CN116842844B - BIM-based bridge high pier construction safety monitoring method and system - Google Patents
BIM-based bridge high pier construction safety monitoring method and system Download PDFInfo
- Publication number
- CN116842844B CN116842844B CN202310860458.3A CN202310860458A CN116842844B CN 116842844 B CN116842844 B CN 116842844B CN 202310860458 A CN202310860458 A CN 202310860458A CN 116842844 B CN116842844 B CN 116842844B
- Authority
- CN
- China
- Prior art keywords
- simulation
- model
- data set
- construction
- bridge
- 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
- 238000010276 construction Methods 0.000 title claims abstract description 255
- 238000012544 monitoring process Methods 0.000 title claims abstract description 145
- 238000000034 method Methods 0.000 title claims abstract description 54
- 238000004088 simulation Methods 0.000 claims abstract description 242
- 238000005094 computer simulation Methods 0.000 claims abstract description 34
- 230000004927 fusion Effects 0.000 claims abstract description 24
- 238000004891 communication Methods 0.000 claims abstract description 8
- 230000003068 static effect Effects 0.000 claims description 77
- 230000035882 stress Effects 0.000 claims description 43
- 230000006353 environmental stress Effects 0.000 claims description 20
- 238000012502 risk assessment Methods 0.000 claims description 8
- 230000008569 process Effects 0.000 claims description 7
- 230000000694 effects Effects 0.000 abstract description 19
- 238000013528 artificial neural network Methods 0.000 description 6
- 210000002569 neuron Anatomy 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 4
- 229910000831 Steel Inorganic materials 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000009430 construction management Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000008707 rearrangement Effects 0.000 description 1
- 239000010959 steel Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- 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
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
-
- 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)
- Evolutionary Computation (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Computer Hardware Design (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Bridges Or Land Bridges (AREA)
Abstract
The invention discloses a BIM-based bridge high pier construction safety monitoring method and system, and relates to the field of construction monitoring, wherein the method comprises the following steps: performing BIM simulation modeling on the first target bridge, and outputting a first bridge simulation model; outputting a first geologic simulation model; performing model fusion according to the first bridge simulation model and the first geological simulation model, and outputting a first construction simulation model; performing construction simulation according to the first construction simulation model to obtain a first simulation data set; inputting a first simulation data set into a multi-element monitoring model, performing safety monitoring according to the multi-element monitoring model, and outputting a first risk coefficient, wherein the multi-element monitoring model is in communication connection with the first construction simulation model; and outputting first reminding information according to the first risk coefficient. The technical problem of in prior art to the construction safety monitoring early warning accuracy of bridge high mound poor, lead to the construction safety monitoring early warning effect of bridge high mound poor is solved.
Description
Technical Field
The invention relates to the field of construction monitoring, in particular to a BIM-based bridge high pier construction safety monitoring method and system.
Background
Along with the continuous increase of traffic and transportation demands, the construction scale of the highway network is rapidly enlarged. The construction of the high pier of the bridge is an important component of the construction of the expressway, and the construction of the high pier of the bridge has the characteristics of high construction difficulty and high safety risk, and the safety monitoring and early warning is an important means for guaranteeing the construction safety of the high pier of the bridge. Therefore, in the construction management of the high pier of the bridge, the construction safety monitoring and early warning of the high pier of the bridge becomes important. In the prior art, the technical problems of poor construction safety monitoring and early warning accuracy aiming at the high bridge pier and poor construction safety monitoring and early warning effect of the high bridge pier exist.
Disclosure of Invention
The application provides a BIM-based bridge high pier construction safety monitoring method and system. The technical problem of in prior art to the construction safety monitoring early warning accuracy of bridge high mound poor, lead to the construction safety monitoring early warning effect of bridge high mound poor is solved. The method achieves the effects of improving the accuracy and the comprehensiveness of the construction safety monitoring and early warning of the high pier of the bridge, improving the construction safety monitoring and early warning effect of the high pier of the bridge and providing powerful guarantee for the construction safety of the high pier of the bridge.
In view of the above problems, the application provides a BIM-based bridge high pier construction safety monitoring method and system.
In a first aspect, the present application provides a bridge high pier construction safety monitoring method based on BIM, wherein the method is applied to a bridge high pier construction safety monitoring system based on BIM, and the method includes: performing BIM simulation modeling on the first target bridge, and outputting a first bridge simulation model; data acquisition is carried out on the construction area of the first target bridge, modeling is carried out through a BIM simulation system, and a first geological simulation model is output; performing model fusion according to the first bridge simulation model and the first geological simulation model, and outputting a first construction simulation model; performing construction simulation according to the first construction simulation model to obtain a first simulation data set, wherein the first simulation data set comprises a static simulation data set and a dynamic simulation data set, the static simulation data set is a static stress simulation data set of the first target bridge caused by high piers of the first target bridge to geology, and the dynamic simulation data set is a geological dynamic stress simulation data set of the first target bridge in the construction process; inputting the first simulation data set into a multi-element monitoring model, performing safety monitoring according to the multi-element monitoring model, and outputting a first risk coefficient, wherein the multi-element monitoring model is in communication connection with the first construction simulation model; and outputting first reminding information according to the first risk coefficient.
