CN111272135B - Automatic linear measurement and control method for continuous beam bridge prefabrication and assembly construction - Google Patents

Automatic linear measurement and control method for continuous beam bridge prefabrication and assembly construction Download PDF

Info

Publication number
CN111272135B
CN111272135B CN202010091032.2A CN202010091032A CN111272135B CN 111272135 B CN111272135 B CN 111272135B CN 202010091032 A CN202010091032 A CN 202010091032A CN 111272135 B CN111272135 B CN 111272135B
Authority
CN
China
Prior art keywords
beam section
strain
wireless
syn
vertical displacement
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
Application number
CN202010091032.2A
Other languages
Chinese (zh)
Other versions
CN111272135A (en
Inventor
王浩
茅建校
卫俊岭
谢以顺
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.)
Southeast University
Original Assignee
Southeast 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 Southeast University filed Critical Southeast University
Priority to CN202010091032.2A priority Critical patent/CN111272135B/en
Publication of CN111272135A publication Critical patent/CN111272135A/en
Application granted granted Critical
Publication of CN111272135B publication Critical patent/CN111272135B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B21/00—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
    • G01B21/32—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring the deformation in a solid
    • E—FIXED CONSTRUCTIONS
    • E01—CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES
    • E01D—CONSTRUCTION OF BRIDGES, ELEVATED ROADWAYS OR VIADUCTS; ASSEMBLY OF BRIDGES
    • E01D21/00—Methods or apparatus specially adapted for erecting or assembling bridges
    • 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

Landscapes

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

Abstract

The invention discloses a linear automatic measurement and control method for continuous beam bridge prefabrication and assembly construction, which comprises the following steps: arrangement of wireless strain sensors: arranging wireless strain sensors on the top plate and the bottom plate of each precast beam section along the bridge direction according to the construction and assembly sequence and the structural form of the beam section; acquiring and transmitting strain information: setting the acquisition time interval of the wireless strain sensor according to requirements, reading the strain data of the beam section at regular intervals, and transmitting the acquired strain data to a cloud end through a wireless network; calculating the vertical displacement of the beam section: the vertical displacement of each beam section is obtained by inputting the acquired strain information into a beam section vertical displacement conversion formula deduced based on a small deformation theory and a corner-displacement formula; and (5) building a neural network model. The invention establishes a complex relation between the strain and the vertical displacement of the beam section which is continuously updated along with the construction process based on the neural network technology, so as to realize the linear control of the structure of the prefabricated and assembled continuous beam bridge.

Description

Automatic linear measurement and control method for continuous beam bridge prefabrication and assembly construction
Technical Field
The invention relates to a bridge line shape automatic measurement and control method, in particular to a line shape automatic measurement and control method for continuous bridge prefabrication and assembly construction.
Background
In recent years, with the rapid development of economy in China, bridge construction is also continuously and rapidly developed. The construction method and the application of the technology thereof for prefabricating and assembling the prestressed concrete bridge greatly promote the development of bridge construction industry in China. The prefabrication and assembly method generally divides a main beam of a bridge into a plurality of sections along the bridge direction, and the prefabricated beam sections are hoisted to corresponding positions and then tensioned with prestress to connect the beam sections into a bridge structure capable of bearing. For the prestressed continuous beam bridge constructed by assembling the cantilever, the final bridge-forming structure of the bridge needs to go through a complex process in construction, and the bridge structure system is continuously changed along with different construction stages during construction. In the construction process, due to factors such as design errors (material characteristics, creep shrinkage and the like), construction errors (beam section weight, installation errors and the like), measurement errors, errors of a structural model and the like, the line shape of the bridge in the construction process has certain deviation from the designed line shape, and the subsequent construction and bridge forming quality of the bridge are seriously influenced. The construction alignment monitoring is mainly used for trial calculation of the elevation of each beam section of the bridge according to the design file and the construction scheme, analyzing errors by combining elevation data of construction monitoring, and adjusting the elevation of subsequent beam sections in time so as to ensure the reliability and safety of the structure in the construction process and ensure the smooth proceeding of closure precision and system conversion.
At present, the main means for line shape measurement in bridge construction are as follows: the sensors include a total station, an automatic total station, a GPS measuring instrument, a laser measuring instrument, etc., which can measure the deformation or displacement of a target accurately to a certain extent, but each of them has insurmountable defects. The total station needs to manually collect a large amount of data, and the automation degree is low; the automatic total station has large workload for interior and exterior operations, and is easily influenced by weather and other external conditions for observation; the GPS measuring instrument is greatly influenced by the satellite condition and the air vision; the laser measuring instrument is greatly influenced by factors such as weather and geographical environment of a measuring target, and large errors are easily generated in precision.
In the present stage, with the outbreak of cloud computing, 5G, big data and AI technologies and internet of things technologies, the technology of wireless sensors gradually enters the visual field of people. The wireless sensor network node is formed by randomly distributed micro nodes integrated with the sensor, the data processing unit and the communication module, and the network is formed in a self-organizing mode. Although the variety of wireless sensors is wide, most of them include four modules, namely a sensing module, an information processing module, a wireless communication module, and an energy supply module. With the continuous development of physical elements such as thermosensitive elements and the like, the sensing module can more accurately measure the physical information of the monitored target. The updating of the microelectronic processor provides a good foundation for the information processing module to store and collect data more quickly and coordinate the work of each node of the sensor. The wireless sensor brings a new rotating machine for the linear rapid monitoring of the prefabricated and assembled bridge structure.
A complex network system in which a neural network is formed by a large number of processing units widely connected to each other is a highly complex nonlinear learning system. After decades of development, neural networks have been widely developed in a variety of research fields such as pattern recognition, automatic control, signal processing, and decision assistance. The neural network is widely applied to the bridge linear control system due to the unique model structure, the inherent nonlinear simulation capability, the outstanding characteristics of high self-adaption, high fault tolerance and the like. On the basis of a linear controller framework structure, a nonlinear adaptive learning mechanism is added, so that the controller has better performance. The neural network is a highly nonlinear system and can effectively establish a complex nonlinear relation among variables.
Therefore, in order to better guide the structural design and exert the structural performance, a linear automatic measurement and control method for the prefabrication, assembly and construction of the continuous beam bridge is urgently needed to be developed.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide an automatic linear measurement and control method for the prefabrication and assembly construction of a continuous bridge, which can quickly and accurately measure the linear shape of the bridge in the prefabrication and assembly construction, solve the defects caused by the traditional manual monitoring, reduce the workload, improve the monitoring efficiency and reduce the labor cost.
The technical scheme is as follows: in order to realize the aim, the invention discloses a linear automatic measurement and control method for continuous beam bridge prefabrication and assembly construction, which comprises the following steps:
(1) arrangement of wireless strain sensors: arranging wireless strain sensors on the top plate and the bottom plate of each precast beam section along the bridge direction according to the construction and assembly sequence and the structural form of the beam section;
(2) acquiring and transmitting strain information: setting the acquisition time interval of the wireless strain sensor according to requirements, reading the strain data of the beam section at regular intervals, and transmitting the acquired strain data to a cloud end through a wireless network;
(3) calculating the vertical displacement of the beam section: the vertical displacement of each beam section is obtained by inputting the acquired strain information into a beam section vertical displacement conversion formula deduced based on a small deformation theory and a corner-displacement formula;
(4) and building a neural network model: firstly converting acquired strain information into vertical displacement information, secondly considering the mutual influence among different beam sections, taking the strain data of the constructed beam section, the constructed cantilever length and the constructed beam section displacement data obtained by calculation as input data, and taking the vertical displacement of the subsequent beam section to be installed as output data, and training a linear control neural network model of the continuous beam bridge structure meeting the error requirement.
The specific arrangement method of the wireless strain sensors in the step (1) comprises the following steps:
numbering the beam sections of the prefabricated assembled bridge from 0 to n symmetrically by taking pier columns in the bridge as axes according to the construction sequence, wherein the length of the nth beam section is L according to the construction numbern(ii) a Wireless strain sensors are installed on the top plate and the bottom plate of the numbered beam sections along the bridge direction, and the number of the wireless strain sensors on the top plate of the nth beam section on the right side of the pier column is SYn u1、SYnu2、SYnu3…SYnun, number SYn of wireless strain sensor at the base plate d1、SYnd2、SYnd3…SYndn, the serial number of the wireless strain sensor at the top plate of the nth beam section on the left side of the pier column is SXnu1、SXnu2、SXnu3…SYnun, number SXn of wireless strain sensor at bottom plate d1、SXnd2、SXnd3…SXndn。
Preferably, the specific method for acquiring and transmitting the strain information in the step (2) is as follows:
the method comprises the steps of reading strain data once when the beam section is initially installed, reading the strain data of the beam section once every time the beam section is installed, namely reading the strain data of the beam section 1 time when the nth beam section is installed, reading the strain data of the n +1 th n-1 th beam section, and transmitting the collected strain data to a cloud end through a wireless network for storage.
Furthermore, the specific calculation method of the vertical displacement of the beam section in the step (3) is as follows:
n number pair and L lengthnWhen the construction is completed, the structure of the beam section generates vertical displacement deltaxnWireless strain sensors SYn arranged on the roof of a beam section u1、SYnu2、SYnu3…SYnuWireless strain sensors SYn for n and beam segment floors d1、SYnd2、SYnd3…SYndn can be changed correspondingly, and the vertical displacement delta x of the top end of the standard beam section generated by bending is generated due to the fact that the beam section structure has high rigidity and the generated vertical bending deformation is small and the central axis of the beam section is assumed to be a straight linenThrough the angle theta of the cross section of the standard beam sectionnObtaining; similarly, when the construction of the n beam section is finished, the n-1 beam section generates a corner thetan-1,nAnd a vertical displacement Δ xn-1,nMeasuring the strain of the beam section through a wireless strain sensor arranged on the beam section, and deducing to obtain the corner theta of the monitoring section of the beam section according to the strain difference value of the top plate and the bottom plate of the monitoring sectionnTo derive the vertical displacement deltax of each beam sectionnThe specific derivation process is as follows:
after the n beam section construction is finished, the 1 st strain reading of each measuring point wireless strain sensor of the top plate is respectively epsilonn,u,1,1、εn,u,2,1、εn,u,3,1、…、εn,u,n,1The reading of the 1 st strain of each measuring point wireless strain sensor of the bottom plate is respectively epsilonn,d,11、εn,d,2,1、εn,d,3,1、…、εn,d,n,1Strain of the top plate Δ εn,1,1Comprises the following steps:
Figure BDA0002383725260000031
wherein, Delta epsilonn,1,1The strain measurement of the top plate at the 1 st time;
