CN112883646B - Building settlement amount extraction method, system and device combining machine learning and soil mechanics model - Google Patents

Building settlement amount extraction method, system and device combining machine learning and soil mechanics model Download PDF

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CN112883646B
CN112883646B CN202110192838.5A CN202110192838A CN112883646B CN 112883646 B CN112883646 B CN 112883646B CN 202110192838 A CN202110192838 A CN 202110192838A CN 112883646 B CN112883646 B CN 112883646B
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宫辉力
陈蓓蓓
曹锦�
孙玉洁
周超凡
雷坤超
史珉
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Abstract

The invention provides a method, a system and a device for extracting building settlement of a combined machine learning and soil mechanics model, wherein the system comprises the following steps: the basic information acquisition module is used for acquiring ground subsidence information, building load space position information, building age data, building additional stress, annual ground water level drop value and compression layer thickness data in a preset area; the ground subsidence model module is used for constructing a ground subsidence model I and a ground subsidence model II based on the GA-BP neural network; and the evaluation module is used for constructing a consolidation settlement model, combining the first ground settlement model and the second ground settlement model, and stripping the settlement value caused by groundwater. The method can strip the influence of underground water exploitation, acquire the settlement value caused by regional scale building load, fully utilize the existing hydrogeologic data and building data, greatly reduce the cost required by building a model, introduce additional stress of the building and influence factors of the building age, and simulate more accurately.

Description

Building settlement amount extraction method, system and device combining machine learning and soil mechanics model
Technical Field
The invention relates to the field of building settlement monitoring, in particular to the field of ground settlement amount extraction caused by building load, and particularly aims at solving the problem of ground settlement under the combined action of a building load stress field and an underground water seepage field.
Background
Ground subsidence is a geological environment phenomenon in which regional ground elevations fall. The method relates to various aspects of economy, environment, resources, society and the like, and is one of the urban problems to be solved urgently at present. The nonlinear process of ground subsidence is known, the formation mechanism of subsidence is revealed, and scientific regulation and control can be better realized. Previous studies have shown that the production of subsurface fluids and building load increases are often important contributors to urban ground subsidence. In recent years, with the acceleration of the global urbanization process, the influence of building loads on ground settlement tends to be further enhanced. Under the background, the influence of underground water exploitation is stripped, and the ground settlement value under the action of building load is obtained, so that the method is important to scientific prevention and control of settlement.
Existing building load and ground settlement relationship studies can be divided into two categories. The first category discusses the relationship between building features and ground subsidence using a spatial statistical analysis method. These characteristics include building add-on stress, height, density, age, and the like. The method only analyzes the correlation between the building load and the ground subsidence, and can not quantitatively extract the subsidence value caused by the building load. The second type is based on the rock-soil constitutive relation, a ground subsidence model under the action of building load is constructed, and the space influence range of the building load on ground subsidence and the consolidation subsidence time caused by the building load are researched. The construction of the model generally requires complex rock-soil parameters and geological drilling data, and the experimental cost is high, so that the model is difficult to popularize in a large range. In addition, the model result only keeps high precision in a small area, and is difficult to be applied to a large-scale sedimentation area. The machine learning model is not limited by acquisition of hydrogeologic parameters of a research area, and can simulate the ground subsidence process of the area scale. In many machine learning models, BP neural networks interact with real-world objects by modeling biological systems. The method has good nonlinear mapping capability and is suitable for simulating ground subsidence. The genetic algorithm is based on genetic theory, and can optimize the sample population. The genetic algorithm is combined with the BP neural network to avoid the BP neural network from sinking into local minima.
In summary, the conventional ground subsidence model under the action of building load has certain applicability only in a small area. As the global subsidence area expands, traditional models are difficult to apply. In a settlement area under the combined action of underground water exploitation and building load, how to peel off the influence of underground water, the settlement value caused by the regional scale building load is obtained, and is important to the prevention and control of settlement.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a building settlement amount extraction model applicable to a large area. The model can simulate the ground subsidence process of the regional scale under the machine learning framework by using the existing hydrogeological data and building load information, and acquire the ground subsidence value caused by building load.
