CN115659846B - BIM-based building stability real-time monitoring method - Google Patents
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Abstract
The invention relates to the technical field of data identification processing, in particular to a BIM-based building stability real-time monitoring method, which comprises the following steps: acquiring humidity data of each position of the bottom of the foundation pit, acquiring an original point according to the humidity data of each position of the bottom of the foundation pit at the current moment, and further dividing all the humidity data into at least two cluster clusters; constructing a ray by taking the original point as a starting point, and acquiring an output value of humidity data of each position on the ray so as to obtain a water seepage boundary line; obtaining a serious water seepage path according to the origin and the water seepage boundary line; obtaining the integral water seepage coefficient of the serious water seepage path according to the function model; inputting the trained neural network according to the serious seepage path corresponding to the original point at each moment and the overall seepage coefficient to obtain the predicted overall seepage coefficient of the original point, and judging whether the building corresponding to the foundation pit is stable or not according to the predicted overall seepage coefficient of the original point; the obtained stability result is more timely and has higher accuracy.
Description
Technical Field
The invention relates to the technical field of data identification processing, in particular to a BIM-based building stability real-time monitoring method.
Background
When the excavation depth of a building foundation pit is lower than the water level below the bottom of the foundation pit, the phenomenon that bottom water rushes into the foundation pit can be caused, the water rushing into the foundation pit generally comes from surrounding rocks and the bottom of the pit, if a confined aquifer is arranged at a shallow part below the bottom surface of the foundation pit, the thickness of a watertight layer at the top of the confined aquifer can be continuously reduced along with the increase of the excavation depth of the foundation pit, the strength is also continuously reduced, and when the water head pressure of the aquifer is greater than the strength of the watertight layer, the confined water can break through the bottom of the foundation pit and rush into the foundation pit, so that the obvious water seepage phenomenon appears at the bottom of the foundation pit.
When the infiltration phenomenon appears in the foundation ditch, can cause very big hidden danger to the stability of building, whether have the infiltration phenomenon to detect bottom the foundation ditch through the vision image now, though detection efficiency is great, only rely on the data analysis of vision image to be objective inadequately, very easily have the error, it is great to building stability influence.
Disclosure of Invention
In order to solve the above technical problems, the present invention aims to provide a building stability real-time monitoring method based on BIM, and the adopted technical scheme is as follows:
one embodiment of the invention provides a BIM-based building stability real-time monitoring method, which comprises the following steps:
arranging a humidity-sensitive capacitor at the bottom of the foundation pit for acquiring humidity data of each position of the bottom of the foundation pit;
acquiring the ratio of humidity data of each position of the bottom of a foundation pit at the current moment to humidity data of the adjacent position of the foundation pit at the current moment, acquiring an original point according to the ratio, and dividing all the humidity data into at least two cluster clusters according to the original point and the humidity data of each position;
constructing a ray by taking the original point as a starting point, acquiring an output value of humidity data of each position on the ray, and obtaining a water seepage boundary line based on the output value; obtaining a serious water seepage path according to the origin and the water seepage boundary line;
presetting a calculation function, substituting the humidity data of each position on the serious seepage path into the calculation function to obtain a function model of an optimal solution, performing derivation on the function model to obtain the seepage coefficient of each position on the serious seepage path, and obtaining the overall seepage coefficient of the serious seepage path according to the average value of the seepage coefficients of all the positions;
obtaining the serious seepage path and the whole seepage coefficient corresponding to the original point in the pit bottom of the foundation pit at each moment, inputting the whole seepage coefficient of the original point into a neural network finished by training at each moment to obtain the predicted whole seepage coefficient of the original point, and judging whether a building corresponding to the foundation pit is stable or not according to the predicted whole seepage coefficient of the original point.
Preferably, the step of obtaining the origin according to the ratio includes:
if the ratio of the humidity data of the current position to the humidity data of the neighborhood position is greater than 1, the current position is the origin; the neighborhood location includes eight neighboring locations to the current location.