In a second aspect, the present application further provides a bridge high pier construction safety monitoring system based on BIM, wherein the system includes: the bridge simulation module is used for performing BIM simulation modeling on the first target bridge and outputting a first bridge simulation model; the geological simulation module is used for collecting data of a construction area of the first target bridge, modeling the construction area through a BIM simulation system and outputting a first geological simulation model; the fusion module is used for carrying out model fusion according to the first bridge simulation model and the first geological simulation model and outputting a first construction simulation model; the construction simulation module is used for performing construction simulation according to the first construction simulation model to obtain a first simulation data set, wherein the first simulation data set comprises a static simulation data set and a dynamic simulation data set, the static simulation data set is a static stress simulation data set of the first target bridge, which is caused by a high pier of the first target bridge, to geology, and the dynamic simulation data set is a geological dynamic stress simulation data set of the first target bridge in the construction process; the safety monitoring module is used for inputting the first simulation data set into a multi-element monitoring model, carrying out safety monitoring according to the multi-element monitoring model and outputting a first risk coefficient, wherein the multi-element monitoring model is in communication connection with the first construction simulation model; and the reminding information output module is used for outputting first reminding information according to the first risk coefficient.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
Performing BIM simulation modeling on a first target bridge to output a first bridge simulation model; the method comprises the steps of acquiring data of a construction area of a first target bridge, modeling by a BIM simulation system and outputting a first geological simulation model; performing model fusion on the first bridge simulation model and the first geological simulation model, and outputting a first construction simulation model; performing construction simulation according to the first construction simulation model to obtain a first simulation data set; and inputting the first simulation data set into the multivariate monitoring model, outputting a first risk coefficient, and generating first reminding information. The method achieves the effects of improving the accuracy and the comprehensiveness of the construction safety monitoring and early warning of the high pier of the bridge, improving the construction safety monitoring and early warning effect of the high pier of the bridge and providing powerful guarantee for the construction safety of the high pier of the bridge.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
In order to more clearly illustrate the technical solution of the embodiments of the present invention, the following description will briefly explain the drawings of the embodiments of the present invention. It is apparent that the figures in the following description relate only to some embodiments of the invention and are not limiting of the invention.
FIG. 1 is a schematic flow chart of a BIM-based bridge high pier construction safety monitoring method;
FIG. 2 is a schematic flow chart of outputting a first construction simulation model in a BIM-based bridge high pier construction safety monitoring method of the present application;
Fig. 3 is a schematic structural diagram of a bridge high pier construction safety monitoring system based on BIM of the present application.
Reference numerals illustrate: the system comprises a bridge simulation module 11, a geological simulation module 12, a fusion module 13, a construction simulation module 14, a safety monitoring module 15 and a reminding information output module 16.
Detailed Description
The application provides a BIM-based bridge high pier construction safety monitoring method and system. The technical problem of in prior art to the construction safety monitoring early warning accuracy of bridge high mound poor, lead to the construction safety monitoring early warning effect of bridge high mound poor is solved. The method achieves the effects of improving the accuracy and the comprehensiveness of the construction safety monitoring and early warning of the high pier of the bridge, improving the construction safety monitoring and early warning effect of the high pier of the bridge and providing powerful guarantee for the construction safety of the high pier of the bridge.
Example 1
Referring to fig. 1, the application provides a bridge high pier construction safety monitoring method based on BIM, wherein the method is applied to a bridge high pier construction safety monitoring system based on BIM, and the method specifically comprises the following steps:
Step S100: performing BIM simulation modeling on the first target bridge, and outputting a first bridge simulation model;
specifically, basic parameters of a first target bridge are collected, and bridge basic information is obtained. And connecting the BIM simulation system, uploading the bridge foundation information to the BIM simulation system, and performing BIM simulation modeling by the BIM simulation system according to the bridge foundation information to obtain a first bridge simulation model. The first target bridge can be any bridge which uses the BIM-based bridge high pier construction safety monitoring system to conduct intelligent construction safety monitoring and early warning. The bridge foundation information comprises data information such as bridge shape, bridge length and bridge width corresponding to the first target bridge, and high pier number, high pier distribution, high pier geometric data and the like. The high pier distribution includes positional information corresponding to each high pier of the first target bridge. The high pier geometry data comprises high pier height parameters, high pier radius, high pier shape and the like corresponding to each high pier of the first target bridge. And the BIM simulation system is in communication connection with the bridge high pier construction safety monitoring system based on BIM. The BIM simulation system may be a BIM simulation modeling system in the prior art. The first bridge simulation model is a BIM simulation model corresponding to bridge foundation information.
