CN108898596B - 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: acquiring point cloud data of a building to be analyzed; subdividing the point cloud data along a first direction of a building to be analyzed to obtain a cross section to be analyzed and the point cloud data thereof; determining the surface area and the projection area of the roof represented by the cross section to be analyzed according to the point cloud data of the cross section to be analyzed, and determining a first destruction coefficient according to the surface area and the projection area of the roof; determining the volume of the roof represented by the cross section to be analyzed according to the point cloud data of the cross section to be analyzed, and determining a second destruction coefficient according to the volume of the roof; and determining the destruction state of the building to be analyzed according to the first destruction coefficient and the second destruction coefficient. The invention divides the point cloud data of the building, respectively calculates the coefficients of each group of cross sections capable of reflecting the damage state of the building after division, and compares the calculated coefficients to 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 a cross section to be analyzed and the point cloud data of the cross section;
determining the surface area and the projection area of the roof represented by the cross section to be analyzed according to the point cloud data of the cross section to be analyzed, and determining a first destruction coefficient according to the surface area and the projection area of the roof;
fourthly, determining the volume of the roof represented by the cross section to be analyzed according to the point cloud data of the cross section to be analyzed, and determining a second destruction coefficient according to the volume of the roof;
and fifthly, determining the destruction state of the building to be analyzed according to the first destruction coefficient and the second destruction coefficient.
According to one embodiment of the invention, cross section subdivision is carried out on the point cloud data at preset intervals along the direction of a building, and the cross section to be analyzed and the point cloud data thereof are extracted from a plurality of obtained sections.
According to one embodiment of the invention, a preset interpolation algorithm is utilized to perform 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:
carrying out horizontal projection on the point cloud data of the cross section to be analyzed to obtain horizontal projection point cloud data;
triangulating the horizontal projection point cloud data, determining points corresponding to vertexes of all triangles in subdivision results in the point cloud data of the cross section to be analyzed, and obtaining vertexes of all triangles forming the roof of the cross section to be analyzed;
calculating the area of each triangle forming the roof of the cross section to be analyzed according to the vertex of each triangle forming the roof of the cross section to be analyzed, and obtaining the surface area of the roof through summation.
According to one embodiment of the invention, the projected area of the roof is obtained by summing the areas of the triangles of the horizontal projection point subdivision result.
According to an embodiment of the present invention, in the third step, the first destruction coefficient is determined according to the following expression:
σs＝As/A0
wherein σsDenotes a first destruction coefficient, AsAnd A0Respectively representing the surface area and the projected area of the roof characterized by the cross-section to be analyzed.
According to an embodiment of the present invention, the fourth step includes:
performing three-dimensional space triangulation on the cross section to be analyzed according to the cross section to be analyzed 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;
and determining the second 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 an embodiment of the present invention, in the fourth step, the second destruction coefficient is determined according to the following expression:
wherein σvDenotes a second destruction coefficient, V and A0Respectively, the volume and the projected area of the roof characterized by the cross-section to be analyzed, and D the width of the cross-section to be analyzed.
According to one embodiment of the present invention, in said step five,
and calculating a difference value between the first destruction coefficient and the second destruction coefficient, and judging whether the difference value is in a preset difference value interval, wherein if the difference value is not in the preset difference value interval, the cross section to be analyzed is judged to be damaged.
The building destruction state detection method provided by the invention divides 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 division, and compares the calculated coefficients to determine the destruction state of the building. 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 shows a flow diagram of a building damage status detection method according to another embodiment of the invention;
FIG. 3 illustrates a first coefficient of failure line plot for each cross-section of a sound house, according to one embodiment of the present invention;
FIG. 4 shows a second failure coefficient line graph for each cross-section of a sound house, according to one embodiment of the present invention;
FIG. 5 shows a line graph of the first destruction factor and the second destruction factor difference for each cross-section of a sound house according to one embodiment of the invention;
FIG. 6 is a schematic view of a cross-sectional body of a building when a first destruction coefficient is calculated by a method for detecting a destruction state of a building according to an embodiment of the present invention;
FIG. 7 is a schematic view of a cross-sectional body of a building when a second destruction coefficient is calculated by the building destruction state detection method according to an embodiment of the present invention;
FIG. 8 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. 9 illustrates a first failure coefficient line graph for longitudinal sections of a building of varying degrees of failure, according to one embodiment of the present invention;
FIG. 10 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. 11 illustrates a second failure coefficient line graph for longitudinal sections of a building of varying degrees of failure, according to one embodiment of the present invention;
FIG. 12 illustrates a line graph of first and second failure coefficients for a partially collapsed building, according to an embodiment of the present invention; and
fig. 13 shows a graph of the difference between the first failure coefficient and the second failure coefficient for various longitudinal sections of a building with different degrees of failure, according to one embodiment of the 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.
