CN114359713A - Road trafficability analysis method based on remote sensing geological conditions - Google Patents
Road trafficability analysis method based on remote sensing geological conditions Download PDFInfo
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Abstract
The invention discloses a road trafficability analysis method based on remote sensing geological conditions, which comprises the following steps: carrying out geological disaster point detection in the high-resolution remote sensing image based on a YOLOv3 target detection framework; grading the basic quality of rock masses according to the hardness degrees and the structural structures of different types of rock masses; calculating theoretical bearing capacity of the soil body according to the basic physical mechanical parameters of the soil body, the soil body deposition era, underground water and other influence factors, and further dividing the hardness degree of the remote sensing soil body construction; according to the OSM road classification, performing initial trafficability division on different roads; and superposing the disaster influence range, the rock mass basic quality grading, the soil body construction hardness degree and the initial trafficability of the road to obtain the final trafficability grading.
Description
Technical Field
The invention belongs to the field of road traffic, and particularly relates to a road traffic analysis method based on remote sensing geological conditions.
Background
When field investigation work is carried out, team advancing routes need to be selected and road trafficability needs to be evaluated, and timely and effective road trafficability evaluation can bring much convenience to the investigation work and can effectively avoid potential dangers. Under the complex field environment, the road environment is complex and may be affected by various geological disasters. The existing traffic analysis methods mostly start from the terrain, namely, the traffic is evaluated through factors such as gradient and the like, and the influence of factors such as the quality of rock mass, the hardness degree of soil mass and geological disasters in the area where the road is located on the road traffic capacity is not considered.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a road trafficability analysis method based on remote sensing geological conditions, which adopts the following technical scheme:
the road trafficability analysis method based on the remote sensing geological conditions comprises the following steps:
s1, carrying out geological disaster detection based on the high-resolution remote sensing image to obtain a disaster influence range;
s2, grading the basic quality grade of the rock mass based on the remote sensing data;
s3, dividing the hardness degree of the remote sensing soil engineering construction based on the basic physical parameters of the soil, the cause of the soil and the factors of the sedimentary era;
s4, classifying the road, and performing road initial trafficability assignment and buffer area construction according to the classification result;
and S5, performing grid superposition summation on the disaster influence range, the rock mass basic quality grading, the soil body construction hardness degree and the initial trafficability of the road, and performing reclassification according to the trafficability, trafficability and difficulty to obtain the final trafficability result of the road.
Further, a specific implementation manner of S1 is as follows;
1.1, superposing the geological disaster vector data of landslide, debris flow and collapse with high-resolution remote sensing image data, and obtaining a geological disaster detection data set after data labeling, cutting and enhancing;
1.2, carrying out model training and testing based on a Yolov3 target detection framework by using the geological disaster detection data set in 1.1;
1.3, carrying out geological disaster detection on the remote sensing image by using the trained geological disaster detection model to obtain a geological disaster point;
and 1.4, constructing a buffer area for the disaster point and rasterizing to obtain a disaster influence range.
Further, a specific implementation manner of S2 is as follows;
2.1, according to the compression strength empirical values of different types of rock masses and the weathering degree of the rock masses, qualitatively dividing the hardness degree of the rock masses into hard rock, harder rock, softer rock, soft rock and extremely soft rock, correspondingly assigning values to rock mass elements, and performing rasterization processing;
2.2, according to the formation principle of the rock mass structure structural surface, carrying out classification assignment on the rock mass structure density by using an ArcGIS linear density analysis tool to obtain the influence factors of the rock mass structure structural surface;
2.3, the primary structure surface formed in the diagenesis stage has greater influence on the characteristics of the rock mass, and the integrity of the primary structure of the rock mass is assigned according to the diagenesis characteristics of various rock masses to obtain influence factors of the primary structure surface of the rock mass;
2.4, carrying out grid superposition summation on the influence factors of the rock mass structural plane and the influence factors of the primary structural plane, and obtaining the rock mass integrity degree grading after reclassification;
and 2.5, performing grid superposition summation by using the hardness degree of the rock mass obtained in the step 2.1 and the integrity degree grading of the rock mass obtained in the step 2.4, and obtaining the basic quality grade grading of the rock mass after reclassification.