Step S200: data acquisition is carried out on the construction area of the first target bridge, modeling is carried out through a BIM simulation system, and a first geological simulation model is output;
Step S300: performing model fusion according to the first bridge simulation model and the first geological simulation model, and outputting a first construction simulation model;
Further, as shown in fig. 2, step S300 of the present application further includes:
Step S310: modeling by a BIM simulation system, and building the first geological simulation model, wherein data acquisition is carried out on a construction area of the first target bridge, wherein the data acquisition comprises a geological layer data set, an inclined slope data set and an embedded part data set;
Specifically, data acquisition is carried out on a construction area of the first target bridge, and a geological data set of the construction area is obtained. Uploading the geological data set of the construction area to a BIM simulation system, and performing BIM simulation modeling by the BIM simulation system according to the geological data set of the construction area to obtain a first geological simulation model. The geological data set of the construction area comprises a geological layer data set, an inclined gradient data set and an embedded part data set. The geological layer data set comprises data information such as geological layer composition, geological layer depth, geological layer water content, geological layer compactness and the like corresponding to the construction area of the first target bridge. The slope data set includes geological slope change information corresponding to a construction area of the first target bridge. The embedded part data set comprises parameters such as the position, the size, the shape and the like of each embedded part corresponding to the construction area of the first target bridge. The embedded parts comprise shielding plate embedded parts, bridge falling-preventing stop blocks, bridge falling-preventing embedded parts, bridge support steel plates, contact net post embedded parts, pier embedded sleeves, foundation bolts and other bridge embedded parts corresponding to the first target bridge. The first geologic simulation model comprises a BIM simulation model corresponding to a geological data set of a construction area.
Step S320: performing geological risk assessment on the construction area of the first target bridge based on the first geological simulation model, and outputting a first geological hidden danger index;
further, step S320 of the present application further includes:
step S321: determining the number of high piers, the distribution of the high piers and the geometric data of the high piers by calling the structural parameters of the first bridge simulation model;
step S322: according to the number of the high piers, the distribution of the high piers and the geometric data of the high piers, carrying out region positioning on the first geological simulation model, and outputting a positioning result of a construction region;
step S323: and carrying out area expansion on the positioning result of the construction area, and determining the range of the construction area.
Step S330: when the first geological hidden danger index is smaller than a preset hidden danger index, a first fusion instruction is obtained;
Step S340: and fusing the first geological simulation model and the first bridge simulation model according to the first fusing instruction, and outputting a first construction simulation model.
And extracting structural parameters of the first bridge simulation model to obtain high pier quantity, high pier distribution and high pier geometric data. And then, carrying out region positioning on the first geological simulation model according to the number of the high piers, the distribution of the high piers and the geometric data of the high piers to obtain a positioning result of the construction region, and carrying out region expansion on the positioning result of the construction region to obtain the range of the construction region. And further, carrying out geological risk assessment on the range of the construction area to obtain a first geological hidden danger index. And judging whether the first geological hidden danger index is smaller than a preset hidden danger index. If the first geological hidden danger index is smaller than the preset hidden danger index, the BIM-based bridge high pier construction safety monitoring system automatically generates a first fusion instruction, and fuses the first geological simulation model with the first bridge simulation model according to the first fusion instruction to obtain a first construction simulation model.
The positioning result of the construction area comprises a model area corresponding to the number of high piers, the distribution of the high piers and the geometric data of the high piers in the first geological simulation model. The first geological risk indicator is data information for characterizing a geological risk of the extent of the construction area. The greater the geological risk of the extent of the construction area, the higher the corresponding first geological hidden danger index. The preset hidden danger index comprises a first geological hidden danger index threshold preset and determined by the BIM-based bridge high pier construction safety monitoring system. The first fusion instruction is instruction information used for representing that the first geological hidden danger index is smaller than a preset hidden danger index and can fuse the first geological simulation model and the first bridge simulation model. The first construction simulation model comprises a first geological simulation model and a first bridge simulation model.
When the positioning result of the construction area is expanded, the edge point of each model area in the positioning result of the construction area is expanded according to the coordinate expansion radius preset and determined by the BIM-based bridge high pier construction safety monitoring system, so that the range of the construction area is obtained.
The method achieves the technical effects that the geological risk assessment is carried out on the construction area of the first target bridge through the first geological simulation model, so that the first geological simulation model and the first bridge simulation model are adaptively fused, and the reliability of the first construction simulation model is improved.
Step S400: performing construction simulation according to the first construction simulation model to obtain a first simulation data set, wherein the first simulation data set comprises a static simulation data set and a dynamic simulation data set, the static simulation data set is a static stress simulation data set of the first target bridge caused by high piers of the first target bridge to geology, and the dynamic simulation data set is a geological dynamic stress simulation data set of the first target bridge in the construction process;
Specifically, a first simulation data set is obtained by performing construction simulation on a first construction simulation model. Wherein the first simulation data set comprises a static simulation data set and a dynamic simulation data set. The static simulation data set is a static stress simulation data set of the high pier of the first target bridge caused by geology, namely, the static simulation data set comprises the pier simulation bearing stress and the simulation environment stress of the first target bridge under the influence of pier-free construction equipment. The simulated environmental stress is a simulated load bearing stress corresponding to the range of the construction area of the first target bridge. The dynamic simulation data set is a geological dynamic stress simulation data set of the first target bridge in the construction process, namely, the dynamic simulation data set comprises pier simulation bearing stress parameters and simulation environment stress parameters of the first target bridge under the influence of pier construction equipment. The influence of the pier construction equipment comprises the simulated construction vibration intensity and the simulated construction vibration duration of the pier construction equipment. The bridge pier construction equipment comprises a crane, a tower crane, a pile driver, an excavator and the like.
