CN117268332B - Method and system for monitoring non-uniform settlement of building - Google Patents
Method and system for monitoring non-uniform settlement of building Download PDFInfo
- Publication number
- CN117268332B CN117268332B CN202311549166.4A CN202311549166A CN117268332B CN 117268332 B CN117268332 B CN 117268332B CN 202311549166 A CN202311549166 A CN 202311549166A CN 117268332 B CN117268332 B CN 117268332B
- Authority
- CN
- China
- Prior art keywords
- sedimentation
- data
- sequence
- settlement
- building
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000012544 monitoring process Methods 0.000 title claims abstract description 30
- 238000004062 sedimentation Methods 0.000 claims abstract description 87
- 238000013528 artificial neural network Methods 0.000 claims abstract description 14
- 238000012806 monitoring device Methods 0.000 claims abstract description 11
- 238000012216 screening Methods 0.000 claims abstract description 5
- 238000012545 processing Methods 0.000 claims description 15
- 239000013598 vector Substances 0.000 claims description 10
- 239000011159 matrix material Substances 0.000 claims description 7
- 238000009825 accumulation Methods 0.000 claims description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000004590 computer program Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000008054 signal transmission Effects 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C5/00—Measuring height; Measuring distances transverse to line of sight; Levelling between separated points; Surveyors' levels
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/14—Receivers specially adapted for specific applications
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention belongs to the technical field of settlement monitoring, and relates to a method and a system for monitoring uneven settlement of a building, wherein settlement deformation amounts of the building are monitored at different time points through a GNSS monitoring device to form a settlement deformation data sequence; describing a functional relation between discrete point data by using a curve fitting method, and screening the data sequence; extracting trend values from the standard sedimentation data sequence by using a time-varying model; predicting and outputting settlement amount by using the extracted trend value through the RBF neural network; and carrying out consistency treatment on the output sedimentation prediction sequence to obtain a sedimentation prediction sequence after consistency treatment, and forming a sedimentation table.
Description
Technical Field
The invention belongs to the technical field of settlement monitoring, and particularly relates to a method and a system for monitoring uneven settlement of a building.
Background
Infrastructure settlement is one of the important contents of urban management work, and is a reflection of the compression of soil layers inside on the ground surface, and once disaster is formed, the disaster is extremely difficult to recover. Along with the acceleration of the urban process, the economic losses caused by ground subsidence and other hazards are gradually highlighted, and the phenomenon that the more developed urban economy is, the more serious the ground subsidence hazard is.
With the development of urban construction, high-rise buildings become one of system engineering with faster development in recent years, settlement monitoring becomes a necessary measure for safe construction of the high-rise buildings, a large amount of discrete and random monitoring data are required to be processed due to the influence of a plurality of factors such as complex engineering geological conditions, rules are sought, particularly, a time sequence for forecasting the settlement monitoring data is widely applied to modeling and forecasting the data, and a statistical rule of random events is sought by utilizing modern statistics and information processing technology.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method for monitoring uneven settlement of a building, which comprises the following steps:
s1, monitoring sedimentation deformation amounts of a building at different time points through a GNSS monitoring device to form a sedimentation deformation data sequence;
s2, describing a functional relation between discrete point data by using a curve fitting method, and screening the data sequence to form a standard sedimentation data sequence;
s3, extracting trend values from the standard sedimentation data sequence by using a time-varying model;
s4, predicting settlement amount by using the extracted trend value through the RBF neural network and outputting the settlement amount;
s5, carrying out consistency treatment on the output sedimentation prediction sequence to obtain a sedimentation prediction sequence after consistency treatment, and forming a sedimentation table.
Further, in step S2, let the sedimentation observation data sequence of a certain monitoring point at m different time points be l= { l 1 ，l 2 ，…，l i ，…l m An ith time point t i Observation data l of (2) i The expansion of (2) is:
（1）；
wherein n is the degree of polynomial fitting, a 0 ，a 1 ，a 2 ，…，a n To expand the coefficient, m are notThe expansion conversion of the sedimentation observation data sequence at the same time point into a matrix L expression is as follows: l=bx;
the equation coefficients of polynomial fitting can be obtained by combining the least square principle, namely:
X=(B T B) -1 B T L；
equation coefficient a to be solved 0 ,a 1 ,…,a n Substituting the equation (1) to obtain a model based on a curve fitting equation.
