CN106780164B - Efficient bridge structure damage identification system - Google Patents
Efficient bridge structure damage identification system Download PDFInfo
- Publication number
- CN106780164B CN106780164B CN201611248084.6A CN201611248084A CN106780164B CN 106780164 B CN106780164 B CN 106780164B CN 201611248084 A CN201611248084 A CN 201611248084A CN 106780164 B CN106780164 B CN 106780164B
- Authority
- CN
- China
- Prior art keywords
- stress
- bridge structure
- data
- bridge
- module
- 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
- 238000012545 processing Methods 0.000 claims abstract description 22
- 230000005540 biological transmission Effects 0.000 claims abstract description 13
- 238000007418 data mining Methods 0.000 claims abstract description 10
- 238000000034 method Methods 0.000 claims abstract description 9
- 238000006073 displacement reaction Methods 0.000 claims description 28
- 238000011156 evaluation Methods 0.000 claims description 24
- 229910000831 Steel Inorganic materials 0.000 claims description 9
- 239000010959 steel Substances 0.000 claims description 9
- 230000002068 genetic effect Effects 0.000 claims description 4
- 238000012935 Averaging Methods 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 claims description 3
- 238000005065 mining Methods 0.000 abstract 1
- 238000005265 energy consumption Methods 0.000 description 3
- 238000012937 correction Methods 0.000 description 2
- 238000013210 evaluation model Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/08—Construction
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M99/00—Subject matter not provided for in other groups of this subclass
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N3/00—Investigating strength properties of solid materials by application of mechanical stress
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N3/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N3/20—Investigating strength properties of solid materials by application of mechanical stress by applying steady bending forces
-
- 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
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Geometry (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Biochemistry (AREA)
- Analytical Chemistry (AREA)
- Chemical & Material Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Business, Economics & Management (AREA)
- Structural Engineering (AREA)
- Tourism & Hospitality (AREA)
- Evolutionary Computation (AREA)
- Economics (AREA)
- Pure & Applied Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- General Engineering & Computer Science (AREA)
- General Business, Economics & Management (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Computational Mathematics (AREA)
- Civil Engineering (AREA)
- Architecture (AREA)
- Aviation & Aerospace Engineering (AREA)
- Bridges Or Land Bridges (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The invention discloses an efficient bridge structure damage identification system which comprises a data mining module, a data transmission module, a data processing module and a damage early warning module, wherein the data mining module is used for mining a bridge structure; the data mining module acquires original data influencing a bridge structure through a sensor and transmits the original data to the data processing module through the data transmission module; the data processing module is used for processing the received data; and the damage early warning module is used for evaluating the damage condition of the bridge according to the processed data. The method has high data processing speed, and can rapidly give the damage grade of the bridge structure, thereby timely and accurately evaluating and early warning the damage condition of the bridge structure.
Description
Technical Field
The invention relates to the technical field of bridge engineering, in particular to a high-efficiency bridge structure damage identification system.
Background
The bridge is the throat of urban traffic, plays an important role in national economy and people's life, and brings damages of different degrees to a plurality of concrete bridge structures along with the increase of urban traffic, vehicle overload, environmental change and other reasons, so that effective damage identification on the bridge structures gradually becomes a considerable research focus for ensuring traffic safety. The traditional bridge structure damage identification system is limited to the change of static and dynamic parameters of a bridge structure at a certain moment, and due to the complexity of the bridge structure and the diversity of factors influencing the damage of the bridge structure, the damage condition of the bridge structure cannot be quickly and effectively predicted by the traditional bridge structure damage identification system.
Disclosure of Invention
In order to solve the above problems, the present invention aims to provide an efficient bridge structure damage identification system.
The purpose of the invention is realized by adopting the following technical scheme:
a high-efficiency bridge structure damage identification system comprises a data mining module, a data transmission module, a data processing module and a damage early warning module; the data mining module acquires original data influencing a bridge structure through a sensor and transmits the original data to the data processing module through the data transmission module; the data processing module is used for processing the received data; and the damage early warning module is used for evaluating the damage condition of the bridge according to the processed data.