Preferably, the step of dividing all humidity data into at least two clusters according to the origin and the humidity data at each position includes:
and taking the original point as a core point, taking the difference value of the humidity data between every two positions as a distance, and carrying out a DBSCAN clustering algorithm based on a preset searching radius, the core point and the distance between every two positions to obtain at least two clustering clusters.
Preferably, the step of acquiring an output value of the humidity data at each position on the ray includes:
substituting the humidity data of each position on the ray into a calculation formula to obtain an output value of each position, wherein the calculation formula is as follows:
wherein, the first and the second end of the pipe are connected with each other,
a position mark corresponding to each selected humidity data is represented; />
Indicates the fifth->
Humidity data corresponding to the position mark; />
Representing a natural constant.
Preferably, the step of obtaining a water seepage boundary line based on the output value includes:
recording the position of which the output value is 0 for the first time as a boundary point, and acquiring the mean value of humidity data corresponding to all positions of which the output values are 0 on the ray as a first mean value;
acquiring the mean value of the humidity data of all the positions in the clustering cluster corresponding to the boundary point, recording the mean value as a second mean value, calculating the difference value between the first mean value and the second mean value, and if the difference value is within a preset range, determining the boundary line of the clustering cluster where the boundary point is located as a water seepage boundary line;
if the difference value is not within the preset range, reconstructing a second ray with the original point as a starting point, wherein the direction of the second ray is different from that of the current ray, acquiring a new boundary point based on humidity data of each position on the second ray, acquiring a first mean value on the second ray and a second mean value of a cluster where the new boundary point is located on the second ray, calculating the difference value between the first mean value and the second mean value, judging whether the difference value is within the preset range, and repeating the steps until the difference value is within the preset range to obtain a water seepage boundary line.
Preferably, the step of obtaining a severe water seepage path according to the origin and the water seepage boundary line includes:
and selecting a point on the water seepage boundary line which is farthest away from the original point, wherein a connecting line between the position of the point and the position of the original point is a serious water seepage path.
Preferably, the step of deriving the function model to obtain the water permeability coefficient of each position on the severe water permeability path includes:
and substituting the humidity data of each position on the serious water seepage path into the derived function model to obtain a corresponding water seepage coefficient.
Preferably, the step of determining whether the building corresponding to the foundation pit is stable according to the predicted overall water permeability coefficient of the origin includes:
presetting a water seepage coefficient threshold value and warning delay time, recording a time interval between the moment corresponding to the predicted overall water seepage coefficient and the current moment when the predicted overall water seepage coefficient is greater than the water seepage coefficient threshold value, and judging that the building corresponding to the foundation pit is unstable if the time interval is less than the warning delay time.
The invention has the following beneficial effects: according to the embodiment of the invention, the humidity data of each position at the bottom of the foundation pit is obtained for analysis, the analysis method for obtaining the humidity data through collection is more visual, the original point of the permeation position is found out based on the humidity data of each position, a plurality of clusters are obtained through division based on the original point and the humidity data, then the permeation boundary line is obtained through ray construction by the original point, analysis is carried out by combining the characteristics of the permeation point and the permeation surface, the permeation boundary line is more reliable and accurate, the serious permeation path obtained according to the permeation boundary line is more reasonable, the whole water permeability coefficient is obtained based on the humidity data of each position on the serious permeation path, the whole water permeability coefficient is predicted in the future based on the whole water permeability coefficient at each historical moment to obtain the predicted whole water permeability coefficient, building stability is judged according to the predicted whole water permeability coefficient, the obtaining means for predicting the whole water permeability coefficient is reasonable and reliable, therefore, the analysis based on the predicted whole water permeability coefficient is more timely, the analysis result is more accurate, the data is visual, and the error is smaller.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flow chart of a building stability real-time monitoring method based on BIM according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a location distribution of clusters according to an embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention for achieving the predetermined objects, the following description, in conjunction with the accompanying drawings and preferred embodiments, describes a building stability real-time monitoring method based on BIM according to the present invention, and the detailed implementation, structure, features and effects thereof are described in detail below. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The embodiment of the invention predicts the permeation condition of the underground water at the bottom of the foundation pit to further detect the building stability, and because the underground water is slowly soaked in the wall at the bottom of the foundation pit, the soaking of the wall at the bottom of the foundation pit is generally started from a certain position, so the monitoring of the soaking of the bottom of the foundation pit by the embodiment of the invention is to analyze the soaking by using a water seepage origin.