Specifically, the invention provides the following technical scheme:
in one aspect, the invention provides a method for extracting building settlement of a combined machine learning and soil mechanics model, which comprises the following steps:
step 1, acquiring SAR images repeatedly passing through the environment for a plurality of times in the same preset area within a preset time range, performing interference processing, extracting a high-coherence target point, modeling phase component information of the high-coherence target point, and separating each phase information to acquire ground subsidence information;
step 2, acquiring building load space position information and building age data in a preset area;
step 3, obtaining building additional stress;
step 4, acquiring annual ground water level drop values and compressed layer thickness data of a preset area;
step 5, respectively constructing a first ground subsidence model and a second ground subsidence model, and extracting a ground subsidence value caused by regional scale building load; the ground settlement model I takes the thickness data of a compression layer, the decline value of the groundwater level in each year, the building age data and the building additional stress as input and the settlement value in each year as output; the ground subsidence model II takes the thickness data of the compression layer and the annual ground water level drop value as input and the annual subsidence value as output;
and 6, constructing a consolidation and settlement model, and evaluating a result obtained based on a machine learning algorithm to determine the accuracy of the settlement value caused by the regional scale building load obtained by the machine learning model.
Preferably, the step 3 further includes: performing thinning treatment on the high-coherence target point; generalizing the buildings in the preset area into rectangular uniform loads; additional stress for each high coherence target point is estimated.
Preferably, in the step 6, the evaluating method is as follows:
selecting a region A less affected by building load as a background field, and constructing a consolidation settlement model A, wherein the model aims to verify whether the accuracy of the consolidation settlement model is reliable when no building load is distributed; in verifying the reliability, preferably, the following manner may be adopted: if the cumulative error of the simulation result and the monitoring value of SAR image data is within 5mm, the model can be used for solving the settlement value caused by the building load;
selecting a target area B with building load distribution, constructing a consolidation settlement model B under the combined action of building load, soil body self-weight stress and underground water exploitation, and obtaining a settlement simulation value M; comparing the monitoring value of the same SAR image data with the model simulation value, and evaluating the precision of the model;
simulating a ground subsidence value N of the area B when no building load is distributed by utilizing a consolidation subsidence model;
the difference value between M and N is the settlement value caused by the building load; and (3) verifying the settlement value caused by the regional scale building load obtained by the first and second simultaneous ground settlement models in the step (5) by using the settlement value.
What needs to be further explained here is: in the step 5, the ground subsidence model I and the ground subsidence model II constructed based on machine learning can be used for stripping the influence of underground water through the two models simultaneously, so as to obtain the ground subsidence value caused by the regional scale building.
The consolidation and settlement model in step 6 also builds two models. The model of region a is to verify that the accuracy of the consolidation settlement model is reliable in the absence of building load distribution. After the model proved to be more reliable, the settlement value caused by the building load was obtained in the region B, and the settlement value was of a monomer scale. And finally, verifying the value obtained by the regional scale of the machine learning model by using the value obtained by the monomer scale of the consolidation settlement model.
Preferably, the consolidation and settlement model is constructed in the following manner:
the consolidation differential equation is established as follows:
solving the consolidation differential equation to obtain a sedimentation quantity value;
wherein,
in the above formulas, z is soil depth, C v For consolidation coefficient, t is any period of time, q represents flow, e 1 For the initial void ratio, h is the head,k is the permeability coefficient of the soil body, i is the hydraulic gradient, A is the cross-sectional area, u is the pore water pressure, e 1 For the initial void ratio, a is the compression coefficient, u/gamma w Is a pressure water head.
Preferably, the time conditions and boundary conditions for solving the consolidation differential equation are:
when t=0, 0<z<In the H state, the pore water pressure is equal to the vertical additional stress; when 0 is<t<When infinity, z=0, the pore water pressure is 0; when 0 is<t<When infinity, z=h,when t= infinity, 0<z<In the H state, the pore water pressure is 0;
wherein H is the soil thickness.
Preferably, in the step 5, the sedimentation value caused by stripping the groundwater is achieved by: and calculating root mean square errors of the first ground subsidence model and the second ground subsidence model aiming at the high coherence point of the specific building age.
In another aspect, the present invention also provides a system for extracting a settlement amount of a building combining machine learning and a soil mechanics model, the system comprising:
the basic information acquisition module is used for acquiring ground subsidence information, building load space position information, building age data, building additional stress, annual ground water level drop value and compression layer thickness data in a preset area; the ground subsidence information is obtained by the following steps: based on SAR images repeatedly passing through a preset area for many times, carrying out interference processing, extracting a high-coherence target point, modeling phase component information of the high-coherence target point, and separating each phase information to obtain ground subsidence information;
the ground subsidence model module is used for constructing a ground subsidence model I and a ground subsidence model II, and combining the ground subsidence model I and the ground subsidence model II to obtain a ground subsidence value caused by regional scale building load; the ground settlement model I takes the thickness data of a compression layer, the decline value of the groundwater level in each year, the building age data and the building additional stress as input and the settlement value in each year as output; the ground subsidence model II takes the thickness data of the compression layer and the annual ground water level drop value as input and the annual subsidence value as output;
and the evaluation module is used for constructing a consolidation settlement model and evaluating the results obtained by the first and second simultaneous ground settlement models to determine the accuracy of the settlement value caused by the obtained regional scale building load.
