CN108957477B - Method for detecting destruction state of building - Google Patents
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Abstract
The invention provides a method for detecting a destruction state of a building, which comprises the following steps: step one, point cloud data of a building to be analyzed is obtained; step two, subdividing the point cloud data along a first direction of a building to be analyzed to obtain each cross section and the point cloud data thereof; thirdly, determining the destruction degree of each cross section according to the point cloud data of each cross section; respectively counting the number of the cross sections in different preset damage degree intervals according to the damage degree value of each cross section; and step five, determining the integral damage degree of the building to be analyzed according to the number of the cross sections in different preset damage degree intervals. The invention groups the point cloud data of the building, respectively calculates the coefficient of each group of cross sections capable of reflecting the damage state of the building after subdivision, and further calculates and determines the damage state of the building through the calculated coefficient. The method provided by the invention is convenient to apply, small in calculated amount and high in precision, and can well determine the damage state of the building.
Description
Technical Field
The invention relates to the field of building space characteristic detection, in particular to a method for detecting a building destruction state.
Background
In real life, building damage caused by earthquake is a main factor of casualties and economic loss, and building damage degree investigation is an important basis for disaster damage assessment. At present, in building space characteristic earthquake damage detection, the existing method mainly calculates the space characteristics such as distance, area, angle, volume, gradient and slope direction between point clouds in a certain neighborhood range to analyze the earthquake damage degree of a building, and a building damage state detection method which is high in precision and convenient to apply is not available.
Accordingly, the present invention provides a method for detecting a state of destruction of a building.
Disclosure of Invention
In order to solve the above problems, the present invention provides a method for detecting a destruction state of a building, the method comprising the steps of:
step one, point cloud data of a building to be analyzed is obtained;
step two, subdividing the point cloud data along the first direction of the building to be analyzed to obtain each cross section and the point cloud data thereof;
thirdly, determining the destruction degree of each cross section according to the point cloud data of each cross section;
respectively counting the number of the cross sections in different preset damage degree intervals according to the damage degree value of each cross section;
and step five, determining the integral damage degree of the building to be analyzed according to the number of the cross sections in different preset damage degree intervals.
According to one embodiment of the invention, in the second step, cross section subdivision is performed on the point cloud data at preset intervals along the direction of the building, and the cross section to be analyzed and the point cloud data thereof are extracted from the obtained multiple sections.
According to an embodiment of the invention, in the first step, a preset interpolation algorithm is used for performing interpolation processing on the original point cloud data of the building to be analyzed, so that the point cloud density of the point cloud data after interpolation is greater than or equal to a preset point cloud density threshold value, and the point cloud data of the building to be analyzed is obtained.
According to an embodiment of the invention, the preset interpolation algorithm comprises any one or several of the following:
nearest neighbor interpolation, reciprocal distance weighted interpolation, spline interpolation, moving average interpolation, and local polynomial interpolation.
According to an embodiment of the present invention, the third step includes:
determining a first destruction coefficient and a second destruction coefficient of each cross section according to the point cloud data of each cross section, and determining the destruction degree of each cross section through the first destruction coefficient and the second destruction coefficient, wherein the first destruction coefficient and the second destruction coefficient are any two of a surface destruction coefficient, a volume destruction coefficient and a reference destruction coefficient.
According to an embodiment of the present invention, the third step includes:
horizontally projecting the point cloud data of each cross section to obtain horizontally projected point cloud data;
triangulating the horizontal projection point cloud data, determining points corresponding to the vertexes of the triangles in the subdivision result in the point cloud data of the cross sections, and obtaining the vertexes of the triangles forming the roofs of the cross sections;
calculating the area of each triangle forming the roof of each cross section according to the vertex of each triangle forming the roof of each cross section, and summing to obtain the surface area of the roof represented by each cross section;
according to the area of each triangle of the horizontal projection point subdivision result, the projection area of the roof is obtained through summation;
determining the surface area failure coefficient according to the surface area of the roof and the projected area thereof.
According to an embodiment of the present invention, the third step includes:
carrying out three-dimensional space triangulation on each cross section according to each cross section and point cloud data thereof to obtain a plurality of tetrahedrons;
respectively calculating the volume of each tetrahedron, and summing to obtain the volume of the roof represented by each cross section;
and determining the volume destruction coefficient by combining the width of the building to be analyzed according to the volume of the roof and the projection area thereof.
According to one embodiment of the invention, the reference destruction coefficients range from: 1 to 1.4142.
According to an embodiment of the present invention, in the step five, the destruction degree is determined according to the following expression:
dσ(i)＝│σ1(i)-σ2(i)│
wherein d σ (i) represents a destruction degree corresponding to the i-th cross section, σ1(i) Represents a first destruction coefficient, σ, corresponding to the ith cross section2(i) A second destruction coefficient corresponding to the ith cross-section is shown.
According to an embodiment of the present invention, in the step five, the overall damage degree is determined according to the following expression:
wherein, gamma represents the whole damage degree, m represents the total number of the preset damage degree value intervals, kiA coefficient, N, corresponding to the ith preset damage value intervaliRepresenting the number of cross sections within the ith pre-determined interval of failure and N representing the total number of cross sections.
The building destruction state detection method provided by the invention groups the point cloud data of the building, respectively calculates the coefficients of each group of cross sections capable of reflecting the destruction state of the building after subdivision, and further calculates and determines the destruction state of the building through the calculated coefficients. The method provided by the invention is convenient to apply, small in calculated amount and high in precision, and can well determine the damage state of the building.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 shows a flow diagram of a building damage status detection method according to one embodiment of the invention;
FIG. 2 is a schematic view of a cross-sectional body of a building when a surface area failure coefficient is calculated by a method for detecting a failure state of a building according to an embodiment of the present invention;
FIG. 3 shows a schematic cross-sectional view of a building when a volume destruction coefficient is calculated by a method for detecting a destruction state of a building according to an embodiment of the invention;
FIG. 4 shows a plot of the surface area damage factors for each cross-section of a sound house according to one embodiment of the present invention;
FIG. 5 shows a plot of the coefficient of volume destruction for each cross-section of a sound house according to one embodiment of the present invention;
FIG. 6 shows a line graph of the surface area failure coefficient and the volume failure coefficient difference for each cross-section of a sound house according to one embodiment of the present invention;
FIG. 7 illustrates a line drawing of surface areas of longitudinal sections of a building with different degrees of failure, according to one embodiment of the present invention;
FIG. 8 is a graph showing surface area failure coefficients for various longitudinal sections of a building with varying degrees of failure, according to one embodiment of the present invention;
FIG. 9 illustrates a line drawing of the volume of longitudinal sections of a building with different degrees of failure, according to one embodiment of the present invention;
FIG. 10 is a line graph illustrating the volumetric failure coefficients of longitudinal sections of a building with different levels of failure, according to one embodiment of the present invention;
FIG. 11 illustrates a plot of surface area failure coefficient versus volume failure coefficient for a partially collapsed building, according to an embodiment of the present invention; and
FIG. 12 shows a plot of the surface area failure coefficient versus the volume failure coefficient difference for various longitudinal sections of a building with varying degrees of failure, according to one embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention are described in further detail below with reference to the accompanying drawings.
In real life, building damage caused by earthquake is a main factor of casualties and economic loss, and building damage degree investigation is an important basis for disaster damage assessment. At present, in building space characteristic earthquake damage detection, the existing method mainly calculates the space characteristics such as distance, area, angle, volume, gradient and slope direction between point clouds in a certain neighborhood range to analyze the earthquake damage degree of a building, and a building damage state detection method which is high in precision and convenient to apply is not available.
In order to assess the damage status of a damaged building, it is necessary to know the image information of the building. Multiple earthquake emergency remote sensing application practices at home and abroad prove that high-resolution satellite and aerial remote sensing become important means for disaster monitoring. The laser radar (LiDAR) is a new active remote sensing technology, can quickly acquire high-precision ground elevation information, And can monitor earthquake secondary disasters such as ground surface deformation, ground surface fracture, landslide, barrage lake And the like caused by an earthquake, And road And building damage.
At present, the main research methods for detecting building damage by using LiDAR data are as follows: 1) a change detection method based on the height difference of the pre-earthquake and post-earthquake point clouds or a Digital Surface Model (DSM) height difference image generated by the pre-earthquake and post-earthquake point clouds; 2) an extraction method for fusing post-earthquake point cloud or DSM with high-resolution images; 3) and (3) a building earthquake damage analysis method based on three-dimensional space parameters such as intensity, echo times, gradient, slope direction, local height difference and the like of the post-earthquake point cloud. The first type of change detection method needs the accumulation of pre-earthquake LiDAR data in disaster areas, so that the method cannot be practically used in actual earthquake emergency disaster monitoring at present; the second and high-resolution image fusion methods have the problem of high-precision registration of multi-source data; the third method is currently an important development direction, but the high-precision earthquake damage detection parameters are relatively few.
Generally, the roof is divided into a flat roof, a sloping roof and various large-span roofs according to different appearances, wherein the slope of the flat roof is not more than 5 percent, and the slope is 2 to 3 percent commonly. The roof with the roof slope of more than 10 percent is a sloping roof and comprises a single slope, a double slope, a four slope, a pyramid tip and the like. Other forms of roofs, such as thin shells, suspended cables, spherical shells, double-sided flat shells, folded plates, net racks, etc., are often used for roofs of larger span structures. The roof not only meets the functional requirements of heat preservation, heat insulation, leakage resistance and the like, but also meets the requirements of water prevention, drainage and the like, and the slope of the roof affects the water prevention and drainage functions. The roof slope is the ratio of the height difference between the highest point and the lowest point of the roof and the horizontal distance between the two points, the slope is mainly determined according to the performance and the structure of the selected roof waterproof layer material, and specific requirements are provided for roof drainage slopes of different roof material types according to the general building code of civil building design (GB 50352-.
TABLE 1 roof drainage gradient general rule for civil building design (GB 50352-
| Roofing category | Roof drainage slope (%) |
| Waterproof rigid waterproof flat roof of coiled material | 2-5 |
| Flat tile | 20-50 |
| Corrugated tile | 10-50 |
| Asphalt felt tile | ≥20 |
| Metal plate with net frame and suspension cable structure | ≥4 |
| Profiled steel sheet | 5-35 |
| Planting soil roof | 1-3 |
Note: 1, the slope of the flat roof is not less than 3% by adopting a structure, and the slope of the flat roof is preferably 2% by adopting a material;
2, the gradient of the coiled material roof is not more than 25%, and when the gradient is more than 25%, measures for fixing and preventing the roof from sliding off are taken;
3 the longitudinal gradient of the gutter and the eave gutter of the coiled material roof is not less than 1 percent, and the water drop at the bottom of the gutter is not more than 200 mm. Drainage of the gutter and the eaves gutter cannot flow through the deformation joint and the firewall;
4, the flat tiles must be laid firmly, and fixed reinforcement measures should be adopted in earthquake fortification areas or roofs with the slope larger than 50%;
the gradient of the 5 overhead heat-insulation roof is not more than 5 percent, and the gradient of the planted roof is not more than 3 percent.
Accordingly, FIG. 1 shows a flow diagram of a building damage status detection method according to one embodiment of the invention.
As shown in fig. 1, in step S101, point cloud data of a building to be analyzed is acquired. According to one embodiment of the invention, the building information may be LiDAR point cloud data, and the point cloud data in the building image information may have uneven density, which may affect the subsequent calculation accuracy, so that the point cloud data in the building image information may be interpolated to make the point cloud data have the preset point cloud density. The method for point cloud interpolation includes: nearest neighbor interpolation, reciprocal distance weighted interpolation, spline interpolation, moving average interpolation, and local polynomial interpolation.
Next, in step S102, the point cloud data is subdivided along the first direction of the building to be analyzed, so as to obtain the cross section to be analyzed and the point cloud data thereof. According to an embodiment of the present invention, the process of subdividing the point cloud data may be: and (4) carrying out cross section subdivision on the point cloud data at preset intervals along the direction of the building, and extracting the cross section to be analyzed and the point cloud data thereof from the obtained multiple sections.