The concrete scheme of the building stability real-time monitoring method based on the BIM provided by the invention is specifically described below by combining the attached drawings.
Referring to fig. 1, a flow chart of a building stability real-time monitoring method based on BIM according to an embodiment of the present invention is shown, where the method includes the following steps:
and S100, arranging a humidity sensitive capacitor at the bottom of the foundation pit for acquiring humidity data of each position of the bottom of the foundation pit.
Humidity sensitive capacitor is generally made with polymer film capacitor, because humidity sensitive capacitor's structural feature, its unable complete laminating is on the surface of foundation ditch bottom wall body, can have the air between humidity sensitive capacitor and the wall body surface, thereby can lead to the dielectric coefficient characterization to be the air, can lead to the air between humidity sensitive capacitor and the wall body to be filled by water when underground confined water infiltrates the wall body, and then can make humidity sensitive capacitor's dielectric coefficient take place obvious change, make its electric capacity that corresponds also change, and the change of capacitance variation and humidity is the direct ratio relation, consequently can adopt humidity sensitive capacitor to carry out data acquisition to the humidity of foundation ditch bottom wall body.
Because the bottom of the foundation pit eroded by the underground water is uneven, the distributed humidity-sensitive capacitor is adopted to monitor the surface humidity of the bottom of the foundation pit, the surface of the bottom of the foundation pit is divided into a plurality of unit areas, and each unit area in the embodiment of the invention is the area with the area of 1 square decimeter; arranging humidity-sensitive capacitors at four corners of an area with unit area size, namely arranging one humidity-sensitive capacitor at each position of the surface of the bottom of the foundation pit with an interval of 1 decimeter, and collecting humidity data of the current position; all humidity-sensitive capacitors at the bottom of the foundation pit form a humidity monitoring network covering the bottom of the foundation pit, and a corresponding humidity information network at the bottom of the foundation pit is obtained based on a BIM technology.
And recording the humidity data acquired by the humidity-sensitive capacitor at each position, wherein the recording frequency is once per minute, and the humidity data corresponding to each position at an interval of one minute can be obtained.
Step S200, acquiring the ratio of the humidity data of each position of the bottom of the foundation pit at the current moment to the humidity data of the adjacent position, acquiring an original point according to the ratio, and dividing all the humidity data into at least two clusters according to the original point and the humidity data of each position.
Considering the restriction of local geological conditions, a large amount of confined water is restricted by the change of the pressure at the bottom of a water layer, so that the construction materials are eroded by the water gushing on the underground water, and the bottom has obvious water seepage; the strength of the building is affected when the building is in a high-humidity environment for a long time, so that the building is not stable enough; and when the seepage quantity of the pressure-bearing water layer is larger, the water of the pressure-bearing layer gradually permeates from the seepage point to the point and the surface in the seepage process, so that the underground water immersion origin can be obtained based on the humidity data of each position.
Specifically, for the current time, the humidity data corresponding to each position at the time can be obtained in step S100, and the humidity data at each position is analyzed: calculating the ratio of the humidity data of the current position to the humidity data corresponding to the eight neighborhood positions, if the ratio of the humidity data of the current position to the humidity data corresponding to the eight neighborhood positions adjacent to the current position is larger than 1, indicating that the humidity data of the current position is larger than the humidity data of the eight positions around the current position, and the humidity data is consistent with the characteristic that the underground water is immersed into the original point and gradually permeates from the original point to the periphery, marking the current position as the original point of the immersed underground water, and marking the positions of all the original points; the groundwater from the origin is immersed in the light wall at the bottom of the pit, which may lead to a reduction in the stability of the building.