strain delta epsilon of the soleplaten,2,1Comprises the following steps:
Figure BDA0002383725260000041
wherein, Delta epsilonn，2，1Strain measurements for the 1 st time of the base plate;
initial corner theta when n number beam section is installednComprises the following steps:
Figure BDA0002383725260000042
wherein, thetanIs the initial corner of the n-th block, D is the height of the precast beam section, epsilonn，d，j，1Strain reading of the wireless strain sensor at each measuring point of the bottom plate of the n number block is 1 st, epsilonn，u，i，1The 1 st strain reading of the wireless strain sensor at each measuring point of the top plate on the n number block, LnThe length of the n-th precast beam section;
after the n beam section is installed, the corner theta of the n-1 beam sectionn-1，nComprises the following steps:
Figure BDA0002383725260000043
wherein D is the height of the precast beam section, εn-1，d，j，n+1Strain reading of n +1 times of wireless strain sensor at each measuring point of bottom plate of No. n-1 block, epsilonn-1，u，i，n+1Strain reading of the wireless strain sensor at each measuring point of the top plate on the n-1 block for the (n + 1) th time, Ln-1The length of the n-1 th precast beam section;
the displacement of each beam section after the construction of the n beam sections is completed is as follows:
Δx0，n＝L0sin(θ0+θ0，1+θ0，2+…+θ0，n) (5)
Δx1，n＝L1sin(θ1+θ1，2+θ1，3+…+θ1，n)+Δx0，n (6)
…
Δxn＝Lnsinθn+Δxn-1，n+Δxn-2，n+…+Δx0，n (7)
wherein L is0Is the length of No. 0 precast beam segment, L1Is the length of No. 1 precast beam segment, LnLength of precast Beam segment n, Δ x0，nVertical displacement, Deltax, of No. 0 beam segment after installation of No. n beam segment1，nVertical displacement, Deltax, of No. 1 beam segment after installation of No. n beam segmentnThe vertical displacement of the n number of beam sections after the n number of beam sections are installed.
Further, the continuous bridge structure linear control neural network model meeting the error requirement in the step (4) is a five-layer continuous bridge structure linear control neural network model.
Has the advantages that: compared with the prior art, the invention has the following remarkable advantages: firstly, establishing a complex relation between the strain and the vertical displacement of a beam section which is continuously updated along with the construction process based on a neural network technology to realize the linear control of the structure of the prefabricated and assembled continuous beam bridge; and then, the wireless strain sensors are arranged on the top plate and the bottom plate of the precast beam section to monitor the line shape of the bridge in real time, so that the line shape monitoring in the bridge assembling construction is more accurate and reliable, the defects brought by the traditional manual monitoring are overcome, the workload is reduced, the monitoring efficiency is improved, the cost is reduced, and the bridge forming quality of the bridge is ensured.
Drawings
FIG. 1 is a schematic diagram of the beam segment numbering and strain information acquisition of the present invention;
FIG. 2 is a schematic plan view of the numbering and arrangement of n beam section sensors on the left side of the pier stud axis;
FIG. 3 is a schematic plan view of the numbering and arrangement of n number beam section sensors on the right side of the pier stud axis according to the present invention;
FIG. 4 is a schematic view of a beam segment variation of the present invention;
FIG. 5 is a schematic diagram of the corner-displacement conversion principle of the present invention;
FIG. 6 is a schematic diagram of a linear control neural network model according to the present invention.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
As shown in FIG. 1, the invention provides a linear automatic measurement and control method for continuous beam bridge prefabrication and assembly construction, which comprises the following steps:
(1) arrangement of wireless strain sensors: arranging wireless strain sensors on the top plate and the bottom plate of each precast beam section along the bridge direction according to the construction and assembly sequence and the structural form of the beam section;
the specific arrangement method of the wireless strain sensor comprises the following steps:
numbering the beam sections of the prefabricated assembled bridge from 0 to n symmetrically by taking pier columns in the bridge as axes according to the construction sequence, wherein the length of the nth beam section is L according to the construction numbernAs shown in fig. 1; wireless strain sensors are installed on the top plate and the bottom plate of the numbered beam sections along the bridge direction, and the number of the wireless strain sensors on the top plate of the nth beam section on the right side of the pier column is SYn u1、SYnu2、SYn u3…SYnun, number SYn of wireless strain sensor at the base plate d1、SYnd2、SYn d3…SYndn, the serial number of the wireless strain sensor at the top plate of the nth beam section on the left side of the pier column is SXn u1、SXnu2、SXn u3…SYnun, number SXn of wireless strain sensor at bottom plate d1、SXnd2、SXn d3…SXndn, as shown in FIG. 2;
(2) acquiring and transmitting strain information: setting the acquisition time interval of the wireless strain sensor according to requirements, reading the strain data of the beam section at regular intervals, and transmitting the acquired strain data to a cloud end through a wireless network;
the specific method for acquiring and transmitting the strain information comprises the following steps:
reading the strain data once when the beam section is initially installed, and subsequently reading the strain data of the beam section once when one beam section is installed, namely reading the strain data of the beam section 1 time when the nth beam section is installed, reading the strain data of the n +1 th n-1 th beam section, and transmitting the acquired strain data to a cloud end for storage through a wireless network, as shown in fig. 3;
(3) calculating the vertical displacement of the beam section: the vertical displacement of each beam section is obtained by inputting the acquired strain information into a beam section vertical displacement conversion formula deduced based on a small deformation theory and a corner-displacement formula;
the specific calculation method of the vertical displacement of the beam section comprises the following steps:
n number pair and L lengthnWhen the construction is completed, the structure of the beam section generates vertical displacement deltaxnWireless strain sensors SYn arranged on the roof of a beam sectionu1、SYnu2、SYnu3…SYnuWireless strain sensors SYn for n and beam segment floorsd1、SYnd2、SYnd3…SYndn will change correspondingly, because the beam section structure has a large rigidity and the generated vertical bending deformation is small, and if the central axis of the beam section is still a straight line, the vertical displacement delta x of the top end of the standard beam section generated by bendingnThrough the angle theta of the cross section of the standard beam sectionnObtaining a deformation schematic diagram of the n-numbered beam section, as shown in FIG. 4; similarly, when the construction of the n beam section is finished, the n-1 beam section generates a corner thetan-1,nAnd a vertical displacement Δ xn-1,nThe strain of the beam section is measured through a wireless strain sensor arranged on the beam section, the principle is shown in figure 5, and the corner theta of the monitoring section of the beam section is obtained through derivation according to the strain difference value of a top plate and a bottom plate of the monitoring sectionnTo derive the vertical displacement deltax of each beam sectionnThe specific derivation process is as follows:
after the n beam section construction is finished, the 1 st strain reading of each measuring point wireless strain sensor of the top plate is respectively epsilonn,u,1,1、εn,u,2,1、εn,u,3,1、…、εn,u,n,1The reading of the 1 st strain of each measuring point wireless strain sensor of the bottom plate is respectively epsilonn,d,11、εn,d,2,1、εn,d,3,1、…、εn,d,n,1Strain of the top plate Δ εn,1,1Comprises the following steps:
Figure BDA0002383725260000061
wherein, Delta epsilonn,1,1The strain measurement of the top plate at the 1 st time;
strain delta epsilon of the soleplaten,2,1Comprises the following steps:
Figure BDA0002383725260000062
wherein, Delta epsilonn,2,1Strain measurements for the 1 st time of the base plate;
initial corner theta when n number beam section is installednComprises the following steps:
Figure BDA0002383725260000063
wherein, thetanIs the initial corner of the n-th block, D is the height of the precast beam section, epsilonn,d,j,1Strain reading of the wireless strain sensor at each measuring point of the bottom plate of the n number block is 1 st, epsilonn,u,i,1The 1 st strain reading of the wireless strain sensor at each measuring point of the top plate on the n number block, LnThe length of the n-th precast beam section;
after the n beam section is installed, the corner theta of the n-1 beam sectionn-1，nComprises the following steps:
Figure BDA0002383725260000071
wherein D is the height of the precast beam section, εn-1，d，j，n+1Strain reading of n +1 times of wireless strain sensor at each measuring point of bottom plate of No. n-1 block, epsilonn-1，u，i，n+1Strain reading of the wireless strain sensor at each measuring point of the top plate on the n-1 block for the (n + 1) th time, Ln-1The length of the n-1 th precast beam section;
the displacement of each beam section after the construction of the n beam sections is completed is as follows:
Δx0，n＝L0sin(θ0+θ0，1+θ0，2+…+θ0，n) (5)
Δx1，n＝L1sin(θ1+θ1，2+θ1，3+…+θ1，n)+Δx0，n (6)
…
Δxn＝Lnsinθn+Δxn-1，n+Δxn-2，n+…+Δx0，n (7)
wherein L is0Is the length of No. 0 precast beam segment, L1Is the length of No. 1 precast beam segment, LnLength of precast Beam segment n, Δ x0，nVertical displacement, Deltax, of No. 0 beam segment after installation of No. n beam segment1，nVertical displacement, Deltax, of No. 1 beam segment after installation of No. n beam segmentnVertical displacement of the n-number beam section after the n-number beam section is installed;
(4) and building a neural network model: firstly converting acquired strain information into vertical displacement information, secondly considering the mutual influence among different beam sections, training a continuous bridge structure linear control neural network model meeting the error requirement by taking the strain data of a constructed beam section, the constructed cantilever length and the constructed beam section displacement data obtained by calculation as input data and the vertical displacement of a beam section to be installed subsequently as output data, wherein the continuous bridge structure linear control neural network model meeting the error requirement is a five-layer continuous bridge structure linear control neural network model as shown in figure 6.

