CN108957477A - Building destruction condition detection method - Google Patents
Building destruction condition detection method Download PDFInfo
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- CN108957477A CN108957477A CN201810689085.7A CN201810689085A CN108957477A CN 108957477 A CN108957477 A CN 108957477A CN 201810689085 A CN201810689085 A CN 201810689085A CN 108957477 A CN108957477 A CN 108957477A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformation in the plane of the image
- G06T3/40—Scaling the whole image or part thereof
- G06T3/4007—Interpolation-based scaling, e.g. bilinear interpolation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
Abstract
The present invention provides a kind of building destruction condition detection method, it includes: Step 1: obtaining the point cloud data of building to be analyzed；Step 2: the first direction along building to be analyzed carries out subdivision to point cloud data, each cross section and its point cloud data are obtained；Step 3: determining the damage degree in each cross section according to the point cloud data in each cross section；Step 4: statistics is in the different quantity for presetting cross section in damage degree sections respectively according to the value of the damage degree in each cross section；Step 5: determining the whole damage degree of building to be analyzed according to the quantity in the cross section in different default damage degree sections.The present invention is grouped the point cloud data of building, calculates separately the coefficient that every group of cross section after subdivision is able to reflect building destruction state, and coefficient obtained by calculation further calculates the collapse state of determining building.Method application provided by the invention is convenient, and calculation amount is small, and precision is high, can be good at the collapse state for determining building.
Description
Technical field
The present invention relates to building space feature detection fields, specifically, being related to a kind of building destruction state-detection
Method.
Background technique
In real life, building destruction caused by earthquake is the principal element of casualties and economic loss, building
The investigation of object extent of the destruction is the important evidence of Disaster Loss Evaluation.Currently, building space feature earthquake detects, existing method master
If calculating the space characteristics analysis such as distance, area, angle, volume, the gradient and the slope aspect in a certain contiguous range between point cloud to build
Object Earthquake hazard degree is built, there is no precision height, using convenient and fast building destruction condition detection method.
Therefore, the present invention provides a kind of building destruction condition detection methods.
Summary of the invention
To solve the above problems, the present invention provides a kind of building destruction condition detection method, the method include with
Lower step:
Step 1: obtaining the point cloud data of building to be analyzed；
Step 2: the first direction along the building to be analyzed carries out subdivision to the point cloud data, each cross is obtained
Section and its point cloud data；
Step 3: determining the damage degree in each cross section according to the point cloud data in each cross section；
Step 4: statistics is in different default damage degree areas respectively according to the value of the damage degree in each cross section
The quantity in interior cross section；
Step 5: determining the entirety of building to be analyzed according to the quantity in the cross section in different default damage degree sections
Damage degree.
According to one embodiment of present invention, in the step 2, preset interval is pressed to the point along building trend
Cloud data carry out cross section subdivision, extract the cross section to be analyzed and its point cloud data from obtained multiple sections.
According to one embodiment of present invention, in said step 1, it to be analyzed is built using default interpolation algorithm to described
The original point cloud data for building object carries out interpolation processing, so that the point cloud density of the point cloud data after interpolation is greater than or equal to preset
Cloud density threshold, to obtain the point cloud data of the building to be analyzed.
According to one embodiment of present invention, the default interpolation algorithm includes any one of item set forth below or several
:
Closest interpolation method, inverse distance weighted interpolation method, spline method, moving average interpolation method and part are more
Item formula interpolation method.
According to one embodiment of present invention, the step 3 includes:
First rupture factor and the second rupture factor in the cross section are determined according to the point cloud data in each cross section, and are led to
It crosses first rupture factor and second rupture factor determines the damage degree in each cross section, wherein described first destroys
Coefficient and second rupture factor are wantonly two in surface area rupture factor, volumetric fracture of rock coefficient and reference rupture factor
?.
According to one embodiment of present invention, the step 3 includes:
Floor projection is carried out to the point cloud data in each cross section, obtains floor projection point cloud data；
Triangulation is carried out to the floor projection point cloud data, determines that each vertex of a triangle is each in subdivision result
Corresponding point, obtains each vertex of a triangle on the roof to form each cross section in the point cloud data in a cross section；
The roof to form each cross section is calculated according to each vertex of a triangle on the roof for forming each cross section
The area of each triangle obtains the surface area on the roof that each cross section is characterized by summation；
According to the area of each triangle of floor projection point subdivision result, the perspective plane on the roof is obtained by summation
Product；
The surface area rupture factor is determined according to the surface area on the roof and its projected area.