Further, clustering the humidity data of all positions based on the humidity data corresponding to each position at the bottom of the foundation pit, wherein the clustering method in the embodiment of the invention adopts a DBSCAN density clustering algorithm, an implementer sets a search radius by himself, the original point is taken as a core point, the search radius is used for judging whether the distance between every two positions meets the search radius, the distance between every two positions is marked as a corresponding humidity change condition, all the humidity data are further divided into at least two cluster clusters, the actual number of the cluster clusters is related to the search radius set by the implementer, the humidity change condition between every two positions is a difference value of the humidity data corresponding to the two positions, and the DBSCAN density clustering algorithm is the prior known technology and is not repeated in detail; because the water at the bottom of the foundation pit is infiltrated by the method and is characterized in that irregular gradual wetting is performed by points and surfaces, and the humidity is gradually reduced from the original point, the position corresponding to each humidity data in the clusters obtained at the bottom of the foundation pit forms an irregular annular region according to the corresponding cluster, as shown in fig. 2, a schematic diagram of the position distribution of each cluster is shown; the characteristics corresponding to the humidity data in each cluster are similar, so that the boundary line of every two clusters can be regarded as an equal humidity line, and the humidity data in the cluster corresponding to the equal humidity line are basically similar.
It should be noted that, because the DBSCAN density clustering algorithm performs range judgment in a clustering manner, when a plurality of origins appear, the final clustering result is not affected, and no matter how many origins appear, the water seepage manner is soaked from point to surface, and corresponding irregular annular region data can be obtained; since each origin is the point at which the humidity data begins to diverge, the origin is located at a more central position of the annular region.
Step S300, constructing a ray by taking an original point as a starting point, acquiring an output value of humidity data of each position on the ray, and obtaining a water seepage boundary line based on the output value; and obtaining a serious water seepage path according to the origin and the water seepage boundary line.
Due to the fact that the humidity-sensitive capacitor at the bottom of the foundation pit has high humidity data in a humid environment, the judgment of the boundary of water seepage is fuzzy while underground water continuously permeates, namely the sensitivity of the DBSCAN density clustering algorithm obtained in the step S200 is reduced, and therefore the actual water seepage boundary is judged by using the soft-argmax function according to continuous humidity data changes.
Firstly, constructing a ray by taking an origin in an irregular annular region as a starting point, wherein the direction of the ray is outward after passing through the annular region, and acquiring an output value of humidity data of each position on the ray, namely putting the humidity data of each position on the ray into the following calculation formula:
wherein, the first and the second end of the pipe are connected with each other,
a position mark corresponding to each selected humidity data is represented; />
Represents a fifth or fifth party>
Humidity data corresponding to the position mark number; />
Representing a natural constant.
Acquiring an output value of each humidity data by using a soft-argmax function, determining a critical value of the humidity, wherein the output of the calculation formula is 1 under the trend of gradually decreasing the data, and when a critical point is reached, the output of the calculation formula is 0, namely the humidity data of the position is data which is not influenced by underground water immersion, and on an actual underground water influence path, the humidity of the position is possibly caused by the humidity of the actual environment and is not caused by underground water immersion; therefore, water seepage boundary points can be obtained through the position with the output result of the calculation formula being 0, and the average value of the humidity corresponding to all the points which do not accord with the water seepage position on the ray is obtained
Pick the mean value>
Recording as a first mean value, wherein the first mean value is the bottom of the foundation pitThe ambient humidity of (a).