Further, eliminating the sedimentation observation data of the time points which do not accord with the curve fitting equation, and reserving the sedimentation observation data of the time points which accord with the curve fitting equation to form a standard sedimentation data sequence as follows:the method comprises the steps of carrying out a first treatment on the surface of the M represents the number of standard sedimentation data, +.>Is the kth standard sedimentation data.
Further, the kth standard sedimentation data and the kth-1 standard sedimentation dataAnd->The time interval between them is t k -t k-1 Wherein t is k Is->And->The total time interval between the two standard sedimentation data is given by the following one accumulation formula:
=/>，k=1；
=/>,k≥1；
wherein,is->Is added up by one time->Is->Is added up once;
the time-varying model is:
；
；
wherein x is 1 Is thatA, u is the parameter to be identified.
Further, in step S4, P trend values within a unit time interval are used as a model input sequence
x （1） (I)：
I=1, 2, …, P; p is the number of samples;
for input sequence x （1） (I) Performing secondary prediction to generate a secondary prediction sequence x （2） (I) The method comprises the following steps:
,I=1,2,…，P；
wherein r is the iteration number;
total accumulated sedimentation amount S (I):
S(I)=s(I)+s(0)=x (2) (I)+s(0)；
wherein s (0) is the initial settling amount.
Further, predicting the total accumulated settlement amount S (I) by adopting a neural network to obtain a settlement prediction sequence;
output of RBF neural network：
,J=1,2,…，P；
In the method, in the process of the invention,predicted sequence for sedimentation->The j-th element of (a)>A connection weight value of the q-th vector, h Jq Is the basis function of the q-th vector; the total number of vectors is Q.
Further, in step S5, the sedimentation prediction sequenceThe elements in (2) are subjected to consistency treatment according to the following formula to obtain a sedimentation prediction sequence after consistency treatment>：
；
Wherein,is constant (I)>Is->Maximum element of->Is->Is the smallest element in the (c) set.
The invention also provides a monitoring system for the uneven settlement of the building, which is used for realizing the monitoring method for the uneven settlement of the building, and comprises the following steps: the system comprises a GNSS monitoring device, a data acquisition unit, a data processing unit, a settlement prediction unit and a prediction result output unit;
the GNSS monitoring device monitors the settlement deformation amount of the building at different time points.
The data acquisition unit monitors settlement deformation data of the building at different time points to form a settlement deformation data sequence, and utilizes a curve fitting method to describe a functional relation among discrete point data, so as to screen the data sequence and form a standard settlement data sequence.
And the data processing unit is used for extracting trend values from the standard sedimentation data sequence by using the time-varying model.
And the settlement prediction unit is used for predicting settlement amount through the RBF neural network by using the extracted trend value and outputting the settlement amount.
And the predicted result output unit is used for carrying out consistency processing on the sedimentation predicted sequence output by the sedimentation predicted unit to obtain a sedimentation predicted sequence after consistency processing, and forming a sedimentation table.
Compared with the prior art, the invention has the following beneficial technical effects:
monitoring the settlement deformation quantity of the building at different time points by using a GNSS monitoring device to form a settlement deformation data sequence; describing a functional relation between discrete point data by using a curve fitting method, and screening the data sequence; extracting trend values from the standard sedimentation data sequence by using a time-varying model; predicting and outputting settlement amount by using the extracted trend value through the RBF neural network; and carrying out consistency treatment on the output sedimentation prediction sequence to obtain a sedimentation prediction sequence after consistency treatment, and forming a sedimentation table. The method solves the problems of variability and complexity of uneven settlement of the building and improves the prediction precision.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flow chart of a method of monitoring differential settlement of a building according to the present invention;
FIG. 2 is a schematic diagram of a system for monitoring differential settlement of a building according to the present invention.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
In the drawings of the specific embodiments of the present invention, in order to better and more clearly describe the working principle of each element in the system, the connection relationship of each part in the device is represented, but only the relative positional relationship between each element is clearly distinguished, and the limitations on the signal transmission direction, connection sequence and the structure size, dimension and shape of each part in the element or structure cannot be constructed.