Preferably, the data transmission module is composed of a wireless sensor network, the wireless sensor network adopts a routing mechanism based on genetic algorithm,defining the initial path of a network node to be Hj＝{h1,h2,...,hpAnd then, defining a fitness function in the algorithm as:
wherein s isjIs the residual energy of node j, c (h)j) Is path hjPath length of (e), e (h)j) Is path hjThe inventor gives empirical values for the three weights according to a large amount of field practices, and the three weights are respectively: 0.3, 0.2, 0.5.
Preferably, the acquired parameter data includes horizontal displacement of the bridge structure, lateral displacement, stress of the concrete structure portion of the bridge, and stress of the steel structure portion of the bridge.
Preferably, the data processing module is configured to perform modification and average calculation on the raw data.
Preferably, the bridge structure damage early warning module comprises a deformation evaluation submodule, a stress evaluation submodule and a comprehensive early warning submodule.
The invention has the beneficial effects that: the method can be used for identifying the damage condition of the bridge structure in real time and efficiently, and the accuracy of the bridge structure identification system is improved.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
FIG. 1 is a schematic view of the structural connection of the present invention;
fig. 2 is a schematic diagram of a bridge structure early warning module according to the present invention.
Reference numerals:
the system comprises a data mining module 1, a data transmission module 2, a data processing module 3, a damage early warning module 4, a deformation evaluation submodule 41, a stress evaluation submodule 42 and a comprehensive early warning submodule 43.
Detailed Description
The invention is further described with reference to the following examples.
Referring to fig. 1 and fig. 2, the efficient bridge structure damage identification system of the embodiment includes a data mining module 1, a data transmission module 2, a data processing module 3, and a damage early warning module 4; the data mining module 1 acquires original data affecting a bridge structure through a sensor and transmits the original data to the data processing module 3 through the data transmission module 2; the data processing module 3 is used for processing the received data; and the damage early warning module 4 is used for evaluating the damage condition of the bridge structure according to the processed data.
Preferably, the data transmission module 2 is formed by a wireless sensor network, the wireless sensor network adopts a routing mechanism based on a genetic algorithm, and an initial path defining a network node is Hj＝{h1,h2,...,hpAnd then, defining a fitness function in the algorithm as:
wherein s isjIs the residual energy of node j, c (h)j) Is path hjPath length of (e), e (h)j) Is path hjThe inventor gives empirical values for the three weights according to a large amount of field practices, and the three weights are respectively: 0.3, 0.2, 0.5.
Compared with the prior art, the embodiment provides a routing mechanism based on a genetic algorithm, and the selected fitness function fully considers the path length of the node, the energy consumption and the residual energy consumption of the node, so that an optimal transmission path is formulated, the energy consumption of a bridge structure damage identification system is greatly reduced, and the service life of the system is prolonged.
Preferably, the acquired parameter data comprises horizontal displacement and transverse displacement of the bridge structure, stress of the concrete structure part of the bridge and stress of the steel structure part of the bridge.
Preferably, the data processing module 3 processes the data by using a data correction and averaging algorithm, and specifically includes: the data acquired by the sensor nodes are corrected, so that the influence of the ambient temperature on the data acquisition is eliminated, and the formula is as follows:
in the formula (I), the compound is shown in the specification,
in order to obtain the corrected data, the data is,
for the original data before correction, T0Using a standard temperature for the sensor, and using T as the actual environment temperature when the sensor is used;
the embodiment corrects the data, eliminates the measurement error caused by the ambient temperature when the sensor collects the data, and increases the accuracy of the bridge structure damage identification system.
As another preferred embodiment, the performing an averaging algorithm on the sensor data specifically includes:
setting the data collected at a certain moment i as yiProcessing the data by adopting a data weighted moving average algorithm, and obtaining processed data y'iComprises the following steps:
in the formula, σ1、σ2、σ3Respectively corresponding weight factors.