Further, a cluster where the water seepage boundary point is located is obtained, and the mean value corresponding to the humidity data of all the positions in the cluster is calculated and recorded as a second mean value
The cluster where the boundary point is located is the edge of underground water at the bottom of the foundation pit, and the difference between the cluster and the environmental humidity is smaller, namely the first mean value->
And the second mean value->
If the difference value of the boundary point is close to zero, the reasonability of the found boundary point at the moment is judged by setting a preset range, and when the difference value between a first average value and a second average value corresponding to the boundary point at the moment is in the preset range, the difference between the environmental data corresponding to the first average value and the permeation boundary data corresponding to the second average value is smaller, and the reasonability that the boundary line of the cluster where the boundary point is located is the water permeation boundary line is higher; the preset range is set by an implementer according to the actual situation, and the preset range is closer to zero.
It should be noted that, when the difference between the first average value and the second average value corresponding to the boundary point is not within the preset range, a second ray is constructed by using the origin, the second ray is inconsistent with the ray, the output value of the humidity data at each position on the second ray is calculated, based on the method for obtaining the same first average value and second average value, the first average value and the second average value corresponding to the boundary point on the second ray are obtained, whether the difference between the first average value and the second average value corresponding to the boundary point on the second ray belongs to the preset range is judged, and so on, until the boundary point is found when the difference belongs to the preset range, the boundary point of the cluster where the boundary point is located is the water seepage boundary line.
Obtaining a serious water seepage path according to the obtained water seepage boundary line, wherein the specific method comprises the following steps: and selecting the point on the water seepage boundary line which is farthest away from the original point, wherein a connecting line between the position of the point and the position of the original point is the serious water seepage path.
And S400, presetting a calculation function, substituting the humidity data of each position on the serious water seepage path into the calculation function to obtain a function model of an optimal solution, deriving the function model to obtain the water seepage coefficient of each position on the serious water seepage path, and obtaining the overall water seepage coefficient of the serious water seepage path according to the average value of the water seepage coefficients of all the positions.
Obtaining a serious water seepage path in the step S300, analyzing all humidity data on the serious water seepage path, and constructing an immersion route function model, wherein the humidity data on the serious water seepage path basically presents linear distribution because the humidity characteristics of underground water seepage are gradually reduced:
middle, or>
Indicates the mark of each position on the serious seepage path, and>
represents the humidity data at each location on the severe hydration path>
And &>
All represent constants; inputting the humidity data of all positions on the serious water seepage path into a path function model for fitting to obtain an optimal solution, wherein the target function for fitting to obtain the optimal solution is ^ er>
，/>
Indicating the substitution of position reference numeralsThe humidity data obtained in the function model->
The actual humidity data corresponding to the position index is considered as the parameter->
And &>
And obtaining the function model of the optimal solution for the optimal constant.
Furthermore, a function model of the optimal solution is derived, and then each position label on the serious water seepage path is calculated by using the derived function model and substituted into the function model to obtain corresponding humidity change data, namely a water seepage coefficient corresponding to each position, so that a path for underground water to be immersed is expressed more visually and clearly by using the mathematical model, the influence of each point on infiltration is considered, if a certain position is locally dry, the range for underground water to be immersed in is reduced, and the influence of the local drying condition can be considered from the whole process.
After the water seepage coefficient corresponding to each position on the serious water seepage path is obtained, the water seepage coefficients corresponding to all the positions on the serious water seepage path are subjected to average value calculation, the obtained average value is recorded as the integral water seepage coefficient of the serious water seepage path, the numerical value of the integral water seepage coefficient expresses the integral water seepage condition of the serious water seepage path at the current moment, and the integral water seepage coefficient is larger, so that the integral water seepage condition is more serious.
And S500, acquiring a serious water seepage path and an integral water seepage coefficient corresponding to the original point at the bottom of the foundation pit at each moment, inputting the integral water seepage coefficient of the original point at each moment into the trained neural network to obtain a predicted integral water seepage coefficient of the original point, and judging whether the building corresponding to the foundation pit is stable or not according to the predicted integral water seepage coefficient of the original point.