Step S500: inputting the first simulation data set into a multi-element monitoring model, performing safety monitoring according to the multi-element monitoring model, and outputting a first risk coefficient, wherein the multi-element monitoring model is in communication connection with the first construction simulation model;
further, the step S500 of the present application further includes:
Step S510: inputting the first simulation data set into a multi-element monitoring model, wherein the multi-element monitoring model comprises a static safety monitoring sub-model and a dynamic safety monitoring sub-model, and a weight sharing channel is arranged between the static safety monitoring sub-model and the dynamic safety monitoring sub-model;
Further, step S510 of the present application further includes:
Step S511: outputting a pier stress sample data set of the first target bridge according to the first construction simulation model connected with the multi-element monitoring model, wherein the pier stress sample data set comprises a bearing stress sample data set and an environment stress sample data set;
Step S512: and taking the pier stress sample data set and the identification information for identifying the safety of the pier as training data sets, and outputting the static safety monitoring sub-model when the model converges.
Specifically, the bridge high pier construction safety monitoring system based on BIM is connected, and a pier stress sample data set of a first target bridge and identification information for identifying the safety of the bridge are collected. The pier stress sample data set comprises a bearing stress sample data set and an environment stress sample data set. The load bearing force sample data set comprises a static load bearing force sample data set and a dynamic load bearing force sample data set. The environmental stress sample data set comprises a static environmental stress sample data set and a dynamic environmental stress sample data set. The identification information for identifying the safety of the bridge pier comprises a static risk sample coefficient set and a dynamic risk sample coefficient set. And then taking the static bearing stress sample data set, the static environment stress sample data set and the static risk sample coefficient set as training data sets, and continuously self-training and learning the training data sets to a convergence state based on the BP neural network to obtain a static safety monitoring sub-model. The static bearing stress sample data set and the static environment stress sample data set respectively comprise a plurality of historical bridge pier bearing stresses and a plurality of historical environment stresses of the first target bridge under the influence of bridge pier-free construction equipment. The static risk sample coefficient set comprises a static bearing stress sample data set and a plurality of historical static risk coefficients corresponding to the static environment stress sample data set. The BP neural network is a multi-layer feedforward neural network trained according to an error back propagation algorithm. The BP neural network comprises an input layer, a plurality of layers of neurons and an output layer. The BP neural network can perform forward calculation and backward calculation. When calculating in the forward direction, the input information is processed layer by layer from the input layer through a plurality of layers of neurons and is turned to the output layer, and the state of each layer of neurons only affects the state of the next layer of neurons. If the expected output cannot be obtained at the output layer, the reverse calculation is carried out, the error signal is returned along the original connecting path, and the weight of each neuron is modified to minimize the error signal. The static security monitoring sub-model comprises an input layer, an implicit layer and an output layer.
Further, after step S512, the method further includes:
Step S513: determining pier construction equipment of the first target bridge;
Step S514: outputting a first dynamic influence data set according to the construction vibration intensity and the construction vibration duration of the bridge pier construction equipment, wherein the first dynamic influence data set is used for identifying influence on the bridge pier of the first target bridge during construction;
Step S515: and outputting the dynamic safety monitoring sub-model when the model converges according to the first dynamic influence data set, the pier stress sample data set and the identification information for identifying the safety of the pier as a training data set.
Specifically, a first dynamic influence data set, a dynamic bearing stress sample data set, a dynamic environment stress sample data set and a dynamic risk sample coefficient set are used as training data sets, and based on a BP neural network, the first dynamic influence data set, the dynamic bearing stress sample data set, the dynamic environment stress sample data set and the dynamic risk sample coefficient set are continuously self-trained and learned to a convergence state, so that a dynamic safety monitoring submodel is obtained. The dynamic bearing stress sample data set and the dynamic environment stress sample data set respectively comprise a plurality of pier historical bearing stress parameters and a plurality of historical environment stress parameters of the first target bridge under the first dynamic influence data set. The first dynamic influence data set comprises a plurality of historical construction vibration intensities and a plurality of historical construction vibration durations of pier construction equipment, and a plurality of historical pier influence coefficients corresponding to the plurality of historical construction vibration intensities and the plurality of historical construction vibration durations. Each historical pier influence coefficient is used for representing influence on the piers of the first target bridge when the bridge is constructed under the historical construction vibration intensity and the historical construction vibration duration. The set of dynamic risk sample coefficients includes a plurality of historical dynamic risk coefficients. The dynamic security monitoring sub-model comprises an input layer, an implicit layer and an output layer.
Step S520: inputting the static simulation data set into the static safety monitoring sub-model for identification, and outputting a first static risk coefficient;
step S530: inputting the dynamic simulation data set into the dynamic safety monitoring sub-model for identification, and outputting a first dynamic risk coefficient;
step S540: and outputting the first risk coefficient according to the first static risk coefficient and the first dynamic risk coefficient.
Step S600: and outputting first reminding information according to the first risk coefficient.