Further, a specific implementation manner of S3 is as follows;
3.1, regarding road construction as common engineering construction, obtaining average physical mechanical indexes of various soil bodies by consulting an engineering geological manual, calculating the theoretical bearing capacity of the remote sensing soil body according to the physical mechanical indexes of the soil body, and obtaining a bearing capacity assigned value diagram of the engineering construction theoretical of the remote sensing soil body after value assignment;
3.2, directly influencing the gravity, cohesive force and internal friction angle of the soil body by influencing the density and porosity of the soil body in the soil body deposition era, dividing all the soil bodies into three types according to deposition time, respectively corresponding to middle-updated and former deposited soil bodies, later-updated deposited soil bodies and new-updated deposited soil bodies, respectively giving land bearing capacity adjustment coefficients, and obtaining an influence factor valuation diagram in the soil body deposition era;
3.3, the groundwater influences the theoretical bearing capacity of the soil body by influencing the gravity of the soil body and the internal friction angle of the soil body, a soil body range is divided into a water saturation area and a natural water containing area by using a Euclidean distance analysis tool in ArcGIS, different adjustment coefficients are respectively given, and a soil body theoretical bearing capacity groundwater influence factor assignment map is obtained;
and 3.4, carrying out grid product calculation on the theoretical bearing capacity of the soil body, the influence factors of the sedimentary era and the influence factors of underground water, specifically multiplying the adjustment coefficient of the influence factors on the basis of the theoretical bearing capacity of the soil body, dividing the soil body into stone-sandwiched soil, soft soil, medium soil and hard soil according to the obtained theoretical bearing capacity of the soil body, and assigning values and classifying to obtain the construction hardness degree of the remote sensing soil body engineering.
Further, a specific implementation manner of S4 is as follows;
4.1, dividing the road into a main urban road, a secondary urban road, an urban branch road, an elevated and express way, a suburban country road, an internal road, a pedestrian road, a bicycle lane and other seven types according to the fclass field in the OSM road data;
4.2, performing initial traffic definition and assignment on the road according to the road category, wherein the urban main road, the urban secondary main road, the urban branch road, the elevated road and the express road are endowed with 'easy traffic', suburban rural roads, internal roads and other categories are endowed with 'easy traffic', the rest categories are endowed with 'difficult traffic', and corresponding values are endowed at the same time;
and 4.3, considering the influence of soil, rock and geological disasters on road traffic, constructing a buffer area on the road, rasterizing the buffer area and endowing the initial trafficability value to the road.
Further, in 2.1, YOLOv3 first scales the original picture to 416 × 416, divides the original image into S × S equally large cells according to the scale size of the feature map, and detects on three scales with feature maps of 13 × 13,26 × 26, and 52 × 52, where each cell has 3 nchor boxes to predict 3 bounding boxes;
4 values are predicted for each bounding box on each cell, i.e. the coordinates of the target box (x, y) and the width w and height h are denoted tx，ty，tw，thThe center of the target is offset in the cell relative to the upper left corner of the image (c)x，cy) The anchor box has a height and a width pw，phThen, the modified bounding box is:
bx＝σ(tx)+cx
by＝σ(ty)+cy
during the training process, the sum of the squares of the errors is used as a loss function, assuming the real coordinate is twThen the gradient can be found by minimizing the loss function, the gradient being the true coordinate value minus the predicted coordinate value:
further, a theoretical bearing capacity calculation formula in 3.1 is as follows;
fa＝Mbγb+Mdγmd+Mcck
in the formula:
fa-a characteristic value of bearing capacity of the foundation, in kPa, determined by a shear strength index of the soil;
Mb、Md、Mc-a load factor;
b, the width of the bottom surface of the foundation is taken as 6m when the width is more than 6m, and is taken as 3m when the width is less than 3 m;
ck-a standard value for the cohesion of the soil within a depth range of one time the width of the short side of the base;
gamma-the soil mass weight below the bottom surface of the foundation, and the effective weight below the underground water;
γmthe weighted average gravity of the soil above the bottom surface of the foundation and the effective gravity below the underground water are obtained.
Furthermore, in 3.2, the middle-updated and former deposited soil bodies are densely deposited and belong to hyperconcentration, and an adjustment coefficient of 13 is given; the sedimentary soil body of the late renewal world belongs to normal consolidation and is endowed with an adjustment coefficient of 10; the new-world deposited soil body has loose deposition and is not consolidated, and an adjustment coefficient of 8 is given.
Further, 3.4 according to the theoretical value F of the basic bearing capacity of the soil bodyaClassifying the hardness degrees of the soil body, and multiplying the basic theoretical bearing capacity of the soil body by the reduction coefficient of the relative influence factors to obtain the theoretical value F of the basic bearing capacity of the soil bodya；
Fa0 belongs to stone-filled soil;
Famore than or equal to 160kPa belongs to hard soil;
16000kPa≥Famore than or equal to 80kPa belongs to medium soil;
Faless than or equal to 80kPa belongs to soft soil.
