CN113255035A - Method for fusing monitoring data of small and medium-sized bridges - Google Patents

Method for fusing monitoring data of small and medium-sized bridges Download PDF

Info

Publication number
CN113255035A
CN113255035A CN202110536825.5A CN202110536825A CN113255035A CN 113255035 A CN113255035 A CN 113255035A CN 202110536825 A CN202110536825 A CN 202110536825A CN 113255035 A CN113255035 A CN 113255035A
Authority
CN
China
Prior art keywords
data
stress
bridge
following
steps
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.)
Granted
Application number
CN202110536825.5A
Other languages
Chinese (zh)
Other versions
CN113255035B (en
Inventor
徐冬阳
游霏
赵佳峻
张逸群
常建涛
张文博
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Xidian University
Original Assignee
Xidian University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Xidian University filed Critical Xidian University
Priority to CN202110536825.5A priority Critical patent/CN113255035B/en
Publication of CN113255035A publication Critical patent/CN113255035A/en
Application granted granted Critical
Publication of CN113255035B publication Critical patent/CN113255035B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/13—Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/04—Ageing analysis or optimisation against ageing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/08—Thermal analysis or thermal optimisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/10—Noise analysis or noise optimisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Evolutionary Computation (AREA)
  • General Physics & Mathematics (AREA)
  • Geometry (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer Hardware Design (AREA)
  • Software Systems (AREA)
  • Artificial Intelligence (AREA)
  • Computing Systems (AREA)
  • Molecular Biology (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Data Mining & Analysis (AREA)
  • Mathematical Physics (AREA)
  • Computational Linguistics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biophysics (AREA)
  • Health & Medical Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)
  • Architecture (AREA)
  • Civil Engineering (AREA)
  • Structural Engineering (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)

Abstract

The invention relates to a method for fusing monitoring data of small and medium-sized bridges, which is characterized by comprising the following steps: at least including the following steps: 1) building a bridge model and a vehicle model; 2) acquiring data of a sensor, including acquiring data of the sensor under no load and the sensor under load; 3) processing the acquired data of the sensor; 4) comprehensive stress analysis is carried out on the bridge by detecting the factors of environment and load through the established model and the sensor data; 5) and giving the comprehensive stress state of the bridge under the influence of environment and load factors. The method is convenient for constructing a stress field model by integrating the environmental temperature and the bridge bearing mechanical model, so as to realize the bridge stress reduction analysis with higher precision.