In the embodiment, the weighted moving average algorithm is adopted to process the data, so that the influence of an abnormal point on the accuracy of the system is avoided to a certain extent, and the accuracy of the evaluation result of the bridge structure early warning module is improved.
Preferably, the bridge structure early warning module 4 includes a deformation evaluation submodule 41, a stress evaluation submodule 42 and a comprehensive early warning submodule 43.
Preferably, the deformation evaluation sub-module 41 is configured to evaluate a deformation degree of the bridge structure according to the horizontal displacement and the lateral displacement obtained by the modules, and specifically includes:
a. and establishing a deformation evaluation submodule based on a fuzzy algorithm, setting upper and lower limit values for each input variable by taking horizontal displacement and transverse displacement as input variables, respectively establishing corresponding weights according to the influence degree of each input variable on the deformation degree of the bridge structure, and defining the same fuzzy state, namely 'serious', 'slight' and 'normal' for the input variables. The deformation degree of the bridge structure is taken as an output quantity, and three fuzzy states, namely 'serious', 'mild' and 'normal', are defined for the deformation degree;
b. according to historical data of horizontal displacement and transverse displacement of the bridge structure collected over the years, a fuzzy rule for reasoning the deformation degree of the bridge structure by taking the horizontal displacement and the transverse displacement as the basis is formulated;
c. inputting variable values, judging sensor faults when the variable values exceed the upper limit range and the lower limit range, reasoning and obtaining membership degrees of all input variables in a fuzzy set according to fuzzy rules when the variable values are in the ranges, and calculating the deformation degree b (i) of the bridge structure by setting the data of the horizontal displacement value and the transverse displacement acquired at the ith moment as P (i) and L (i) respectively:
b(i)＝γ1μ(p)+γ2μ(l)
wherein, γ1And μ (p) are the weight and degree of membership, γ, respectively, of the horizontal shift P (i)2And μ (l) are the weight and degree of membership, respectively, of the lateral shift L (i);
compared with the prior art, the bridge structure monitoring and evaluating submodule 41 based on the fuzzy algorithm provided by the preferred embodiment obtains the deformation degree of the bridge structure by using a fuzzy evaluation model to reason according to the horizontal displacement and the transverse displacement which affect the deformation of the bridge structure, better handles the problems of fuzziness, subjective judgment and the like, and effectively diagnoses the deformation degree of the bridge structure;
preferably, the stress evaluation sub-module 42 is configured to evaluate the stress state of the bridge structure according to the stress of the concrete structure part of the bridge and the stress of the steel structure part of the bridge obtained by the above modules, and specifically includes:
A. establishing a stress evaluation submodule based on a fuzzy algorithm, setting upper and lower limit values for each input variable by taking stress and stress as input variables, respectively establishing corresponding weights according to the influence of each input variable on the stress state of the bridge structure, and defining the same fuzzy state, namely high, normal and low, for the input variables. Defining three fuzzy states, namely high, normal and low, of the stress state by taking the stress state of the bridge structure as an output quantity;
B. according to historical data collected over the years, establishing a fuzzy rule for reasoning the stress state of the bridge structure by taking the stress of the concrete structure part of the bridge and the stress of the steel structure part of the bridge as the basis;
C. inputting variable values, judging the faults of the sensors when the variable values exceed the upper limit range and the lower limit range, reasoning and obtaining the membership degree of each input variable in fuzzy concentration according to a fuzzy rule when the variable values are in the range, and calculating the stress state q (i) of the bridge structure by setting the stress of the bridge concrete part and the stress of the bridge steel structure part acquired at the ith moment as Y (i) and R (i) respectively:
q(i)＝ρ1μ(y)+ρ2μ(r)
where ρ is1And μ (y) is the weight and degree of membership, ρ, of the stress Y (i), respectively2And μ (r) is the weight and degree of membership, respectively, of stress R (i);