According to one embodiment of present invention, the step 3 includes:
Three-dimensional space triangulation is carried out to each cross section according to each cross section and its point cloud data, it is more to obtain composition
A tetrahedron；
Each tetrahedral volume is calculated separately, the volume on the roof that each cross section is characterized is obtained by summation；
According to the volume and its projected area on the roof, the volume is determined in conjunction with the width of the building to be analyzed
Rupture factor.
According to one embodiment of present invention, the range with reference to rupture factor are as follows: 1 to 1.4142.
According to one embodiment of present invention, in the step 5, the damage degree is determined according to following expression:
D σ (i)=│ σ1(i)-σ2(i)│
Wherein, d σ (i) indicates the corresponding damage degree in i-th of cross section, σ1(i) i-th of cross section corresponding first is indicated
Rupture factor, σ2(i) corresponding second rupture factor in i-th of cross section is indicated.
According to one embodiment of present invention, in the step 5, the whole destruction is determined according to following expression
Degree:
Wherein, γ indicates that whole damage degree, m indicate the sum of default damage degree value interval, kiI-th of expression default broken
The corresponding coefficient of bad degree value interval, NiIndicate the quantity in the cross section in i-th of default damage degree section, N indicates cross-sectional
The sum in face.
Building destruction condition detection method provided by the invention is grouped the point cloud data of building, counts respectively
Every group of cross section is able to reflect the coefficient of building destruction state after calculation subdivision, and coefficient obtained by calculation is further counted
Calculate the collapse state for determining building.Method application provided by the invention is convenient, and calculation amount is small, and precision is high, can be good at really
Determine the collapse state of building.
Other features and advantages of the present invention will be illustrated in the following description, also, partly becomes from specification
It obtains it is clear that understand through the implementation of the invention.The objectives and other advantages of the invention can be by specification, right
Specifically noted structure is achieved and obtained in claim and attached drawing.
Detailed description of the invention
Attached drawing is used to provide further understanding of the present invention, and constitutes part of specification, with reality of the invention
It applies example and is used together to explain the present invention, be not construed as limiting the invention.In the accompanying drawings:
Fig. 1 shows building destruction condition detection method flow chart according to an embodiment of the invention；
Fig. 2 shows building destruction condition detection method computational chart area destruction according to an embodiment of the invention
Building section body schematic diagram when coefficient；
Fig. 3 shows that building destruction condition detection method according to an embodiment of the invention calculates volumetric fracture of rock system
Building section body schematic diagram when number；
Fig. 4 shows each cross section surface area rupture factor broken line in intact house according to an embodiment of the invention
Figure；
Fig. 5 shows each cross section volumetric fracture of rock coefficient line chart in intact house according to an embodiment of the invention；
Fig. 6 shows each cross section surface area rupture factor in intact house according to an embodiment of the invention and body
The line chart of product rupture factor difference；
Fig. 7 shows different extent of the destruction buildings according to an embodiment of the invention longitudinally each segment table area broken line
Figure；
Fig. 8 shows different extent of the destruction buildings according to an embodiment of the invention longitudinally each segment table area destruction
Coefficient line chart；
Fig. 9 shows different extent of the destruction buildings according to an embodiment of the invention longitudinally each segment body product broken line
Figure；
Figure 10 shows different extent of the destruction buildings according to an embodiment of the invention longitudinally each section of volumetric fracture of rock
Coefficient line chart；
Figure 11 shows local collapsed building surface area rupture factor according to an embodiment of the invention and volume
Rupture factor line chart；And
Figure 12 shows different extent of the destruction buildings according to an embodiment of the invention, and longitudinally each segment table area is broken
Bad coefficient and volumetric fracture of rock coefficient differentials line chart.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, the embodiment of the present invention is made below in conjunction with attached drawing
Further it is described in detail.
In real life, building destruction caused by earthquake is the principal element of casualties and economic loss, building
The investigation of object extent of the destruction is the important evidence of Disaster Loss Evaluation.Currently, building space feature earthquake detects, existing method master
If calculating the space characteristics analysis such as distance, area, angle, volume, the gradient and the slope aspect in a certain contiguous range between point cloud to build
Object Earthquake hazard degree is built, there is no precision height, using convenient and fast building destruction condition detection method.