And classifying according to the principle to obtain the soil engineering construction hardness degree.
Further, in S5, buffers with a radius of 500m are established along both sides of the road for rating the road trafficability.
Compared with the prior art, the invention has the following advantages and remarkable effects:
(1) the method can evaluate and grade the trafficability of the road from the perspective of geological disasters and geological conditions, and can provide favorable help for tasks such as field geological investigation and the like.
(2) Geological disaster detection in a specific area is carried out based on a remote sensing technology, and large-scale rapid road trafficability evaluation can be realized by combining rock mass and soil mass data.
Drawings
FIG. 1 is a detailed flow chart of the present invention.
Detailed Description
The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
Step 1: and carrying out geological disaster detection based on the high-resolution remote sensing image.
Firstly, marking a 0.8m remote sensing image, storing the remote sensing image as an XML file in a Pascal format, and then converting the XML file into a TXT file in a format of < label, x, y, w, h > by a format conversion script. The data samples with better quality are selected from the cut image. The network architecture is based on Python's Pythrch library. In the training process, in order to prevent overfitting, data enhancement processing is added. A self-programming procedure is used to accomplish the data enhancement process. And finally obtaining sample data of collapse detection after image clipping, contrast enhancement, brightness enhancement, noise enhancement and other data enhancement processing. And importing the pictures into Labelimg software, and combining the position information of the vector spring points to manufacture a collapse image training data set.
The YOLO algorithm proposed by Redmon et al in 2016 converts the target detection task into a regression problem, and greatly accelerates the detection speed. The YOLOv3 is provided on the basis of YOLOv2, the detection speed of YOLOv2 is kept, and the detection accuracy is greatly improved, particularly on the detection and identification of small targets.
YOLOv3 first scales the original picture to 416 × 416, divides the original image into S × S equal-sized cells according to the scale size of the feature map, and detects on three scales with feature maps of 13 × 13,26 × 26, and 52 × 52, where each cell has 3 nchor boxes predicting 3 bounding boxes.
The convolutional neural network predicts 4 values for each bounding box on each cell, namely the coordinates of the target box (x, y) and the width w and height h are respectively marked as tx，ty，tw，th. The center of the target is offset in the cell relative to the upper left corner of the image (c)x，cy) The anchor box has a height and a width pw，phThen, the modified bounding box is:
bx＝σ(tx)+cx
by＝σ(ty)+cy
during the training process, the sum of the squared errors is used as a loss function. Assuming the true coordinate is twThen the gradient can be found by minimizing the loss function, the gradient being the true coordinate value minus the predicted coordinate value:
YOLOv3 used a completely new design of Darknet-53 in the feature extraction stage. A large amount of residual error connection is used in the network, so that the depth of the network is increased; and combining with the FPN network structure, sampling two feature maps behind the network, aggregating with the feature maps with corresponding sizes in the early stage of the network, and obtaining a prediction result through a convolution network. In the target detection stage, the YOLOv3 firstly performs convolution processing on the 13 × 13 characteristic diagram and then predicts the characteristic diagram to obtain a first detection result; then, the 13 x 13 feature map is up-sampled to obtain a 26 x 26 feature map, the 26 x 26 feature map in the previous network is fused to form a new feature map, and multiple convolutions are carried out to send the new feature map into a detection layer to obtain a second detection result; and then, the 26 x 26 feature map upsampling and the previous layer are fused to obtain a 52 x 52 feature map, and the 52 x 52 feature map is convolved for multiple times and sent into the detection layer to obtain a third detection result. And performing non-maximum suppression processing on the three obtained detection results to obtain a final identification result.
And constructing a buffer area of the disaster point obtained by detection, and converting the disaster point into a grid to obtain a disaster point influence range result.
Step 2: and grading the basic quality grade of the rock mass based on the remote sensing data.