compared with the prior art, the stress evaluation submodule 42 based on the fuzzy algorithm provided by the preferred embodiment obtains the stress state of the bridge structure by using a fuzzy evaluation model to reason according to the stress of different parts of the bridge influencing the stress state of the bridge, better handles the problems of fuzziness, subjective judgment and the like, and effectively diagnoses the stress state of the bridge structure;
preferably, the comprehensive early warning sub-module 43 is used for further comprehensively evaluating the damage condition of the bridge structure according to the deformation degree b (i) and the stress state q (i) of the bridge structure,
defining the damage evaluation coefficient of the bridge structure as follows:
wherein mu and sigma are weights of the deformation degree of the bridge structure and the influence degree of the stress state on the damage condition of the bridge structure, which are determined according to historical data, respectively, and k represents the number of the deformation degree b (i) and the stress condition q (i) obtained within 2 hours;
the method comprises the following steps of formulating a boundary value theta of the early warning level of the bridge structure according to the monitoring data of the past year, and dividing different early warning levels according to the relation between an evaluation coefficient and the boundary value, wherein the method specifically comprises the following steps:
the comprehensive early warning submodule 43 provided in the preferred embodiment performs comprehensive early warning on the bridge structure according to the obtained deformation degree and stress state of the bridge structure, and compared with the prior art, analyzes the damage condition of the bridge structure aiming at the diversity of factors affecting the damage of the bridge structure, and forms a relatively comprehensive and accurate early warning system for the damage of the bridge structure.
Based on the above embodiment, a series of tests were performed according to different parameter information in the database, and the following are evaluation results obtained by the tests:
finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (4)
1. An efficient bridge structure damage identification system is characterized in that: the system comprises a data mining module, a data transmission module, a data processing module and a damage early warning module; the data mining module acquires original data influencing a bridge structure through a sensor and transmits the original data to the data processing module through the data transmission module; the data processing module is used for processing the received data; the damage early warning module is used for evaluating the damage condition of the bridge according to the processed data; the damage early warning module comprises a deformation evaluation submodule, a stress evaluation submodule and a comprehensive early warning submodule, wherein the deformation evaluation submodule is used for evaluating the deformation degree of the bridge structure according to the processed horizontal displacement and the processed transverse displacement, and specifically comprises the following steps:
a. establishing a deformation evaluation submodule based on a fuzzy algorithm, setting upper and lower limit values for each input variable by taking horizontal displacement and transverse displacement as input variables, respectively making corresponding weights according to the influence degree of each input quantity on the deformation degree of the bridge structure, defining the same fuzzy state, namely 'serious', 'slight' and 'normal' for the input variables, and defining three fuzzy states, namely 'serious', 'slight' and 'normal' for the deformation degree by taking the deformation degree of the bridge structure as an output quantity;
b. according to historical data of horizontal displacement and transverse displacement of the bridge structure collected over the years, a fuzzy rule for reasoning the deformation degree of the bridge structure by taking the horizontal displacement and the transverse displacement as the basis is formulated;
c. inputting variable values, judging sensor faults when the variable values exceed the upper limit range and the lower limit range, reasoning and obtaining membership degrees of all input variables in a fuzzy set according to fuzzy rules when the variable values are in the ranges, and calculating the deformation degree b (i) of the bridge structure by setting the data of the horizontal displacement value and the transverse displacement acquired at the ith moment as P (i) and L (i) respectively:
b(i)＝γ1μ(p)+γ2μ(l)
wherein, γ1And μ (p) are the weight and degree of membership, γ, respectively, of the horizontal shift P (i)2And μ (l) are the weight and degree of membership, respectively, of the lateral shift L (i); the stress evaluation submodule is used for evaluating the stress state of the bridge structure according to the stress of the processed bridge concrete structure part and the stress of the bridge steel structure part, and specifically comprises:
A. establishing a stress evaluation submodule based on a fuzzy algorithm, setting upper and lower limit values for each input variable by taking stress and stress as input variables, respectively making corresponding weights according to the influence of each input quantity on the stress state of the bridge structure, defining the same fuzzy state, namely high, normal and low, for the input variables, and defining three fuzzy states, namely high, normal and low, for the stress state by taking the stress state of the bridge structure as an output quantity;
B. according to historical data collected over the years, establishing a fuzzy rule for reasoning the stress state of the bridge structure by taking the stress of the concrete structure part of the bridge and the stress of the steel structure part of the bridge as the basis;
C. inputting variable values, judging the faults of the sensors when the variable values exceed the upper limit range and the lower limit range, reasoning and obtaining the membership degree of each input variable in fuzzy concentration according to a fuzzy rule when the variable values are in the range, and calculating the stress state q (i) of the bridge structure by setting the stress of the bridge concrete part and the stress of the bridge steel structure part acquired at the ith moment as Y (i) and R (i) respectively:
q(i)＝ρ1μ(y)+ρ2μ(r)
where ρ is1And μ (y) is the weight and degree of membership, ρ, of the stress Y (i), respectively2And μ (r) is the weight and degree of membership, respectively, of stress R (i); the comprehensive early warning sub-module is used for further comprehensively warning the damage condition of the bridge structure according to the deformation degree b (i) and the stress state q (i) of the bridge structureThe evaluation was carried out by the following method,
defining the damage evaluation coefficient of the bridge structure as follows:
wherein mu and sigma are weights of the deformation degree of the bridge structure and the influence degree of the stress state on the damage condition of the bridge structure, which are determined according to historical data, respectively, and k represents the number of the deformation degree b (i) and the stress condition q (i) obtained within 2 hours;
the method comprises the following steps of formulating a boundary value theta of the early warning level of the bridge structure according to the monitoring data of the past year, and dividing different early warning levels according to the relation between an evaluation coefficient and the boundary value, wherein the method specifically comprises the following steps:
2. the system of claim 1, wherein the system is further characterized by: the data transmission module is composed of a wireless sensor network, the wireless sensor network adopts a routing mechanism based on a genetic algorithm, and the initial path of a network node is defined as Hj＝{h1,h2,...,hpAnd then, defining a fitness function in the algorithm as:
wherein s isjIs the residual energy of node j, c (h)j) Is path hjPath length of (e), e (h)j) Is path hjThe inventor gives empirical values for the three weights according to a large amount of field practices, and the three weights are respectively: 0.3, 0.2, 0.5.
3. The system of claim 2, wherein the system is further characterized by: the data collected includes horizontal displacement of the bridge structure, lateral displacement, stress of the concrete structural portion of the bridge, and stress of the steel structural portion of the bridge.
4. The system of claim 3, wherein the system is further characterized by: and the data processing module is used for correcting and averaging the original data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611248084.6A CN106780164B (en) | 2016-12-29 | 2016-12-29 | Efficient bridge structure damage identification system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611248084.6A CN106780164B (en) | 2016-12-29 | 2016-12-29 | Efficient bridge structure damage identification system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106780164A CN106780164A (en) | 2017-05-31 |
| CN106780164B true CN106780164B (en) | 2021-04-13 |
Family
ID=58928172
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611248084.6A Active CN106780164B (en) | 2016-12-29 | 2016-12-29 | Efficient bridge structure damage identification system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106780164B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109577385B (en) * | 2018-12-05 | 2020-12-22 | 交通运输部公路科学研究所 | Early warning method and early warning system for foundation of deep water high-rise pile cap |