In order to carry out collapse state evaluation to by the building destroyed, need to know the image information of building.It is domestic
Outer repeatedly earthquake emergency remote sensing application is it was verified that high-resolution satellite and air remote sensing have become the important hand of disaster monitoring
Section.Laser radar (Light Detection And Ranging, LiDAR) is a kind of emerging active remote sensing technology, can be quick
Ground Nuclear Magnetic Resonance elevation information is obtained, the earthquakes such as Ground Deformation, surface rupture caused by earthquake, landslide, barrier lake can be monitored
Secondary disaster and road, building damage.
Currently, carrying out building damage detection main approaches using LiDAR data has: 1) putting cloud after being based on shake foreshock
The variation detection side of difference in height or the digital surface model being generated by it (Digital Surface Model, DSM) high difference image
Method；2) cloud or the extracting method of DSM and high score visual fusion are put after shaking；3) based on intensity, echo times and the slope for putting cloud after shake
The earthquake damage to building analysis method of the three-dimensional space parameters such as degree, slope aspect, local height difference.First kind change detecting method, needs calamity
The accumulation of LiDAR data before Qu Youzhen causes such method at present can not be practical in actual seismic emergency disaster surveillance；Second
There is multi-source data high registration accuracy in class and high score image fusing method；Third class method developing direction important at present,
But high-precision earthquake detection parameters are relatively fewer.
In general, roof is divided into flat roof, inclined roof and various Long Span Roofs, flat roof pitch according to shape difference
Gradient 2%-3% is commonly used on roof no more than 5%.Roof of the roof pitch greater than 10% is inclined roof, there is Dan Po, Shuan Po, four
Point is collected together etc. in slope.The roof of other forms, such as shell, suspension cable, spherical shell, two-sided shallow shell, folded plate, rack, are chiefly used in compared with large span
The roof of structure.Roof should also meet the requirement such as waterproof, draining, room in addition to meeting the functional requirements such as heat preservation, heat-insulated, anti-leakage
The face gradient influences guarded drainage function.The slope of roof is the ratio of roof the highest point and the lowest point depth displacement and two o'clock horizontal distance,
Gradient size is mainly determined according to the performance of selected roof waterproof layer material and construction, according to construction standards " civil buildings
Design general rule " there is specific requirement to the roof drainage gradient for selecting different roof Material classifications in (GB 50352-2005), it is as follows
Shown in table, as can be seen from the table, the stringent flat-top for being 0 almost without the gradient, therefore building can be passed through
The gradient reacts whether building damages.
The 1 roof drainage gradient of table " civil buildings design general rule " (GB 50352-2005)
| Roofing classification | The roof drainage gradient (%) |
| Waterproof coiled material, rigid waterproofing flat roof deck | 2-5 |
| Plain tile | 20-50 |
| Corrugated tile | 10-50 |
| Asphalt felt tile | ≥20 |
| Rack, suspended-cable structure metal plate | ≥4 |
| Profiled sheet | 5-35 |
| Planting soil roofing | 1-3 |
Note: 1 flat roof deck looks for slope to be no less than 3% using structure, and looking for slope using material is preferably 2%；
The gradient of 2 prepared roofings is not preferably greater than 25%, should take fixed when the gradient is greater than 25% and prevent what is slid to arrange
It applies；
3 prepared roofing gutters, eaves gutter head fall are no less than 1%, and ditch bottom water drop must not exceed 200mm.Gutter, eaves
Ditch water discharge must not flow through deformation joint and firewall；
4 plain tiles must be laid securely, and the roofing of earthquake protection area or the gradient greater than 50% should take fixed reinforcement to arrange
It applies；
The 5 overhead heat insulation slopes of roof are not preferably greater than 5%, and the planted roof gradient is not preferably greater than 3%.
Therefore, Fig. 1 shows building destruction condition detection method flow chart according to an embodiment of the invention.
As shown in Figure 1, in step s101, obtaining the point cloud data of building to be analyzed.A reality according to the present invention
Example is applied, building information can be LiDAR point cloud data, and the point cloud data in building image information may be density unevenness
, therefore the computational accuracy after will affect can carry out interpolation processing to the point cloud data in building image information, make
It obtains point cloud data and has preset cloud density.Carrying out a mode for cloud interpolation includes: closest interpolation method, inverse distance weighting
Interpolation method, spline method, moving average interpolation method and Local Polynomial interpolation method.