As shown in fig. 1, a flow chart of a method for monitoring uneven settlement of a building according to the present invention includes the following steps:
s1, monitoring the settlement deformation quantity of the building at different time points through a GNSS monitoring device to form a settlement deformation data sequence.
S2, describing a functional relation among the discrete point data by using a curve fitting method, and screening the data sequence to form a standard sedimentation data sequence.
The curve fitting method is as follows:
let the sedimentation observation data sequence of a certain monitoring point at m different time points be l= { l 1 ，l 2 ，…，l i ，…l m Modeling according to polynomial curve fitting method, i-th time point t i Observation data l of (2) i The expansion of (2) is:
（1）；
wherein n is the degree of polynomial fitting, a 0 ，a 1 ，a 2 ，…，a n For the expansion coefficients, the expansion of the sedimentation observation data sequences at m different time points is converted into a matrix L expression as follows: l=bx.
；
Wherein B is a time point matrix, and X is a coefficient matrix.
The equation coefficients of polynomial fitting can be obtained by combining the least square principle, namely:
X=(B T B) -1 B T L。
equation coefficient a to be solved 0 ,a 1 ,…,a n Substituting the equation (1) to obtain a model based on a curve fitting equation. Eliminating sedimentation observation data of time points which do not accord with the curve fitting equation, and reserving sedimentation observation data of time points which accord with the curve fitting equation to form a standard sedimentation data sequence as follows:the method comprises the steps of carrying out a first treatment on the surface of the M represents the number of standard sedimentation data, +.>Is the kth standard sedimentation data.
S3, extracting trend values from the standard sedimentation data sequence by using a time-varying model.
Extracting trend values by using a time-varying model:
the standard sedimentation data sequences formed were:the method comprises the steps of carrying out a first treatment on the surface of the Kth standard sedimentation data and kth-1 standard sedimentation data +.>And->The time interval between them is t k -t k-1 Wherein t is k Is->And->The total time interval between the two standard sedimentation data is given by the following one accumulation formula:
=/>，k=1；
=/>,k≥1；
wherein,is->Is one-time accumulation of (a)，/>Is->Is added up once;
the time-varying model is:
；
；
wherein x is （1） Is thatA, u is the parameter to be identified.
S4, predicting settlement amount through the RBF neural network by using the extracted trend value and outputting the settlement amount.
To P trend values x within a unit time interval 1 Input sequence x as a model 1 (I)：
I=1, 2, …, P; p is the number of samples;
for input sequence x （1） (I) Performing secondary prediction to generate a secondary prediction sequence x （2） (I) The method comprises the following steps:
,I=1,2,…，P；
wherein r is the iteration number;
the number of samples selected in this step settles the part of the convergence phase of the table, so it is necessary to add an initial settlement s (0) to be equal to the total cumulative settlement:
S(I)=s(I)+s(0)=x (2) (I)+s(0)。
predicting total accumulated sedimentation S (I) by using a neural network to obtain a sedimentation prediction sequence。
Output of RBF neural networkThe method comprises the following steps:
,J=1,2,…，P；
in the method, in the process of the invention,predicted sequence for sedimentation->The j-th element of (a)>A connection weight value of the q-th vector, h Jq Is the basis function of the q-th vector; the total number of vectors is Q.
；
To expand constant, c q Is the q-th vector parameter.
S5, carrying out consistency processing on the sedimentation prediction sequence output by the sedimentation prediction unit to obtain a sedimentation prediction sequence after consistency processing, and forming a sedimentation table.
Sedimentation prediction sequenceThe elements in (2) are subjected to consistency treatment according to the following formula to obtain a sedimentation prediction sequence after consistency treatment>：
；
Wherein,is a constant and can be determined experimentally.