Based on the method for obtaining the same overall water seepage coefficient of the serious water seepage path corresponding to the origin at the current moment in the step S400, the overall water seepage coefficient corresponding to the origin at each historical moment is obtained. Predicting the overall water seepage coefficient at a future moment according to the overall water seepage coefficient corresponding to an original point at each historical moment, wherein the prediction method in the embodiment of the invention adopts an lstm long-short term memory neural network, a training sample of the long-short term memory neural network is the overall water seepage coefficient corresponding to the original point at each historical moment, a loss function adopts a mean square error loss function, the specific training method is the prior known technology, and details are not repeated in the embodiment of the invention; and predicting the whole water seepage coefficient of the future time according to the trained long-short term memory neural network and the whole water seepage coefficient of each time.
The stability of the building is judged according to the whole water seepage coefficient predicted on the serious water seepage path corresponding to the origin obtained by prediction, an implementer sets a corresponding water seepage coefficient threshold according to the requirements of the implementer and the actual address condition, and the water seepage coefficient threshold is used as the judgment basis of water seepage warning; setting warning delay time, wherein the setting time is 1 year in the embodiment of the invention, when the predicted overall water seepage coefficient is greater than the water seepage coefficient threshold value, recording the time interval between the moment corresponding to the predicted overall water seepage coefficient and the current moment, and if the time interval is less than the warning delay time for 1 year, determining that the current building has an unstable risk; if the time interval is more than the warning delay time for 1 year, the building at the moment is considered to have no risk, and monitoring is continued so as to facilitate follow-up timely warning; the embodiment of the invention is based on the prediction design of building BIM on the stability of the foundation pit, and an implementer can obtain a result in a system for detecting the service life of the foundation pit of BIM.
In summary, in the embodiments of the present invention, the humidity-sensitive capacitor is disposed at the bottom of the foundation pit for acquiring humidity data of each position of the bottom of the foundation pit; acquiring the ratio of humidity data of each position of the bottom of a foundation pit at the current moment to humidity data of the adjacent position of the foundation pit at the current moment, acquiring an original point according to the ratio, and dividing all the humidity data into at least two cluster clusters according to the original point and the humidity data of each position; constructing a ray by taking an original point as a starting point, acquiring an output value of humidity data of each position on the ray, and obtaining a water seepage boundary line based on the output value; obtaining a serious water seepage path according to the origin and the water seepage boundary line; presetting a calculation function, substituting the humidity data of each position on the serious water seepage path into the calculation function to obtain a function model of an optimal solution, deriving the function model to obtain the water seepage coefficient of each position on the serious water seepage path, and obtaining the overall water seepage coefficient of the serious water seepage path according to the average value of the water seepage coefficients of all the positions; the method comprises the steps of obtaining a serious seepage path and a whole seepage coefficient corresponding to an original point in the pit bottom of the foundation pit at each moment, inputting the whole seepage coefficient of the original point at each moment into a trained neural network, obtaining a predicted whole seepage coefficient of the original point, judging whether a building corresponding to the foundation pit is stable or not according to the predicted whole seepage coefficient of the original point, monitoring the seepage condition of the foundation pit in time, early warning in time of the unstable condition, and reducing loss due to the fact that the obtained result is more reliable and accurate.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that are within the spirit of the present invention are intended to be included therein.
Claims (7)
1. A BIM-based building stability real-time monitoring method is characterized by comprising the following steps:
arranging a humidity-sensitive capacitor at the bottom of the foundation pit for acquiring humidity data of each position of the bottom of the foundation pit;
acquiring the ratio of the humidity data of each position of the bottom of the foundation pit at the current moment to the humidity data of the adjacent positions of the foundation pit, acquiring an original point according to the ratio, and dividing all the humidity data into at least two cluster clusters according to the original point and the humidity data of each position;
constructing a ray by taking the original point as an initial point, acquiring an output value of humidity data of each position on the ray, and obtaining a water seepage boundary line based on the output value; obtaining a serious water seepage path according to the origin and the water seepage boundary line;
presetting a calculation function, substituting the humidity data of each position on the serious seepage path into the calculation function to obtain a function model of an optimal solution, performing derivation on the function model to obtain the seepage coefficient of each position on the serious seepage path, and obtaining the overall seepage coefficient of the serious seepage path according to the average value of the seepage coefficients of all the positions;
obtaining a severe seepage path and an overall seepage coefficient corresponding to the original point in the pit bottom of the foundation pit at each moment, inputting the overall seepage coefficient of the original point at each moment into a trained neural network to obtain a predicted overall seepage coefficient of the original point, and judging whether a building corresponding to the foundation pit is stable or not according to the predicted overall seepage coefficient of the original point;
the step of obtaining a severe water seepage path according to the origin and the water seepage boundary line comprises the following steps:
and selecting a point on the water seepage boundary line which is farthest away from the original point, wherein a connecting line between the position of the point and the position of the original point is a serious water seepage path.