Then, in step S103, a surface area and a projection area of the roof represented by the cross section to be analyzed are determined according to the point cloud data of the cross section to be analyzed, and a first destruction coefficient is determined according to the surface area and the projection area of the roof.
According to one embodiment of the invention, the way of calculating the surface area of the roof may be: and horizontally projecting the point cloud data of the cross section to be analyzed to obtain horizontally projected point cloud data. And then 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 section to be analyzed, and obtaining the vertexes of the triangles forming the roof of the cross section to be analyzed. And finally, calculating the area of each triangle forming the roof of the cross section to be analyzed according to the vertex of each triangle forming the roof of the cross section to be analyzed, and summing to obtain the surface area of the roof.
According to one embodiment of the present invention, the manner of calculating the projected area of the roof may be: and obtaining the projection area of the roof through summation according to the area of each triangle of the horizontal projection point subdivision result.
Theoretically, the first destruction factor 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 the first destruction factors of each cross section calculated for buildings with the same roof slope are theoretically equal.
Next, in step S104, a volume of the roof represented by the cross section to be analyzed is determined according to the point cloud data of the cross section to be analyzed, and a second destruction coefficient is determined according to the volume of the roof. According to an embodiment of the present invention, the manner of calculating the second destruction coefficient may be: and carrying out three-dimensional space triangulation on the cross section to be analyzed according to the cross section to be analyzed and the point cloud data of the cross section to be analyzed to obtain a plurality of tetrahedrons. Then, the volume of each tetrahedron is calculated separately, and the volume of the roof is obtained by summation. And finally, determining the second destruction coefficient by combining the width of the building to be analyzed according to the volume of the roof and the projection area thereof.
The second failure coefficient can also reflect the value of the sloping edge of the roof to the horizontal edge in the roof building height meter, which represents how many times the sloping edge is longer than the horizontal edge, and the calculated first failure coefficients of each cross section are theoretically equal for buildings with the same roof slope. It should be noted that the first destruction coefficient and the second destruction coefficient are theoretically equal for the same building.
Finally, in step S105, the destruction state of the building to be analyzed is determined from the first destruction coefficient and the second destruction coefficient. According to one embodiment of the invention, the manner of determining the building damage status may be: and calculating a difference value between the first destruction coefficient and the second destruction coefficient, and judging whether the difference value is in a preset difference value interval, wherein if the difference value is not in the preset difference value interval, judging that the cross section to be analyzed is damaged.
The flow chart shown in fig. 1 utilizes two main characteristics of similarity of roofs and earthquake damage of the same building to cause height mutation of the building, point cloud data of the building are divided, coefficients of which each group of cross sections can reflect the damage state of the building are calculated respectively after division, and the damage state of the building is determined by comparing 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 shows a flow chart of a building destruction state detection method according to another embodiment of the present invention. As shown in fig. 2, in step S201, the point cloud data is interpolated. In one embodiment, the interpolation algorithm includes any one or more of the following: nearest neighbor interpolation, reciprocal distance weighted interpolation, spline interpolation, moving average interpolation, and local polynomial interpolation.
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 same building is countingThe point cloud density is different and is influenced by point cloud errors, the calculation of the space geometric characteristics of the sectional body of the building can be influenced to a certain degree due to the different point cloud densities, and in order to enable the three-dimensional space characteristics of the earthquake damage of each block of the building to be comparable, the point clouds of samples of each building are interpolated before the calculation of the first destruction coefficient and the second destruction coefficient.
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-order parameter which controls the weight coefficient to decrease as the grid point distance increases, the larger k, the closer point is given a higher weight, the smaller k isThe weight is distributed to each point in the neighborhood more evenly; 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.