Specifically, the multivariate monitoring model includes a static safety monitoring sub-model and a dynamic safety monitoring sub-model. And a weight sharing channel is arranged between the static safety monitoring sub-model and the dynamic safety monitoring sub-model. And inputting the static simulation data set into a static safety monitoring sub-model to obtain a first static risk coefficient. And inputting the dynamic simulation data set into a dynamic safety monitoring sub-model to obtain a first dynamic risk coefficient. And then, transmitting the first static risk coefficient and the first dynamic risk coefficient to a weight sharing channel, wherein the weight sharing channel comprises a static risk weight value and a dynamic risk weight value which are preset and determined by the BIM-based bridge high pier construction safety monitoring system. And then, according to a weight sharing weighting formula, carrying out weighted calculation on the first static risk coefficient and the first dynamic risk coefficient according to the static risk weight value and the dynamic risk weight value to obtain a first risk coefficient, and generating first reminding information. The first static risk coefficient is used for representing the bearing risk of the bridge pier and the environmental stress risk of the first target bridge under the influence of bridge pier-free construction equipment. The first dynamic risk coefficient is used for representing the bridge pier bearing risk and the environment stress risk of the first target bridge under the influence of bridge pier construction equipment. The first reminding information is data information for carrying out early warning and prompting on the first risk coefficient.
Preferably, the weight sharing weighting formula is
Z＝α*X+β*Y，
Wherein Z is the first risk coefficient of the output, X is the first static risk coefficient of the input, Y is the first dynamic risk coefficient of the input, and alpha and beta are the static risk weight value and the dynamic risk weight value respectively.
The method achieves the technical effects of accurately and efficiently analyzing the static simulation data set and the dynamic simulation data set through the multi-element monitoring model, obtaining a reliable first risk coefficient and improving the construction safety monitoring early warning accuracy of the bridge high pier.
Further, step S514 of the present application further includes:
Step S514-1: outputting a second dynamic influence data set according to the construction vibration intensity and the construction vibration duration of the pier construction equipment, wherein the second dynamic influence data set is used for identifying the influence on the geology of a construction area during construction;
step S514-2: and identifying hidden danger levels of the pier construction equipment according to the second dynamic influence data set and the first dynamic influence data set, and carrying out safety management on the pier construction equipment according to identification information.
Specifically, a second dynamic influence data set is obtained based on the construction vibration intensity and the construction vibration duration of the pier construction equipment. The second dynamic influence data set comprises a plurality of historical construction vibration intensities and a plurality of historical construction vibration durations of pier construction equipment, and a plurality of historical geological influence coefficients corresponding to the plurality of historical construction vibration intensities and the plurality of historical construction vibration durations. The historical geological influence coefficient is used for representing the influence on the geology of a construction area when the construction is performed under the historical construction vibration intensity and the historical construction vibration duration. And then, inputting the simulated construction vibration intensity and the simulated construction vibration duration of the pier construction equipment into a first dynamic influence data set and a second dynamic influence data set, marking hidden danger levels of the pier construction equipment through the first dynamic influence data set and the second dynamic influence data set, obtaining identification information, and carrying out safety management on the pier construction equipment according to the identification information. The identification information comprises pier influence coefficients and geological influence coefficients corresponding to the simulated construction vibration intensity and the simulated construction vibration duration. The higher the pier influence coefficient and the geological influence coefficient are, the higher the hidden danger level of the pier construction equipment is under the corresponding simulated construction vibration intensity and simulated construction vibration duration.
In summary, the bridge high pier construction safety monitoring method based on BIM provided by the application has the following technical effects:
1. Performing BIM simulation modeling on a first target bridge to output a first bridge simulation model; the method comprises the steps of acquiring data of a construction area of a first target bridge, modeling by a BIM simulation system and outputting a first geological simulation model; performing model fusion on the first bridge simulation model and the first geological simulation model, and outputting a first construction simulation model; performing construction simulation according to the first construction simulation model to obtain a first simulation data set; and inputting the first simulation data set into the multivariate monitoring model, outputting a first risk coefficient, and generating first reminding information. The method achieves the effects of improving the accuracy and the comprehensiveness of the construction safety monitoring and early warning of the high pier of the bridge, improving the construction safety monitoring and early warning effect of the high pier of the bridge and providing powerful guarantee for the construction safety of the high pier of the bridge.
2. And carrying out geological risk assessment on the construction area of the first target bridge through the first geological simulation model, so as to carry out adaptive fusion on the first geological simulation model and the first bridge simulation model, thereby improving the reliability of the first construction simulation model.
Example two
Based on the same inventive concept as the bridge high pier construction safety monitoring method based on BIM in the foregoing embodiment, the invention also provides a bridge high pier construction safety monitoring system based on BIM, please refer to fig. 3, the system comprises:
the bridge simulation module 11 is used for performing BIM simulation modeling on the first target bridge and outputting a first bridge simulation model;
The geological simulation module 12 is used for collecting data of a construction area of the first target bridge, modeling the construction area through a BIM simulation system and outputting a first geological simulation model;
The fusion module 13 is used for carrying out model fusion according to the first bridge simulation model and the first geological simulation model, and outputting a first construction simulation model;
The construction simulation module 14 is configured to perform construction simulation according to the first construction simulation model, and obtain a first simulation data set, where the first simulation data set includes a static simulation data set and a dynamic simulation data set, the static simulation data set is a static stress simulation data set of the first target bridge caused by the high pier on the geology, and the dynamic simulation data set is a geological dynamic stress simulation data set of the first target bridge in the construction process;
the safety monitoring module 15 is configured to input the first simulation data set into a multi-element monitoring model, perform safety monitoring according to the multi-element monitoring model, and output a first risk coefficient, where the multi-element monitoring model is in communication connection with the first construction simulation model;
the reminding information output module 16, the reminding information output module 16 is configured to output first reminding information according to the first risk coefficient.