The main idea of the step is that the hardness (without considering weathering factor) of the same lithology in the adjacent areas is basically the same as the density of the original structure surface; the density of the structural faces of the structure is linked to the structural phenomena (including folds and faults). Establishing a relation between the density of the structural structure surface and the structural phenomenon, deducing the volume joint density of the rock mass by combining the density of the primary structural surface, and qualitatively grading the integrity degree of the rock mass. And carrying out qualitative grading on the basic quality grade of the rock mass according to the grading structure and the rock hardness grading. The method mainly comprises the steps of searching and establishing various physical and mechanical property parameters, lithology, structure and other basic attributes of various rock masses according to the existing data. And comprehensively analyzing all parameters by combining a remote sensing technical means and adopting a human-computer interaction mode, and calculating the basic quality grade of the rock mass by using a high-grade calculation analysis tool in ArcGIS software through a grid.
First, the rock hardness needs to be graded, which is mainly based on prior empirical values due to the lack of field saturated uniaxial compressive strength tests. Through data collection, empirical values of the compressive strengths of different rocks are determined, the rocks are defined under the fixed condition of slightly weathering, and the contents of all the rocks are divided into five types according to the national standard of the people's republic of China, namely the engineering rock grading Standard (GB/T50218-. The hardness of the rock under other weathering conditions should be adjusted. Namely, the moderate weathering is reduced to the first level, the strong weathering is reduced to two to three levels, and the full weathering is directly extremely soft rock. For calculation and analysis, the rock body element graph is subjected to grid processing in ArcGIS software, and is respectively assigned with '1', 2 ', 3', 4 'and 6', and respectively correspond to hard rock, harder rock, softer rock, soft rock and extremely soft rock. In the above steps, the value "6" is assigned mainly to facilitate the classification calculation of the basic quality class, and the subsequent steps will be described in detail.
Secondly, the integrity of the rock mass is graded, the rock mass being composed of rock and structural planes together. The structural plane refers to a plane, a seam and a strip-shaped geological interface which is formed in the rock body and has different directions, scales, forms and characteristics. The structural surfaces are mainly divided into primary structural surfaces, structural surfaces and secondary structural surfaces according to the cause. The secondary structural surface mainly comprises a weathered structural surface, an unloading structural surface and the like. The weathering structure surface of mountain and extremely high mountain landscape environment in the survey area is the dominant factor. Efflorescence factors are difficult to be regionally defined by principle analysis. The rock hardness grading is the same as the rock hardness grading, the rock integrity degree does not consider weathering factors, and the rock integrity degree is only analyzed and calculated from the structural surface and the primary structural surface.
The structural plane is a fracture plane generated by the action of structural stress in the rock mass, such as cleft, joint, fault, interlayer dislocation and the like. These geographic structures are currently not possible to implement using remote sensing means. In order to grade the tectonic surface of the rock mass, a model is constructed to associate the regional geological structure with it. Regional geological formations consist primarily of faults and folds. Basically the same principle as the formation principle of the structural surface of the structure is caused by the structural stress. Generally, the greater the tectonic movement density, the stronger the tectonic stress and the greater the tectonic surface density of the rock mass. According to the principle, the influence factors of the structural surface of the rock mass are graded.
According to the structure interpretation graph, according to the density of the structure motion, a linear density analysis tool of ArcGIS is used for classifying the structure density, five classes are assigned, and 0, 1, 2, 3 and 4 are respectively assigned, wherein the 0 value corresponds to a block with zero structure density, the other density values are equally divided into four classes, and the density values are respectively corresponding to 1, 2, 3 and 4 from small to large.
The primary structural surface is a structural surface formed in a diagenetic stage and is characterized by being closely related to the cause of a rock mass, such as bedding, sheet, plate, thousand-piece, and the like. According to the basic diagenetic characteristics of various rock masses, the integrity of the primary structure of the rock mass is basically assigned according to the national standard of the people's republic of China, namely the geotechnical engineering survey standard (GB50021-2001), and the specific assignment information is detailed in Table 1.
TABLE 1 basic rock assignment table
And (4) obtaining the influence factors of the primary structural plane of the rock mass after value assignment according to the method.
The integrity degree of the rock mass is that the rock mass is divided by comprehensive primary and structural factors. And performing grid calculation on the native structure density assigned map and the structural structure density assigned map by using a grid calculator in a Spatial analysis tool of ArcGIS. The specific principle is that the assignment of the corresponding graphic elements is summed to obtain the structure density and the value X. According to the corresponding relation in the table 2, the graphs are reclassified and assigned, and the integrity of the rock mass is divided into five types, namely complete, broken, relatively broken and extremely broken.
TABLE 2 rock integrity and assignment mapping table
Note: bands mainly to facilitate basic quality grade calculation classification
And (5) obtaining the rock integrity degree grading after the value is assigned according to the method.