2. The BIM-based construction stability real-time monitoring method according to claim 1, wherein the step of obtaining the origin point according to the ratio comprises:
if the ratio of the humidity data of the current position to the humidity data of the neighborhood position is larger than 1, the current position is the original point; the neighborhood location includes eight neighboring locations to the current location.
3. The BIM-based building stability real-time monitoring method according to claim 1, wherein the step of dividing all humidity data into at least two clusters according to the origin and the humidity data of each position comprises:
and taking the original point as a core point, taking the difference value of the humidity data between every two positions as a distance, and carrying out a DBSCAN clustering algorithm based on a preset searching radius, the core point and the distance between every two positions to obtain at least two clustering clusters.
4. The BIM-based construction stability real-time monitoring method according to claim 1, wherein the step of obtaining the output value of the humidity data at each position on the ray comprises:
substituting the humidity data of each position on the ray into a calculation formula to obtain an output value of each position, wherein the calculation formula is as follows:
wherein the content of the first and second substances,
a position label corresponding to each selected humidity data; />
Indicates the fifth->
Humidity data corresponding to the position mark; />
Representing a natural constant.
5. The BIM-based construction stability real-time monitoring method according to claim 1, wherein the step of obtaining a water seepage boundary line based on the output value comprises:
recording the position of which the output value is 0 for the first time as a boundary point, and acquiring the mean value of humidity data corresponding to all positions of which the output values are 0 on the ray as a first mean value;
acquiring the mean value of the humidity data of all the positions in the clustering cluster corresponding to the boundary point, recording the mean value as a second mean value, calculating the difference value between the first mean value and the second mean value, and if the difference value is within a preset range, determining the boundary line of the clustering cluster where the boundary point is located as a water seepage boundary line;
if the difference value is not within the preset range, reconstructing a second ray with the original point as a starting point, wherein the direction of the second ray is different from that of the current ray, acquiring a new boundary point based on humidity data of each position on the second ray, acquiring a first mean value on the second ray and a second mean value of a cluster where the new boundary point on the second ray is located, calculating the difference value between the first mean value and the second mean value, judging whether the difference value is within the preset range, and repeating the steps until the difference value is within the preset range to obtain a water seepage boundary line.
6. The BIM-based construction stability real-time monitoring method according to claim 1, wherein the step of deriving the function model to obtain the water permeability coefficient of each position on the severe water permeability path comprises:
and substituting the humidity data of each position on the serious water seepage path into the derived function model to obtain a corresponding water seepage coefficient.
7. The BIM-based building stability real-time monitoring method according to claim 1, wherein the step of judging whether the building corresponding to the foundation pit is stable according to the predicted overall water permeability coefficient of the origin comprises:
presetting a water seepage coefficient threshold value and warning delay time, recording a time interval between the moment corresponding to the predicted overall water seepage coefficient and the current moment when the predicted overall water seepage coefficient is greater than the water seepage coefficient threshold value, and judging that the building corresponding to the foundation pit is unstable if the time interval is less than the warning delay time.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN202211568183.8A CN115659846B (en) | 2022-12-08 | 2022-12-08 | BIM-based building stability real-time monitoring method |
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