Wherein,is->Maximum element of->Is->Is the smallest element in the (c) set.
As shown in the following table, the sedimentation prediction sequence after the consistency processing is prepared into a sedimentation data table, and the sedimentation data table is output and displayed.
The invention also provides a structural schematic diagram of a monitoring system for the uneven settlement of the building, and fig. 2 is a structural schematic diagram of the monitoring system for the uneven settlement of the building, wherein the monitoring system comprises: the system comprises a GNSS monitoring device, a data acquisition unit, a data processing unit, a settlement prediction unit and a prediction result output unit.
The GNSS monitoring device monitors the settlement deformation amount of the building at different time points.
The data acquisition unit monitors settlement deformation data of the building at different time points to form a settlement deformation data sequence, and utilizes a curve fitting method to describe a functional relation among the discrete point data so as to screen the data sequence.
And the data processing unit is used for extracting trend values from the standard sedimentation data sequence by using the time-varying model.
And the settlement prediction unit is used for predicting settlement amount through the RBF neural network by using the extracted trend value and outputting the settlement amount.
And the predicted result output unit is used for carrying out consistency processing on the sedimentation predicted sequence output by the sedimentation predicted unit to obtain a sedimentation predicted sequence after consistency processing, and forming a sedimentation table.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted across a computer-readable storage medium. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (4)
1. A method for monitoring uneven settlement of a building, comprising the steps of:
s1, monitoring sedimentation deformation amounts of a building at different time points through a GNSS monitoring device to form a sedimentation deformation data sequence;
s2, describing a functional relation between discrete point data by using a curve fitting method, and screening the data sequence to form a standard sedimentation data sequence;
let the sedimentation observation data sequence of a certain monitoring point at m different time points be l= { l 1 ，l 2 ，…，l i ，…l m An ith time point t i Observation data l of (2) i The expansion of (2) is:
（1）；
wherein n is the degree of polynomial fitting, a 0 ，a 1 ，a 2 ，…，a n For the expansion coefficients, the expansion of the sedimentation observation data sequences at m different time points is converted into a matrix L expression as follows: l=bx;
；
wherein B is a time point matrix, and X is a coefficient matrix;
the equation coefficients of polynomial fitting can be obtained by combining the least square principle, namely:
X=(B T B) -1 B T L；
equation coefficient a to be solved 0 ,a 1 ,…,a n Substituting the model into the model (1) to obtain a model based on a curve fitting equation;
eliminating sedimentation observation data of time points which do not accord with the curve fitting equation, and reserving sedimentation observation data of time points which accord with the curve fitting equation to form a standard sedimentation data sequence as follows:the method comprises the steps of carrying out a first treatment on the surface of the M represents the number of standard sedimentation data, +.>Is the kth standard sedimentation data;
s3, extracting trend values from the standard sedimentation data sequence by using a time-varying model;
kth standard sedimentation data and kth-1 standard sedimentation dataAnd->The time interval between them is t k -t k-1 Wherein t is k Is thatAnd->The total time interval between the two standard sedimentation data is given by the following one accumulation formula:
=/>，k=1；
=/>,k≥1；
wherein,is->Is added up by one time->Is->Is added up once;
the time-varying model is:
；
；
wherein x is （1） Is thatA, u are parameters to be identified;
s4, predicting and outputting settlement amount through a neural network by using the extracted trend value;
s5, carrying out consistency treatment on the output sedimentation prediction sequence to obtain a sedimentation prediction sequence after consistency treatment, and forming a sedimentation table;
sedimentation prediction sequenceThe elements in (2) are subjected to consistency treatment according to the following formula to obtain a sedimentation prediction sequence after consistency treatment>：
；
Wherein,is constant (I)>Is->Maximum element of->Is->Is the smallest element in the (c) set.