And finally, dividing the basic quality of the rock mass, wherein the basic quality grade of the rock mass has good reference function on the engineering construction, anti-explosion striking and structural self-stability of the rock mass. The common grading method for the basic quality grade of the rock mass comprises grading according to the qualitative characteristics of the basic quality of the rock mass and grading according to the basic quality index BQ of the rock mass. And in consideration of the specificity of remote sensing work, a qualitative classification method is adopted.
TABLE 3 basic quality class classification of rock masses
According to the standard requirements, the rock mass basic quality grades are classified according to the table 3. And performing grid calculation on the rock hardness degree graph and the rock integrity degree graph by using a grid calculator in a Spatial analysis tool of ArcGIS. The principle is that the evaluation of the rock mass hardness degree and the evaluation of the rock mass integrity degree are added to obtain a sum value Y, and classification is carried out according to the rock mass grade distribution rule shown in the table 3 and the following method:
TABLE 4 rock mass quality grade
Because the value of the extremely soft rock and the extremely broken rock is assigned to be 6, the special cases of the upper right corner and the lower left corner of the table 3 just correspond to the calculation result, and therefore the basic quality result of the rock mass is obtained.
And step 3: dividing the hardness degree of the engineering construction of the remote sensing soil body based on the factors such as the basic physical parameters of the soil body, the cause of the soil body, the deposition era and the like.
The step first needs to calculate the theoretical bearing capacity of the soil body. The invention divides the hard degree of soil into four types of stone-filled soil, hard soil, medium soil and soft soil. The physical properties of the stone-included soil are special, and the basic bearing capacity is determined by the contact mode of the skeleton particles, the physical properties of the filler and the proportion of the filler. The survey area belongs to the appearance of mountains and extremely high mountains. The gravels deposited in the valleys can be classified into the category of included gravels. The perennial frozen soil in the special soil is higher in elevation, mostly in steep valleys, and can be classified as included stone soil. The mucky soil in the special soil has extremely weak bearing capacity and can be directly classified into soft soil.
The ultimate bearing capacity of the foundation soil refers to the corresponding minimum foundation bottom pressure when the foundation soil is sheared and damaged and is about to lose the overall stability. The foundation bearing capacity is generally determined by field load tests or other in-situ tests, formula calculation. According to the basic design code of building foundation (GB5007-2002), except for a first-class building, the method can be used for calculating by using a theoretical formula without carrying out an in-situ test. The field verification of the measuring area is difficult, and a theoretical formula can be used for calculation. And comprehensively comparing and determining, namely allowing the foundation soil to have a certain plastic zone to develop by adopting the critical pressure calculation when the soil enters the plastic zone within a certain range, wherein the certain plastic zone is specified to have the maximum depth not more than one fourth of the base width. The detailed theoretical bearing capacity calculation formula is as follows:
fa＝Mbγb+Mdγmd+Mcck
in the formula:
fa-a characteristic value of foundation bearing capacity (kPa) determined from the shear strength index of the soil;
Mb、Md、Mcthe load factor, as can be determined from table 5;
b, taking a value according to 6m when the width (m) of the bottom surface of the foundation is more than 6m, and taking a value according to 3m when the sand soil is less than 3 m;
ck-a standard value for the cohesion of the soil within a depth range of one time the width of the short side of the base;
gamma-the soil mass weight below the bottom surface of the foundation, and the effective weight below the underground water;
γmthe weighted average gravity of the soil above the bottom surface of the foundation and the effective gravity below the underground water are obtained.
TABLE 5 coefficient of bearing capacity Mb, Md, Mc
From the theoretical formula, it can be seen that: the soil body bearing capacity determining factors are mainly the soil body heavy gamma and gammamInternal friction angle phi k of soil body and soil body cohesive force ck. Under the condition that the geotechnical test cannot be carried out and only soil classification information is available, the average physical and mechanical indexes of various soil bodies can be known by looking up an engineering geological manual, and the theoretical bearing capacity of the soil body can be calculated according to a formula.
Secondly, influence factors of the soil body deposition era need to be calculated, the deposition time influences the density and porosity of the soil body, and the gravity, cohesive force and internal friction angle of the soil body are directly influenced. All soil bodies are divided into three types according to the deposition time, and are respectively assigned as ' 0 ', 1 ' and ' 2 ' from morning to evening, and the three types of soil bodies respectively correspond to middle-updated and former deposited soil bodies, later-updated deposited soil bodies and new-updated deposited soil bodies. The deposited soil body in the middle and old updated generations is deposited compactly, belongs to hyperconjugation and is endowed with an adjustment coefficient of 13; the sedimentary soil body of the late renewal world belongs to normal consolidation and is endowed with an adjustment coefficient of 10; the new-generation sedimentary soil body has loose sediment and belongs to under-consolidation, and 8 adjustment coefficients are given to obtain the influence factors of the sedimentary era.
And then calculating soil body hydrological influence factors, wherein the influence of the underground water on the soil body bearing capacity mainly comprises the soil body weight and the soil body internal friction angle. Firstly, the soil mass gravity is taken as the effective gravity below the underground water, namely 10 is subtracted from the soil mass gravity in a saturated state; secondly, according to the engineering geological handbook, the water content of the soil body is high, and the internal friction angle of the soil body is smaller.
In order to distinguish the influence range of the underground water on the soil body bearing capacity, the soil body range is divided into a water saturation area and a natural water containing area by using a Euclidean distance analysis tool in a Spatial analysis tool of ArcGIS based on a river water network of a survey area. The saturated water area is endowed with a regulating coefficient of 7, the natural water-bearing area is endowed with a regulating coefficient of 10, and the underground water influence factor is obtained.
And finally, carrying out grid quadrature calculation on a soil engineering construction theoretical bearing capacity assigned value graph, a sedimentary time influence factor assigned value graph and a groundwater influence factor assigned value graph by using a grid calculator in a Spatial analysis tool of ArcGIS. The concrete principle is that a theoretical value F of the basic bearing capacity of the soil body is obtained by multiplying a reduction coefficient of relative influence factors on the basis of the basic theoretical bearing capacity of the soil bodya. The fractional number cannot be used by the Spatial analysis tool based on ArcGIS, all reduction coefficients are increased by 10 times, and the result is calculated and then divided by 100 to obtain the theoretical value of the basic bearing capacity of the soil body.
According to the engineering construction experience, soil body hardness degree classification is carried out according to the theoretical value of the basic bearing capacity of the soil body:
Fa0 belongs to the claystone soil ("0" value is merely a convenient calculation and does not represent a specific actual bearing capacity);
Famore than or equal to 160kPa belongs to hard soil;
16000kPa≥Famore than or equal to 80kPa belongs to medium soil;
Faless than or equal to 80kPa belongs to soft soil.
And classifying according to the principle to obtain the soil engineering construction hardness degree.
And 4, step 4: and (4) initial road trafficability assignment and buffer area construction.
The method comprises the steps of firstly classifying roads, dividing OSM road data into 27 types according to fclass fields, and summarizing the 27 types into 9 types including urban main roads, urban secondary roads, urban branches, elevated roads, express roads, suburban country roads, interior roads, pedestrian roads, bicycle lanes and the like. In the aspect of initial trafficability, the construction condition of a highway is considered, the initial trafficability of paved roads such as roads, viaducts and expressways in a city is set to be easy to pass, in other road categories, the rock-soil property of unpaved roads is considered to have a large influence on the trafficability, the initial trafficability is not set, and the final road trafficability grade is obtained through assignment of the rock-soil property of the roads in the follow-up process. The road width and initial traffic assignment is given in the following table.
TABLE 6 road width and initial trafficability assignment table
And according to the road width and initial traffic assignment table, performing width and initial traffic assignment on the road data by using a field calculator function in ArcGIS software. Considering the influence of rock mass, soil mass and disaster occurrence conditions near the road on the road traffic condition, buffer zones with the radius of 500m are established along the two sides of the road and used for grading the road traffic.
And 5: and analyzing road trafficability.
The method comprises the steps of carrying out grid superposition summation on the disaster influence range, the basic quality grading of rock mass, the soil body construction hardness degree and the initial trafficability of the road, and carrying out reclassification according to the easiness in traffic, the easiness in traffic and the difficulty in traffic to obtain the final trafficability result of the road.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (10)
1. The road trafficability analysis method based on the remote sensing geological condition is characterized by comprising the following steps of:
s1, carrying out geological disaster detection based on the high-resolution remote sensing image to obtain a disaster influence range;
s2, grading the basic quality grade of the rock mass based on the remote sensing data;
s3, dividing the hardness degree of the remote sensing soil engineering construction based on the basic physical parameters of the soil, the cause of the soil and the factors of the sedimentary era;
s4, classifying the road, and performing road initial trafficability assignment and buffer area construction according to the classification result;
and S5, performing grid superposition summation on the disaster influence range, the rock mass basic quality grading, the soil body construction hardness degree and the initial trafficability of the road, and performing reclassification according to the trafficability, trafficability and difficulty to obtain the final trafficability result of the road.