Description

Method for fusing monitoring data of small and medium-sized bridges
Technical Field
The invention relates to a method for fusing monitoring data of small and medium-sized bridges, which realizes comprehensive stress analysis and evaluation on in-service bridges under the coupling effect of environmental and load multi-factors.
Background
The safety and durability problems of a large bridge structure in an operation state are more and more emphasized by people, and a plurality of practices are also paid, wherein a structural state monitoring system with research properties is installed on a Shanghai Xupu bridge, and the monitoring contents comprise vehicle load, elevation and natural vibration characteristics of a midspan girder, temperature and strain of a midspan section, and cable force and vibration level of a stay cable; the health monitoring system installed on the bridge of Nanjing Changjiang river is mainly used for monitoring the temperature, the wind speed and the wind direction, the earthquake, the impact of ships, the settlement of pier positions, the constant-load geometric linear shape, the structural vibration, the stress of rod pieces, the displacement of supports and the like. At present, a monitoring data analysis method for a large bridge is mature, but complex and complicated, and is not completely suitable for small and medium bridges. Compared with large bridges, bridge monitoring is not considered in the construction period of most of small and medium-sized bridges in service, so that the positioning of the monitoring sensors has difficulty, the monitoring data has high distortion possibility, and the monitoring cost, the type of the monitoring data, the monitoring range span and the load fatigue effect are greatly different from those of the large bridges. Due to the limitation of all factors, the realization of high-precision stress analysis of small and medium-sized bridges becomes a great difficulty.
Disclosure of Invention
In order to realize comprehensive stress analysis and evaluation of small and medium-sized bridges, the invention provides a method for fusing monitoring data of small and medium-sized bridges, so that a stress field model is constructed by integrating environmental temperature and a bridge bearing mechanical model, and the bridge stress reduction analysis with higher precision is realized.
The technical scheme adopted by the invention for solving the technical problems is as follows: a method for fusing monitoring data of small and medium-sized bridges is characterized by comprising the following steps: at least including the following steps:
1) building a bridge model and a vehicle model;
2) acquiring data of a sensor, including acquiring data of the sensor under no load and the sensor under load;
3) processing the acquired data of the sensor;
4) comprehensive stress analysis is carried out on the bridge by detecting the factors of environment and load through the established model and the sensor data;
5) and giving the comprehensive stress state of the bridge under the influence of environment and load factors.
The establishment of the vehicle model is realized by establishing a mechanical equation of the vehicle:
Figure BDA0003070002260000021
Figure BDA0003070002260000022
Figure BDA0003070002260000023
Figure BDA0003070002260000024
Msis the mass of the vehicle; m1 and m2 are the under-vehicle-front-part mass and the rear-part mass, respectively; ks is the product of1And ks2Front and rear suspension stiffness respectively; cs is1And cs2Is the damping constant of the front part of the vehicle and the damping constant of the rear part of the vehicle; kt1And kt2Is the stiffness of the front and rear tires; ct1And ct2Damping of the front and rear tires, respectively; i isvIs the moment of inertia body of the vehicle; z, Z1And Z2The vehicle shakes, namely the vehicle shakes up and down in the process of bridge running; is the pitch rotation of the vehicle; la1And la2Is the distance between the center of the vehicle and the front and back.
The bridge model building method comprises the following steps:
1) acquiring a distributed load applied to the bridge through a pose kinetic equation of the bridge;
Figure BDA0003070002260000031
EI，mband c represents flexural stiffness, mass and viscous damping per unit length, respectively, solving the equation:
Figure BDA0003070002260000032
where δ represents the Dirac function, δ (x-x)1) Because the vehicle equivalent model established before is equivalent to two points, so that the value can be obtained only at the point x1 or the point x 2;
2) the lateral displacement is along the normal direction by the shape function, so that the lateral displacement of the bridge can be described by the following equation:
Figure BDA0003070002260000033
wherein phinr(x) Is the shape function of the nth bending mode, the r-th span, ηt(t) is the corresponding generalized time-dependent normal coordinate;
3) introduce the shape function variable phinr(x) The variables, shape functions, can be solved homogeneously by using the following equations:
φnr(x)＝Anrsinβnrxr+Bnrcosβnrxr+Cnrsinhβnrxr+Dnrcosβnrxr
wherein beta isnrxrThe expression frequency parameter, i.e., the unit length in the nth mode, is given by the following equation:
Figure BDA0003070002260000041
4) all equations are put into solution, initial variables are input, boundary conditions are obtained, and the bridge is obtained by splicing a section of the bridge, so that all parameters at the end of the first section and the starting point of the second section should be equal, and the support displacement should be 0, namely the following equation:
yr(xr＝lr，t)＝0
y(r+1)(xr+1＝lr，t)＝0
finally, substituting all parameters into an equation to obtain a final vehicle-bridge kinetic equation:
Figure BDA0003070002260000042
where [ M ], [ C ] and [ K ] are the system mass, damping matrix and stiffness matrix, respectively. F (t) is a force vector, u (t) is a response vector, and vector u (t) includes generalized coordinates Z, θ, Z1, Z2, 1, 2, 3.., n; the size of the system matrix with nb bridge effective bending modes will be 4+ nb.
And 3) processing the data of the acquired sensor by the step 3) comprises data missing value processing and data noise processing.
The data missing value processing comprises the following steps:
firstly, importing data, wherein the data comprises: the sensor uploads stress monitoring, temperature monitoring, expansion joint monitoring and settlement monitoring data values every hour, then data are analyzed and processed, and an analysis processing algorithm of the adopted data is as follows:
step 1) recording all data of one day, wherein the data are uploaded once an hour, and only the number of times of the data in one day is needed, if the data only appear 23 times in a list in one day, one data is missing, all the existing data are traversed in a circulating mode, whether the data are missing in each day can be obtained, and if the data are missing, the data of the missing days can be obtained; storing the number of the missing data into a list for storage;
step 2) performing 24-hour traversal on the number of the missing data to obtain specific missing time, and storing the time into a record;
and 3) constructing a dataframe, storing all data to be inserted into the dataframe, adding a column to record the order of the dataframe, circularly traversing from the first data, storing the dataframe under the newly established dataframe if a missing value is met, otherwise, storing the dataframe in an original data table, combining the two dataframes, and sequencing according to the order field, thereby inserting the required data into the position which is required by the user.
The data noise processing comprises the following steps:
step 1) randomly selecting psi point sample points from training data as a sample subset and putting the psi point sample points into a root node of a tree;
step 2) randomly appointing a dimension, and randomly generating a cutting point p in the current node data, wherein the cutting point is generated between the maximum value and the minimum value of the appointed dimension in the current node data;
step 3) generating a hyperplane by using the cutting point, and then dividing the data space of the current node into 2 subspaces: placing data smaller than p in the specified dimension at the left child node of the current node, and placing data larger than or equal to p at the right child node of the current node;
step 4) recursion steps 2) and 3) in the child nodes, and new child nodes are continuously constructed until only one data in the child nodes or the child nodes reach the defined height;
step 5) whether to generate T isolated trees without circulation 1) to 4), generating T isolated trees iTree and continuing step 6)
Step 6) after T iTrees are obtained, the iForest training is finished, and then the generated iForest is used for evaluating test data; for each data point xiTraverse each isolated tree iTree, calculate point xiAverage height h in the forestxiNormalizing the average height of all the points; the formula for calculating the outlier score is as follows:
Figure BDA0003070002260000061
wherein:
Figure BDA0003070002260000062
Hithe harmonic number can be estimated through an Euler constant, and the smaller the score is, the more abnormal the data is;
step 7) adopting a quartile range method noise processing of a statistical model, namely, considering data beyond the range of the quartile range as an abnormal value greatly influenced by noise, wherein the quartile range (IQR) is the difference value of an upper quartile and a lower quartile, and the method is specified by taking 1.5 times of IQR as a standard: points exceeding the upper quartile by +1.5 times the IQR distance or the lower quartile by-1.5 times the IQR distance are abnormal values;
and 8) finding an abnormal value which exceeds the upper quartile by 1.5 times or the lower quartile by 1.5 times, replacing the abnormal value with na, and filling the abnormal value with a median to obtain required data.
The sensor comprises:
the stress sensors are distributed and fixed at different stress points of the bridge and used for detecting stress strain values so as to be integrated with the final comprehensive stress, and the obtained final stress is introduced into the model to be solved;