Then, in step s 102, subdivision is carried out to point cloud data along the first direction of building to be analyzed, obtained wait divide
Analyse cross section and its point cloud data.According to one embodiment of present invention, the process for carrying out subdivision to point cloud data may is that edge
Building trend carries out cross section subdivision to point cloud data by preset interval, extracts from obtained multiple sections to be analyzed cross-sectional
Face and its point cloud data.
Then, in step s 103, the damage degree in each cross section is determined according to the point cloud data in each cross section.One
In a embodiment, first rupture factor and the second rupture factor in the cross section are determined according to the point cloud data in each cross section,
And the damage degree in each cross section is determined by the first rupture factor and the second rupture factor, wherein the first rupture factor and
Two rupture factors are wantonly two in surface area rupture factor, volumetric fracture of rock coefficient and reference rupture factor.
In one embodiment, damage degree is determined according to following expression:
D σ (i)=│ σ1(i)-σ2(i)│
Wherein, d σ (i) indicates the corresponding damage degree in i-th of cross section, σ1(i) i-th of cross section corresponding first is indicated
Rupture factor, σ2(i) corresponding second rupture factor in i-th of cross section is indicated.
Finally, according to the value of the damage degree in each cross section, statistics is in different default broken respectively in step S104
The quantity in cross section in bad degree section.
In one embodiment, whole damage degree is determined according to following expression:
Wherein, γ indicates that whole damage degree, m indicate the sum of default damage degree value interval, kiI-th of expression default broken
The corresponding coefficient of bad degree value interval, NiIndicate the quantity in the cross section in i-th of default damage degree section, N indicates cross-sectional
The sum in face.
Flow chart as shown in Figure 1 causes depth of building to be mutated two masters using same building object roof phase Sihe earthquake
Characteristic is wanted, the point cloud data of building is grouped, calculating separately every group of cross section after subdivision, to be able to reflect building broken
The coefficient of bad state, coefficient obtained by calculation further calculate the collapse state of determining building.The present invention provides
Method application it is convenient, calculation amount is small, and precision is high, can be good at the collapse state for determining building.
Fig. 2 shows building destruction condition detection method computational chart area destruction according to an embodiment of the invention
Building section body schematic diagram when coefficient.
As shown in Fig. 2, building long L, wide D, determine the cross section according to the point cloud data in each cross section first is broken
Bad coefficient and the second rupture factor, and determine by the first rupture factor and the second rupture factor the damage degree in each cross section,
Wherein, the first rupture factor and the second rupture factor are surface area rupture factor, volumetric fracture of rock coefficient and refer to rupture factor
In wantonly two.
According to one embodiment of present invention, the mode of computational chart area rupture factor may is that each cross section
Point cloud data carries out floor projection, obtains floor projection point cloud data.Triangulation is carried out to floor projection point cloud data, is determined
Each vertex of a triangle point corresponding in the point cloud data in each cross section, obtains being formed each cross-sectional in subdivision result
Each vertex of a triangle on the roof in face.It is calculated to be formed according to each vertex of a triangle on the roof for forming each cross section
The area of each triangle on the roof in each cross section obtains the surface on the roof that each cross section is characterized by summation
Product.According to the area of each triangle of floor projection point subdivision result, the projected area on the roof is obtained by summation；Root
Surface area rupture factor is determined according to the surface area and its projected area on roof.
Fig. 3 shows that building destruction condition detection method according to an embodiment of the invention calculates volumetric fracture of rock system
Building section body schematic diagram when number.
According to one embodiment of present invention, the mode for calculating volumetric fracture of rock coefficient may is that according to each cross section and
Its point cloud data carries out three-dimensional space triangulation to each cross section, obtains forming multiple tetrahedrons.Calculate separately each four
The volume of face body obtains the volume on the roof that each cross section is characterized by summation.According to the volume on roof and its perspective plane
Product, determines the volumetric fracture of rock coefficient in conjunction with the width of building to be analyzed.
Volume factor is able to reflect roofing bevel edge in roof building height meter indicates bevel edge than horizontal side length than the value of horizontal sides
How many times, building identical for the roof pitch of building, the every group of volumetric fracture of rock coefficient calculated is theoretically
It is equal.It should be noted that surface area rupture factor is theoretically phase with volumetric fracture of rock coefficient for same building object
Deng.
By a cloud interpolation, the precision for calculating rupture factor can be improved.In one embodiment, original point cloud data is flat
Equal density is 2.5points/m2, but real data midpoint cloud and non-uniform Distribution, in band overlay region, point cloud density is reachable
7points/m2, the regional 2points/m that has2, same building object is different in its cloud density of statistics, the influence of receptor site cloud error,
Point cloud density difference may influence to calculate the space geometry feature of building section body to a certain extent, in order to make building
Each block earthquake three-dimensional space feature has comparativity each other, is carrying out surface area rupture factor and the calculating of volumetric fracture of rock coefficient
It is preceding that interpolation is carried out to each solitary building sample point cloud.
Currently used interpolation method includes: closest interpolation method, inverse distance weighted interpolation method, spline method, shifting
Dynamic interpolation of average value method and Local Polynomial interpolation method.The basic theories of each interpolation method is as follows:
Closest interpolation method is also known as Tyson (Thiessen) polygon method, is that Dutch meteorologist A.H.Thiessen is proposed
For calculating the analysis method of average rainfall from the rainfall product data of discrete distribution weather station, in present geospatial analysis
Quick valuation is carried out through common this method.Assignment method is, it is assumed that the attribute value of any mesh point P (x, y) is nearest away from him
The attribute value of location point, when data are uniformly distributed or data closely completely only have base point not have value, using closest interpolation
Method rapidly and accurately interpolation can go out grid, and when a cloud is unevenly distributed, this method can not accurately reflect area without data
Attribute value variation.
Inverse distance weighted interpolation method is that meteorologist and geologist propose first, and it is flat to be chiefly used in atural object marginal point
Sliding processing.The advantages of gradual changed method of its proximal point algorithm for combining Thiessen polygon and image factoring, basic principle
For if P (xi,yi,zi) be space in discrete point, the z of heightiDistance for all data points in its local neighborhood adds
Weight average value.Formula are as follows:
In formula: dijFor P in planejWith point set point Q in P neighborhoodiDistance, k be degree parameter, which control weight coefficient with
Grid points distance increase and decline, k is bigger, and closer point is by a quiet higher weight, and k is smaller, and weight is relatively uniform
Distribute to each point in neighborhood in ground；ZiFor point QiAttribute value.
Spline method is that a certain interpolation is distinguished into several pieces, and different polynomial of degree n curved surfaces is defined to each piece, should
Curved surface must be all continuous with n-1 subderivatives all on the boundary of adjacent block, just can guarantee smooth between curved surface and adjacent piecemeal
Property, polynomial of degree n is spline function.
Moving average interpolation method is to treat the attribute values of all the points in interpolation point close region to be averaged, as to
The attribute of interpolation point, this method are a kind of interpolation methods of local smoothing method, and the mass data collection suitable for distribution rule is inserted
Value.
Local Polynomial interpolation method is the method for carrying out an interpolation according to certain trend of local data's prediction.Usually
In the local close region of interpolation point, interpolation is carried out using weighted least-squares method polynomial fitting.The interpolation method is also
A kind of smooth interpolation method, the data set smoother suitable for itself.
Below according to the influence before and after an example analysis site cloud interpolation to space geometry feature, earthquake causes building broken
Bad that various damage -forms are presented, the point cloud level difference for destroying part changes greatly.According to one embodiment of present invention, using triangle
Net interpolation method carries out interpolation processing to building object point cloud using the interval 0.1m, and point cloud density is 100points/m after interpolation2。
Step are as follows: create the triangulation of original point first, then traverse triangulation data structure finds encirclement interpolation point three
It is angular, later, the Z value of interpolation point is acquired according to the point and triangle selection interpolated sample method.
Nearest neighbor method, linear approach, cubic convolution method etc. can be selected in interpolated sample method.Different interpolation calculation values have
Difference, nearest neighbor method interpolation curved surface is discontinuous, other interpolation curved surfaces are continuous.According to experimental data, linear approach, cubic convolution method have
Smoothing effect, building surface continuous and derivable, and interpolation result almost indifference, but cubic convolution calculated result is larger in difference
When, the result of generation can expand this species diversity.
Further to select suitable interpolation method, to nearest neighbor method, linear approach, the cubic convolution of intact building object point cloud
Interpolation result, have estimated the surface area rupture factor σ of each interpolation method points, volumetric fracture of rock factor sigmavAnd difference between the two
dσ.The line chart of calculated result is as shown in Fig. 4, Fig. 5 and Fig. 6.Same section of roof difference interpolation method σ as seen from the figures, σv,
D σ is different.Due to original point cloud uneven distribution, σ between each section of house roof in Fig. 4sIt changes greatly, the difference of maximin is close
Twice；σ between each section of roof of arest neighbors interpolationsThere are minute differences, but its value is bigger；Each section of roof of linear approach and cubic convolution method
Between σsIt is almost the same, it is worth also relatively small.
The σ that volumetric method is estimated in Fig. 5v, the σ of original point cloud and the estimation of nearest neighbor method relative areasBecome smaller, σ between each sectionvIt deposits
Height fluctuations are presented in different.Similitude is big between each block among linear approach and cubic convolution method, substantially linearly
Distribution, the latter end σ of cubic convolution methodvSlightly become larger.
Surface area rupture factor difference d σ distribution is as can be seen that d σ difference is larger between each section of roof of original point cloud, recently in Fig. 6
D σ is similar between each section of roofing of adjacent method, but integral value is all bigger than normal, and each section of d σ facet of linear approach and cubic convolution method is in 0 or so, three
Secondary convolution method most end section d σ slightly becomes larger with respect to the value of other each section of roofing.
σs、σv, d σ anomalous variation will lead to the erroneous judgement of house destruction, therefore, the biggish nearest neighbor method of results change is uncomfortable
For the present invention, cubic convolution method is almost the same with linear interpolation method distribution of results form, still, multiple fitting of a polynomial
It may result in cubic convolution method and individual abnormal points occur in house edge, building destruction degree error in judgement can be brought.
In summary, different difference approach results show that linear approach was both real to building surface product and volumentary geometry Analysis of Parameter Effect
The encryption of point cloud is showed, has also maintained the similitude between each section of complete roofing, be the preferable interpolation method that compares, therefore, at one
The present invention carries out interpolation encryption using this method to owned building sample point cloud in embodiment.
For the sensibility for further analyzing different earthquake damage characteristics, grades are destroyed not to different after Unitary coordinate in research
Interpolation is carried out by 0.1 meter of grid size and is adopted using the linear interpolation method based on the triangulation network with the building sample of roofing shape
Sample divides section along house length direction by 1 meter of interval, calculates surface area, volume and the related ratio parameter of roofing point cloud.
It chooses top of the slope and change of the three-dimensional space characteristic parameter in different destructions is compared in the two kinds of building sample analysis of flat-top
Change.
It is the surface area of the top of the slope and flat-top house that calculate in Fig. 7, abscissa is along house longitudinal divisions serial number, from figure
In, it is apparent that the almost the same linear distribution of each segment table area in intact house, the one-storey house and top of the slope house of local failure
Position value is destroyed at it and has fluctuations, and roofing destroys light position, surface area values ASThere is minor change with adjacent sections, but
Destroying heavier position, ASIt can change greatly, and damage each section of house A completelySChange it is very big, adjacent sections i.e. occur compared with
Big variation, surface area variation size directly reflect roofing extent of the destruction.But between the house of different scales size, ASSize
It can not compare each other.
Surface area rupture factor σ in Fig. 8sDistribution and surface area ASDistribution trend is identical, does not destroy the surface area of roofing in figure
Rupture factor σs1.0 or so are substantially distributed in, the roofing section σ seriously destroyedsMajor part is distributed in 1.1 or more, locally collapses more
Serious position σsVariation is bigger, collapses and obviously distinguishes with the roofing that do not collapse.Know ASIt can detecte out house roof to collapse but not
Collapse the subtle disruption that visual interpretation does not go out on this image.
Fig. 9 and Figure 10 is top of the slope and each segment body product V, volumetric fracture of rock factor sigma in flat-top house respectivelyvDistribution map.Similarly
As can be seen that each segment body product V, volumetric fracture of rock factor sigma in housevIt is also more sensitive to destroying, destroy more serious σvVariation is bigger,
Roofing destroys the variation for being mainly shown as height, therefore and ASCompare, what V changed when house destroys becomes apparent.
Collapse the σ in top of the slope house for certain partvAnd σsAs shown in figure 11, the σ of surface area estimationsPart ratio is destroyed in house
σvGreatly, part σ is not destroyedvWith σsIt is almost the same.It can be seen that the surface area rupture factor and volume of the different slopes of roof in Figure 11
The difference d σ of rupture factor, unbroken d σ are distributed near 0, and the d σ that house destroys part is significantly greater than 0；Collapse more serious
House d σ it is bigger, local collapsed house collapse part d σ it is big, the part d σ that do not collapse is distributed near 0.It can be seen that d σ energy
Enough reflection each section of house destruction situations very well, can be used as the characteristic parameter of earthquake detection.
Rupture factor σ is related to the gradient, and the reduction of its roof point causes the gradient to become smaller after house destroys, therefore calculates 0-
The reason σ of 100% gradient0, and assume each gradient of former roofing reduce 2%, 10%, 20%, 30% when, the variation of rupture factor σ
Value, as shown in table 2.In table when grade reduction 2%, maximum difference is 0.0141, and substantially intact section d σ is both less than 0.0141, comprehensive
The influence for considering error is closed, maximum value 0.0141 is determined whether each section of roofing has the threshold value obviously destroyed as d σ.Roofing collapses
The more serious d σ that collapses is mutated, and the top of the slope of local failure, flat-top house d σ become in look-up table 2 in the gradient near 0.14
When changing 20%, maximum changing value d σ is 0.1336, the threshold value that it is seriously destroyed as roofing.The roofing to collapse completely destroys d σ
All bigger, substantially all 0.2 or more, ruling grade variation about 30%, corresponding 0.1935 as damaging completely in table 2
Threshold value.For above three threshold value from being suitble to the different slopes of roof to carry out selection, division is relatively coarse, but can detecte each section of roofing
Different extent of the destruction, therefore study in (- ∞, 0.0141], (and 0.0141,0.1336], (0.1336,0.1935),
[0.1935 ,+∞), by each section of roofing be divided into it is intact, have obvious destruction, moderate damage and seriously destroy four grades.
Grade is destroyed according to roofing, grade assessment formula is destroyed using house and calculates building destruction rate, i=1 in formula, 2,
3,4, respectively indicate it is intact, have obvious destruction, moderate damage and seriously destroy four grades, corresponding average failure coefficient kiRespectively
Value is 0.01,0.3., 0.7,1.0.Building can be divided into according to destructive rate γ collapse I, part collapses II, not collapsing has
It is obvious to destroy III and do not collapse without 4 destruction grades such as obvious destructions IV, γ first take [0,0.1], (0.1,0.5), [0.5,
1.0], destruction grade in house is judged to not collapsing respectively, part collapses and collapses, rank of not collapsing is further according to d σ to not collapsing
Grade is further assessed, if d σ ∈ (0.0141,0.1336), do not collapse as have it is obvious destroy III a, otherwise not collapse without bright
It is aobvious to destroy III b.
The different following roof pitch variety d σ changing values of table 2
For the ability for verifying the proposed actually detected earthquake damage to building of method and index, 46 solitary building points are had chosen
House is divided into several segments at equal intervals after pretreatment, with 1.0 meters, calculates every section of roofing table by cloud, LiDAR point cloud data
Area AS, volume V, rupture factor σs、σv, rupture factor difference d σ and every house the parameters such as destruction ratio γ, further according to above-mentioned
Destructive rate determines each threshold value for destroying grade, determined the destruction grade in every house respectively.The results are shown in Table 3.It is same in table
When give the destruction grade of visual interpretation.
According to level results are destroyed in table, in 46 houses, there are 42 houses to determine result automatically and visually confirm result
It is completely the same.Overall accuracy is that 91%, kappa coefficient is 0.82.
3 building d σ of table, γ and destruction grade
Building destruction condition detection method provided by the invention is grouped the point cloud data of building, counts respectively
Every group of cross section is able to reflect the coefficient of building destruction state after calculation subdivision, and coefficient obtained by calculation is further counted
Calculate the collapse state for determining building.Method application provided by the invention is convenient, and calculation amount is small, and precision is high, can be good at really
Determine the collapse state of building.
It should be understood that disclosed embodiment of this invention is not limited to specific structure disclosed herein, processing step
Or material, and the equivalent substitute for these features that those of ordinary skill in the related art are understood should be extended to.It should also manage
Solution, term as used herein is used only for the purpose of describing specific embodiments, and is not intended to limit.
" one embodiment " or " embodiment " mentioned in specification means the special characteristic described in conjunction with the embodiments, structure
Or characteristic is included at least one embodiment of the present invention.Therefore, the phrase " reality that specification various places throughout occurs
Apply example " or " embodiment " the same embodiment might not be referred both to.
While it is disclosed that embodiment content as above but described only to facilitate understanding the present invention and adopting
Embodiment is not intended to limit the invention.Any those skilled in the art to which this invention pertains are not departing from this
Under the premise of the disclosed spirit and scope of invention, any modification and change can be made in the implementing form and in details,
But scope of patent protection of the invention, still should be subject to the scope of the claims as defined in the appended claims.
Claims (10)
1. a kind of building destruction condition detection method, which is characterized in that the method comprises the steps of:
Step 1: obtaining the point cloud data of building to be analyzed；
Step 2: the first direction along the building to be analyzed carries out subdivision to the point cloud data, each cross section is obtained
And its point cloud data；
Step 3: determining the damage degree in each cross section according to the point cloud data in each cross section；
Step 4: statistics is in different default damage degree sections respectively according to the value of the damage degree in each cross section
The quantity in cross section；
Step 5: determining that the whole of building to be analyzed destroys according to the quantity in the cross section in different default damage degree sections
Degree.
2. the method as described in claim 1, which is characterized in that in the step 2, press preset interval along building trend
Cross section subdivision is carried out to the point cloud data, extracts the cross section to be analyzed and its point cloud number from obtained multiple sections
According to.
3. method according to claim 1 or 2, which is characterized in that in said step 1, using default interpolation algorithm to institute
State building to be analyzed original point cloud data carry out interpolation processing so that the point cloud density of the point cloud data after interpolation be greater than or
Equal to preset cloud density threshold, to obtain the point cloud data of the building to be analyzed.
4. method as claimed in claim 3, which is characterized in that the default interpolation algorithm includes any in item set forth below
Or several:
Closest interpolation method, inverse distance weighted interpolation method, spline method, moving average interpolation method and Local Polynomial
Interpolation method.
5. method as described in any one of claims 1 to 4, which is characterized in that the step 3 includes:
First rupture factor and the second rupture factor in the cross section are determined according to the point cloud data in each cross section, and pass through institute
It states the first rupture factor and second rupture factor determines the damage degree in each cross section, wherein first rupture factor
It is surface area rupture factor, volumetric fracture of rock coefficient and with reference to wantonly two in rupture factor with second rupture factor.
6. method as claimed in claim 5, which is characterized in that the step 3 includes:
Floor projection is carried out to the point cloud data in each cross section, obtains floor projection point cloud data；
Triangulation is carried out to the floor projection point cloud data, determines that each vertex of a triangle is in each cross in subdivision result
Corresponding point, obtains each vertex of a triangle on the roof to form each cross section in the point cloud data of section；
The each of the roof to form each cross section is calculated according to each vertex of a triangle on the roof for forming each cross section
The area of triangle obtains the surface area on the roof that each cross section is characterized by summation；
According to the area of each triangle of floor projection point subdivision result, the projected area on the roof is obtained by summation；
The surface area rupture factor is determined according to the surface area on the roof and its projected area.
7. method as claimed in claim 6, which is characterized in that the step 3 includes:
Three-dimensional space triangulation is carried out to each cross section according to each cross section and its point cloud data, obtains forming multiple four
Face body；
Each tetrahedral volume is calculated separately, the volume on the roof that each cross section is characterized is obtained by summation；
According to the volume and its projected area on the roof, the volumetric fracture of rock is determined in conjunction with the width of the building to be analyzed
Coefficient.
8. the method for claim 7, which is characterized in that the range with reference to rupture factor are as follows: 1 to 1.4142.
9. method as described in any of claims 8, which is characterized in that in the step 5, according to following expression
Determine the damage degree:
D σ (i)=│ σ1(i)-σ2(i)│
Wherein, d σ (i) indicates the corresponding damage degree in i-th of cross section, σ1(i) indicate that system is destroyed in i-th of cross section corresponding first
Number, σ2(i) corresponding second rupture factor in i-th of cross section is indicated.
10. method as claimed in claim 9, which is characterized in that in the step 5, according to following expression determination
Whole damage degree:
Wherein, γ indicates that whole damage degree, m indicate the sum of default damage degree value interval, kiIndicate i-th of default damage degree
The corresponding coefficient of value interval, NiIndicate the quantity in the cross section in i-th of default damage degree section, N indicates cross section
Sum.
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| US11703457B2 (en) | 2020-12-29 | 2023-07-18 | Industrial Technology Research Institute | Structure diagnosis system and structure diagnosis method |
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