2. The method for road trafficability analysis based on remote sensing geological conditions of claim 1, wherein: the specific implementation of S1 is as follows;
1.1, superposing the geological disaster vector data of landslide, debris flow and collapse with high-resolution remote sensing image data, and obtaining a geological disaster detection data set after data labeling, cutting and enhancing;
1.2, carrying out model training and testing based on a Yolov3 target detection framework by using the geological disaster detection data set in 1.1;
1.3, carrying out geological disaster detection on the remote sensing image by using the trained geological disaster detection model to obtain a geological disaster point;
and 1.4, constructing a buffer area for the disaster point and rasterizing to obtain a disaster influence range.
3. The method for road trafficability analysis based on remote sensing geological conditions of claim 1, wherein: the specific implementation of S2 is as follows;
2.1, according to the compression strength empirical values of different types of rock masses and the weathering degree of the rock masses, qualitatively dividing the hardness degree of the rock masses into hard rock, harder rock, softer rock, soft rock and extremely soft rock, correspondingly assigning values to rock mass elements, and performing rasterization processing;
2.2, according to the formation principle of the rock mass structure structural surface, carrying out classification assignment on the rock mass structure density by using an ArcGIS linear density analysis tool to obtain the influence factors of the rock mass structure structural surface;
2.3, the primary structure surface formed in the diagenesis stage has greater influence on the characteristics of the rock mass, and the integrity of the primary structure of the rock mass is assigned according to the diagenesis characteristics of various rock masses to obtain influence factors of the primary structure surface of the rock mass;
2.4, carrying out grid superposition summation on the influence factors of the rock mass structural plane and the influence factors of the primary structural plane, and obtaining the rock mass integrity degree grading after reclassification;
and 2.5, performing grid superposition summation by using the hardness degree of the rock mass obtained in the step 2.1 and the integrity degree grading of the rock mass obtained in the step 2.4, and obtaining the basic quality grade grading of the rock mass after reclassification.
4. The method for road trafficability analysis based on remote sensing geological conditions of claim 1, wherein: the specific implementation of S3 is as follows;
3.1, regarding road construction as common engineering construction, obtaining average physical mechanical indexes of various soil bodies by consulting an engineering geological manual, calculating the theoretical bearing capacity of the remote sensing soil body according to the physical mechanical indexes of the soil body, and obtaining a bearing capacity assigned value diagram of the engineering construction theoretical of the remote sensing soil body after value assignment;
3.2, directly influencing the gravity, cohesive force and internal friction angle of the soil body by influencing the density and porosity of the soil body in the soil body deposition era, dividing all the soil bodies into three types according to deposition time, respectively corresponding to middle-updated and former deposited soil bodies, later-updated deposited soil bodies and new-updated deposited soil bodies, respectively giving land bearing capacity adjustment coefficients, and obtaining an influence factor valuation diagram in the soil body deposition era;
3.3, the groundwater influences the theoretical bearing capacity of the soil body by influencing the gravity of the soil body and the internal friction angle of the soil body, a soil body range is divided into a water saturation area and a natural water containing area by using a Euclidean distance analysis tool in ArcGIS, different adjustment coefficients are respectively given, and a soil body theoretical bearing capacity groundwater influence factor assignment map is obtained;
and 3.4, carrying out grid product calculation on the theoretical bearing capacity of the soil body, the influence factors of the sedimentary era and the influence factors of underground water, specifically multiplying the adjustment coefficient of the influence factors on the basis of the theoretical bearing capacity of the soil body, dividing the soil body into stone-sandwiched soil, soft soil, medium soil and hard soil according to the obtained theoretical bearing capacity of the soil body, and assigning values and classifying to obtain the construction hardness degree of the remote sensing soil body engineering.
5. The method for road trafficability analysis based on remote sensing geological conditions of claim 1, wherein: the specific implementation of S4 is as follows;
4.1, dividing the road into a main urban road, a secondary urban road, an urban branch road, an elevated and express way, a suburban country road, an internal road, a pedestrian road, a bicycle lane and other seven types according to the fclass field in the OSM road data;
4.2, performing initial traffic definition and assignment on the road according to the road category, wherein the urban main road, the urban secondary main road, the urban branch road, the elevated road and the express road are endowed with 'easy traffic', suburban rural roads, internal roads and other categories are endowed with 'easy traffic', the rest categories are endowed with 'difficult traffic', and corresponding values are endowed at the same time;
and 4.3, considering the influence of soil, rock and geological disasters on road traffic, constructing a buffer area on the road, rasterizing the buffer area and endowing the initial trafficability value to the road.