a temperature sensor: the system is used for detecting the temperature of the current environment, and extra stress can be generated by the extra temperature so as to be integrated with the final comprehensive stress, and the final stress is obtained and then is brought into a model for solving;
the displacement sensor is used for non-contact detection and is used for detecting the displacement change of a current bridge relative to an unchangeable reference point, namely the stress concentration is generated by the pavement settlement change-pavement settlement, so that extra stress is generated and is integrated with the final integrated stress, and the obtained final stress is introduced into a model for solving;
a humidity sensor: the method is used for detecting the humidity of the current environment, and the mechanical property of the bridge material can be influenced.
The comprehensive stress analysis of the bridge by the established model and the sensor data detection under the environment and load multi-factors comprises the following steps: analysis of bridge stress by temperature and analysis of bridge stress by settlement.
The analysis of the temperature to the bridge stress is realized by establishing a temperature field distribution model in the bridge, and the method comprises the following steps:
1) the three-dimensional unsteady heat conduction equation of the concrete structure is listed through the solid heat conduction theory:
Figure BDA0003070002260000081
in the formula: a (a ═ lambda/c gamma) is the thermal conductivity coefficient of the concrete, and lambda is the thermal conductivity coefficient of the concrete; c is the specific heat of the concrete; gamma is the gravity of the concrete; omega is the heat given off per unit volume of concrete.
Wherein the four parameters can be confirmed by the materials used for bridge construction;
2) confirming a temperature stress field by the temperature field, and dividing a bridge section grid by adopting triangular units to obtain a unit temperature stress displacement expression as follows:
Figure BDA0003070002260000082
when the unit division degree reaches a certain degree, the displacement of any point in the unit can be approximately considered to be a linear function of the unit coordinate, namely:
Figure BDA0003070002260000083
wherein u (x, y) v (x, y) are all displacement functions of the triangular units;
3) and substituting the triangle unit coordinates (i, j, m) obtained by division into the parallel simultaneous expression to obtain:
Figure BDA0003070002260000084
since the displacement at the cell boundaries is linear, the three directional components of the temperature stress can be represented by:
Figure BDA0003070002260000091
the analysis of the bridge stress by settlement comprises the following steps:
the analysis of the bridge stress by settlement adopts a stress area method to calculate the settlement;
firstly, dividing and calculating soil layers according to natural layering surfaces of foundation soil, introducing the concept of soil layer average additional stress, converting the additional stress in the depth range from z (i-1) to zi in the foundation below the center of a substrate into the distributed stress in the rectangular distribution in the same depth range according to the equal area principle through the average additional stress coefficient, and then calculating the compression amount of the soil layers according to the rectangular distributed stress condition, wherein the sum of the compression amounts of all the soil layers is the calculated settlement amount of the foundation;
the average settlement of the foundation can be expressed as:
Figure BDA0003070002260000092
in the formula: n is the number of soil layers divided by the settlement calculation depth range; p is a radical of0Applying pressure to the substrate; esiIs the compressive modulus of the ith layer;
Figure BDA0003070002260000093
adding stress to mean direction of stiffnessCoefficient of zi-1And ziRespectively represent the distance from the substrate;
the derivation process of the basic calculation formula of the stress area method is as follows:
Figure BDA0003070002260000094
in the formula
Figure BDA0003070002260000095
Is 0 to z below the center point of the substrateiDepth range additional stress area, using AiTo represent;
Figure BDA0003070002260000101
is 0 to z below the center point of the substratei-1Depth range additional stress area, using Ai-1To indicate. And Δ Ai＝Ai-Ai-1Is below the center of the substrate zi-1～ziAdditional stress area within the depth range, and thus the amount of compression of the ith layer as described above, can again be expressed as
Figure BDA0003070002260000102
According to the relationship between the additional stress area and the additional stress coefficient and the additional stress, the stress area can be expressed as
Figure BDA0003070002260000103
Figure BDA0003070002260000104
Therefore it has the advantages of
Figure BDA0003070002260000105
To improve the calculation accuracy, the specification specifies a peak-settling calculation empirical coefficient of Shinshou, which is calculated according to the above formula
Figure BDA0003070002260000106
The ratio of the final peak settling amount s calculated according to the ground peak settling observation data to the s calculated by the above formula is defined, and is generally determined according to the region peak settling observation data and experience, and can also be found according to the following table 1:
TABLE 1 Settlement calculation empirical coefficients
Figure BDA0003070002260000107
Figure BDA0003070002260000108
In summary, the final settlement amount of the foundation by the stress area method is calculated as follows:
Figure BDA0003070002260000111
finally, the two sides of the equation are derived, simplified and arranged to obtain an expression of the stress on the settlement, which is as follows:
Figure BDA0003070002260000112
in the formula: Δ piTo center z in the foundation below the center of the foundationi-1～ziAdding stress variation to the depth range; p0 as a substrate applied pressure EsiThe compressive modulus of the ith layer, kPa;
Figure BDA0003070002260000113
calculating an empirical coefficient for peak settling;
Figure BDA0003070002260000114
is the derivative of the final amount of foundation settling.
Step 5) is to convert all external influence factors into stress, the stress is the final influence factor, and the only parameter required by the fatigue theory is the stress cycle number, so that the bridge state is evaluated according to the fatigue theory:
estimating the fatigue degree:
the degree of fatigue damage is calculated according to the following formula:
Figure BDA0003070002260000115
estimating the fatigue life:
given fatigue life LfComprises the following steps:
Figure BDA0003070002260000116
wherein D isIIs cumulative damage index, DjIs incremental injury, njIs the number of stress cycles, NjThe number of cycles is at a constant stress level, and achieving this number of cycles results in fatigue failure.
The invention has the advantages that: firstly, a mechanical equivalent model is established, basic mechanical equations are respectively obtained by simplifying a trolley and a bridge, on the basis, a team considers that a temperature field exists at the temperature to generate extra stress, so that the stress field generated by the temperature field is taken into consideration range, the bridge generates settlement when being loaded, stress concentration is generated, the stress concentration generated by settlement is also taken into consideration range, and then long-time memory neural networks are established to fuse and predict multidimensional data, so that the multidimensional data obtained by monitoring the bridge can be fused and the stress prediction in the future can be predicted for a period of time. And (3) evaluating the current health condition of the bridge by a fatigue theory according to the final stress obtained by fusing the multidimensional data, thereby realizing comprehensive stress analysis evaluation and service life prediction of the bridge.
Drawings
The invention is further illustrated with reference to the accompanying drawings of embodiments:
FIG. 1 is a mechanical equivalent model of a vehicle;
FIG. 2 is a structural variation;
FIG. 3 is data missing value handling code;
FIG. 4 is a denoising algorithm;
FIG. 5 is data noise detection;
FIG. 6 is a schematic diagram of a quartile bit difference method;
FIG. 7 shows the result of the four-part bit difference denoising process
FIG. 8 is a stress area method for calculating the final settlement of the foundation.
Detailed Description
A method for fusing monitoring data of small and medium-sized bridges at least comprises the following steps:
1) modeling
At least comprises the following steps: establishing a bridge model, establishing a vehicle model and establishing a temperature field distribution model in the bridge.
(1) Building vehicle model
The vehicle is modeled as a rigid beam connected in front and back in consideration of the vehicle stress at the key part of the span girder bridge. The mass of the body is centered at the center of mass of the beam, referred to as the spring-like mass. The mass system of the wheel, tire and partial suspension is known as the unsprung mass. From fig. 1, the mechanical equation of the vehicle can be derived:
Figure BDA0003070002260000131
Figure BDA0003070002260000132
Figure BDA0003070002260000133
Figure BDA0003070002260000134
Msis the mass of the vehicle; m1 and m2 are the under-vehicle-front-part mass and the rear-part mass, respectively; ks is the product of1And ks2Front and rear suspension stiffness respectively; cs is1And cs2Are the damping constant of the vehicle front portion and the damping constant of the vehicle rear portion. kt1And kt2Is the stiffness of the front and rear tires; ct1And ct2Damping for the front and rear tires respectively. I isvIs the moment of inertia body of the vehicle; z, Z1And Z2The vehicle shakes, namely the vehicle shakes up and down in the process of bridge running; is the pitch rotation of the vehicle. la1And la2Is the distance between the center of the vehicle and the front and back.
(2) Building bridge model