Further, the system further comprises:
The first execution module is used for modeling through a BIM simulation system and building the first geological simulation model, wherein data acquisition is carried out on a construction area of the first target bridge, and the first geological simulation model comprises a geological layer data set, a slope data set and an embedded part data set;
The geological risk assessment module is used for carrying out geological risk assessment on the construction area of the first target bridge based on the first geological simulation model and outputting a first geological hidden danger index;
The fusion instruction acquisition module is used for acquiring a first fusion instruction when the first geological hidden danger index is smaller than a preset hidden danger index;
And the second execution module is used for fusing the first geological simulation model and the first bridge simulation model according to the first fusion instruction and outputting a first construction simulation model.
Further, the system further comprises:
The structural parameter retrieving module is used for determining the number of high piers, the distribution of the high piers and the geometric data of the high piers by retrieving the structural parameters of the first bridge simulation model;
The region positioning module is used for performing region positioning on the first geological simulation model according to the number of the high piers, the distribution of the high piers and the geometric data of the high piers and outputting a positioning result of a construction region;
and the area expansion module is used for carrying out area expansion on the positioning result of the construction area and determining the range of the construction area.
Further, the system further comprises:
The third execution module is used for inputting the first simulation data set into a multi-element monitoring model, wherein the multi-element monitoring model comprises a static safety monitoring sub-model and a dynamic safety monitoring sub-model, and a weight sharing channel is arranged between the static safety monitoring sub-model and the dynamic safety monitoring sub-model;
the static risk coefficient obtaining module is used for inputting the static simulation data set into the static safety monitoring sub-model for identification and outputting a first static risk coefficient;
The dynamic risk coefficient module is used for inputting the dynamic simulation data set into the dynamic safety monitoring sub-model for identification and outputting a first dynamic risk coefficient;
the first risk coefficient output module is used for outputting the first risk coefficient according to the first static risk coefficient and the first dynamic risk coefficient.
Further, the system further comprises:
The bridge pier stress sample data set obtaining module is used for outputting a bridge pier stress sample data set of the first target bridge according to the first construction simulation model connected with the multi-element monitoring model, and comprises a bearing stress sample data set and an environment stress sample data set;
and the fourth execution module is used for taking the pier stress sample data set and the identification information for identifying the safety of the pier as training data sets, and outputting the static safety monitoring sub-model when the model converges.
Further, the system further comprises:
the device determining module is used for determining pier construction devices of the first target bridge;
the dynamic influence data set obtaining module is used for outputting a first dynamic influence data set according to the construction vibration intensity and the construction vibration duration of the bridge pier construction equipment, wherein the first dynamic influence data set is used for identifying influence on the bridge pier of the first target bridge during construction;
And the fifth execution module is used for outputting the dynamic safety monitoring sub-model according to the first dynamic influence data set, the pier stress sample data set and the identification information for identifying the safety of the pier as a training data set when the model converges.
Further, the system further comprises:
The sixth execution module is used for outputting a second dynamic influence data set according to the construction vibration intensity and the construction vibration duration of the pier construction equipment, wherein the second dynamic influence data set is used for identifying the influence on the geology of a construction area during construction;
the safety management module is used for identifying hidden danger levels of the bridge pier construction equipment according to the second dynamic influence data set and the first dynamic influence data set and carrying out safety management on the bridge pier construction equipment according to identification information.
The bridge high pier construction safety monitoring system based on the BIM provided by the embodiment of the invention can execute the bridge high pier construction safety monitoring method based on the BIM provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
All the included modules are only divided according to the functional logic, but are not limited to the above-mentioned division, so long as the corresponding functions can be realized; in addition, the specific names of the functional modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present invention.
The application provides a BIM-based bridge high pier construction safety monitoring method, wherein the method is applied to a BIM-based bridge high pier construction safety monitoring system, and the method comprises the following steps: performing BIM simulation modeling on a first target bridge to output a first bridge simulation model; the method comprises the steps of acquiring data of a construction area of a first target bridge, modeling by a BIM simulation system and outputting a first geological simulation model; performing model fusion on the first bridge simulation model and the first geological simulation model, and outputting a first construction simulation model; performing construction simulation according to the first construction simulation model to obtain a first simulation data set; and inputting the first simulation data set into the multivariate monitoring model, outputting a first risk coefficient, and generating first reminding information. The technical problem of in prior art to the construction safety monitoring early warning accuracy of bridge high mound poor, lead to the construction safety monitoring early warning effect of bridge high mound poor is solved. The method achieves the effects of improving the accuracy and the comprehensiveness of the construction safety monitoring and early warning of the high pier of the bridge, improving the construction safety monitoring and early warning effect of the high pier of the bridge and providing powerful guarantee for the construction safety of the high pier of the bridge.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (6)
1. The BIM-based bridge high pier construction safety monitoring method is characterized by comprising the following steps of:
performing BIM simulation modeling on the first target bridge, and outputting a first bridge simulation model;
data acquisition is carried out on the construction area of the first target bridge, modeling is carried out through a BIM simulation system, and a first geological simulation model is output;
modeling is performed through a BIM simulation system, and a first geological simulation model is output, wherein the method comprises the following steps:
modeling by a BIM simulation system, and building the first geological simulation model, wherein data acquisition is carried out on a construction area of the first target bridge, wherein the data acquisition comprises a geological layer data set, an inclined slope data set and an embedded part data set;
Performing geological risk assessment on the construction area of the first target bridge based on the first geological simulation model, and outputting a first geological hidden danger index;
When the first geological hidden danger index is smaller than a preset hidden danger index, a first fusion instruction is obtained;
Fusing the first geological simulation model and the first bridge simulation model according to the first fusing instruction, and outputting a first construction simulation model;
Performing model fusion according to the first bridge simulation model and the first geological simulation model, and outputting a first construction simulation model;
performing construction simulation according to the first construction simulation model to obtain a first simulation data set, wherein the first simulation data set comprises a static simulation data set and a dynamic simulation data set, the static simulation data set is a static stress simulation data set of the first target bridge caused by high piers of the first target bridge to geology, and the dynamic simulation data set is a geological dynamic stress simulation data set of the first target bridge in the construction process;
Inputting the first simulation data set into a multi-element monitoring model, performing safety monitoring according to the multi-element monitoring model, and outputting a first risk coefficient, wherein the multi-element monitoring model is in communication connection with the first construction simulation model, and the method comprises the following steps:
inputting the first simulation data set into a multi-element monitoring model, wherein the multi-element monitoring model comprises a static safety monitoring sub-model and a dynamic safety monitoring sub-model, and a weight sharing channel is arranged between the static safety monitoring sub-model and the dynamic safety monitoring sub-model;
inputting the static simulation data set into the static safety monitoring sub-model for identification, and outputting a first static risk coefficient;
inputting the dynamic simulation data set into the dynamic safety monitoring sub-model for identification, and outputting a first dynamic risk coefficient;
outputting the first risk coefficient according to the first static risk coefficient and the first dynamic risk coefficient;
And outputting first reminding information according to the first risk coefficient.
2. The method of claim 1, wherein the method further comprises:
determining the number of high piers, the distribution of the high piers and the geometric data of the high piers by calling the structural parameters of the first bridge simulation model;
According to the number of the high piers, the distribution of the high piers and the geometric data of the high piers, carrying out region positioning on the first geological simulation model, and outputting a positioning result of a construction region;
and carrying out area expansion on the positioning result of the construction area, and determining the range of the construction area.
3. The method of claim 1, wherein the method further comprises:
Outputting a pier stress sample data set of the first target bridge according to the first construction simulation model connected with the multi-element monitoring model, wherein the pier stress sample data set comprises a bearing stress sample data set and an environment stress sample data set;
and taking the pier stress sample data set and the identification information for identifying the safety of the pier as training data sets, and outputting the static safety monitoring sub-model when the model converges.
4. A method as claimed in claim 3, wherein the method further comprises:
Determining pier construction equipment of the first target bridge;
outputting a first dynamic influence data set according to the construction vibration intensity and the construction vibration duration of the bridge pier construction equipment, wherein the first dynamic influence data set is used for identifying influence on the bridge pier of the first target bridge during construction;
and outputting the dynamic safety monitoring sub-model when the model converges according to the first dynamic influence data set, the pier stress sample data set and the identification information for identifying the safety of the pier as a training data set.
5. The method of claim 4, wherein the method further comprises:
Outputting a second dynamic influence data set according to the construction vibration intensity and the construction vibration duration of the pier construction equipment, wherein the second dynamic influence data set is used for identifying the influence on the geology of a construction area during construction;
And identifying hidden danger levels of the pier construction equipment according to the second dynamic influence data set and the first dynamic influence data set, and carrying out safety management on the pier construction equipment according to identification information.