To enter intoSelecting a proper interpolation method, and estimating a first damage coefficient sigma of each interpolation method point for interpolation results of nearest neighbor method, linear method and cubic convolution of perfect building point cloudsSecond coefficient of destruction sigmavAnd the absolute value d σ of the difference between the two. The line graphs of the calculation results are shown in fig. 3, 4, and 5. The figure shows that different interpolation methods sigma of the same section of the roofs，σvAnd d σ is different. Sigma between the roofs of the house sections in FIG. 3 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. 4vSigma 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 damage coefficient difference d σ in fig. 5, the difference of d σ between the roofs of the segments of the original point cloud is large, d σ between the roofs of the segments of the nearest neighbor method is similar, but the overall value is large, while the division of d σ between the segments of the linear method and the cubic convolution method is about 0, and the value of the last segment d σ of the cubic convolution method is slightly larger than that of the roofs of other segments.
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 in one embodiment is a relatively good interpolation methodThe method is adopted to carry out interpolation encryption on all the point clouds of the building sample.
Then, in step S202, the building is subdivided to obtain the cross section to be analyzed and the point cloud data thereof. In one embodiment, the point cloud data is subjected to cross section subdivision at preset intervals along the direction of the building, and a cross section to be analyzed and the point cloud data thereof are extracted from the obtained multiple sections.
Then, in step S203, horizontal projection and planar triangulation are performed on the cross section to be analyzed. In one embodiment, the point cloud data of the cross section to be analyzed is subjected to horizontal projection to obtain horizontal projection point cloud data. And then 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 section to be analyzed, and obtaining the vertexes of the triangles forming the roof of the cross section to be analyzed.
Next, in step S205, the surface area of the roof characterized by the cross-section to be analyzed is calculated. According to one embodiment of the invention, the area of each triangle forming the roof of the cross-section to be analyzed is calculated from the vertices of each triangle forming the roof of the cross-section to be analyzed, the surface area of the roof being obtained by summation.
In step S207, a first destruction coefficient is calculated. According to another aspect of the invention, the first failure coefficient is determined from a surface area of the roof and a projected area.
Meanwhile, in step S204, a three-dimensional space subdivision is performed on the cross section to be analyzed. According to one embodiment of the invention, the cross section to be analyzed is subjected to three-dimensional space triangulation according to the cross section to be analyzed and the point cloud data thereof, so that a plurality of tetrahedrons are formed.
Then, in step S206, the volume of the roof characterized by the cross section to be analyzed is calculated. According to one embodiment of the invention, the volume of each tetrahedron is calculated separately, and the volume of the roof is obtained by summing.
Next, in step S208, a second destruction coefficient is calculated. According to one embodiment of the invention, the second destruction coefficient is determined in combination with the width of the building to be analyzed, on the basis of the volume of the roof and its projected area.
Finally, in step S209, the house destruction state is determined. According to one embodiment of the invention, a difference value between the first destruction coefficient and the second destruction coefficient is calculated, and whether the difference value is in a preset difference value interval is judged, wherein if the difference value is not in the preset difference value interval, the fact that the cross section to be analyzed is damaged is judged.
Fig. 6 is a schematic view showing a cross-sectional body of a building when a first destruction coefficient is calculated by the building destruction state detection method according to an embodiment of the present invention.
As shown in fig. 6, the building has a length L and a width D, and the cross sections are grouped along the length direction to obtain a plurality of cross sections to be analyzed and point cloud data thereof. The process of calculating the first destruction coefficient includes: and horizontally projecting the point cloud data of the cross section to be analyzed to obtain horizontally projected point cloud data. And triangulating the horizontal projection point cloud data, determining points corresponding to the vertexes of all triangles in the subdivision result in the point cloud data of the cross section to be analyzed, and obtaining the vertexes of all triangles forming the roof of the cross section to be analyzed. The area of each triangle forming the roof of the cross-section to be analyzed is calculated from the vertices of each triangle forming the roof of the cross-section to be analyzed, and the surface area of the roof is obtained by summation.
The process of calculating the projected area of the roof characterized by the cross section to be analyzed comprises: and obtaining the projection area of the roof through summation according to the area of each triangle of the horizontal projection point subdivision result.
According to one embodiment of the invention, the first destruction factor is determined according to the following expression:
σs＝As/A0
wherein σsDenotes a first destruction coefficient, AsAnd A0Respectively representing the surface area and the projected area of the roof characterized by the cross-section to be analyzed.
Fig. 7 is a schematic view showing a cross-sectional body of a building when a second destruction coefficient is calculated by the building destruction state detection method according to an embodiment of the present invention.