Claims (2)

1. A linear automatic measurement and control method for continuous beam bridge prefabrication and assembly construction is characterized by comprising the following steps:
(1) arrangement of wireless strain sensors: arranging wireless strain sensors on the top plate and the bottom plate of each precast beam section along the bridge direction according to the construction and assembly sequence and the structural form of the beam section; the specific arrangement method of the wireless strain sensor comprises the following steps: numbering the beam sections of the prefabricated assembled bridge from 0 to n symmetrically by taking pier columns in the bridge as axes according to the construction sequence, wherein the length of the nth beam section is L according to the construction numbern(ii) a Wireless strain sensors are installed on the top plate and the bottom plate of the numbered beam sections along the bridge direction, and the number of the wireless strain sensors on the top plate of the nth beam section on the right side of the pier column is SYnu1、SYnu2、SYnu3…SYnun, number of wireless strain sensors at the base plateSYnd1、SYnd2、SYnd3…SYndn, the serial number of the wireless strain sensor at the top plate of the nth beam section on the left side of the pier column is SXnu1、SXnu2、SXnu3…SYnun, number SXn of wireless strain sensor at bottom plated1、SXnd2、SXnd3…SXndn；
(2) Acquiring and transmitting strain information: setting the acquisition time interval of the wireless strain sensor according to requirements, reading the strain data of the beam section at regular intervals, and transmitting the acquired strain data to a cloud end through a wireless network; the specific method for acquiring and transmitting the strain information comprises the following steps: reading the strain data once when the beam section is initially installed, and subsequently reading the strain data of the beam section once when one beam section is installed, namely reading the strain data of the beam section 1 time when the nth beam section is installed, reading the strain data of the n +1 th n-1 th beam section, and transmitting the acquired strain data to a cloud end for storage through a wireless network;
(3) calculating the vertical displacement of the beam section: the vertical displacement of each beam section is obtained by inputting the acquired strain information into a beam section vertical displacement conversion formula deduced based on a small deformation theory and a corner-displacement formula; the specific calculation method of the vertical displacement of the beam section comprises the following steps: n number pair and L lengthnWhen the construction is completed, the structure of the beam section generates vertical displacement deltaxnWireless strain sensors SYn arranged on the roof of a beam sectionu1、SYnu2、SYnu3…SYnuWireless strain sensors SYn for n and beam segment floorsd1、SYnd2、SYnd3…SYndn can be changed correspondingly, and the vertical displacement delta x of the top end of the standard beam section generated by bending is generated due to the fact that the beam section structure has high rigidity and the generated vertical bending deformation is small and the central axis of the beam section is assumed to be a straight linenThrough the angle theta of the cross section of the standard beam sectionnObtaining; similarly, when the construction of the n beam section is finished, the n-1 beam section generates a corner thetan-1,nAnd a vertical displacement Δ xn-1,nMeasuring the strain of the beam section by a wireless strain sensor arranged on the beam section and breaking the strain according to the monitoringDeducing the strain difference value of the top plate and the bottom plate to obtain the corner theta of the monitoring section of the beam sectionnTo derive the vertical displacement deltax of each beam sectionnThe specific derivation process is as follows:
after the n beam section construction is finished, the 1 st strain reading of each measuring point wireless strain sensor of the top plate is respectively epsilonn,u,1,1、εn,u,2,1、εn,u,3,1、…、εn,u,n,1The reading of the 1 st strain of each measuring point wireless strain sensor of the bottom plate is respectively epsilonn,d,1,1、εn,d,2,1、εn,d,3,1、…、εn,d,n,1Strain of the top plate Δ εn,1,1Comprises the following steps:
Figure FDA0002851006980000021
wherein, Delta epsilonn,1,1The strain measurement of the top plate at the 1 st time;
strain delta epsilon of the soleplaten,2,1Comprises the following steps:
Figure FDA0002851006980000022
wherein, Delta epsilonn,2,1Strain measurements for the 1 st time of the base plate;
initial corner theta when n number beam section is installednComprises the following steps:
Figure FDA0002851006980000023
wherein, thetanIs the initial corner of the n-th block, D is the height of the precast beam section, epsilonn,d,j,1Strain reading of the wireless strain sensor at each measuring point of the bottom plate of the n number block is 1 st, epsilonn,u,i,1The 1 st strain reading of the wireless strain sensor at each measuring point of the top plate on the n number block, LnThe length of the n-th precast beam section;
after the n beam section is installed, the corner theta of the n-1 beam sectionn-1,nComprises the following steps:
Figure FDA0002851006980000024
wherein D is the height of the precast beam section, εn-1,d,j,n+1Strain reading of n +1 times of wireless strain sensor at each measuring point of bottom plate of No. n-1 block, epsilonn-1,u,i,n+1Strain reading of the wireless strain sensor at each measuring point of the top plate on the n-1 block for the (n + 1) th time, Ln-1The length of the n-1 th precast beam section;
the displacement of each beam section after the construction of the n beam sections is completed is as follows:
Δx0，n＝L0sin(θ0+θ0，1+θ0，2+…+θ0，n) (5)
Δx1，n＝L1sin(θ1+θ1，2+θ1，3+…+θ1，n)+Δx0，n (6)
…
Δxn＝Lnsinθn+Δxn-1，n+Δxn-2，n+…+Δx0，n (7)
wherein L is0Is the length of No. 0 precast beam segment, L1Is the length of No. 1 precast beam segment, LnLength of precast Beam segment n, Δ x0,nVertical displacement, Deltax, of No. 0 beam segment after installation of No. n beam segment1,nVertical displacement, Deltax, of No. 1 beam segment after installation of No. n beam segmentnVertical displacement of the n-number beam section after the n-number beam section is installed;
(4) and building a neural network model: firstly converting acquired strain information into vertical displacement information, secondly considering the mutual influence among different beam sections, taking the strain data of the constructed beam section, the constructed cantilever length and the constructed beam section displacement data obtained by calculation as input data, and taking the vertical displacement of the subsequent beam section to be installed as output data, and training a linear control neural network model of the continuous beam bridge structure meeting the error requirement.
2. The linear automatic measurement and control method for the continuous beam bridge prefabrication and assembly construction according to claim 1, characterized in that: the continuous bridge structure linear control neural network model meeting the error requirement in the step (4) is a five-layer continuous bridge structure linear control neural network model.
CN202010091032.2A 2020-02-13 2020-02-13 Automatic linear measurement and control method for continuous beam bridge prefabrication and assembly construction Active CN111272135B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010091032.2A CN111272135B (en) 2020-02-13 2020-02-13 Automatic linear measurement and control method for continuous beam bridge prefabrication and assembly construction

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010091032.2A CN111272135B (en) 2020-02-13 2020-02-13 Automatic linear measurement and control method for continuous beam bridge prefabrication and assembly construction

Publications (2)

Publication Number Publication Date
CN111272135A CN111272135A (en) 2020-06-12
CN111272135B true CN111272135B (en) 2021-03-19

Family

ID=70997038

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010091032.2A Active CN111272135B (en) 2020-02-13 2020-02-13 Automatic linear measurement and control method for continuous beam bridge prefabrication and assembly construction

Country Status (1)

Country Link
CN (1) CN111272135B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111678434A (en) * 2020-06-16 2020-09-18 中国工程物理研究院机械制造工艺研究所 Device and method for simultaneously detecting six-degree-of-freedom errors of machine tool linear shaft operation

Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111576230B (en) * 2020-05-28 2021-09-28 广西交科集团有限公司 Method for controlling lifting displacement of lattice beam of arch bridge with small construction disturbance
CN111860202A (en) * 2020-06-28 2020-10-30 中铁大桥科学研究院有限公司 Beam yard pedestal state identification method and system combining image identification and intelligent equipment
CN111962400A (en) * 2020-09-14 2020-11-20 上海同禾工程科技股份有限公司 Bridge dynamic linear monitoring system and monitoring method for bridge construction period
CN112683226B (en) * 2021-01-13 2022-02-25 中南大学 High-precision dynamic testing method for corner
CN112796227B (en) * 2021-04-09 2021-06-29 湖南久钰电子有限公司 Bridge deck construction monitoring method and system
CN114199152A (en) * 2021-11-03 2022-03-18 上海传输线研究所（中国电子科技集团公司第二十三研究所） Wing shape variation measuring method and device
CN116524382A (en) * 2023-05-22 2023-08-01 西南交通大学 Bridge swivel closure accuracy inspection method system and equipment

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH0660349B2 (en) * 1987-12-17 1994-08-10 新日本製鐵株式会社 Iron loss value reduction device for grain-oriented electrical steel
JP2917839B2 (en) * 1994-11-30 1999-07-12 鹿島建設株式会社 Method and apparatus for manufacturing precast block for bridge
JP5234546B2 (en) * 2009-02-20 2013-07-10 独立行政法人産業技術総合研究所 Stress light emission analysis apparatus, stress light emission analysis method, stress light emission analysis program, and recording medium
CN101694745A (en) * 2009-06-16 2010-04-14 同济大学 Safety detection method based on freeway geometry linear comprehensive technical indexes
EP2637010B1 (en) * 2012-03-05 2015-06-24 EADS Construcciones Aeronauticas, S.A. Method and system for monitoring a structure
CN204142505U (en) * 2014-09-02 2015-02-04 江苏育亿信息科技有限公司 Highway bridge monitor and early warning system
CN104318780B (en) * 2014-10-31 2016-07-13 重庆大学 Consider the freeway incident detection method of meteorological factor, road alignment factor
CN107894254A (en) * 2017-11-16 2018-04-10 中铁四局集团有限公司 A kind of alignment control intelligent management system and its method for Construction of continuous beam

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111678434A (en) * 2020-06-16 2020-09-18 中国工程物理研究院机械制造工艺研究所 Device and method for simultaneously detecting six-degree-of-freedom errors of machine tool linear shaft operation

Also Published As

Publication number Publication date
CN111272135A (en) 2020-06-12

Similar Documents

Publication Publication Date Title
CN111272135B (en) Automatic linear measurement and control method for continuous beam bridge prefabrication and assembly construction
CN109680615B (en) Three-dimensional coordinate measuring construction method of short-line method section beam
CN111121733A (en) Construction quality detection method and system based on BIM and three-dimensional laser scanning
CN110030967B (en) Method, device and system for monitoring horizontal deformation of support
CN108226399A (en) A kind of beam-string structure damage combined recognising method based on modal parameter
CN101775906B (en) Construction stretching control method used for spoke type roof structure
CN108827158B (en) Laser monitoring device and method for main tower deviation of long-span bridge
CN112081285B (en) Method for determining length of prestressed stay cable of cable structure
CN112016147B (en) Revit platform-based steel truss girder pre-assembly precision detection method
CN106441101B (en) A kind of engineering monitoring high-precision configuration displacement measurement method
CN111660422B (en) BIM-based box girder segment prefabricating method by adopting short line method
CN102880769B (en) The construction control method of Long span concrete deck cable stayed bridge and camber computing method
CN105547586A (en) Calibration device of bending moment sensor
CN113513173A (en) Accumulated continuous pre-assembly construction method for large deformed steel structure
CN108385538A (en) The method that the prefabricated head tower of cable-stayed bridge is installed on concrete king-post
CN106403858B (en) A kind of superaltitude large cantilever steel platform tip deflection monitoring method
CN111946070A (en) Skirt-swing type installation method for spatial irregular steel grids
CN110344327A (en) A kind of cable-stayed bridge upper rail control net real-time grid DEM method of CPIII point
CN112395767B (en) Calculation method for point pose change of tool nose point of machine tool caused by uniform settlement deformation of foundation
CN113267170A (en) Three-main-truss steel beam transverse height difference adjusting method based on 3D laser scanning and model calculation data comparison feedback
CN112883608A (en) Health index evaluation method and system for truss bridge
CN111982677A (en) Initial effective prestress field detection method for prestressed concrete frame structure
CN117418463A (en) Linear control method and system for integral erection of large-section steel girder
CN203908719U (en) Cable force monitor for pre-stress stay cable steel structure
Han et al. Study on bridge structure damage and health diagnosis method based on health monitoring

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