Next, in step S103, the destruction degree of each cross section is determined from the point cloud data of each cross section. In one embodiment, a first destruction coefficient and a second destruction coefficient of each cross section are determined according to the point cloud data of the cross section, and the destruction degree of each cross section is determined through the first destruction coefficient and the second destruction coefficient, wherein the first destruction coefficient and the second destruction coefficient are any two of a surface destruction coefficient, a volume destruction coefficient and a reference destruction coefficient.
In one embodiment, the degree of corruption is determined according to the expression:
dσ(i)＝│σ1(i)-σ2(i)│
wherein d σ (i) represents a destruction degree corresponding to the i-th cross section, σ1(i) Represents a first destruction coefficient, σ, corresponding to the ith cross section2(i) A second destruction coefficient corresponding to the ith cross-section is shown.
Finally, in step S104, the number of the cross sections in different preset damage degree intervals is respectively counted according to the damage degree value of each cross section.
In one embodiment, the overall degree of corruption is determined according to the expression:
wherein, gamma represents the whole damage degree, m represents the total number of the preset damage degree value intervals, kiA coefficient, N, corresponding to the ith preset damage value intervaliRepresenting the number of cross sections within the ith pre-determined interval of failure and N representing the total number of cross sections.
The flow chart shown in fig. 1 utilizes two main characteristics of similarity of roofs and earthquake damage of the same building, which cause high sudden change of the buildings, to group point cloud data of the buildings, respectively calculate coefficients of each group of cross sections after subdivision, which can reflect the damage state of the buildings, and further calculate and determine the damage state of the buildings through the calculated coefficients. The method provided by the invention is convenient to apply, small in calculated amount and high in precision, and can well determine the damage state of the building.
Fig. 2 is a schematic view of a cross-sectional body of a building when a surface area destruction coefficient is calculated by a building destruction state detection method according to an embodiment of the present invention.
As shown in fig. 2, the building length L and width D, a first destruction coefficient and a second destruction coefficient of each cross section are determined according to the point cloud data of the cross section, and the destruction degree of each cross section is determined according to the first destruction coefficient and the second destruction coefficient, wherein the first destruction coefficient and the second destruction coefficient are any two of a surface destruction coefficient, a volume destruction coefficient and a reference destruction coefficient.
According to one embodiment of the present invention, the way to calculate the surface area damage factor may be: and horizontally projecting the point cloud data of each cross section to obtain horizontally projected point cloud data. And triangulating the horizontal projection point cloud data, determining points corresponding to the vertexes of the triangles in the subdivision result in the point cloud data of the cross sections, and obtaining the vertexes of the triangles forming the roofs of the cross sections. The area of each triangle forming the roof of each cross-section is calculated from the vertices of each triangle forming the roof of each cross-section, and the surface area of the roof characterized by each cross-section is obtained by summing. According to the area of each triangle of the horizontal projection point subdivision result, the projection area of the roof is obtained through summation; the surface area failure coefficient is determined from the surface area of the roof and its projected area.
Fig. 3 is a schematic view showing a cross-sectional body of a building when a volume destruction coefficient is calculated by a building destruction state detection method according to an embodiment of the present invention.
According to one embodiment of the present invention, the way to calculate the volume destruction coefficient may be: and carrying out three-dimensional space triangulation on each cross section according to each cross section and point cloud data thereof to obtain a plurality of tetrahedrons. The volume of each tetrahedron is calculated separately and summed to obtain the volume of the roof characterized by each cross section. And determining the volume destruction coefficient according to the volume of the roof and the projection area thereof and combining the width of the building to be analyzed.
The volume coefficient can reflect the value of the sloping edge of the roof to the horizontal edge in the roof building height meter, and represents how many times the sloping edge is longer than the horizontal edge, and for buildings with the same roof slope, the calculated volume destruction coefficients of each group are theoretically equal. It should be noted that the surface area failure coefficient and the volume failure coefficient are theoretically equal to each other for the same building.
Through point cloud interpolation, the accuracy of calculating the damage coefficient can be improved. In one embodiment, the average density of the raw point cloud data is 2.5points/m2However, the point clouds in the actual data are not uniformly distributed, and the point cloud density in the strip overlapping area can reach 7points/m2Some areas 2points/m2The point cloud density of the same building is different in statistics and is influenced by point cloud errors, the difference of the point cloud density can influence the calculation of the space geometric characteristics of a building section body to a certain extent, and in order to enable the three-dimensional space characteristics of earthquake damage of each block section of the building to be comparable, interpolation is carried out on the point clouds of each building sample before the calculation of the surface area damage coefficient and the volume damage coefficient is carried out.
Currently, commonly used interpolation methods include: nearest neighbor interpolation, reciprocal distance weighted interpolation, spline interpolation, moving average interpolation, and local polynomial interpolation. The basic theory for each interpolation is as follows:
nearest neighbor interpolation, also known as Thiessen (Thiessen) polygon, is an analytical method proposed by netherlands meteorologist a.h. Thiessen for calculating average rainfall from rainfall data from discrete distributed weather stations, and is now commonly assigned rapidly in geospatial analysis. The assignment method is that assuming that the attribute value of any grid point P (x, y) is the attribute value of the position point nearest to the grid point, when the data is uniformly distributed or the data is compact and complete and only a few points have no value, the grid can be quickly and accurately interpolated by applying the nearest interpolation method, and when the point cloud is not uniformly distributed, the method cannot accurately reflect the attribute value change of a non-data area.
The distance inverse weighted interpolation method is firstly proposed by meteorologists and geologists and is mainly used for smoothing the edge points of the ground objects. The method integrates the advantages of a proximity point method of a Thiessen polygon and a gradient method of a multiple regression method, and has the basic principle that P (x) is seti,yi,zi) Is a discrete point in space, the height z thereofiA weighted average of the distances of all data points within its local neighborhood. The formula is as follows:
in the formula dijIs in plane PjAnd P neighborhood inner point set QiK is a square parameter which controls the weight coefficient to decrease along with the increase of the distance of the grid points, the larger k is, the closer point is given a higher weight, the smaller k is, and the weights are distributed to each point in the neighborhood more uniformly; ziIs a point QiThe attribute value of (2).
The spline interpolation method is to divide an interpolation area into a plurality of blocks, define different n-degree polynomial surfaces for each block, ensure the smoothness between the surface and the adjacent blocks only by continuing all n-1-degree derivatives on the boundary of the surface and the adjacent blocks, and the n-degree polynomial is a spline function.
The moving average value interpolation method is to take the average value of the attribute values of all the points in the area near the point to be interpolated as the attribute of the point to be interpolated.
Local polynomial interpolation is a method of interpolating points according to some trend of local data prediction. In general, a weighted least square method is adopted to fit a polynomial to interpolate in a local adjacent area of a point to be interpolated. The interpolation method is also a smooth interpolation method, and is suitable for a data set which is smooth per se.
The influence of the point cloud interpolation on the space geometric characteristics is analyzed according to an example, the building damage caused by earthquake presents various damage forms, and the point cloud height difference of the damage part is large in change. According to one embodiment of the invention, the building point cloud is interpolated at 0.1m intervals by adopting a triangulation network interpolation method, and the density of the point cloud after interpolation is 100points/m2. The method comprises the following steps: firstly, triangulation of an original point is created, then a triangulation data structure is traversed to find a triangle surrounding an interpolation point, and then an interpolation sampling method is selected according to the point and the triangle to obtain a Z value of the interpolation point.
The interpolation sampling method can be selected from a nearest neighbor method, a linear method, a cubic convolution method and the like. Different interpolation calculation values have difference, the nearest neighbor interpolation curved surface is discontinuous, and other interpolation curved surfaces are continuous. According to experimental data, a linear method and a cubic convolution method have a smoothing effect, a building surface is continuous and smooth, interpolation results are almost the same, and when point differences are large in a cubic convolution calculation result, the generated results can enlarge the differences.
In order to further select a proper interpolation method, the surface area damage coefficient sigma of each interpolation method point is estimated for the interpolation results of the nearest neighbor method, the linear method and the cubic convolution of the perfect building point cloudsVolume destruction coefficient sigmavAnd the difference d σ between the two. The line graphs of the calculation results are shown in fig. 4, 5, and 6. The same segment of house is seen from the figureMethod of interpolation of top differences sigmas，σvAnd d σ is different. Sigma between the roofs of the house sections in FIG. 4 due to uneven distribution of the original point cloudsThe variation is large, and the difference of the maximum value and the minimum value is nearly two times; nearest neighbor interpolation between each segment of roof sigmasThe difference is slight, but the value is larger; sigma between sections of roofs by linear method and cubic convolution methodsAlmost the same, the values are also relatively small.
Sigma estimated by volume method in FIG. 5vSigma of relative area estimation of original point cloud and nearest neighbor methodsBecome smaller, σ between the segmentsvThere is a certain difference that exhibits a change in elevation. The similarity between each block in the middle of the linear method and the cubic convolution method is large and is basically in straight line distribution, and the last segment sigma of the cubic convolution methodvSlightly larger.
As can be seen from the distribution of the surface area destruction coefficient difference d σ in fig. 6, the difference of d σ between each segment of the roofs of the original point cloud is large, d σ between each segment of the roofs of the nearest neighbor method is similar, but the overall value is large, the d σ facets of each segment of the linear method and the cubic convolution method are about 0, and the value of the last segment d σ of the cubic convolution method is slightly larger than that of each segment of the roofs.
σs、σvAnd abnormal changes of d sigma can cause misjudgment of house damage, so that the nearest neighbor method with large result change is not suitable for the method, the result distribution form of the cubic convolution method is almost the same as that of the linear interpolation method, but the cubic convolution method can cause individual abnormal points at the edge of the house by multiple polynomial fitting, and the judgment error of the building damage degree can be brought. By combining the above, the analysis of the influence of the results of different difference methods on the surface area and volume geometric parameters of the building shows that the linear method not only realizes point cloud encryption, but also keeps the similarity between the sections of complete roofs, and is a relatively good interpolation method, so that the method is adopted for interpolation encryption of all point clouds of the building sample in one embodiment.
In order to further analyze the sensitivity of different earthquake damage characteristics, in the research, interpolation sampling is carried out on building samples with different damage levels and different roof shapes after coordinate normalization according to the size of a 0.1-meter grid by adopting a linear interpolation method based on a triangular network, sections are divided along the length direction of a house at intervals of 1 meter, and the surface area, the volume and related ratio parameters of the roof point cloud are calculated. And selecting two types of building samples of a slope top and a flat top to analyze and compare the change of the three-dimensional space characteristic parameters under different damage conditions.
In FIG. 7, the calculated surface areas of the flat-topped and flat-topped houses are shown, and the abscissa is the serial number along the longitudinal section of the house, it is obvious from the figure that the surface areas of the sections of the intact house are basically consistent and linearly distributed, the damaged positions of the flat-topped house and the flat-topped house which are locally damaged have fluctuation, the damaged position of the roof is light, and the surface area value A isSWith slight variations from adjacent sections, but in more heavily damaged parts, ASWill change greatly and completely destroy each section A of the houseSThe change is very large, the adjacent sections have large changes, and the surface area change directly reflects the roof damage degree. But between houses of different sizes, ASThe sizes cannot be compared with each other.
FIG. 8 shows the surface area destruction coefficient σsDistribution and surface area ASThe distribution trends are the same, and the surface area damage coefficient sigma of the undamaged roof in the graphsThe roof section sigma which is distributed about 1.0 and is seriously damagedsMost of the areas are distributed over 1.1, the more severe the local collapsesThe larger the change, the more clearly the collapsed and the non-collapsed roof is distinguished. It is known that ASSubtle damage that is not visually discernable on an image of a house roof collapse but not collapse can be detected.
FIGS. 9 and 10 show the volume V and the volume destruction factor σ of each section of a pitched roof and flat roof house, respectivelyvAnd (5) distribution diagram. Similarly, it can be seen that the volume V and the volume destruction coefficient sigma of each section of the housevIs also sensitive to damage, the more severely the damage σvThe greater the change, the greater the roof damage is mainly manifested as a change in height, hence aSIn comparison, V changes more significantly when the house is broken.