The multi-span highway bridge was modeled as a continuous beam with inflexible support, with mass, stiffness and damping characteristics assumed to be uniform along the beam. Firstly, structural stress simulation is carried out on a bridge in matlab, the bridge is found to generate deflection when stressed, and the simulation result is shown in figure 2:
based on the simulation result of matlab, the pose of the bridge can be described by the following kinetic equation:
Figure BDA0003070002260000141
EI，mband c represents flexural rigidity, mass and viscous damping per unit length, respectively, above which the vehicle, due to which the bridge is subjected to forces, where f (x)rT) is the point of contact and the distributed load applied to the bridge is represented by f (x)rT), the expression is given as follows:
Figure BDA0003070002260000142
where δ represents the Dirac function, δ (x-x)1) Because the previously established vehicle equivalent model is equivalent to two points, so that only two points are used for the equivalent modelThere will be values at either x1 or x 2.
So far, the force that the bridge is subjected to has been, so in order to solve the equation, the displacement of the bridge is needed, the displacement of the bridge is represented by adopting the lateral displacement, because of the consistency of the nodes, the lateral displacement is more conveniently represented, the lateral displacement is along the normal direction through the shape function, and therefore the lateral displacement of the bridge can be described by the following equation:
Figure BDA0003070002260000151
wherein phinr(x) Is the shape function of the nth bending mode, the r-th span, ηtAnd (t) is the corresponding generalized time-dependent normal coordinate, so that the shape function of any node can be obtained along the normal direction.
At this time, phi is newly introducednr(x) Variables, that is, shape function variables, shape functions can be solved homogeneously by using the following equation:
φnr(x)＝Anrsinβnrxr+Bnrcosβnrxr+Cnrsinhβnrxr+Dnrcosβnrxr
wherein beta isnrxrThe expression frequency parameter, i.e., the unit length in the nth mode, is given by the following equation:
Figure BDA0003070002260000152
at this time, all parameters are present, the equation is all brought into solution, and in order to solve the equation, we must have initial variables to obtain the boundary condition, since the bridge is obtained by splicing a section of the bridge, all parameters at the end of the first section and the start of the second section should be equal, and the support displacement should be 0, that is, the following equation:
yr(xr＝lr，t)＝0
y(r+1)(xr+1＝lr，t)＝0
finally, substituting all parameters into an equation to obtain a final vehicle-bridge kinetic equation:
Figure BDA0003070002260000153
where [ M ], [ C ] and [ K ] are the system mass, damping matrix and stiffness matrix, respectively. F (t) is a force vector, u (t) is a response vector, and the vector u (t) contains generalized coordinates Z, θ, Z1, Z2, 1, 2, 3. The size of the system matrix with nb bridge effective bending modes will be 4+ nb.
(3) Establishing a temperature field distribution model in the bridge:
to establish a temperature field distribution model inside a bridge, a heat transfer mode needs to be firstly determined. Generally, heat transfer is divided into thermal conduction, thermal convection, and thermal radiation. The bridge is usually in the external environment that blows to the sun, changes the multiterminal, and the sun passes through thermal radiation and makes the bridge outside intensification, inside the bridge, usually with heat-conduction mode to inside transfer heat, because the thermal radiation intensity that the difference received is different, leads to the inside temperature field that exists of bridge. According to the temperature data returned by the bridge observation points in real time, the following three-dimensional unsteady bridge heat conduction equation can be established:
regardless of the discontinuity within a concrete structure, theoretical studies conducted by solid heat conduction theory, assuming that the concrete material is homogeneous and isotropic, can list the three-dimensional unsteady thermal conductivity equation of a concrete structure:
Figure BDA0003070002260000161
in the formula: a (a ═ lambda/c gamma) is the thermal conductivity coefficient of the concrete, and lambda is the thermal conductivity coefficient of the concrete; c is the specific heat of the concrete; gamma is the gravity of the concrete; omega is the heat given off per unit volume of concrete.
Wherein the four parameters can be confirmed by the materials used for bridge construction.
The temperature stress field was confirmed from the temperature field:
for the bridge structure, the temperature change of the main beam along the longitudinal direction is slow, the temperature field distribution in the transverse direction can be directly considered under the general condition, then the three-dimensional temperature field is converted into the two-dimensional temperature field, the problem can be simplified into the problem of plane strain under the elastic mechanics, and then the solution of the problem of plane strain of the elastic mechanics can be solved by adopting a finite element method. The triangular units are adopted to divide the cross section grids of the bridge, and the obtained unit temperature stress displacement expression is as follows:
Figure BDA0003070002260000171
when the unit division degree reaches a certain degree, the displacement of any point in the unit can be approximately considered to be a linear function of the unit coordinate, namely:
Figure BDA0003070002260000172
where u (x, y) v (x, y) are all displacement functions of the triangle elements. And substituting the triangle unit coordinates (i, j, m) obtained by division into the parallel simultaneous expression to obtain:
Figure BDA0003070002260000173
since the displacement at the cell boundaries is linear, the three directional components of the temperature stress can be represented by:
Figure BDA0003070002260000174
2) acquiring data of a sensor, including acquiring data of the sensor under no load and the sensor under load;
in the above established model, the model can be solved only by external stress and data, and the sensor is firstly required to collect data, and the adopted sensor includes:
and the stress sensors are distributed and fixed at different stress points of the bridge and used for detecting stress strain values so as to be integrated with the final comprehensive stress, and the obtained final stress is introduced into the model to be solved.
A temperature sensor: the method is used for detecting the temperature of the current environment, and extra stress can be generated by the extra temperature so as to be integrated with the final integrated stress, and the final stress is obtained and then is brought into a model for solving.
The displacement sensor is used for non-contact detection and is used for detecting the displacement change of a current bridge relative to an unchanging reference point, namely the stress concentration is generated by the road surface settlement change-road surface settlement, so that extra stress is generated and is integrated with the final integrated stress, and the obtained final stress is introduced into a model for solving.
A humidity sensor: the method is used for detecting the humidity of the current environment, and the mechanical property of the bridge material can be influenced.
3) Processing the acquired data of the sensor;
the collected data is definitely noisy and has missing values, and because data missing is inevitably generated in the data transmission process, environmental noise and the like exist, preprocessing of the data is very necessary. The data is divided into two steps, whether missing values exist or not is firstly detected, if the missing values exist, the missing values are supplemented, and then the data is denoised.
Data missing value handling
Missing data in the training dataset may reduce the fit of the model or may lead to model bias, which may lead to erroneous predictions or classifications because the behavior and relationships of the variables are not analyzed correctly.
Firstly, data are imported, the data of the project are all derived from data measured by the sensors, the sensors upload data values of stress monitoring, temperature monitoring, expansion joint monitoring and settlement monitoring every hour, monitoring points of each data type are marked by capital letters A-H, and numbers represent different acquisition sensors, for example, A1 and A2 represent two different sensors of the A point. Data is a time sequence, so whether missing values exist on the time sequence or not needs to be considered in the first step, an algorithm is written to detect the time sequence, specific missing moments can be found out in real time, then all data are analyzed and processed, and 3 delta criterion is adopted to insert the data.
All data of one day are recorded, and as the data are uploaded once an hour, only the number of times of a certain day is needed, and if the certain day only appears 23 times in the list, one data is missing. And circularly traversing all the existing data to obtain whether the data is missing every day, and if the data is missing, knowing how many days the data is missing. And storing the number of the missing data into a list for storage.
And performing a 24-hour traversal method on the number of the missing data to obtain the specific missing time, and storing the time into a record.
Constructing a dataframe, storing all the data to be inserted into the dataframe, adding a column to record the order of the dataframe, starting from the first data to cycle through, if a missing value is encountered, storing the dataframe under the newly established dataframe, otherwise, storing the dataframe in the original data table, merging the two dataframes, and sorting according to the order field, thereby inserting the required data into the desired position, wherein the specific code is shown in FIG. 3.
Data noise processing
Data Noise (Noise): disturbance data in the dataset (data that is inaccurate for scene description), i.e. random errors or variances in the measured variables. Observed quantity (Measurement) is real Data (True Data) + Noise (Noise), and Noise has a large influence on model training, which not only increases Data quantity and causes errors in original information, but also increases calculation quantity, increases computer memory and calculation overhead, and also increases calculation errors. Particularly, the linear algorithm obtains the optimal solution through iteration, if the data contains a large amount of noise data, the convergence speed of the data is greatly influenced, and even the accuracy of the training generated model has great side effect. Methods commonly used for removing noise include 3 δ denoising, dbscan denoising, isolated forest (Isolation Tree), etc., as shown in fig. 4.
Here we first analyze the data using isolated forests, the following is the principle of isolated forest algorithm:
psi point sample points are randomly selected from the training data as a sample subset and placed into the root node of the tree.
A dimension (feature) is randomly specified, and a cut point p is randomly generated in the current node data (the cut point is generated between the maximum value and the minimum value of the specified dimension in the current node data).
A hyperplane is generated by the cut point, and then the data space of the current node is divided into 2 subspaces: and placing the data smaller than p in the specified dimension at the left child node of the current node, and placing the data larger than or equal to p at the right child node of the current node.
And (3) recursion steps (2) and (3) in the child nodes, and new child nodes are continuously constructed until only one datum in the child nodes (the cutting can not be continued) or the child nodes reach the defined height.
And (4) circulating from (1) to (4) until T isolated trees iTree are generated.
After T itrees are obtained, the iForest training is terminated and we can then evaluate the test data with the generated ifoest. For each data point xiTraverse each isolated tree iTree, calculate point xiAverage height h in the forestxiAnd normalizing the average height of all the points. The formula for calculating the outlier score is as follows:
Figure BDA0003070002260000211
wherein:
Figure BDA0003070002260000212
Hiis a harmonic number and can be estimated by the euler constant. Smaller scores indicate more anomalies in the data.
Applying the above algorithm to our data set to obtain the result shown in fig. 5, it is obvious that the data has large noise in the transmission communication process, and we solve this problem by using the quartile bit difference method.
The noise processing adopts a quartile bit difference method of a statistical model, namely data beyond the range of the quartile guard distance is considered as an abnormal value greatly influenced by noise, and the quartile distance (IQR) is the difference value of an upper quartile and a lower quartile. And we stipulate by the norm 1.5 times IQR: points that exceed the upper quartile +1.5 times the IQR distance, or the lower quartile-1.5 times the IQR distance, are outliers.
As fig. 6 is the algorithm we adopt, in the process we find the outlier that exceeds the upper quartile by 1.5 times the distance of the quartile or the lower quartile by 1.5 times the distance, replace the outlier by na, and then fill it with the median.
The following fig. 7 shows a quartile difference constant detection image of bridge stress and temperature data.
4) Substituting the data into the model;
5) comprehensive stress analysis is carried out on the bridge by detecting the factors of environment and load through the established model and the sensor data;
after the sensor collects data, the temperature value, the settlement displacement value and the stress value exist, and now the temperature value, the settlement displacement value and the stress value need to be integrated, so that the final stress index is obtained. (Mr. Prior Art points of innovation)
The relationship between temperature and stress was first analyzed:
analysis of temperature versus bridge stress
The cause of the temperature stress:
the temperature stress means that the temperature field in the bridge changes along with the change of solar radiation and external temperature. However, the elastic modulus of the bridge is high, the internal points are constrained mutually and are constrained by external supports and the like, the bridge cannot deform freely, and temperature stress occurs at the same time.
The research significance of the temperature stress is as follows:
the temperature stress belongs to constantly changing dynamic load, and analysis and fatigue test research according to the bridge collapse accident in the past show that constantly changing dynamic load can make structures such as bridges and the like produce fatigue failure lower than a static load strength standard value, and the failure belongs to brittle failure, and the danger degree is far higher than that of energetic failure.
Analysis of bridge stress by settlement
The settlement of the bridge deck can generate certain stress concentration, so that additional stress is generated, and particularly, the serious settlement of one section of the bridge can have serious influence on the service life of the bridge. We used the area of stress method to calculate the settlement.
In the final settlement calculation by the stress area method, soil layers are divided and calculated according to the natural layering surface of foundation soil, the concept of the average additional stress of the soil layers is introduced, the additional stress in the depth range from z (i-1) to zi in the foundation below the center of the substrate is converted into the distributed stress in the rectangular distribution in the same depth range according to the principle of equal area through the average additional stress coefficient, then the compression amount of the soil layers is calculated according to the rectangular distributed stress condition, and the sum of the compression amounts of all the soil layers is the calculated settlement amount of the foundation, which is shown in figure 8. The theoretical basis of the average sedimentation amount can be expressed as
Figure BDA0003070002260000231
In the formula: n is the number of soil layers divided by the settlement calculation depth range; p is a radical of0Applying pressure to the substrate; esiIs the compressive modulus of the ith layer;
Figure BDA0003070002260000232
adding a stress coefficient for the average direction of stiffness, zi-1And ziRespectively, represent the distance from the substrate.
The derivation process of the basic calculation formula of the stress area method is as follows:
Figure BDA0003070002260000233
in the formula
Figure BDA0003070002260000234
Is 0 to z below the center point of the substrateiDepth range additional stress area, using AiTo represent;
Figure BDA0003070002260000235
is 0 to z below the center point of the substratei-1Depth range additional stress area, using Ai-1To indicate. And Δ Ai＝Ai-Ai-1Is below the center of the substrate zi-1～ziAdditional stress area within the depth range, and thus the amount of compression of the ith layer as described above, can again be expressed as
Figure BDA0003070002260000236
According to the relationship between the additional stress area and the additional stress coefficient and the additional stress, the stress area can be expressed as
Figure BDA0003070002260000241
Figure BDA0003070002260000242
Therefore it has the advantages of
Figure BDA0003070002260000243
To improve the calculation accuracy, the specification specifies a peak-settling calculation empirical coefficient of Shinshou, which is calculated according to the above formula
Figure BDA0003070002260000244
Is defined as based on foundation settlementThe ratio of the final peak settling amount s calculated from the peak observation data to s calculated by the above formula is generally determined according to the local peak settling observation data and experience, and can also be found according to the following table.
TABLE 2 Settlement calculation empirical coefficients
Figure BDA0003070002260000245
Figure BDA0003070002260000246
In summary, the final settlement amount of the foundation by the stress area method is calculated as follows:
Figure BDA0003070002260000247
finally, we can get the expression of the stress on the settlement by deriving the two sides of the above equation, and simplifying the arrangement, as follows:
Figure BDA0003070002260000248
in the formula: Δ piTo center z in the foundation below the center of the foundationi-1～ziAdding stress variation to the depth range; p0 as a substrate applied pressure EsiThe compressive modulus of the ith layer, kPa;
Figure BDA0003070002260000249
calculating an empirical coefficient for peak settling;
Figure BDA0003070002260000251
is the derivative of the final amount of foundation settling.
To date, we have converted the effects of temperature and sedimentation into stress in the simplest expression.
6) And giving the comprehensive stress state of the bridge under the influence of environment and load factors.
All external influence factors are converted into stress, the stress is the final influence factor, and the only parameter required by the fatigue theory is the stress cycle number, so that the bridge state is evaluated according to the fatigue theory.
Fatigue level estimation
When the material or the part is subjected to stress higher than the fatigue limit, the material is damaged to a certain extent in each cycle, and the damage can be accumulated, and when the damage is accumulated to a critical value, the material is damaged, so that the estimation of the fatigue degree of the bridge material is crucial.
The degree of fatigue damage is calculated according to the following formula:
Figure BDA0003070002260000252
wherein DIIs cumulative damage index, DjIs incremental injury, njIs the number of stress cycles, NjThe number of cycles is at a constant stress level, and achieving this number of cycles results in fatigue failure.
Fatigue life estimation
Fatigue life is predicted using the Miner's criterion and the cumulative damage accumulation under variable amplitude loading for a particular stress history sample is given by: the process of evaluating the cumulative damage index is repeated for all simulated response samples and the average is used to find the fatigue life, given the fatigue life LfComprises the following steps:
Figure BDA0003070002260000253