6. The method for road trafficability analysis based on remote sensing geological conditions of claim 2, wherein: in 2.1, YOLOv3 first scales an original picture to 416 × 416, divides the original image into S × S equal-sized cells according to the scale size of a feature map, detects on three scales with the feature maps of 13 × 13,26 × 26 and 52 × 52, and predicts 3 bounding boxes by 3 nchor boxes in each cell;
4 values are predicted for each bounding box on each cell, i.e. the coordinates of the target box (x, y) and the width w and height h are denoted tx，ty，tw，thThe center of the target is offset in the cell relative to the upper left corner of the image (c)x，cy) The anchor box has a height and a width pw，phThen, the modified bounding box is:
bx＝σ(tx)+cx
by＝σ(ty)+cy
during the training process, the sum of the squares of the errors is used as a loss function, assuming the real coordinate is twThe gradient can be obtained by minimizing a loss function, the gradient being the real coordinate value minusRemoving the predicted coordinate value:
7. the method for road trafficability analysis based on remote sensing geological conditions of claim 4, wherein: 3.1, a theoretical bearing capacity calculation formula is as follows;
fa＝Mbγb+Mdγmd+Mcck
in the formula:
fa-a characteristic value of bearing capacity of the foundation, in kPa, determined by a shear strength index of the soil;
Mb、Md、Mc-a load factor;
b, the width of the bottom surface of the foundation is taken as 6m when the width is more than 6m, and is taken as 3m when the width is less than 3 m;
ck-a standard value for the cohesion of the soil within a depth range of one time the width of the short side of the base;
gamma-the soil mass weight below the bottom surface of the foundation, and the effective weight below the underground water;
γmthe weighted average gravity of the soil above the bottom surface of the foundation and the effective gravity below the underground water are obtained.
8. The method for road trafficability analysis based on remote sensing geological conditions of claim 4, wherein: 3.2, the middle-updated and former deposited soil bodies are densely deposited and belong to hyperconcentration, and an adjustment coefficient of 13 is given; the sedimentary soil body of the late renewal world belongs to normal consolidation and is endowed with an adjustment coefficient of 10; the new-world deposited soil body has loose deposition and is not consolidated, and an adjustment coefficient of 8 is given.
9. The method for road trafficability analysis based on remote sensing geological conditions of claim 4, wherein: 3.4 according to the theoretical value F of the basic bearing capacity of the soil bodyaClassifying the hardness degree of the soil body and laying the foundation of the basic theoretical bearing capacity of the soil bodyMultiplying the theoretical value by a reduction coefficient of relative influence factors to obtain a theoretical value F of the basic bearing capacity of the soil bodya；
Fa0 belongs to stone-filled soil;
Famore than or equal to 160kPa belongs to hard soil;
16000kPa≥Famore than or equal to 80kPa belongs to medium soil;
Faless than or equal to 80kPa belongs to soft soil.
And classifying according to the principle to obtain the soil engineering construction hardness degree.
10. The method for road trafficability analysis based on remote sensing geological conditions of claim 1, wherein: in S5, buffers with a radius of 500m are established along both sides of the road for rating the road trafficability.
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| CN115408907A (en) * | 2022-08-26 | 2022-11-29 | 中国地质调查局军民融合地质调查中心 | Method and system for evaluating anti-knock striking performance of earth surface |
| CN116257805A (en) * | 2023-05-16 | 2023-06-13 | 中国地质大学（武汉） | Traffic prediction model construction method and traffic prediction method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115408907A (en) * | 2022-08-26 | 2022-11-29 | 中国地质调查局军民融合地质调查中心 | Method and system for evaluating anti-knock striking performance of earth surface |
| CN115408907B (en) * | 2022-08-26 | 2024-02-23 | 中国地质调查局军民融合地质调查中心 | Method and system for evaluating surface antiknock striking performance |
| CN116257805A (en) * | 2023-05-16 | 2023-06-13 | 中国地质大学（武汉） | Traffic prediction model construction method and traffic prediction method |
| CN116257805B (en) * | 2023-05-16 | 2023-08-15 | 中国地质大学（武汉） | Traffic prediction model construction method and traffic prediction method |
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