Preferably, in the basic information acquisition module, the method for acquiring the building additional stress is as follows:
performing thinning treatment on the high-coherence target point; generalizing the buildings in the preset area into rectangular uniform loads; additional stress for each high coherence target point is estimated.
Preferably, the evaluation module evaluates the following modes:
selecting a region A less affected by building load as a background field, and constructing a consolidation settlement model A to verify the accuracy of the consolidation settlement model when no building load is distributed;
preferably, the verification of the accuracy can be specifically performed in the following manner: if the cumulative error of the simulation result and the monitoring value of SAR image data is within 5mm, the model can be used for solving the settlement value caused by the building load;
when the precision of the consolidation and settlement model A meets the requirement, selecting a target area B with building load distribution, constructing a consolidation and settlement model B under the combined action of building load, soil body self-weight stress and underground water exploitation, and obtaining a settlement simulation value M; comparing the monitoring value of the same SAR image data with the model simulation value, and evaluating the precision of the model;
simulating a ground subsidence value N of the area B when no building load is distributed by utilizing a consolidation subsidence model;
the difference value between M and N is the settlement value caused by the building load; and verifying the subsidence value caused by the regional scale building load obtained by the first ground subsidence model and the second ground subsidence model of the combined ground subsidence model by utilizing the subsidence value.
Preferably, the consolidation and settlement model is constructed in the following manner:
the consolidation differential equation is established as follows:
solving the consolidation differential equation to obtain a sedimentation quantity value;
wherein,
in the above formulas, z is soil depth, C v For consolidation coefficient, t is any period of time, q represents flow, e 1 For the initial pore ratio, h is the water head, k is the permeability coefficient of the soil body, i is the hydraulic ramp down, A is the cross-sectional area, u is the pore water pressure, e 1 For the initial void ratio, a is the compression coefficient, u/gamma w Is a pressure water head.
Preferably, the time conditions and boundary conditions for solving the consolidation differential equation are:
when t=0, 0<z<In the H state, the pore water pressure is equal to the vertical additional stress; when 0 is<t<When infinity, z=0, the pore water pressure is 0; when 0 is<t<When infinity, z=h,when t= infinity, 0<z<In the H state, the pore water pressure is 0;
wherein H is the soil thickness.
In addition, the invention also provides a building settlement amount extraction device combining machine learning and a soil mechanics model, which comprises the following components:
a processor; the method comprises the steps of,
and a memory in which computer program instructions are stored which, when executed by the processor, perform the building settlement amount extraction method of the combined machine learning and soil mechanics model described above.
Compared with the prior art, the scheme of the invention has at least the following beneficial effects:
1) The invention combines machine learning and soil mechanics model, can peel off the influence of underground water exploitation, and obtains the settlement value caused by regional scale building load, thereby providing basis for preventing and treating ground settlement.
2) In the aspect of model input data, the scheme can fully utilize the existing hydrogeologic data and building data, greatly reduces the cost required for constructing the model, and has strong popularization.
3) The scheme creatively introduces additional stress and age influence factors of the building while considering the hydrogeological background, and simulates the formation process of ground subsidence from the soil mechanics level. In the research area with sufficient groundwater level data, the scheme can also be used for predicting ground subsidence.
Drawings
FIG. 1 is a PS-InSAR flow chart;
FIG. 2 shows the additional stresses at different locations under a rectangular uniform load, (a) the stresses at corner points; (b) stress at any point below the building; (c) stress at any point outside the rectangular frame of the building;
FIG. 3 is a BP neural network model according to an embodiment of the present invention;
FIG. 4 is a zone A consolidation settlement model according to an embodiment of the invention;
FIG. 5 is a zone B consolidation settlement model according to an embodiment of the invention;
FIG. 6 is a flow chart of a settling volume extraction method according to an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without any inventive effort, are intended to be within the scope of the invention.
First, the meaning of the condensed rate words according to the present invention will be described as follows:
GA-BP: genetic algorithm-error back propagation, genetic Algorithm-Error Back Propagation;
PS-InSAR: permanent scatterer synthetic aperture radar interferometry, persistent Scatterer Interferometric Synthetic Aperture Radar;
SAR: synthetic aperture radar, synthetic Aperture Radar;
SLC: single view plural images Single Look Complex;
SRTM: the aerospace plane radar topography mission, shuttle Radar Topography Mission;
DEM: a digital elevation model, digital Elevation Model;
LOS: line of Sight;
PS: a permanent diffuser, persistent Scatterer;
RMSE: root mean square error, root Mean Square Error.