6. A BIM-based bridge high pier construction safety monitoring system for performing the method of any one of claims 1 to 5, the system comprising:
The bridge simulation module is used for performing BIM simulation modeling on the first target bridge and outputting a first bridge simulation model;
modeling is performed through a BIM simulation system, and a first geological simulation model is output, wherein the method comprises the following steps:
modeling by a BIM simulation system, and building the first geological simulation model, wherein data acquisition is carried out on a construction area of the first target bridge, wherein the data acquisition comprises a geological layer data set, an inclined slope data set and an embedded part data set;
Performing geological risk assessment on the construction area of the first target bridge based on the first geological simulation model, and outputting a first geological hidden danger index;
When the first geological hidden danger index is smaller than a preset hidden danger index, a first fusion instruction is obtained;
Fusing the first geological simulation model and the first bridge simulation model according to the first fusing instruction, and outputting a first construction simulation model;
the geological simulation module is used for collecting data of a construction area of the first target bridge, modeling the construction area through a BIM simulation system and outputting a first geological simulation model;
the fusion module is used for carrying out model fusion according to the first bridge simulation model and the first geological simulation model and outputting a first construction simulation model;
The construction simulation module is used for performing construction simulation according to the first construction simulation model to obtain a first simulation data set, wherein the first simulation data set comprises a static simulation data set and a dynamic simulation data set, the static simulation data set is a static stress simulation data set of the first target bridge, which is caused by a high pier of the first target bridge, to geology, and the dynamic simulation data set is a geological dynamic stress simulation data set of the first target bridge in the construction process;
the safety monitoring module is used for inputting the first simulation data set into a multi-element monitoring model, carrying out safety monitoring according to the multi-element monitoring model, and outputting a first risk coefficient, wherein the multi-element monitoring model is in communication connection with the first construction simulation model, and comprises the following components:
inputting the first simulation data set into a multi-element monitoring model, wherein the multi-element monitoring model comprises a static safety monitoring sub-model and a dynamic safety monitoring sub-model, and a weight sharing channel is arranged between the static safety monitoring sub-model and the dynamic safety monitoring sub-model;
inputting the static simulation data set into the static safety monitoring sub-model for identification, and outputting a first static risk coefficient;
inputting the dynamic simulation data set into the dynamic safety monitoring sub-model for identification, and outputting a first dynamic risk coefficient;
outputting the first risk coefficient according to the first static risk coefficient and the first dynamic risk coefficient;
and the reminding information output module is used for outputting first reminding information according to the first risk coefficient.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310860458.3A CN116842844B (en) | 2023-07-13 | 2023-07-13 | BIM-based bridge high pier construction safety monitoring method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310860458.3A CN116842844B (en) | 2023-07-13 | 2023-07-13 | BIM-based bridge high pier construction safety monitoring method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116842844A CN116842844A (en) | 2023-10-03 |
| CN116842844B true CN116842844B (en) | 2024-09-03 |
Family
ID=88163250
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310860458.3A Active CN116842844B (en) | 2023-07-13 | 2023-07-13 | BIM-based bridge high pier construction safety monitoring method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116842844B (en) |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116305797A (en) * | 2023-02-07 | 2023-06-23 | 广东电网有限责任公司广州供电局 | Construction risk assessment system based on BIM and multisource quality information fusion |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108460231A (en) * | 2018-03-23 | 2018-08-28 | 中交公路长大桥建设国家工程研究中心有限公司 | A kind of bridge builds foster overall process intellectual monitoring assessment early warning decision system and method |
| US11468206B2 (en) * | 2018-08-20 | 2022-10-11 | Sri International | Machine learning system for building renderings and building information modeling data |
| CN109209403A (en) * | 2018-11-16 | 2019-01-15 | 中铁投资集团有限公司 | A kind of karsts developing area region multi-source geology BIM modeling anticipation guiding construction method |
| CN115761155A (en) * | 2022-10-27 | 2023-03-07 | 石家庄铁道大学 | Railway three-dimensional geological route selection method and system |
-
2023
- 2023-07-13 CN CN202310860458.3A patent/CN116842844B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116305797A (en) * | 2023-02-07 | 2023-06-23 | 广东电网有限责任公司广州供电局 | Construction risk assessment system based on BIM and multisource quality information fusion |
Non-Patent Citations (1)
| Title |
|---|
| 公路大桥主桥设计中的BIM 技术的应用探讨;朱永杰等;《公路工程》;20170831;第42卷(第4期);第169-172页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116842844A (en) | 2023-10-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wu et al. | Critical review of data-driven decision-making in bridge operation and maintenance | |
| Ding et al. | Development of web-based system for safety risk early warning in urban metro construction | |
| Ceylan et al. | Neural networks applications in pavement engineering: A recent survey | |
| Babovic et al. | Neural networks as routine for error updating of numerical models | |
| Dogan et al. | Detection of damages caused by earthquake and reinforcement corrosion in RC buildings with Deep Transfer Learning | |
| CN115267945A (en) | Thunder and lightning early warning method and system based on graph neural network | |
| Oumeraci et al. | XtremRisK—integrated flood risk analysis for extreme storm surges at open coasts and in estuaries: methodology, key results and lessons learned | |
| Huang et al. | Physics-guided deep neural network for structural damage identification | |
| CN105678417A (en) | Prediction method and device for tunnel face water inflow of construction tunnel | |
| CN117556197A (en) | Typhoon vortex initialization method based on artificial intelligence | |
| Romano et al. | Artificial neural network for tsunami forecasting | |
| CN117272232A (en) | Tunnel monitoring method and device for data fusion, computer equipment and storage medium | |
| Xu et al. | A Bayesian approach for site-specific extreme load prediction of large scale bridges | |
| CN116842844B (en) | BIM-based bridge high pier construction safety monitoring method and system | |
| Rohaimi et al. | 3 Hours ahead of time flood water level prediction using NNARX structure: Case study pahang | |
| CN110826290B (en) | Safety early warning method for offshore floating system | |
| CN117437204A (en) | Insulator defect rapid detection method and system based on YOLO-MID | |
| Chun et al. | An application of data fusion technology in structural health monitoring and damage identification | |
| Mendler | Minimum diagnosable damage and optimal sensor placement for structural health monitoring | |
| CN113888380A (en) | Method, device, equipment and medium for predicting man overboard trajectory | |
| Ginan et al. | Applications of AI in Health Monitoring of Structures in Potential Seismic Areas—A Review | |
| Rani et al. | Gridlock Prediction using a Gated Recurrent Neural Network for Smart Transport Systems | |
| Song et al. | Probabilistic modeling of long-term joint wind and wave load conditions via generative adversarial network | |
| Xie | Deep Learning in Earthquake Engineering: A Comprehensive Review | |
| Pamuncak | Deep learning for structural 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 |