2. The method for monitoring the differential settlement of a building according to claim 1, wherein in the step S4, P trend values within a unit time interval are used as the model input sequence x （1） (I)：
I=1, 2, …, P; p is the number of samples;
for input sequence x （1） (I) Performing secondary prediction to generate a secondary prediction sequence x （2） (I) The method comprises the following steps:
,I=1,2,…，P；
wherein r is the iteration number;
total accumulated sedimentation amount S (I):
S(I)=s(I)+s(0)=x (2) (I)+s(0)；
wherein s (0) is the initial settling amount.
3. The method for monitoring uneven settlement of a building according to claim 2, wherein a neural network is adopted to predict the total accumulated settlement amount S (I) to obtain a settlement prediction sequence;
output of RBF neural network：
,J=1,2,…，P；
In the method, in the process of the invention,predicted sequence for sedimentation->The j-th element of (a)>A connection weight value of the q-th vector, h Jq Is the basis function of the q-th vector; the total number of vectors is Q.
4. A system for monitoring the differential settlement of a building, characterized by implementing a method for monitoring the differential settlement of a building as claimed in any one of claims 1 to 3, comprising: the system comprises a GNSS monitoring device, a data acquisition unit, a data processing unit, a settlement prediction unit and a prediction result output unit;
the GNSS monitoring device monitors the settlement deformation amount of the building at different time points;
the data acquisition unit monitors settlement deformation data of the building at different time points to form a settlement deformation data sequence, and utilizes a curve fitting method to describe a functional relation among discrete point data, so as to screen the data sequence and form a standard settlement data sequence;
the data processing unit is used for extracting trend values from the standard sedimentation data sequence by using a time-varying model;
the settlement prediction unit is used for predicting settlement amount through the RBF neural network by using the extracted trend value and outputting the settlement amount;
and the predicted result output unit is used for carrying out consistency processing on the sedimentation predicted sequence output by the sedimentation predicted unit to obtain a sedimentation predicted sequence after consistency processing, and forming a sedimentation table.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311549166.4A CN117268332B (en) | 2023-11-21 | 2023-11-21 | Method and system for monitoring non-uniform settlement of building |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311549166.4A CN117268332B (en) | 2023-11-21 | 2023-11-21 | Method and system for monitoring non-uniform settlement of building |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117268332A CN117268332A (en) | 2023-12-22 |
| CN117268332B true CN117268332B (en) | 2024-02-06 |
Family
ID=89206666
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311549166.4A Active CN117268332B (en) | 2023-11-21 | 2023-11-21 | Method and system for monitoring non-uniform settlement of building |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117268332B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117725843B (en) * | 2024-02-08 | 2024-04-30 | 天津大学 | House structure deformation prediction method based on deep learning |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR102232918B1 (en) * | 2020-10-30 | 2021-03-26 | 재단법인 경북아이티융합 산업기술원 | Subsidence sensor of real-time artificial intelligence based building condition diagnosis system |
| CN115077474A (en) * | 2022-06-14 | 2022-09-20 | 天津华北地质勘查总院 | Ground settlement trend prediction method and system based on machine learning |
| CN217604973U (en) * | 2022-07-11 | 2022-10-18 | 元泰(山东)检测鉴定有限公司 | Building engineering foundation pit settlement detection device |
| CN115494501A (en) * | 2022-09-29 | 2022-12-20 | 南宁市勘测设计院集团有限公司 | Urban infrastructure deformation monitoring method based on high-resolution PS-InSAR |
| CN219178547U (en) * | 2022-12-15 | 2023-06-13 | 广西戈宏检测科技有限公司 | But cyclic utilization subsides observation point instrument |
| CN116465361A (en) * | 2023-03-14 | 2023-07-21 | 沈阳建筑大学 | Foundation settlement and side slope deformation monitoring and processing method |
| CN116929293A (en) * | 2023-07-18 | 2023-10-24 | 河北省北斗导航位置服务有限公司 | Intelligent building settlement deformation monitoring system based on Beidou |