Aiming at the defects in the prior art, the invention is mainly improved by the following steps:
1) Building load information is characterized by additional stress. In the past, a ground subsidence model based on machine learning is generally used for qualitatively representing building loads by using characteristic values such as building height, density and the like. The method is based on the soil mechanics, and the additional stress of the regional scale building is calculated, so that the sedimentation process can be simulated from a mechanism level.
2) The construction of the model introduces a building age influencing factor. The conventional machine learning model generally does not consider the influence of the age of the building. According to the principle of the effective stress of the Taisha: total stress = effective stress + pore water pressure. Building additional stresses increase the effective stress, causing soil consolidation, a process that may last decades. However, during soil consolidation, the sedimentation rate is gradually slowed down as the age of the building increases. The model is thus constructed taking into account the influence of the age of the building.
3) The cost required for constructing the model is reduced. In the past, the construction of the water and soil model needs complex rock and soil parameters and geological drilling data, and the experimental cost is high. The machine learning model is not limited by acquisition of hydrogeologic parameters of a research area, and can acquire settlement values caused by building loads by using existing hydrogeologic data and building load information under the machine learning framework.
4) The model has wide space applicability. Because of the complex stratum structure, the traditional water and soil model is only applicable to a small area. The neural network model has good nonlinear mapping capability through self-adaptive learning of sample points, and can simulate the ground subsidence process of the regional scale. Therefore, the space applicability is wider.
Based on the four improvements described above, in a specific embodiment, the solution of the present invention can be preferably implemented by the following method:
according to the scheme provided by the invention, the settlement value caused by the regional scale building load can be obtained. The input data required by the machine learning algorithm are as follows: ground subsidence values from year to year, building add-on stresses from year to year, age of the building at year to year, ground water level drop values from year to year, and compressional layer thickness. The main technical route is as follows: the method comprises the steps of preprocessing original data to obtain data required by a machine learning model. And constructing a GA-BP neural network model to obtain the ground subsidence value caused by the building load. And finally, verifying the estimation result through a traditional soil mechanics model.
In general, the method mainly comprises the following key steps: firstly, processing a multisource SAR image by using PS-InSAR, and acquiring ground subsidence information of each year. And secondly, based on ArcGIS and Google Earth images, calculating building space position information and building age information. Third, building add-on stress was estimated using cloth Xin Naisi gram solutions. And fourthly, calculating the annual ground water level drop value, and collecting the information of the thickness of the compression layer in the research area. Fifthly, constructing a GA-BP neural network model by utilizing the preprocessing data in two cases: i.e. taking into account the building factor (additional stress, building age) and not taking into account the building factor. And estimating the ground subsidence value caused by building load through the two GA-BP neural network models. And sixthly, constructing a consolidation model by using the rock-soil data and the geological drilling data, and verifying a machine learning result. Specifically, in a more detailed embodiment, as shown in connection with FIG. 6, the method includes:
s1: and acquiring ground subsidence information based on PS-InSAR.
The PS-InSAR processing flow is shown in figure 1. And in a certain time range, carrying out interference processing on SAR images repeatedly passing through the same region for a plurality of times. And extracting a high-coherence target, namely a permanent scatterer, modeling phase component information of the target, and separating each phase information to obtain annual surface deformation information. First, a primary image is selected from the original Single Look Complex (SLC) image and registered with other secondary images. Then, the interference process is carried out on the main image and the auxiliary image. Removing reference ellipsoid phase by satellite orbit data, selecting DEM data acquired by US SRTM (Shuttle Radar Topography Mission), removing terrain phase, and generating time sequence differential interference phaseCan be expressed as:
wherein the method comprises the steps ofIs the residual topography phase caused by SRTM, +.>The phase is a linear deformation phase, and the two phases are estimated through a PS point phase difference model; decomposing by filtering->Nonlinear deformation phase->Atmospheric phase and->Noise phase. Will->And->And (5) superposing to obtain LOS (Line of Sight) surface deformation, and obtaining vertical surface deformation by utilizing trigonometric function transformation. Where θ is the radar incident angle.
d v ＝d los /cosθ (2)
S2: and (5) obtaining building age information.
According to the Google Earth historical images, the ArcGIS is utilized to count the spatial position information and the age attribute information of the building load. If the scale of the research area is larger, the construction load space distribution and age information can be extracted by utilizing Landsat and Sentinel-2 optical images.
S3: building additional stress information extraction
The additional stress is mainly the stress caused by the building load. According to the principle of the effective stress of the Taisha foundation, the additional stress can increase the effective stress, thereby causing the soil body to compress and inducing the ground to subside. Firstly, in order to reduce data redundancy and improve the convergence rate of a model, PS points acquired by InSAR are subjected to thinning treatment, and a Subset Feature in an ArcGIS is selected as a thinning method. Then, the building is generalized to rectangular uniform load, and the additional stress at the 1m embedded depth of each PS point is estimated. Building additional stress values at different PS points each year are thus obtained. The foundation is assumed to be a semi-infinite space elastomer. As shown in FIG. 2a, with point b as the origin and the load intensity P, the integral dσ of the normal stress at M (x, y, z) point directly below point b z The method comprises the following steps:
additional stress sigma z As shown in equation 4. Where m=i/j, n=z/j:
and then calculating the additional stress at any sedimentation point M' by using a corner method. When M' is located directly below the building load, as in FIG. 2b, the additional stress is as shown in equation 5. Middle sigma z1 ，σ z2 ，σ z3 ，σ z4 Corner stresses for rectangles bhM 'e, eM' fc, hagM ', M' gdf, respectively:
σ zM′ ＝σ z1 +σ z2 +σ z3 +σ z4 (5)
when M' is outside the rectangular range of building loads, as in FIG. 2c, the additional stresses are:
σ zM′ ＝σ z1 +σ z2 -σ z3 -σ z4 (6)
s4: groundwater level data and compressed layer thickness pre-processing.
The groundwater level data that is available is typically a point element or a line element in the form of a contour. The water level contour is interpolated into planes using the kriging method of ArcGIS. Because of the differences in geologic structures, the burial depth of the groundwater level may not be directly related to the ground subsidence, and thus it is necessary to calculate the annual groundwater level drop value. Here, the annual groundwater level drop is calculated using the grid calculator function of the ArcGIS, and in addition, the investigation region compressed layer thickness data is collected, which relates to the total thickness of highly compressible soil layers such as clay, powdery clay, etc.
S5: and (5) constructing a neural network model.
The neural network comprises three layers, namely an input layer, an output layer and a hidden layer. In a preferred embodiment, the model is built in two situations. The first input layer of the model is the thickness of a compression layer, the decline value of the underground water level of each year, the building age and the building additional stress at the PS point position of each year; the output layer is the annual sedimentation value. The second input layer of the model comprises the thickness of the compression layer and the annual groundwater level drop value, and the rest parameters are the same as those of the first model. The parameter settings of the neural network are shown in table 1. In the GA part, the population size is 50 and the algebra of evolution is 100. Individuals with high fitness are selected by using the roulette method, and the crossover probability and the mutation probability are set to 0.6 and 0.5, respectively. A plurality of global solutions are obtained through GA, and then the BP neural network is utilized to carry out iterative operation to obtain the optimal global solution. Through multiple experiments, when the hidden layer is determined to be provided with seven neurons, the errors of the training set and the verification set are minimum. The output layer is provided with a neuron, namely the ground settlement corresponding to different years. The Sigmoid function was chosen as the activation function, the learning rate and error were set to 0.01, and epochs were set to 1500.
TABLE 1 GA-BP neural network model parameters
75% of the data are used as sample data, and 25% of the data are used as verification data. By R 2 Maximum absolute error, minimum absolute error, root mean square error verifies the simulation accuracy of the model. The PS point obtained by preprocessing, taking 2018 PS point as an example, includes the following information: the settlement amount in 2018, the thickness of a compression layer, the ground water level drop value in 2017-2018, the building age at each point position and the building additional stress at each point position. Taking the PS point with the building age of 10 as an example, the root mean square error of the first model and the second model is calculated respectively and is recorded as RMSE 1 And RMSE 2 . From the definition of root mean square error, it can be seen that: the root mean square error is the square root of the ratio of the square of the deviation of the predicted value from the true value to the number of observations n. That is, RMSE 1 And RMSE 2 Is the amount of settlement caused by a building of 10 years of age. The above example is to obtain the settlement value caused by building load through the simultaneous model one and the simultaneous model two, thereby stripping the ground settlement caused by underground water exploitationFor descent purposes, the magnitude of settlement induced by the building load can be further determined. And whether the settlement value caused by the building load is accurate or not, namely whether the combined model I and the combined model II meet the actual detection accuracy requirement or not is judged, and the soil mechanics model is constructed for verification.
S6: and constructing a soil mechanics verification model.
The sedimentation area is simultaneously affected by the combined action of underground water exploitation and building load. How to peel off the influence of underground water exploitation, and further obtain the ground settlement caused by building load is a main problem solved by the soil mechanics model.
The first and second ground subsidence models constructed by machine learning are obtained as subsidence amounts caused by regional scale building loads, and the soil mechanics verification model is obtained as subsidence amounts caused by building loads at a certain point. The earth mechanical model has high precision, but the data acquisition difficulty required by constructing the model is extremely high. The invention uses the soil mechanics model to verify the result of the machine learning model, if the accuracy of the machine learning model is lower, the model is retrained by introducing new data and/or adjusting the structure or main parameters of the machine learning and other modes.