Claims (10)

1. a method for fusing monitoring data of small and medium-sized bridges is characterized by comprising the following steps: at least including the following steps:
1) building a bridge model and a vehicle model;
2) acquiring data of a sensor, including acquiring data of the sensor under no load and the sensor under load;
3) processing the acquired data of the sensor;
4) comprehensive stress analysis is carried out on the bridge by detecting the factors of environment and load through the established model and the sensor data;
5) and giving the comprehensive stress state of the bridge under the influence of environment and load factors.
2. The method for fusing the monitoring data of the small and medium-sized bridges as claimed in claim 1, wherein the method comprises the following steps: the establishment of the vehicle model is realized by establishing a mechanical equation of the vehicle:
Figure FDA0003070002250000011
Figure FDA0003070002250000012
Figure FDA0003070002250000013
Figure FDA0003070002250000014
Msis the mass of the vehicle; m1 and m2 are the under-vehicle-front-part mass and the rear-part mass, respectively; ks is the product of1And ks2Front and rear suspension stiffness respectively; cs is1And cs2Is the damping constant of the front part of the vehicle and the damping constant of the rear part of the vehicle; kt1And kt2Is the stiffness of the front and rear tires; ct1And ct2Damping of the front and rear tires, respectively; i isvIs the moment of inertia body of the vehicle; z, Z1And Z2The vehicle shakes, namely the vehicle shakes up and down in the process of bridge running; is the pitch rotation of the vehicle; la1And la2At a distance from the center of the vehicleDistance.
3. The method for fusing the monitoring data of the small and medium-sized bridges as claimed in claim 1, wherein the method comprises the following steps: the bridge model building method comprises the following steps:
1) acquiring a distributed load applied to the bridge through a pose kinetic equation of the bridge;
Figure FDA0003070002250000021
EI，mband c represents flexural stiffness, mass and viscous damping per unit length, respectively, solving the equation:
Figure FDA0003070002250000022
where δ represents the Dirac function, δ (x-x)1) Because the vehicle equivalent model established before is equivalent to two points, so that the value can be obtained only at the point x1 or the point x 2;
2) the lateral displacement is along the normal direction by the shape function, so that the lateral displacement of the bridge can be described by the following equation:
Figure FDA0003070002250000031
wherein phinr(x) Is the shape function of the nth bending mode, the r-th span, ηt(t) is the corresponding generalized time-dependent normal coordinate;
3) introduce the shape function variable phinr(x) The variables, shape functions, can be solved homogeneously by using the following equations:
φnr(x)＝Anrsinβnrxr+Bnrcosβnrxr+Cnrsinhβnrxr+Dnrcosβnrxr
wherein beta isnrxrThe expression frequency parameter, i.e., the unit length in the nth mode, is given by the following equation:
Figure FDA0003070002250000032
4) all equations are put into solution, initial variables are input, boundary conditions are obtained, and the bridge is obtained by splicing a section of the bridge, so that all parameters at the end of the first section and the starting point of the second section should be equal, and the support displacement should be 0, namely the following equation:
yr(xr＝lr，t)＝0
y(r+1)(xr+1＝lr，t)＝0
finally, substituting all parameters into an equation to obtain a final vehicle-bridge kinetic equation:
Figure FDA0003070002250000033
where [ M ], [ C ] and [ K ] are the system mass, damping matrix and stiffness matrix, respectively. F (t) is a force vector, u (t) is a response vector, and vector u (t) includes generalized coordinates Z, θ, Z1, Z2, 1, 2, 3.., n; the size of the system matrix with nb bridge effective bending modes will be 4+ nb.
4. The method for fusing the monitoring data of the small and medium-sized bridges as claimed in claim 1, wherein the method comprises the following steps: and 3) processing the data of the acquired sensor by the step 3) comprises data missing value processing and data noise processing.
5. The method for fusing the monitoring data of the small and medium-sized bridges as claimed in claim 4, wherein the method comprises the following steps: the data missing value processing comprises the following steps:
firstly, importing data, wherein the data comprises: the sensor uploads stress monitoring, temperature monitoring, expansion joint monitoring and settlement monitoring data values every hour, then data are analyzed and processed, and an analysis processing algorithm of the adopted data is as follows:
step 1) recording all data of one day, wherein the data are uploaded once an hour, and only the number of times of the data in one day is needed, if the data only appear 23 times in a list in one day, one data is missing, all the existing data are traversed in a circulating mode, whether the data are missing in each day can be obtained, and if the data are missing, the data of the missing days can be obtained; storing the number of the missing data into a list for storage;
step 2) performing 24-hour traversal on the number of the missing data to obtain specific missing time, and storing the time into a record;
and 3) constructing a dataframe, storing all data to be inserted into the dataframe, adding a column to record the order of the dataframe, circularly traversing from the first data, storing the dataframe under the newly established dataframe if a missing value is met, otherwise, storing the dataframe in an original data table, combining the two dataframes, and sequencing according to the order field, thereby inserting the required data into the position which is required by the user.
6. The method for fusing the monitoring data of the small and medium-sized bridges as claimed in claim 4, wherein the method comprises the following steps: the data noise processing comprises the following steps:
step 1) randomly selecting psi point sample points from training data as a sample subset and putting the psi point sample points into a root node of a tree;
step 2) randomly appointing a dimension, and randomly generating a cutting point p in the current node data, wherein the cutting point is generated between the maximum value and the minimum value of the appointed dimension in the current node data;
step 3) generating a hyperplane by using the cutting point, and then dividing the data space of the current node into 2 subspaces: placing data smaller than p in the specified dimension at the left child node of the current node, and placing data larger than or equal to p at the right child node of the current node;
step 4) recursion steps 2) and 3) in the child nodes, and new child nodes are continuously constructed until only one data in the child nodes or the child nodes reach the defined height;
step 5) whether to generate T isolated trees without circulation 1) to 4), generating T isolated trees iTree and continuing step 6)
Step 6) after T iTrees are obtained, the iForest training is finished, and then the generated iForest is used for evaluating test data; for each data point xi, traversing each isolated tree iTree, calculating the average height hxi of the point xi in the forest, and normalizing the average height of all the points; the formula for calculating the outlier score is as follows:
Figure FDA0003070002250000051
wherein:
Figure FDA0003070002250000052
Hithe harmonic number can be estimated through an Euler constant, and the smaller the score is, the more abnormal the data is;
step 7) adopting a quartile range method noise processing of a statistical model, namely, considering data beyond the range of the quartile range as an abnormal value greatly influenced by noise, wherein the quartile range (IQR) is the difference value of an upper quartile and a lower quartile, and the method is specified by taking 1.5 times of IQR as a standard: points exceeding the upper quartile by +1.5 times the IQR distance or the lower quartile by-1.5 times the IQR distance are abnormal values;
and 8) finding an abnormal value which exceeds the upper quartile by 1.5 times or the lower quartile by 1.5 times, replacing the abnormal value with na, and filling the abnormal value with a median to obtain required data.
7. The method for fusing the monitoring data of the small and medium-sized bridges as claimed in claim 1, wherein the method comprises the following steps: the sensor comprises:
the stress sensors are distributed and fixed at different stress points of the bridge and used for detecting stress strain values so as to be integrated with the final comprehensive stress, and the obtained final stress is introduced into the model to be solved;
a temperature sensor: the system is used for detecting the temperature of the current environment, and extra stress can be generated by the extra temperature so as to be integrated with the final comprehensive stress, and the final stress is obtained and then is brought into a model for solving;
the displacement sensor is used for non-contact detection and is used for detecting the displacement change of a current bridge relative to an unchangeable reference point, namely the stress concentration is generated by the pavement settlement change-pavement settlement, so that extra stress is generated and is integrated with the final integrated stress, and the obtained final stress is introduced into a model for solving;
a humidity sensor: the method is used for detecting the humidity of the current environment, and the mechanical property of the bridge material can be influenced.
8. The method for fusing the monitoring data of the small and medium-sized bridges as claimed in claim 1, wherein the method comprises the following steps: the comprehensive stress analysis of the bridge by the established model and the sensor data detection under the environment and load multi-factors comprises the following steps: analysis of bridge stress by temperature and analysis of bridge stress by settlement.
9. The method for fusing the monitoring data of the small and medium-sized bridges as claimed in claim 8, wherein the method comprises the following steps: the analysis of the temperature to the bridge stress is realized by establishing a temperature field distribution model in the bridge, and the method comprises the following steps:
1) the three-dimensional unsteady heat conduction equation of the concrete structure is listed through the solid heat conduction theory:
Figure FDA0003070002250000071
in the formula: a (a ═ lambda/c gamma) is the thermal conductivity coefficient of the concrete, and lambda is the thermal conductivity coefficient of the concrete; c is the specific heat of the concrete; gamma is the gravity of the concrete; omega is the heat given off per unit volume of concrete.
Wherein the four parameters can be confirmed by the materials used for bridge construction;
2) confirming a temperature stress field by the temperature field, and dividing a bridge section grid by adopting triangular units to obtain a unit temperature stress displacement expression as follows:
Figure FDA0003070002250000072
when the unit division degree reaches a certain degree, the displacement of any point in the unit can be approximately considered to be a linear function of the unit coordinate, namely:
Figure FDA0003070002250000081
wherein u (x, y) v (x, y) are all displacement functions of the triangular units;
3) and substituting the triangle unit coordinates (i, j, m) obtained by division into the parallel simultaneous expression to obtain:
Figure FDA0003070002250000082
since the displacement at the cell boundaries is linear, the three directional components of the temperature stress can be represented by:
Figure FDA0003070002250000083
the analysis of the bridge stress by settlement comprises the following steps:
the analysis of the bridge stress by settlement adopts a stress area method to calculate the settlement;
firstly, dividing and calculating soil layers according to natural layering surfaces of foundation soil, introducing the concept of soil layer average additional stress, converting the additional stress in the depth range from z (i-1) to zi in the foundation below the center of a substrate into the distributed stress in the rectangular distribution in the same depth range according to the equal area principle through the average additional stress coefficient, and then calculating the compression amount of the soil layers according to the rectangular distributed stress condition, wherein the sum of the compression amounts of all the soil layers is the calculated settlement amount of the foundation;
the average settlement of the foundation can be expressed as:
Figure FDA0003070002250000091
in the formula: n is the number of soil layers divided by the settlement calculation depth range; p is a radical of0Applying pressure to the substrate; esiIs the compressive modulus of the ith layer;
Figure FDA0003070002250000092
adding a stress coefficient for the average direction of stiffness, zi-1And ziRespectively represent the distance from the substrate;
the derivation process of the basic calculation formula of the stress area method is as follows:
Figure FDA0003070002250000093
in the formula
Figure FDA0003070002250000094
Is 0 to z below the center point of the substrateiDepth range additional stress area, using AiTo represent;
Figure FDA0003070002250000095
is 0 to z below the center point of the substratei-1Depth range additional stress area, using Ai-1To indicate. And Δ Ai＝Ai-Ai-1Is below the center of the substrate zi-1～ziAdditional stress area within the depth range, and thus the amount of compression of the ith layer as described above, can again be expressed as
Figure FDA0003070002250000096
According to the relationship between the additional stress area and the additional stress coefficient and the additional stress, the stress area can be expressed as
Figure FDA0003070002250000097
Figure FDA0003070002250000098
Therefore it has the advantages of
Figure FDA0003070002250000099
To improve the calculation accuracy, the specification specifies a peak-settling calculation empirical coefficient of Shinshou, which is calculated according to the above formula
Figure FDA0003070002250000101
The ratio of the final peak settling amount s calculated according to the ground peak settling observation data to the s calculated by the above formula is defined, and is generally determined according to the region peak settling observation data and experience, and can also be found according to the following table 1:
TABLE 1 Settlement calculation empirical coefficients
Figure FDA0003070002250000102
Figure FDA0003070002250000103
In summary, the final settlement amount of the foundation by the stress area method is calculated as follows:
Figure FDA0003070002250000104
finally, the two sides of the equation are derived, simplified and arranged to obtain an expression of the stress on the settlement, which is as follows:
Figure FDA0003070002250000105
in the formula: Δ piTo center z in the foundation below the center of the foundationi-1～ziAdding stress variation to the depth range; p0 as a substrate applied pressure EsiThe compressive modulus of the ith layer, kPa;
Figure FDA0003070002250000106
calculating an empirical coefficient for peak settling;
Figure FDA0003070002250000107
is the derivative of the final amount of foundation settling.
10. The method for fusing the monitoring data of the small and medium-sized bridges as claimed in claim 1, wherein the method comprises the following steps: step 5) is to convert all external influence factors into stress, the stress is the final influence factor, and the only parameter required by the fatigue theory is the stress cycle number, so that the bridge state is evaluated according to the fatigue theory:
estimating the fatigue degree:
the degree of fatigue damage is calculated according to the following formula:
Figure FDA0003070002250000111
estimating the fatigue life:
given fatigue life LfComprises the following steps:
Figure FDA0003070002250000112
wherein D isIIs cumulative damage index, DjIs incremental injury, njIs the number of stress cycles, NjThe number of cycles is at a constant stress level, and achieving this number of cycles results in fatigue failure.
CN202110536825.5A 2021-05-17 2021-05-17 Method for fusing monitoring data of small and medium-sized bridges Active CN113255035B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110536825.5A CN113255035B (en) 2021-05-17 2021-05-17 Method for fusing monitoring data of small and medium-sized bridges

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110536825.5A CN113255035B (en) 2021-05-17 2021-05-17 Method for fusing monitoring data of small and medium-sized bridges

Publications (2)

Publication Number Publication Date
CN113255035A true CN113255035A (en) 2021-08-13
CN113255035B CN113255035B (en) 2024-04-02

Family

ID=77182397

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110536825.5A Active CN113255035B (en) 2021-05-17 2021-05-17 Method for fusing monitoring data of small and medium-sized bridges

Country Status (1)

Country Link
CN (1) CN113255035B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116881819A (en) * 2023-09-07 2023-10-13 成都理工大学 Stay cable working state monitoring method based on isolated forest

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108763763A (en) * 2018-05-28 2018-11-06 东南大学 A kind of bridge structure strain-responsive abnormity early warning method
CN109060393A (en) * 2018-08-09 2018-12-21 招商局重庆交通科研设计院有限公司 A kind of bridge structure dead load response Time Domain Fusion analysis method
WO2020042753A1 (en) * 2018-08-30 2020-03-05 长沙理工大学 Method for predicting service life of reinforced concrete bridge under conditions of seasonal corrosion and fatigue coupling

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108763763A (en) * 2018-05-28 2018-11-06 东南大学 A kind of bridge structure strain-responsive abnormity early warning method
CN109060393A (en) * 2018-08-09 2018-12-21 招商局重庆交通科研设计院有限公司 A kind of bridge structure dead load response Time Domain Fusion analysis method
WO2020042753A1 (en) * 2018-08-30 2020-03-05 长沙理工大学 Method for predicting service life of reinforced concrete bridge under conditions of seasonal corrosion and fatigue coupling

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
李爱群;丁幼亮;王浩;郭彤;: "桥梁健康监测海量数据分析与评估――"结构健康监测"研究进展", 中国科学:技术科学, no. 08 *
辛学忠;苏木标;陈树礼;夏禾;: "大跨度铁路桥梁健康状态评估的统计对比诊断方法研究", 铁道学报, no. 02 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116881819A (en) * 2023-09-07 2023-10-13 成都理工大学 Stay cable working state monitoring method based on isolated forest
CN116881819B (en) * 2023-09-07 2023-11-14 成都理工大学 Stay cable working state monitoring method based on isolated forest

Also Published As

Publication number Publication date
CN113255035B (en) 2024-04-02

Similar Documents

Publication Publication Date Title
CN107330176B (en) Strain gauge and accelerometer joint layout method based on structural modal estimation
Lee et al. Gage‐free stress estimation of a beam‐like structure based on terrestrial laser scanning
Umesha et al. Crack detection and quantification in beams using wavelets
CN105930571B (en) Big across steel bridge correction method for finite element model based on unit temperature response monitor value
CN103776416B (en) The indirect measurement method that a kind of antenna structure based on strain transducer is out of shape
CN108846149B (en) Structure distributed dynamic load identification method based on multi-source uncertainty
Tessler et al. Shape sensing of plate and shell structures undergoing large displacements using the inverse finite element method
CN112989491B (en) Strain field reconstruction visualization method and system based on load strain linear superposition
CN107862170B (en) Finite element model correction method based on dynamic polycondensation
CN111609805A (en) Tunnel structure state diagnosis method based on full-distribution strain measurement point section curvature
Nguyen et al. An approach to calculating large strain accumulation for discrete element simulations of granular media
CN112949131B (en) Probability damage positioning vector method for continuous bridge cluster damage diagnosis
Mirrashid et al. Innovative computational intelligence-based model for vulnerability assessment of RC frames subject to seismic sequence
CN113255035A (en) Method for fusing monitoring data of small and medium-sized bridges
CN115455793A (en) High-rise structure complex component stress analysis method based on multi-scale model correction
CN106997410B (en) Damage occurrence judgment method based on modal strain energy
Shadan et al. Structural damage identification based on strain frequency response functions
Zhang et al. Internal force monitoring and estimation of a long‐span ring beam using long‐gauge strain sensing
Oh et al. A measured data correlation-based strain estimation technique for building structures using convolutional neural network
Lyu et al. Condition assessment of heritage timber buildings in operational environments
Chen et al. Condition assessment on thermal effects of a suspension bridge based on SHM oriented model and data
CN110321593B (en) Bridge dynamic deflection vibration mode matrix construction method based on accumulated modal mass participation rate
CN112231821A (en) Reliability analysis-based basic seismic isolation structure system optimization method
CN113688465B (en) Aircraft structural strength digital twin method based on combination of load and state
Wong et al. Structural health monitoring of cable-supported bridges in Hong Kong

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