Sigma of a house with locally collapsed roofvAnd σsAs shown in FIG. 11, σ for surface area estimationsIn house destruction part ratio sigmavLarge, undamaged part sigmavAnd σsAre almost identical. In fig. 11, it can be seen that the difference d σ between the surface area destruction coefficient and the volume destruction coefficient of different roof slopes is obtained, the undamaged d σ is distributed around 0, and the damaged portion d σ of the house is significantly larger than 0; the house d σ with more severe collapse is larger, the d σ of the collapsed part of the house with local collapse is larger, and the d σ of the non-collapsed part is distributed in the vicinity of 0. Therefore, d sigma can well reflect the damage condition of each section of the house and can be used as a characteristic parameter for earthquake damage detection.
The coefficient of failure sigma is related to the slope, and the decrease of the roof point of the house after the house is broken leads to the slope becoming smaller, so the principle sigma of 0-100% slope is calculated0And assuming that the change value of the damage coefficient sigma is reduced by 2%, 10%, 20% and 30% when the original roof slope is reduced, as shown in table 2. In the table, when the gradient is reduced by 2%, the maximum difference is 0.0141, and the almost perfect cross section d σ is less than 0.0141, and the maximum value of 0.0141 is used as the threshold value for d σ to determine whether or not each segment of roof is significantly damaged, taking the influence of the error into consideration. The d sigma of the house with the serious roof collapse is suddenly changed, the d sigma of the house with the locally damaged top slope and flat top is about 0.14, and when the slope changes by 20 percent in the lookup table 2, the maximum change value d sigma is 0.1336 and is used as the threshold value of the serious roof damage. The fully collapsed roof damage d σ is relatively large, generally above 0.2, with a maximum slope change of about 30%, which is identified in table 2 as the threshold for complete damage by correspondence of 0.1935. The three thresholds mentioned above are selected to suit different roof slopes, with a relatively rough division, but different degrees of damage of the sections of the roof can be detected, so that (— ∞, 0.0141) was studied]，(0.0141，0.1336](0.1336, 0.1935), [0.1935, + ∞), each section of the roof is classified into four grades of intact, clearly damaged, moderately damaged, and severely damaged.
Calculating the damage rate of the building by utilizing a house damage grade evaluation formula according to the roof damage grade, wherein i is 1,2,3 and 4, the four grades of good, obvious damage, medium damage and serious damage are respectively represented, and the average damage coefficient k corresponds toiThe values are 0.01, 0.3, 0.7 and 1.0 respectively. According to the destruction rate gamma, the building can be divided into 4 buildings, such as collapse I, local collapse II, non-collapse obvious damage III and non-collapse obvious damage IVThe destruction rating is first determined by taking [0,0.1 ] for gamma]、(0.1,0.5)、[0.5,1.0]And respectively judging the house damage levels as non-collapse, local collapse and collapse, further evaluating the non-collapse level according to d sigma, if d sigma belongs to (0.0141,0.1336), determining that the house damage levels are non-collapse with obvious damage IIIa, otherwise, determining that the house damage levels are non-collapse without obvious damage IIIb.
TABLE 2 slope change d sigma of different roofs
In order to verify the capability of the proposed method and index for actually detecting the earthquake damage of the building, 46 building point clouds are selected, LiDAR point cloud data of the 46 building point clouds are preprocessed, the house is divided into a plurality of sections at equal intervals of 1.0 meter, and the surface area A of the roof of each section is calculatedSVolume V, coefficient of failure σs、σvAnd a destruction coefficient difference d sigma and a destruction ratio gamma of each house, and determining a threshold value of each destruction level based on the destruction rate, thereby determining the destruction level of each house. The results are shown in Table 3. The visually perceived damage levels are also given in the table.
According to the results of the destruction levels in the table, the automatic judgment result of 42 houses is completely consistent with the visual judgment result of 46 houses. The overall accuracy was 91% and the kappa coefficient was 0.82.
TABLE 3 buildings d σ, γ and failure rating
The building destruction state detection method provided by the invention groups the point cloud data of the building, respectively calculates the coefficients of each group of cross sections capable of reflecting the destruction state of the building after subdivision, and further calculates and determines the destruction state of the building through the calculated coefficients. The method provided by the invention is convenient to apply, small in calculated amount and high in precision, and can well determine the damage state of the building.
It is to be understood that the disclosed embodiments of the invention are not limited to the particular structures, process steps, or materials disclosed herein but are extended to equivalents thereof as would be understood by those ordinarily skilled in the relevant arts. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.
Reference in the specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Thus, the appearances of the phrase "one embodiment" or "an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment.
Although the embodiments of the present invention have been described above, the above description is only for the convenience of understanding the present invention, and is not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. A method for detecting a state of destruction of a building, the method comprising the steps of:
step one, point cloud data of a building to be analyzed is obtained;
step two, subdividing the point cloud data along the first direction of the building to be analyzed to obtain each cross section and the point cloud data thereof;
determining the destruction degree of each cross section according to the point cloud data of each cross section, wherein a first destruction coefficient and a second destruction coefficient of each cross section are determined according to the point cloud data of each cross section, and the destruction degree of each cross section is determined through the first destruction coefficient and the second destruction coefficient, wherein the first destruction coefficient and the second destruction coefficient are any two of a surface destruction coefficient, a volume destruction coefficient and a reference destruction coefficient;
respectively counting the number of the cross sections in different preset damage degree intervals according to the damage degree value of each cross section;
and step five, determining the integral damage degree of the building to be analyzed according to the number of the cross sections in different preset damage degree intervals.
2. The method of claim 1, wherein in the second step, the point cloud data is subjected to cross section subdivision at preset intervals along the building trend, and the cross section to be analyzed and the point cloud data thereof are extracted from the obtained plurality of cross sections.
3. The method of claim 1 or 2, wherein in the first step, a preset interpolation algorithm is used to interpolate the original point cloud data of the building to be analyzed, so that the point cloud density of the point cloud data after interpolation is greater than or equal to a preset point cloud density threshold value, thereby obtaining the point cloud data of the building to be analyzed.
4. A method according to claim 3, wherein the pre-set interpolation algorithm comprises any one or more of:
nearest neighbor interpolation, reciprocal distance weighted interpolation, spline interpolation, moving average interpolation, and local polynomial interpolation.
5. The method of claim 1, wherein step three comprises:
horizontally projecting the point cloud data of each cross section to obtain horizontally projected point cloud data;
triangulating the horizontal projection point cloud data, determining points corresponding to the vertexes of the triangles in the subdivision result in the point cloud data of the cross sections, and obtaining the vertexes of the triangles forming the roofs of the cross sections;
calculating the area of each triangle forming the roof of each cross section according to the vertex of each triangle forming the roof of each cross section, and summing to obtain the surface area of the roof represented by each cross section;
according to the area of each triangle of the horizontal projection point subdivision result, the projection area of the roof is obtained through summation;
determining the surface area failure coefficient according to the surface area of the roof and the projected area thereof.
6. The method of claim 5, wherein step three comprises:
carrying out three-dimensional space triangulation on each cross section according to each cross section and point cloud data thereof to obtain a plurality of tetrahedrons;
respectively calculating the volume of each tetrahedron, and summing to obtain the volume of the roof represented by each cross section;
and determining the volume destruction coefficient by combining the width of the building to be analyzed according to the volume of the roof and the projection area thereof.
7. The method of claim 6, wherein the reference destruction coefficients range from: 1 to 1.4142.
8. The method as recited in claim 7, wherein in said third step, said destruction level is determined according to the following expression:
dσ(i)＝│σ1(i)-σ2(i)│
wherein d σ (i) represents a destruction degree corresponding to the i-th cross section, σ1(i) Represents a first destruction coefficient, σ, corresponding to the ith cross section2(i) A second destruction coefficient corresponding to the ith cross-section is shown.
9. The method of claim 8, wherein in said step five, said overall degree of destruction is determined according to the expression:
wherein, gamma represents the whole damage degree, m represents the total number of the preset damage degree value intervals, kiA coefficient, N, corresponding to the ith preset damage value intervaliRepresenting the number of cross sections within the ith pre-determined interval of failure and N representing the total number of cross sections.
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| CN106504328A (en) * | 2016-10-27 | 2017-03-15 | 电子科技大学 | A kind of complex geological structure modeling method reconstructed based on sparse point cloud surface |
| CN105243276B (en) * | 2015-10-14 | 2017-03-22 | 中国地震局地壳应力研究所 | Building seismic damage analysis method |
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