Because the research area is simultaneously subjected to underground water exploitation and building load, the ground settlement value caused by building load cannot be directly obtained. The specific implementation mode is as follows:
at a certain depth z of the soil body, V 1 Volume of solid, V 2 As void volume, both are expressed as:
in the above, e 1 Is the initial void ratio. At any time period, the void volume reduction is equal to the net outflow of water, expressed as:
in the above formula, q represents the flow rate. From equation (8) and equation (9), the saturated soil consolidation equation can be derived:
the relationship between the compression coefficient a and the void ratio and the vertical stress P is:
at this time, in the formula (10)Can be expressed as:
when the water head is h, the permeability coefficient of the soil body is k, the hydraulic gradient is i, and the cross-sectional area is A, the Darcy law can be expressed as:
combining equations (12) and (14), substituting equation (10) yields the consolidation differential equation:
wherein C is v As consolidation coefficient, u/gamma w The pressure water head has the following relation with the pore ratio and the compression coefficient:
according to different time conditions and boundary conditions, the differential equation can be solved by utilizing the Fourier series. The following four conditions are included: assuming that the soil thickness is H, when t=0, 0<z<In the H state, the pore water pressure is equal to the vertical additional stress; when 0 is<t<When infinity, z=0, the pore water pressure is 0; when 0 is<t<When infinity, z=h,when t= infinity, 0<z<And H, the pore water pressure is 0.
The model for simulating the soil consolidation and settlement process requires input values such as rock-soil data, geological drilling data, water level observation well data and the like.
In a preferred embodiment, the geotechnical data parameters may include: soil mass density, void ratio, plasticity index, liquid index, compression modulus, compression index, poisson ratio. Geological borehole data is primarily aimed at acquiring formation information. The water level well observation data are used for acquiring the time sequence variation of the underground water level. The construction process of the soil mechanics verification model is as follows:
1) The area A less affected by the building load is selected as a background field, and a settlement verification model is constructed as shown in fig. 4. The model is mainly used for checking whether the settlement simulation value is reliable only under the conditions of underground water exploitation and soil body dead weight stress. And comparing the InSAR monitoring value with the model simulation value, and evaluating the precision of the model.
2) And selecting a target area B with building load distribution, constructing a consolidation settlement model under the combined action of building load, soil body self-weight stress and underground water exploitation, and obtaining a settlement simulation value M. The model is mainly used for simulating ground subsidence caused by various influencing factors in a real environment. And verifying the simulation result by using the InSAR result, and evaluating the precision of the model.
3) And simulating the ground subsidence value N of the area B in the absence of building load distribution by using the consolidation model. Since the experiments in region a have demonstrated the reliability of the model in the absence of building load distribution, it is reasonable to consider the sedimentation value N reliable. The difference between M and N is the settlement value caused by the building load. And verifying the GA-BP neural network model result through the consolidation settlement model result.
What needs to be further explained here is: and S5, a first ground subsidence model and a second ground subsidence model which are constructed based on machine learning can be used for stripping the influence of underground water through the two models, and the ground subsidence value caused by the regional scale building is obtained.
The consolidation and settlement model in S6 also constructs two models. The model of region a is to verify that the accuracy of the consolidation settlement model is reliable in the absence of building load distribution. After the model proved to be more reliable, the settlement value caused by the building load was obtained in the region B, and the settlement value was of a monomer scale. And finally, verifying the value obtained by the regional scale of the machine learning model by using the value obtained by the monomer scale of the consolidation settlement model.
In yet another embodiment, the technical solution of the present invention may be further implemented by:
a system for extracting a building settlement amount combining machine learning and a soil mechanics model, the system comprising:
the basic information acquisition module is used for acquiring ground subsidence information, building load space position information, building age data, building additional stress, annual ground water level drop value and compression layer thickness data in a preset area; the ground subsidence information is obtained by the following steps: based on SAR images repeatedly passing through a preset area for many times, carrying out interference processing, extracting a high-coherence target point, modeling phase component information of the high-coherence target point, and separating each phase information to obtain ground subsidence information;
the ground subsidence model module is used for constructing a first ground subsidence model and a second ground subsidence model based on a GA-BP neural network algorithm and calculating the ground subsidence value caused by the regional scale building load; the ground settlement model I takes the thickness data of a compression layer, the decline value of the groundwater level in each year, the building age data and the building additional stress as input and the settlement value in each year as output; the ground subsidence model II takes the thickness data of the compression layer and the annual ground water level drop value as input and the annual subsidence value as output;
and the evaluation module is used for constructing a consolidation settlement model and evaluating the result acquired based on the machine learning algorithm so as to determine the accuracy of the settlement value caused by the regional scale building load acquired by the machine learning model.
Preferably, in the basic information acquisition module, the method for acquiring the building additional stress is as follows:
performing thinning treatment on the high-coherence target point; generalizing the buildings in the preset area into rectangular uniform loads; additional stress for each high coherence target point is estimated.
Preferably, the evaluation module evaluates the following modes:
selecting an area A less affected by building load as a background field, and constructing a consolidation settlement model, wherein the model aims to verify whether the accuracy of the consolidation settlement model is reliable when no building load is distributed; in a preferred embodiment, if the cumulative error of the simulation result and the monitored value of the SAR image data is within 5mm, the model can be used for solving the settlement value caused by the building load;
selecting a target area B with building load distribution, constructing a consolidation settlement model under the combined action of building load, soil body self-weight stress and underground water exploitation, and obtaining a settlement simulation value M; comparing the monitoring value of the same SAR image data with the model simulation value, and evaluating the precision of the model;
simulating a ground subsidence value N of the area B when no building load is distributed by utilizing a consolidation subsidence model;
the difference value between M and N is the settlement value caused by the building load; and (3) verifying the settlement value caused by the regional scale building load obtained by the first and second simultaneous ground settlement models in the step (5) by using the settlement value.
Preferably, the consolidation and settlement model is constructed in the following manner:
the consolidation differential equation is established as follows:
solving the consolidation differential equation to obtain a sedimentation quantity value;
wherein,
in the above formulas, z is soil depth, C v For consolidation coefficient, t is any period of time, q represents flow, e 1 For the initial pore ratio, h is the water head, k is the permeability coefficient of the soil body, i is the hydraulic ramp down, A is the cross-sectional area, u is the pore water pressure, e 1 For the initial void ratio, a is the compression coefficient, u/gamma w Is a pressure water head.
Preferably, the time conditions and boundary conditions for solving the consolidation differential equation are:
when t=0, 0<z<In the H state, the pore water pressure is equal to the vertical additional stress; when 0 is<t<When infinity, z=0, the pore water pressure is 0; when 0 is<t<When infinity, z=h,when t= infinity, 0<z<In the H state, the pore water pressure is 0;
wherein H is the soil thickness.
In addition, in yet another embodiment, the present invention may also be implemented by a construction settlement amount extraction apparatus combining machine learning and a soil mechanics model, which in a general embodiment, includes:
a processor; the method comprises the steps of,
and a memory in which computer program instructions are stored which, when executed by the processor, perform the building settlement amount extraction method of the combined machine learning and soil mechanics model described above.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), or the like.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (7)

1. The method for extracting the building settlement amount by combining machine learning and a soil mechanics model is characterized by comprising the following steps of:
step 1, acquiring SAR images repeatedly passing through the environment for a plurality of times in the same preset area within a preset time range, performing interference processing, extracting a high-coherence target point, modeling phase component information of the high-coherence target point, and separating each phase information to acquire ground subsidence information;
step 2, acquiring building load space position information and building age data in a preset area;
step 3, obtaining building additional stress;
step 4, acquiring annual ground water level drop values and compressed layer thickness data of a preset area;
step 5, respectively constructing a first ground subsidence model and a second ground subsidence model, stripping the subsidence value caused by underground water, and extracting the ground subsidence value caused by regional scale building load; the ground settlement model I takes the thickness data of a compression layer, the decline value of the groundwater level in each year, the building age data and the building additional stress as input and the settlement value in each year as output; the ground subsidence model II takes the thickness data of the compression layer and the annual ground water level drop value as input and the annual subsidence value as output;
step 6, constructing a consolidation settlement model, and evaluating the accuracy of the ground settlement value caused by the regional scale building load obtained in the step 5;
in the step 5, a first ground subsidence model and a second ground subsidence model are constructed based on machine learning;
in the step 6, the evaluating method is as follows:
selecting a region A less affected by building load as a background field, and constructing a consolidation settlement model A to verify the accuracy of the consolidation settlement model when no building load is distributed;
when the consolidation and settlement model A meets the precision requirement, selecting a target area B with building load distribution, constructing a consolidation and settlement model B under the combined action of building load, soil body self-weight stress and underground water exploitation, and obtaining a settlement simulation value M; comparing the monitoring value of the same SAR image data with the model simulation value, and evaluating the precision of the model;
simulating a ground subsidence value N of the area B when no building load is distributed by utilizing a consolidation subsidence model;
the difference value between M and N is the settlement value caused by the building load; verifying the subsidence value caused by the regional scale building load obtained by the first and second concurrent ground subsidence models in the step 5 by utilizing the subsidence value;
the consolidation and settlement model is constructed in the following manner:
the consolidation differential equation is established as follows:
solving the consolidation differential equation to obtain a sedimentation quantity value;
wherein,
in the above formulas, z is soil depth, C v For consolidation coefficient, t is any period of time, q represents flow, e 1 For the initial pore ratio, h is the water head, k is the permeability coefficient of the soil body, i is the hydraulic ramp down, A is the cross-sectional area, u is the pore water pressure, e 1 For the initial void ratio, a is the compression coefficient, u/gamma w Is a pressure water head.