| CN109556848A (en) * | 2018-12-17 | 2019-04-02 | 交通运输部公路科学研究所 | A kind of simply supported girder bridge structure damage monitoring system based on Transverse Distribution |
| CN110567661B (en) * | 2019-09-11 | 2021-02-09 | 重庆大学 | Bridge damage identification method based on generalized pattern search algorithm and axle coupling |
| CN114324140B (en) * | 2021-12-15 | 2024-04-09 | 东风汽车集团股份有限公司 | Road guardrail damage monitoring method, device and equipment |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102238686A (en) * | 2011-07-04 | 2011-11-09 | 南京邮电大学 | Wireless sensor network routing method for modeling quantum genetic algorithm |
| CN103269489A (en) * | 2013-04-22 | 2013-08-28 | 南京邮电大学 | Wireless sensor network optimization method for environment monitoring |
| CN105841661A (en) * | 2016-03-22 | 2016-08-10 | 韦醒妃 | Bridge dynamic health real-time monitoring device |
-
2016
- 2016-12-29 CN CN201611248084.6A patent/CN106780164B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102238686A (en) * | 2011-07-04 | 2011-11-09 | 南京邮电大学 | Wireless sensor network routing method for modeling quantum genetic algorithm |
| CN103269489A (en) * | 2013-04-22 | 2013-08-28 | 南京邮电大学 | Wireless sensor network optimization method for environment monitoring |
| CN105841661A (en) * | 2016-03-22 | 2016-08-10 | 韦醒妃 | Bridge dynamic health real-time monitoring device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106780164A (en) | 2017-05-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106780164B (en) | Efficient bridge structure damage identification system | |
| WO2019080367A1 (en) | Method for evaluating health status of mechanical device | |
| CN106790604A (en) | A kind of high-precision dam safety remote supervision system | |
| CN102393881B (en) | A kind of high-precision detecting method of real-time many sensing temperatures data fusion | |
| CN104766175A (en) | Power system abnormal data identifying and correcting method based on time series analysis | |
| CN111401529B (en) | River ecological flow supervision method based on remote sensing technology | |
| CN106251242B (en) | Wind power output interval combination prediction method | |
| CN116543560B (en) | Intelligent road condition prediction system and method based on deep learning | |
| CN107679715A (en) | A kind of electric energy meter comprehensive error process merit rating method and evaluation system based on SPC | |
| CN115759450B (en) | Carbon emission accounting early warning analysis system based on big data | |
| CN115994692B (en) | Intelligent river and lake management platform based on 5G and big data | |
| CN108830405B (en) | Real-time power load prediction system and method based on multi-index dynamic matching | |
| CN115342859A (en) | Multifunctional grain condition detection system and detection method | |
| CN114608741A (en) | Pressure sensor acquisition system based on big data | |
| CN117422310A (en) | Electric control decision optimization method for all-steel inner liner linkage line group | |
| CN109584208A (en) | A kind of method of inspection for industrial structure defect intelligent recognition model | |
| CN110851897A (en) | Aqueduct stress-strain prediction method under multi-factor correlation | |
| CN116681307B (en) | River four-disorder supervision traceability display method and system based on multi-terminal fusion feedback | |
| CN115600747B (en) | Tunnel state monitoring and management method and system based on Internet of things | |
| CN113435794B (en) | Intelligent monitoring method for post-processing of nozzle casting based on image processing | |
| CN115512531B (en) | Landslide hazard multi-monitoring-point fusion early warning method based on deformation order | |
| CN118549620A (en) | Fuel monitoring system and monitoring method | |
| CN116861286B (en) | Multi-mode landslide displacement intelligent prediction method based on fuzzy calculation | |
| CN112351071B (en) | Traditional chinese medicine production remote monitering system based on cloud calculates | |
| CN106874557A (en) | A kind of forecasting wind speed bearing calibration based on ratio distribution |
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 | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20210324 Address after: 1-3 / F, 1161 Panyu Avenue North, Donghuan street, Panyu District, Guangzhou, Guangdong 511400 Applicant after: GUANGDONG SHENGXIANG TRAFFIC ENGINEERING DETECTION Co.,Ltd. Address before: Room 1206, Nanmen Zhongke Building, No. 9 Nanmen, Nanshan District, Shenzhen City, Guangdong Province Applicant before: SHENZHEN DATU KECHUANG TECHNOLOGY DEVELOPMENT Co.,Ltd. |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |