIM model of the highway main body to implement detection data driven BIM parametric modeling of the pavement distresses.
Further, the step 105A comprises the following steps:
step 105A1, identifying the distress locations by using terminal equipment,
and marking the pavement distress model reconstructed from the last
detection data to the current distress location, so as to achieve augmented
reality intuitive comparison between the preceding distress and the actual
distress;
step 105A2, rapidly comparing key parameters and prediction
development results of the preceding 3D distress model with current actual
distress key sampling data according to current distress key images and data
collected and uploaded by mobile terminals, so as to obtain the comparison
difference among the preceding data, the current data and prediction
sampling data;
step 105A3, obtaining a rapid judgment result of the development degree
of the pavement distresses according to the comparison difference among the
preceding data, the current data and the prediction sampling data;
and step 105A4, obtaining corresponding collection, alarm and other
linkage operations according to the rapid judgment result of the development
degree of the pavement distresses, so as to achieve augmented reality mobile
inspection of the pavement distresses.
Further, the expression describing the model coefficients is:
(ko, -> i, --- En) = arg min (kO,-,k,-kn Ifevaluation (9I>9standard)112 ) and/or, the evaluation function fevaluation of the 3D distress model is: fevaluation a IDgeometricl+a2|DphysicaI+3|DmaterialI fevaluation is used to measure the difference between the reconstructed 3D distress model and the real distress;
Dgeometric is the geometric characteristic difference between the
reconstructed 3D distress model and the real distress;
Dphysical is the physical change characteristic difference between the
reconstructed model and the real distress;
Dmaterial is the main material decay characteristic difference between the
reconstructed model and the real distress;
a1 is the first difference weight, a2 is the second difference weight, a3 is
the third difference weight, and al, a2 and 3are all larger than or equal to 0.
Compared with the prior art, the BIM (Building Information Modeling)
parametric modeling and augmented reality mobile inspection method for the
pavement distresses has the following beneficial effects:
(1) The BIM parametric modeling method for typical pavement distresses
is created, and different pavement distresses are rapidly visualized. By the aid
of digitalization, parameterization and visualization of BIM, superior
advantages are provided for the visual expression of pavement distresses and
auxiliary maintenance decisions. However, the research on modeling and
application of the pavement distresses based on BIM at home and abroad is
still blank at present. The invention has created the BIM parametric modeling
method for the typical pavement distresses to rapidly visualize different
pavement distress models and form a BIM full parametric model library of the typical distresses, and therefore has good demonstration and popularization values in the transportation industry.
(2) Data driven 3D dynamic evolution and development prediction of the
pavement distresses is formed and an intuitive model and analysis tool is
provided for highway maintenance. Semantic description based BIM modeling
and detection data based 3D reconstruction method are advantageous in
construction of the pavement distresses in a certain state, but may hardly
effectively simulate and predict 3D dynamic changes and development of
distresses, due to the lack of support of continuous change rules and physical
evolution models. In the invention, detection data driven 3D dynamic evolution
simulation of the pavement distresses is constructed by combining a dynamic
evolution physical model, a pavement service performance decay model, a
probability statistical model and the like, based on the parametric
characteristics of BIM; and the typical distresses are subject to 3D dynamic
development prediction driven by pavement detection data based on analysis
of change characteristics of historical detection data, and thus the intuitive
model and analysis tool is provided for highway maintenance.
(3) The augmented reality mobile inspection method for the pavement
distresses is formed, and the pavement distresses are rapidly judged and
alarmed. Mobile phones or augmented reality glasses are adopted to
augment the preceding model, key parameters and development prediction
results of the pavement distresses to the current distress location, and to
implement augmented reality mobile inspection. The preceding key
parameters, current key parameters and prediction key parameters are
compared so as to rapidly judge the distress development status, and corresponding alarms or other operations are triggered. Therefore, augmented reality rapid mobile inspection of the asphalt pavement distresses is achieved.
The appended drawings described herein are intended to provide a
further understanding of the present invention and constitute a part of the
present invention. The exemplary embodiments of the present invention and
the description are used to explain the present invention but are not
constructed as an improper limitation of the present invention. In the drawings:
Fig. 1 is a schematic diagram of a technical route of a BIM parametric
modeling and augmented reality mobile inspection method for pavement
distresses in an embodiment of the present invention.
Fig. 2 is a flow block diagram of a technical route of a BIM parametric
modeling and augmented reality mobile inspection method for pavement
distresses in an embodiment of the present invention.
Fig. 3 is a flow block diagram of an optimal mathematical descriptive
model for obtaining pavement distresses in an embodiment of the present
invention.
For convenience of clearly describe the technical solutions of the
embodiments of the present invention, words, such as "first" and "second", are
employed to distinguish identical items or similar items with basically identical
functions and effects in the embodiments of the present invention. For
example, a first threshold and a second threshold are for distinguishing
different thresholds merely, but not limiting the sequence of the thresholds.
Those skilled in the art will appreciate that the words, such as "first" and
"second" do not limit an amount and an execution order, and these are not to
be limited to.
It should be noted that, in the present invention, words, such as
"exemplary" or "for example", are used for expressing examples, illustrations
or explanations. Any embodiment or design scheme described to be
"exemplary" or "for example" in the present invention should not be interpreted
as being more preferable or having more advantages than other embodiments
or design schemes. Rather, use of words, such as "exemplary" or "for
example", aims to present relevant concepts in a concrete manner.
In the present invention, "at least one" refers to one or more, and "a
plurality of' refers to two or more. "And/or" is an association relationship
describing associated objects and represents that there may be three
relationships, for example, A and/or B may indicates that A exists alone; A
and B exist simultaneously; and B exists alone, where the numbers of A and
B may be a single number or a plural number. The character "" in this
specification indicates that the related objects are in an "or" relationship. "At
least one of the following" or a similar expression thereof refers to any
combination of these items, including a single item or any combination of
plural items. For example, at least one of a, b or c may indicate combination
of a, b, c and d, combination of a and c, combination of b and c or
combination of a, b and c, where, the numbers of a, b and c may be a single
number or a plural number.
The pavement distresses disclosed by the present invention refers to
asphalt surface layer structural destruction and surface layer performance degradation distresses, including cracks, block cracks, transverse cracks, longitudinal cracks, ruts, slippage, pits, bleeding, raveling, repair, settlement and upheavals. According to descriptions and classifications on asphalt pavement distresses in newly published Highway Performance Assessment
Standards JTG5210-2018 and Technical Specifications for Maintenance of
Highway Asphalt Pavement JTG5421-2018 and different layers at which the
distresses occur, the asphalt pavement distresses may be divided into types
of an unstable subgrade structure, base structure destruction, asphalt surface
layer structure destruction, asphalt surface layer performance degradation
and the like. The classification of the main distresses is as shown in Table 1.
Table 1 Classification Table of Asphalt Pavement distresses
Low Main crack lumpiness is between 0.2 m and 0.5 m, and average crack width is smaller than 2 mm. Cracks Medium Main crack lumpiness is smaller than 0.2 m, and average crack width is between 2 mm and 5 mm. severe Main crack lumpiness is smaller than 0.2 m, and average crack width is larger than 5 mm. Block Low Main crack lumpiness is smaller than 1.0 m, and average crack cracks width is between 1 mm and 2 mm. cracks severe Main crack lumpiness is between 0.5 m and 1.0 m, and average crack width is larger than 2 mm. Longitudinal Low Main crack width is smaller than or equal to 3 mm Asphalt cracks severe Main crack width is larger than 3 mm surface Transverse Low Main crack width is smaller than or equal to 3 mm layer cracks severe Main crack width is larger than 3 mm structure Low Settlement depth is between 10 mm and 25 mm, and there is no destruction Settlement obvious bumping feeling in driving. severe Settlement depth is larger than 25 mm, and there is obvious bumping feeling in driving. Ruts Low Rut depth is between 10 mm and 15 mm severe Rut depth is larger than 15mm Low Height difference between wave crest and wave trough is Wavy between 10 mm and 25 mm upheavals severe Height difference between wave crest and wave trough is larger than 25 mm Depth of the pits is2 smaller than 25 mm, or an area of the pits is Pits Low smaller than 0.1 m severe Depth of the pits is larger than or equal2 to 25 mm, or an area of the pits is larger than or equal to 0.1 m Low The pavement surface is subject to fine aggregates loss, peeling AsphaltRaveling nd pitting. Asphal Raveling The pavement surface is subject to coarse aggregates loss, slae s peeling and pitting. er No A thin oil layer occurs on the surface of the pavement performance Bleeding classification degradation No Repair of damages, such as the cracks, the pits, raveling, p lassificationsettlement and the ruts
The present invention provides a BIM parametric modeling and
augmented reality mobile inspection method for pavement distresses and
aims to achieve BIM based pavement distress modeling, dynamic evolution,
3D development prediction, augmented reality mobile inspection and the like.
Key technologies of BIM parametric modeling and data driven dynamic
evolution for typical asphalt pavement distresses are created, so that
inspection data driven rapid visualization of the asphalt pavement distresses
is achieved. The evolution mechanism of the asphalt pavement is further
researched, which provides data and technical supports for asphalt pavement
service performance degradation analysis and highway digital asset
management, and helps increase the highway maintenance technical level
and investment benefit. The BIM technology is superior in digitization,
visualization, multidimensional information integration and the like of
engineering information; and by means of the technology, traditional 2D
distress marking information is expressed in a more intuitive 3D form; and
engineering information of BIM model transfer in design and construction
stages is integrated for more intuitive prediction of the pavement distresses
and management of highway assets, and an aided decision making support
basis is provided for managers.
Fig. 1 shows a schematic diagram of a technical route of a BIM
parametric modeling and augmented reality mobile inspection method for
pavement distresses in an embodiment of the present invention. As illustrated
in Fig. 1, the technical route of the method is as follows:
The semantic description based BIM modeling method for the pavement distresses provides an initial model base, a standard data set and a model base used for experiment comparison are constructed based on 3D reconstruction of inspection data, and an evaluation function of a model reconstruction result is defined by integrating geometric properties, physical properties and material properties; BIM based parametric modeling is achieved through function selection, parameter fitting, case experiments, model verification and iteration optimization; temporal variation characteristics of model parameters and the inspection data on the standard data set are analyzed, and data driven 3D distress model dynamic evolution and development prediction are achieved by correlation of time sequence modeling, difference analysis and physical property model; and external equipment such as mobile phones and augmented reality glasses is adopted to mark the distress model, parameters and key information augmented reality to locations of the pavement distresses, and the change state of the distresses is rapidly judged and alarmed by comparison among preceding distress sampling, current distress sampling and prediction distress sampling.
Fig. 2 shows a schematic diagram of a technical route of a BIM
parametric modeling and augmented reality mobile inspection method for
pavement distresses in an embodiment of the present invention. As illustrated
in Fig. 2, the method comprises the following steps:
step 099, selecting inspection data of typical pavement distresses,
conducting high-precision 3D reconstruction on the typical distresses by using
the inspection data, and constructing a typical pavement distress standard
model base; and executing step 099A-step 099C for each type of pavement
distress: step 099A, selecting a distress sample of typical significance, and collecting distress sample data by using a pavement distress inspection tool and method. It should be noted here that the pavement distress inspection tool and the pavement distress inspection method are both well-known by those skilled in the art, and thus will not be described herein.
step 099B, reconstructing a 3D model of the distress sample based on
the inspection data, and establishing an association relationship between the
inspection data of the distress sample and the 3D reconstruction model;
step 099C, organizing distress collection data, the reconstruction model
and the correlation relationship therebetween to form an asphalt typical
pavement distress standard model base, wherein in the standard model base,
each sample comprises distress semantic description, inspection data and a
3D model; the inspection data may comprise pictures, laser scanned point
cloud data, manually measured data and the like.
step 100, combining geometric characteristics, physical characteristics
and material decay characteristics of the distress and defining a distress 3D
model evaluation function fevaluation(g, gstandard) (N>j>0, wherein N is an allowed
maximum number of iterations in the present invention) for measuring a
difference between the constructed distress 3D model gj and a standard
model gstandard,
Where, fevaluation is the distress 3D model evaluation function for
measuring the difference between the constructed 3D distress model and a
real distress, particularly as follows:
fevaluation QiDgeometricl+a2|DphysicalI+Q3|Dmateriall,
where, fevaluation is used to measure the difference between the constructed 3D distress model and the real distress;
Dgeometric is the geometric characteristic difference between the
reconstructed 3D distress model and the real distress and can be specifically
defined according to the distress geometric characteristics;
Dphysical is a physical change characteristic difference between the
constructed model and the real distress and can be specifically defined
according to physical factors, such as environment, stress and load capacity
of a vehicle during operation, applied to the distress as a whole object;
Dmaterial is a main material decay characteristic difference between the
reconstructed model and the real distress and can be specifically defined
according to the material properties and decay law of a main material of an
asphalt pavement;
a1 is a first difference weight, a2 is a second difference weight, a3 is a
third difference weight, and a1, a2 and a are all larger than or equal to 0;
and when a1=1, a2=a3=0, the distress 3D model evaluation function
fevaluation is degenerated to be an evaluation function considering merely the
geometric difference between the models.
step 101, based on characteristic analysis and semantic description of
typical pavement distresses, mathematically describing pavement distress
information, and constructing a BIM initial descriptive model go(koxo, . . , kixi, . . ,
knxn) of the typical distresses, n>i>0, where, ko, . . , ki, . . , kn are coefficients of
the descriptive model, xo, ..., Xi ..., n are parameters of the descriptive model ,
and n is the number of parameters demanded by a descriptive function;
It should be noted here that the BIM initial descriptive model of the typical
distresses is that, for a certain type of distress, primitive geometric elements are adopted to express combination thought, description function, boundary definition and function declaration of the distress. As geometric profiles of different distresses differ significantly, the descriptive function which facilitates expression of the geometric appearances of the distress and can reflect further characteristic development changes of the distress is demanded to be selected for each distress. Proper descriptive functions are selected for different distresses and change characteristics of the distresses, which provides a significant research basis for BIM parametric modeling and development prediction of the pavement distresses.
Particularly, the step 101 comprises the following steps:
step 101A, for each typical pavement distress, segmenting primitive
geometric elements;
It should be noted here that the primitive geometric elements refer to
geometric units which can clearly express distress semantics and also easy in
combined expression.
step 101B, for regular shapes, performing dimension reduction on
primitive geometric elements or performing conversion to other domains for
characteristic analysis;
It should be noted here that reducing dimensions of the primitive
geometric elements, or converting the primitive geometric elements to other
domains for characteristic analysis may include but are not limited to: a
pavement distress may be expressed with a radius if being circular; if the
pavement distress is a bowl-shaped pit, the dimension of a primitive
geometric element may be reduced to 1/4 arc firstly for expressing; and if the
pavement distress is a complex model, the primitive geometric element of the pavement distress may be converted to a frequency domain for analysis.
step 101C, obtaining the initial descriptive model go(koxo, ... , kixi, ... , knxn)
of the typical distresses according to the mathematical expression function of
the primitive geometric elements, n>i>, where, ko, ... , ki ... , kn are
coefficients of the descriptive model, xo, ..., Xi ..., n are parameters of the
descriptive model, and n is the number of parameters demanded by the
descriptive function;
step 101D, analyzing the common distress joint expression mode and
distress continuous expression rule based on the drawing location and
distress primitive elements;
and step 101E, performing relationship analysis and relationship mapping
calculation on primitive parameters and detection data demanded for drawing
by combining the relationship between distress model expression and drawing,
so as to obtain data and method for detection data driven rapid distress model
drawing.
step 102, analyzing the difference between the BIM initial descriptive
model of each type of pavement distress and a standard 3D model of the
pavement distresses by using an evaluation function to obtain the difference
of the pavement distresses; and minimizing the difference of the pavement
distresses to obtain coefficients (ko, --- , i --- n )of the descriptive model;
the expression of the coefficients of the descriptive model is:
(ko, -, ki, ... kn = arg min Ifevaluation (9,9standard )112 (kO,...,ki,...kn )
step 103, returning to the step 102 and updating the coefficients
(ko, -, ,- a ) of the descriptive model to obtain an optimized descriptive model of the pavement distresses; and when the difference of the pavement distresses is lower than a difference threshold or iteration is larger than a preset number of times, performing algorithm convergence to obtain an optimal mathematical descriptive model goptima(koxo, ... , kixi, ... , knxn) of the pavement distresses;
Fig. 3 shows a flow block diagram of step 103 of the BIM parametric
modeling and augmented reality mobile inspection method for pavement
distresses in an embodiment of the present invention. As illustrated in Fig. 3,
obtaining of the optimal mathematical descriptive model of the pavement
distresses comprises the following steps:
step 103A, updating the coefficients k0 '-- i --- n =n of
the descriptive model to obtain an optimized model; and taking the optimized
model as the current descriptive model gj(koxo, ... , kixi, ... , knxn);
step 103B, obtaining updated fevaluation(gi, gstandard) according to the
optimized model, and judging whether the difference between the current
model and the standard model is smaller than a certain threshold E or the
iteration exceeds a certain number of times j>N,
step 103C, if no, continuing to calculate the coefficients of the descriptive
model at step 220, and then returning to step 230A;
and step 103D, if yes, performing algorithm convergence and outputting
the optimal mathematical descriptive model goptimai(koxo, ... , kixi, ... , knxn)
describing the type of distress.
step 104, obtaining a BIM parametric model of typical distresses by using
a Dynamo visual programming method, according to the optimal mathematical
descriptive model goptima(koxo, ... , kixi, ... , knxn); and based on distress locations, coupling the BIM parametric model of the typical distresses with a
BIM model of the highway main body to obtain a detection data driven BIM
parametric model of the pavement distresses at each moment;
It should be noted here that the adoption of the Dynamo visual
programming method for BIM parametric modeling of typical distresses
particularly comprises the steps as follows: the abstract function is
programmed and expressed by the Dynamo visual programming method,
pavement distress parameters extracted from pavement distress detection
data are added to Dynamo, and the parametric modeling of the pavement
distresses is implemented by Dynamo programming. Particularly, the step 104
comprises the following steps:
step 104A, expressing limiting conditions of general distresses by
combining primitive geometric elements, and constructing a BIM parametric
model of the typical distresses by using the modeling tool Dynamo according
to the optimized mathematical descriptive model goptima(koxo, . . , kixi, . . , knxn) ;
step 104B, marking the constructed distress model to the corresponding
location of the BIM model of the highway main body, and performing 3D
geometric model coupling based on locations of detection points and the BIM
model of the highway main body to implement detection data driven BIM
parametric modeling of the pavement distresses.
step 105A, obtaining pavement distress location information and
sampling data sent by mobile terminal equipment, and marking the distress
models to the distress locations in real time to obtain augmented reality visual
comparison between the preceding distress and actual distresses; obtaining
rapid judgment results of the development degree of the pavement distresses according to the difference of the sampling data at different moments; and obtaining alarm linkage of the development degree of the pavement distresses according to rapid judgment results of the development degree;
It should be noted here that the mobile terminal equipment includes, but
is not limited to, mobile phones or augmented reality glasses.
Particularly, the step 105A comprises the following steps:
step 105A1, identifying the distress locations by using terminal equipment,
and marking the pavement distress model reconstructed from the last
detection data to the current distress location by combining the technologies
such as GPS, mobile phone positioning and image matching, so as to achieve
augmented reality intuitive comparison between the preceding distress and
the actual distress; it should be noted that the pavement distress model
reconstructed from the last detection data is a pavement distress parametric
model reconstructed from the last detection data;
step 105A2, rapidly comparing key parameters and prediction
development results of the preceding 3D distress model with current actual
distress key sampling data according to current distress key images and data
collected and uploaded by mobile terminals, so as to obtain the comparison
difference among the preceding data, the current data and prediction
sampling data;
step 105A3, obtaining the pavement distresses according to the
comparison difference among the preceding data, the current data and the
prediction sampling data;
and step 105A4, obtaining corresponding collection, alarm and other
linkage operations according to the rapid judgment result of the development degree of the pavement distresses, so as to achieve augmented reality mobile inspection of the pavement distresses.
step 105B, selecting detection data of the same distress at two adjacent
times, expressing models (xo, ..., Xi ..., Xn)t and (xo, ..., Xi ..., Xn)(t+i) of the same
distress at two adjacent time points (t, t+1) by using the optimal mathematical
descriptive model of the pavement distresses obtained in step 103, and based
on the parameter difference between the two models, implementing 3D
dynamic evolution simulation of the pavement distresses by combining
physical properties of the pavement distresses and analysis of a material
decay change model.
Particularly, the step 105B comprises the following steps:
step 105B1, obtaining mathematical descriptive models (xo, ..., Xi ..., Xn)t
and (xo, ... , Xi ..., n)(t+1) of the same distress at two adjacent time points (t, t+1)
according to the detection data of the same distress at two adjacent time
points;
and step 105B2, obtaining 3D dynamic evolution simulation of the
pavement distresses according to the parameter difference between the
mathematical descriptive models of the same distress at two adjacent time
points (t, t+1), by combining physical properties of the pavement distresses
and analysis of the material decay change model;
step 105C, obtaining a mathematical descriptive model of the same
distress in time series according to detection data of the same distress at at
least three adjacent time points ((t-1), t, (t+1), . . ); and fitting the change
function ht(xo, ..., Xi ..., Xn) of model parameters according to physical
properties of the pavement distresses and analysis results of the material decay change model, so as to obtain 3D development prediction of the distress in a certain range.
Particularly, the step 105C comprises the following steps:
step 105C1, for each type of typical distress, selecting detection data and
a standard model of the same distress at least three adjacent time points
((t+1), t, (t+1), . . );
step 105C2, obtaining distress descriptive model parameters (xo, . . , xi . .
, Xn)(t-1), (XO, ... , Xi ... , Xn)t, (xo, ... , Xi ... , Xn)(t1), ... of the same distress in time
series (t-1), t, (t+1), . . by adopting the optimized model in step 103;
step 105C3, fitting the change function ht(xo, ..., Xi ..., Xn) of the model
parameters (xo, ..., Xi ..., Xn) in time series by combining the physical
properties of the pavement distresses and the analysis results of the material
decay change model;
and step 105C4, based on the fitted change function, obtaining
parameter values (xo, . . , Xi ..., Xn)(t+2) at the next time interval by outward
interpolation, and implementing 3D development prediction of the typical
pavement distresses within a certain range.
step 105D, comparing the prediction result of the step 105C with actual
detection data, and obtaining aided decision suggestions for highway
maintenance by combining the physical properties of the pavement distresses
and analysis of the material decay change model.
It should be noted here that the prediction result of the step 105C is
specifically the prediction result of pavement distress model parameters. The
actual detection data are the detection data of the current pavement distress
at the corresponding moment.
Particularly, the step 105D comprises the following steps:
step 105D1, comparing data of the pavement distress prediction result at
the moment (t+2) predicted by the distress models in time series (t-1), t, (t+1)
in the step 105C with data of the distress detection result actually detected at
the moment (t+2) to obtain the parameter difference between the prediction
result and the actual detection result;
step 105D2, obtaining the aided decision suggestions for highway
maintenance according to the parameter difference between the prediction
result and the actual detection result, by combining the physical properties of
the pavement distresses and the analysis results of the material decay
change model;
and step 105D3, determining that the distress detection result change
obtained by actual detection is larger than the prediction change obtained by
the distress model, and obtaining corresponding preventive maintenance or
minor repair treatment according to the parameter difference between the
prediction result and the actual detection result.
The invention creates the BIM parametric modeling method for typical
asphalt pavement distresses, and pavement distress inspection data are
rapidly visualized. 3D modeling of the asphalt pavement distresses is usually
implemented by image, point cloud or semantic description, while the BIM
based modeling method has superior advantages in pavement distress
parameterization. At present, BIM is adopted to model structures such as
highways, bridges, tunnels and houses, instead of pavement distresses.
Therefore, the invention may help fill in the gap of specialized research in the
field.
The present invention can help further research the evolution mechanism
of asphalt pavement distresses and increase the technical level of highway
maintenance and investment benefit. Based on the analysis of historical
inspection data of the same highway section, the development and change
process of typical asphalt pavement distresses such as cracks, ruts, pits and
upheaval is intuitively analyzed, which can help further grasp the evolution
mechanism of distresses and increase the maintenance decision level and
investment benefit.
In the present invention, the pavement distresses and highway assets are
managed more intuitively, and the scientificity of maintenance decision is
improved accordingly. BIM model is superior in digitalization, visualization and
multi-dimensional information integration of engineering information.
Traditional 2D distress labeling information is expressed in a more intuitive 3D
form. Prediction of the pavement distresses and management of the highway
assets are more intuitive by combining the engineering information
transmitted by BIM model in design and construction stages, which provides
an aided decision making support basis is provided for managers. The parts
of the present invention that are not elaborated are known by those skilled in
the art.
Although the present invention has been described herein by combining
various embodiments, in the process of implementing the claimed invention,
other variations of the disclosed embodiments can be understood and
implemented by those skilled in the art by checking the drawings, the
disclosure and the appended claims. In the claims, the word "comprising"
does not exclude other components or steps, and "a" or "an" does not exclude plural cases. A single processor or other units can implement a plurality of functions as listed in the claims. Some measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce excellent results.
Although the present invention has been described by combining specific
characteristics and embodiments thereof, it is noticeable that various
modifications and combinations can be made without departing from the spirit
and scope of the present invention. Accordingly, this specification and
drawings are merely illustrative for the present invention as defined by the
appended claims, and are deemed to cover any and all modifications,
variations, combinations or equivalents within the scope of the invention.
Obviously, those skilled in the art can make various modifications and
variations without departing from the spirit and scope of the present invention.
Thus, if these modifications and variations of the present invention fall within
the scope of the claims and their equivalents, the present invention is also
intended to comprise these modifications and variations.
Claims (10)
1. The invention provides a BIM parametric modeling and augmented
reality mobile inspection method for pavement distresses, the method
characterized by comprising the following steps:
step 101, based on characteristic analysis and semantic description of
typical pavement distresses, mathematically describing pavement distress
information, and constructing a BIM initial descriptive model go(koxo, ... , kixi, ...
, knxn) of the typical distresses, n>i>O, where, ko, ... , ki, ... , kn are coefficients of
the descriptive model, xo, ... , Xi ..., Xn are parameters of the descriptive model
, and n is the number of parameters demanded by a descriptive function;
step 102, analyzing the difference between the BIM initial descriptive
model of each type of pavement distress and a standard 3D model of the
pavement distresses by using an evaluation function to obtain the difference
of the pavement distresses; and minimizing the difference of the pavement
distresses to obtain the coefficients (k, -- ki- --- n ) of the descriptive model;
step 103, returning to the step 102 and updating the coefficients
(ko, -j, -n ) of the descriptive model to obtain an optimized descriptive
model of the pavement distresses; and when the difference of the pavement
distresses is lower than a difference threshold or iteration is larger than a
preset number of times, performing algorithm convergence to obtain an
optimal mathematical descriptive model goptimai(koxo, ... , kixi, ... , knxn) of the
pavement distresses;
step 104, obtaining a BIM parametric model of typical distresses by using
a Dynamo visual programming method, according to the optimal mathematical
descriptive model goptima(koxo, ... , kixi, ... , knxn); and based on distress locations, coupling the BIM parametric model of the typical distresses with a
BIM model of a highway main body to obtain a detection data driven BIM
parametric model of the pavement distresses at each moment;
step 105A, obtaining pavement distress location information and
sampling data sent by mobile terminal equipment, and marking the distress
models to the distress locations in real time to obtain augmented reality visual
comparison between the preceding distress and actual distresses; obtaining
rapid judgment results of the development degree of the pavement distresses
according to the difference of the sampling data at different moments; and
obtaining alarm linkage of the development degree of the pavement
distresses according to rapid judgment results of the development degree of
the pavement distresses;
2. The BIM parametric modeling and augmented reality mobile inspection
method for pavement distresses of claim 1, characterized by further
comprising the following steps after the step 104:
step 105B, selecting detection data of the same distress at two adjacent
times, expressing models (xo, ..., Xi ..., Xn)t and (xo, ..., Xi ..., Xn)(t+i) of the same
distress at two adjacent time points (t, t+1) by using the optimal mathematical
descriptive model of the pavement distresses obtained in step 103, and based
on the parameter difference between the two models, implementing 3D
dynamic evolution simulation of the pavement distresses by combining
physical properties of the pavement distresses and analysis of a material
decay change model; or, after the step 104, the method further comprising the
following steps:
step 105C, obtaining an optimal mathematical descriptive model of the same distress in time series according to detection data of the same distress at least three adjacent time points ((t-1), t, (t+1), . . ); and fitting the change function ht(xo, ..., Xi ..., Xn) of model parameters by combining physical properties of the pavement distresses and analysis results of the material decay change model, so as to obtain 3D development prediction of the distress in a certain range.
3. The BIM parametric modeling and augmented reality mobile inspection
method for pavement distresses of claim 2, characterized in that the step
105B comprises the following steps:
step 105B1, obtaining mathematical descriptive models (xo, ..., Xi ..., Xn)t
and (xo, ..., Xi ..., n)(t+) of the same distress at two adjacent time points (t, t+1)
according to detection data of the same distress at two adjacent time points;
and step 105B2, obtaining 3D dynamic evolution simulation of the
pavement distresses according to the parameter difference between the
mathematical descriptive models of the same distress at two adjacent time
points (t ,t+1), by combining physical properties of the pavement distresses
and analysis results of the material decay change model; or,
the step 105C comprises the following steps:
step 105C1, for each type of typical distress, selecting detection data and
a standard model of the same distress at least three adjacent time points
((t+1), t, (t+1), . . );
step 105C2, obtaining distress descriptive model parameters (xo, . . , xi . . ,
Xn)(t-1), (XO, ... , Xi ... , Xn)t, (xo, ... , Xi ... , Xn)(t1), ... of the same distress in time
series (t-1), t, (t+1), . . by adopting the optimized model in step 103;
step 105C3, fitting the change function ht(xo, ..., Xi ..., n) of the model parameters (xo, ..., Xi ..., Xn) in time series by combining the physical properties of the pavement distresses and analysis of the material decay change model; and step 105C4, based on the fitted change function, obtaining parameter values (xo, . . , Xi ..., Xn)(t+2) at the next time interval by outward interpolation, and implementing 3D development prediction of the typical pavement distresses within a certain range.
4. The BIM parametric modeling and augmented reality mobile inspection
method for pavement distresses of claims 1-3, characterized by further
comprising the following steps after the step 104:
step 105D, comparing the prediction result of the step 105C with actual
detection data, and obtaining aided decision suggestions for highway
maintenance by combining the physical properties of the pavement distresses
and analysis of the material decay change model.
5. The BIM parametric modeling and augmented reality mobile inspection
method for pavement distresses of claim 4, characterized in that the step
105D comprises the following steps:
step 105D1, comparing data of the pavement distress prediction result at
the moment (t+2) predicted by the distress models in time series (t-1), t, (t+1)
in the step 105C with data of the distress detection result actually detected at
the moment (t+2) to obtain the parameter difference between the prediction
result and the actual detection result;
step 105D2, obtaining the aided decision suggestions for highway
maintenance according to the parameter difference between the prediction
result and the actual detection result, by combining the physical properties of the pavement distresses and the analysis results of the material decay change model; and step 105D3, determining that the distress detection result change obtained by actual detection is larger than the prediction change obtained by the distress model, and obtaining corresponding preventive maintenance or minor repair treatment according to the parameter difference between the prediction result and the actual detection result.
6. The BIM parametric modeling and augmented reality mobile inspection
method for pavement distresses of claim 1, characterized in that the step 101
comprises the following steps:
step 101A, for each typical pavement distress, segmenting primitive
geometric elements;
step 101B, for regular shapes, performing dimension reduction on
primitive geometric elements or performing conversion to other domains for
characteristic analysis;
step 101C, obtaining the initial descriptive model go(koxo, . . , kixi, . . , knxn)
of the typical distresses according to the mathematical expression function of
the primitive geometric elements, n>i>0, where, ko, . . , ki . . , kn are
coefficients of the descriptive model, xo, ..., Xi ..., Xn are parameters of the
descriptive model, and n is the number of parameters demanded by the
descriptive function;
step 101D, analyzing the common distress joint expression mode and
distress continuous expression rule based on the drawing location and
distress primitive elements;
and step 101E, performing relationship analysis and relationship mapping calculation on primitive parameters and detection data demanded for drawing by combining the relationship between distress model expression and drawing, so as to obtain data and method for detection data driven rapid distress model drawing.
7. The BIM parametric modeling and augmented reality mobile inspection
method for pavement distresses of claim 1, the method characterized in that
the step 103 comprises the following steps:
step 103A, updating the coefficients= ... , ==-i --- n =n of
the descriptive model to obtain an optimized model; and taking the optimized
model as the current descriptive model gj(koxo, ... , kixi, ... , knxn);
step 103B, obtaining updated fevaluation(gi, gstandard) according to the
optimized model, and judging whether the difference between the current
model and the standard model is smaller than a certain threshold F or the
iteration exceeds a certain number of times j>N;
step 103C, if no, continuing to calculate the coefficients of the descriptive
model at step 102, and then returning to step 103A;
and step 103D, if yes, performing algorithm convergence and outputting
the optimal mathematical descriptive model goptimai(koxo, ..., kixi, ..., knxn)
describing the type of distress.
8. The BIM parametric modeling and augmented reality mobile inspection
method for pavement distresses of claim 1, characterized in that the step 104
comprises the following steps:
step 104A, expressing limiting conditions of general distresses by
combining primitive geometric elements, and constructing a BIM parametric
model of the typical distresses by using the modeling tool Dynamo according to the optimized mathematical descriptive model goptima(koxo, . . , kixi, . . , knxn); step 104B, marking the constructed distress model to the corresponding location of the BIM model of the highway main body, and performing 3D geometric model coupling based on locations of detection points and the BIM model of the highway main body to implement detection data driven BIM parametric modeling of the pavement distresses.
9. The BIM parametric modeling and augmented reality mobile inspection
method for pavement distresses of claim 1, characterized in that the step
105A comprises the following steps:
step 105A1, identifying the distress locations by using terminal equipment,
and marking the pavement distress model reconstructed from the last
detection data to the current distress location by combining the technologies
such as GPS, mobile phone positioning and image matching, so as to achieve
augmented reality intuitive comparison between the preceding distress and
the actual distress;
step 105A2, rapidly comparing key parameters and prediction
development results of the preceding 3D distress model with current actual
distress key sampling data according to current distress key images and data
collected and uploaded by mobile terminals, so as to obtain the comparison
difference among preceding data, current data and prediction sampling data;
step 105A3, obtaining a rapid judgment result of the development degree
of the pavement distresses according to the comparison difference among the
preceding data, the current data and the prediction sampling data;
and step 105A4, obtaining corresponding collection, alarm and other
linkage operations according to the rapid judgment result of the development degree of the pavement distresses, so as to achieve augmented reality mobile inspection of the pavement distresses.
10. The BIM parametric modeling and augmented reality mobile
inspection method for pavement distresses of claims 1-3, characterized in that
the expression of the coefficients of the descriptive model is:
(k, ... , ki, ... i) = arg min Ifevaluation (gj,standard )12 (kO,...,kj ,..kn
and/or,
the evaluation function fevaluation of the 3D distress model is: evaluation=
a1|Dgeometric|+a2|DphysicaI|+as|Dmaterial|
fevaluation is used to measure the difference between the reconstructed 3D
distress model and the real distress; Dgeometric is the geometric characteristic
difference between the reconstructed 3D distress model and the real distress;
Dphysical is the physical change characteristic difference between the
reconstructed model and the real distress; Dmateriai is the main material decay
characteristic difference between the reconstructed model and the real
distress; al is the first difference weight, a2 is the second difference weight,
a3 is the third difference weight, and al, a2 and a3 are all larger than or equal
to 0.
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| CN114282298A (en) * | 2021-12-28 | 2022-04-05 | 长安大学 | Road technical condition processing method |
| CN115168929A (en) * | 2022-09-07 | 2022-10-11 | 中建一局集团第二建筑有限公司 | Dynamo-based BIM model element coding information input method |
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| CN114282298A (en) * | 2021-12-28 | 2022-04-05 | 长安大学 | Road technical condition processing method |
| CN114282298B (en) * | 2021-12-28 | 2022-11-22 | 长安大学 | Road technical condition processing method |
| CN115168929A (en) * | 2022-09-07 | 2022-10-11 | 中建一局集团第二建筑有限公司 | Dynamo-based BIM model element coding information input method |
| CN115168929B (en) * | 2022-09-07 | 2023-01-31 | 中建一局集团第二建筑有限公司 | Dynamo-based BIM model element coding information input method |
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