2. The method according to claim 1, wherein the step 3 further comprises: performing thinning treatment on the high-coherence target point; generalizing the buildings in the preset area into rectangular uniform loads; additional stress for each high coherence target point is estimated.
3. The method of claim 1, wherein the time conditions and boundary conditions for solving the consolidation differential equation are:
when t=0, 0<z<In the H state, the pore water pressure is equal to the vertical additional stress; when 0 is<t<When infinity, z=0, the pore water pressure is 0; when 0 is<t<When infinity, z=h,when t= infinity, 0<z<In the H state, the pore water pressure is 0;
wherein H is the soil thickness.
4. The method according to claim 1, wherein the sedimentation value caused by stripping the groundwater in step 5 is achieved by: and calculating root mean square errors of the first ground subsidence model and the second ground subsidence model aiming at the high coherence point of the specific building age.
5. A system for extracting a building settlement amount by combining machine learning and a soil mechanics model, the system comprising:
the basic information acquisition module is used for acquiring ground subsidence information, building load space position information, building age data, building additional stress, annual ground water level drop value and compression layer thickness data in a preset area; the ground subsidence information is obtained by the following steps: based on SAR images repeatedly passing through a preset area for many times, carrying out interference processing, extracting a high-coherence target point, modeling phase component information of the high-coherence target point, and separating each phase information to obtain ground subsidence information;
the ground subsidence model module is used for constructing a first ground subsidence model and a second ground subsidence model, and the first ground subsidence model and the second ground subsidence model are combined to obtain a ground subsidence value caused by regional scale building load; the ground settlement model I takes the thickness data of a compression layer, the decline value of the groundwater level in each year, the building age data and the building additional stress as input and the settlement value in each year as output; the ground subsidence model II takes the thickness data of the compression layer and the annual ground water level drop value as input and the annual subsidence value as output;
the evaluation module is used for constructing a consolidation settlement model and evaluating the results obtained by the first and second simultaneous ground settlement models to determine the accuracy of the settlement value caused by the obtained regional scale building load;
the first ground subsidence model and the second ground subsidence model are constructed based on machine learning;
the evaluation mode of the evaluation module is as follows:
selecting a region A less affected by building load as a background field, and constructing a consolidation settlement model A to verify the accuracy of the consolidation settlement model when no building load is distributed;
selecting a target area B with building load distribution, constructing a consolidation settlement model B under the combined action of building load, soil body self-weight stress and underground water exploitation, and obtaining a settlement simulation value M; comparing the monitoring value of the same SAR image data with the model simulation value, and evaluating the precision of the model;
simulating a ground subsidence value N of the area B when no building load is distributed by utilizing a consolidation subsidence model;
the difference value between M and N is the settlement value caused by the building load; verifying the accuracy of the subsidence value caused by the regional scale building load obtained by the first ground subsidence model and the second ground subsidence model of the simultaneous ground subsidence model by utilizing the subsidence value;
the consolidation and settlement model is constructed in the following manner:
the consolidation differential equation is established as follows:
solving the consolidation differential equation to obtain a sedimentation quantity value;
wherein,
in the above formulas, z is soil depth, C v For consolidation coefficient, t is any period of time, q represents flow, e 1 For the initial pore ratio, h is the water head, k is the permeability coefficient of the soil body, i is the hydraulic ramp down, A is the cross-sectional area, u is the pore water pressure, e 1 For the initial void ratio, a is the compression coefficient, u/gamma w Is a pressure water head.
6. The system of claim 5, wherein the basic information acquisition module acquires the building additional stress by:
performing thinning treatment on the high-coherence target point; generalizing the buildings in the preset area into rectangular uniform loads; additional stress for each high coherence target point is estimated.
7. A building settlement amount extraction device combining machine learning and a soil mechanics model, the device comprising:
a processor; the method comprises the steps of,
a memory in which computer program instructions are stored which, when executed by the processor, perform the method of building settlement amount extraction for a combined machine learning and soil mechanics model as claimed in any one of the preceding claims 1 to 4.
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