As shown in fig. 7, the point cloud data set is subdivided in a three-dimensional space to obtain a plurality of cross sections to be analyzed and point cloud data thereof, and the process of calculating the second destruction coefficient includes: and carrying out three-dimensional space triangulation on the cross section to be analyzed according to the cross section to be analyzed and the point cloud data of the cross section to be analyzed to obtain a plurality of tetrahedrons. The volume of each tetrahedron is calculated separately and the volume of the roof is obtained by summation. And determining a second destruction coefficient according to the volume of the roof and the projection area thereof and combining the width of the building to be analyzed.
According to one embodiment of the invention, the second destruction coefficient is determined according to the following expression:
wherein σvRepresenting the second destruction coefficients, V and S0Respectively, the volume and the projected area of the roof characterized by the cross-section to be analyzed, and D the width of the cross-section to be analyzed.
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. 8, the calculated surface areas of the flat-topped and flat-topped houses are plotted with the abscissa as the serial number along the longitudinal segment of the house, and it is apparent from the plot that the surface areas of the segments of the intact house are substantially uniform and linearly distributed, the damaged portions of the flat-topped and flat-topped houses have fluctuation, the damaged portions of the roofs have light damage, and the surface area value A is a valueSWith 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 different gaugesBetween the houses of modular size, ASThe sizes cannot be compared with each other.
First destruction coefficient σ in fig. 9sDistribution and surface area ASThe distribution trends are the same, and the first 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. 10 and 11 show the volume V and the second destruction coefficient σ of each section of a building with a sloping roof and a flat roof, respectivelyvAnd (5) distribution diagram. It can also be seen that the volume V of each section of the house, the second coefficient of failure σvIs 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. 12, σ for surface area estimationsIn house destruction part ratio sigmavLarge, undamaged part sigmavAnd σsAre almost identical. In fig. 13, it can be seen that the difference d σ between the first destruction coefficient and the second destruction coefficient of different roof slopes is obtained, the undamaged d σ is distributed around 0, and the damaged part d σ of the house is obviously greater 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 building destruction state detection method provided by the invention divides 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 division, and compares the calculated coefficients to determine the destruction state of the building. 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 (10)
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 a cross section to be analyzed and the point cloud data of the cross section;
determining the surface area and the projection area of the roof represented by the cross section to be analyzed according to the point cloud data of the cross section to be analyzed, and determining a first destruction coefficient according to the surface area and the projection area of the roof;
fourthly, determining the volume of the roof represented by the cross section to be analyzed according to the point cloud data of the cross section to be analyzed, and determining a second destruction coefficient according to the volume of the roof;
and fifthly, determining the destruction state of the building to be analyzed according to the first destruction coefficient and the second destruction coefficient.
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:
carrying out horizontal projection on the point cloud data of the cross section to be analyzed to obtain horizontal projection point cloud data;
triangulating the horizontal projection point cloud data, determining points corresponding to vertexes of all triangles in subdivision results in the point cloud data of the cross section to be analyzed, and obtaining vertexes of all triangles forming the roof of the cross section to be analyzed;
calculating the area of each triangle forming the roof of the cross section to be analyzed according to the vertex of each triangle forming the roof of the cross section to be analyzed, and obtaining the surface area of the roof through summation.
6. The method of claim 5, wherein in step three, the projected area of the roof is obtained by summing up the areas of the triangles of the horizontal projection point subdivision result.
7. The method of claim 1, wherein in step three, the first destruction coefficient is determined according to the following expression:
σs＝As/A0
wherein σsDenotes a first destruction coefficient, AsAnd A0Respectively representing the surface area and the projected area of the roof characterized by the cross-section to be analyzed.
8. The method of claim 1, wherein step four comprises:
performing three-dimensional space triangulation on the cross section to be analyzed according to the cross section to be analyzed 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;
and determining the second destruction coefficient by combining the width of the building to be analyzed according to the volume of the roof and the projection area thereof.
9. The method of claim 8, wherein in step four, the second destruction coefficient is determined according to the following expression:
wherein σvDenotes a second destruction coefficient, V and A0Respectively, the volume and the projected area of the roof characterized by the cross-section to be analyzed, and D the width of the cross-section to be analyzed.
10. The method as claimed in claim 1, wherein in the fifth step, a difference between the first destruction coefficient and the second destruction coefficient is calculated, and whether the difference is within a preset difference interval is determined, wherein if the difference is not within the preset difference interval, it is determined that the cross section to be analyzed is damaged.
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