| CN116956433A (en) * | 2023-08-09 | 2023-10-27 | 苏州科技大学 | PPLN-based time sequence combination prediction model |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8282571B2 (en) * | 2009-10-15 | 2012-10-09 | Win Patrick H | Biaxial test head |
-
2023
- 2023-11-21 CN CN202311549166.4A patent/CN117268332B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR102232918B1 (en) * | 2020-10-30 | 2021-03-26 | 재단법인 경북아이티융합 산업기술원 | Subsidence sensor of real-time artificial intelligence based building condition diagnosis system |
| CN115077474A (en) * | 2022-06-14 | 2022-09-20 | 天津华北地质勘查总院 | Ground settlement trend prediction method and system based on machine learning |
| CN217604973U (en) * | 2022-07-11 | 2022-10-18 | 元泰(山东)检测鉴定有限公司 | Building engineering foundation pit settlement detection device |
| CN115494501A (en) * | 2022-09-29 | 2022-12-20 | 南宁市勘测设计院集团有限公司 | Urban infrastructure deformation monitoring method based on high-resolution PS-InSAR |
| CN219178547U (en) * | 2022-12-15 | 2023-06-13 | 广西戈宏检测科技有限公司 | But cyclic utilization subsides observation point instrument |
| CN116465361A (en) * | 2023-03-14 | 2023-07-21 | 沈阳建筑大学 | Foundation settlement and side slope deformation monitoring and processing method |
| CN116929293A (en) * | 2023-07-18 | 2023-10-24 | 河北省北斗导航位置服务有限公司 | Intelligent building settlement deformation monitoring system based on Beidou |
| CN116956433A (en) * | 2023-08-09 | 2023-10-27 | 苏州科技大学 | PPLN-based time sequence combination prediction model |
Non-Patent Citations (1)
| Title |
|---|
| 建筑物沉降监测时序分析方法与预测应用;刘志强;勘 察 科 学 技 术(第6期);52-54 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117268332A (en) | 2023-12-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117268332B (en) | Method and system for monitoring non-uniform settlement of building | |
| Kaul | Stochastic characterization of earthquakes through their response spectrum | |
| Copeland et al. | Reconstructing the inflaton potential: In principle and in practice | |
| Uzielli et al. | Risk analysis for the Ancona landslide—II: estimation of risk to buildings | |
| Spence et al. | An efficient framework for the reliability-based design optimization of large-scale uncertain and stochastic linear systems | |
| TWI464443B (en) | Artificial intelligence earthquake early warning method and earthquake detecting system | |
| US10634580B2 (en) | Systems and methods for analyzing flutter test data using damped sine curve fitting with the closed form shape fit | |
| Yuan et al. | Modified dam deformation monitoring model considering periodic component contained in residual sequence | |
| CN110736400B (en) | Underwater drilling blasting vibration velocity calculation method considering internal rock structure | |
| Sudret et al. | Assessment of the lognormality assumption of seismic fragility curves using non-parametric representations | |
| CN104615866A (en) | Service life prediction method based on physical statistic model | |
| CN105674943A (en) | General multipoint non-linear overall deformation prediction method | |
| Askari et al. | Application of Kalman filtering methods to online real-time structural identification: A comparison study | |
| Shu et al. | A multi-task learning-based automatic blind identification procedure for operational modal analysis | |
| CN108228959A (en) | Using the method for Random censorship estimating system virtual condition and using its wave filter | |
| Huang et al. | Application of optimized GM (1, 1) model based on EMD in landslide deformation prediction | |
| Guo et al. | Groundwater depth forecasting using configurational entropy spectral analyses with the optimal input | |
| US8549012B2 (en) | Processing exception handling | |
| CN115329973A (en) | Simulation method, device, equipment and storage medium | |
| Song et al. | A sliding sequence importance resample filtering method for rolling bearings remaining useful life prediction based on two Wiener-process models | |
| Yan et al. | One dimensional time-domain non-linear site seismic response analysis program integrating two hysteresis models of soil | |
| CN109409591A (en) | A kind of historical relic displacement prediction method and device | |
| CN113204855B (en) | Stratum shortening rate determining method, system, equipment and readable medium | |
| CN117725843B (en) | House structure deformation prediction method based on deep learning | |
| Wang et al. | Investigation of granite deformation process under axial load using LSTMBased architectures |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |