CN117390938B - High-pile wharf operation and maintenance period structure health monitoring method and system - Google Patents
High-pile wharf operation and maintenance period structure health monitoring method and system Download PDFInfo
- Publication number
- CN117390938B CN117390938B CN202311704418.6A CN202311704418A CN117390938B CN 117390938 B CN117390938 B CN 117390938B CN 202311704418 A CN202311704418 A CN 202311704418A CN 117390938 B CN117390938 B CN 117390938B
- Authority
- CN
- China
- Prior art keywords
- wharf
- data
- monitoring
- pile
- model
- 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
- 238000012544 monitoring process Methods 0.000 title claims abstract description 144
- 230000036541 health Effects 0.000 title claims abstract description 79
- 238000012423 maintenance Methods 0.000 title claims abstract description 40
- 238000000034 method Methods 0.000 title claims abstract description 32
- 238000003062 neural network model Methods 0.000 claims abstract description 83
- 238000012549 training Methods 0.000 claims abstract description 53
- 238000009826 distribution Methods 0.000 claims abstract description 37
- 238000007405 data analysis Methods 0.000 claims abstract description 21
- 238000007781 pre-processing Methods 0.000 claims abstract description 21
- 238000013506 data mapping Methods 0.000 claims abstract description 16
- 230000008859 change Effects 0.000 claims description 25
- 238000004364 calculation method Methods 0.000 claims description 19
- 238000013461 design Methods 0.000 claims description 15
- 238000006073 displacement reaction Methods 0.000 claims description 15
- 238000003860 storage Methods 0.000 claims description 13
- 238000004458 analytical method Methods 0.000 claims description 12
- 238000012360 testing method Methods 0.000 claims description 12
- 238000013507 mapping Methods 0.000 claims description 9
- 238000012545 processing Methods 0.000 claims description 9
- 238000012795 verification Methods 0.000 claims description 8
- 238000013528 artificial neural network Methods 0.000 claims description 5
- 238000004140 cleaning Methods 0.000 claims description 5
- 238000000605 extraction Methods 0.000 claims description 5
- 238000011478 gradient descent method Methods 0.000 claims description 5
- 238000005259 measurement Methods 0.000 claims description 4
- 230000006870 function Effects 0.000 description 18
- 238000011156 evaluation Methods 0.000 description 4
- 230000003862 health status Effects 0.000 description 4
- 238000011960 computer-aided design Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000005672 electromagnetic field Effects 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 239000012530 fluid Substances 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000004062 sedimentation Methods 0.000 description 2
- 230000035882 stress Effects 0.000 description 2
- 238000012916 structural analysis Methods 0.000 description 2
- 230000001960 triggered effect Effects 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000003628 erosive effect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000002787 reinforcement Effects 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 238000009991 scouring Methods 0.000 description 1
- 238000006467 substitution reaction Methods 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
- G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0499—Feedforward networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/10—Numerical modelling
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Artificial Intelligence (AREA)
- Software Systems (AREA)
- Biophysics (AREA)
- Computer Hardware Design (AREA)
- Mathematical Physics (AREA)
- Geometry (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biomedical Technology (AREA)
- Computing Systems (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Testing Or Calibration Of Command Recording Devices (AREA)
Abstract
The invention discloses a method and a system for monitoring structural health of a high-pile wharf in an operation and maintenance period, wherein a high-pile wharf BIM model is built in the operation and maintenance period; importing model training data into a preset neural network model for training, and obtaining a weight relation between the parameters of the high-pile wharf and the prediction result based on the preset neural network model; according to the weight relation between the parameters of the high-pile wharf and the prediction result, the high-pile wharf BIM model is combined to analyze the wharf structure health position and area, and a wharf monitoring and sensing network is generated based on the operation period influence factors; acquiring a wharf monitoring data set in an operation period based on the wharf monitoring sensing network and preprocessing data; importing a wharf monitoring data set into a preset neural network model to perform wharf structure integral internal force distribution and comprehensive wharf health state assessment based on data analysis prediction, and generating prediction result data; and carrying out data mapping on the prediction result data and the high pile wharf BIM model, and displaying the prediction result data through preset terminal equipment.
Description
Technical Field
The invention relates to the field of digital twin information, in particular to a method and a system for monitoring structural health of a high pile wharf in an operation and maintenance period.
Background
The high pile wharf is adversely affected by various factors such as siltation, scouring, aging of materials, environmental erosion, abnormal impact of ships, change of surrounding environmental conditions and the like during operation, so that the wharf is greatly deformed, the stress state of pile foundations and upper structures possibly exceeds the design allowable range, normal use is affected, and even disastrous sudden accidents are caused under extreme conditions. How to realize grasping the stress and deformation state of the wharf in real time through the monitoring of the safe practical health condition of the high pile wharf structure, and can provide basis for the periodic maintenance, repair and even reinforcement and reconstruction of the wharf.
The current monitoring means mainly adopts sensor equipment to monitor displacement, deformation, settlement and the like of a wharf component in real time and performs early warning through the relation between a numerical value and a threshold value, but the method can only realize health monitoring of the component where the sensor is positioned at present, has isolated data, is non-real-time and discontinuous, lacks analysis and evaluation, and is difficult to comprehensively evaluate the wharf health state. If the whole wharf structure is to be monitored, a large number of sensors are caused, and the maintenance is inconvenient and the manufacturing cost is high.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides a method and a system for monitoring the structural health of a high-pile wharf in the operation and maintenance period.
The first aspect of the invention provides a structural health monitoring method for a high pile wharf in an operation and maintenance period, which comprises the following steps:
in the operation and maintenance period, obtaining design data of a target pile wharf, and constructing a corresponding high pile wharf BIM model based on the design data;
constructing a finite element numerical calculation model of a target pile wharf, establishing different working condition information based on an operation period influence factor, analyzing internal force change and deformation characteristic data of a corresponding wharf structural member according to the working condition information, and taking the internal force change and deformation characteristic data as model training data;
the model training data is imported into a preset neural network model for training, and a weight relation between the parameters of the high-pile wharf and the predicted result is obtained based on the preset neural network model;
according to the weight relation between the parameters of the high-pile wharf and the prediction result, carrying out wharf structure health position and area analysis by combining a high-pile wharf BIM model, and generating a wharf monitoring and sensing network based on an operation period influence factor;
acquiring a wharf monitoring data set in an operation period based on the wharf monitoring sensing network and preprocessing data;
Importing a wharf monitoring data set into a preset neural network model to perform wharf structure integral internal force distribution and comprehensive wharf health state assessment based on data analysis prediction, and generating prediction result data;
and carrying out data mapping on the prediction result data and the high pile wharf BIM model, and displaying the prediction result data through preset terminal equipment.
In the scheme, a finite element numerical calculation model of a target pile wharf is constructed, different working condition information is established based on operation period influence factors, corresponding wharf structural member internal force change and deformation characteristic data are analyzed according to the working condition information, the internal force change and deformation characteristic data are used as model training data, the working condition information comprises a plurality of types, and each working condition corresponds to different operation period influence factors.
In this scheme, the model training data is imported into a preset neural network model for training, and a weight relation between the parameters of the high pile wharf and the predicted result is obtained based on the preset neural network model, specifically:
ST1 builds a multilayer feedforward neural network model;
ST2, initializing neural network model parameters, wherein the initialized parameters comprise wharf equipment external force parameters, natural external force parameters, ship impact force parameters and wharf self attribute parameters during the operation of a high-pile wharf;
ST3 inputs model training data into a neural network model, and transmits the data from an input layer to an output layer through forward propagation to generate a prediction result;
ST4 defines a Mean Square Error (MSE) loss function to measure the gap between the predicted result and the actual numerical calculation result;
ST5 calculates the gradient of the loss function to the network parameters through a back propagation algorithm, adjusts the network parameters according to the gradient to gradually reduce the loss function value, and updates the parameters of the neural network by adopting a gradient descent method to enable the loss function to be converged;
repeating the steps ST 3-ST 5 to update the training model and parameters until the value of the loss function meets the preset requirement;
and obtaining the weight relation between the parameters of the high pile wharf and the predicted result through the trained multilayer feedforward neural network model.
In this scheme, according to the weight relation between the high pile wharf parameter and the prediction result, the high pile wharf BIM model is combined to perform wharf structure health position and area analysis, and based on the operation period influence factor, a wharf monitoring and sensing network is generated, specifically:
according to the weight relation between the high-pile wharf parameters and the prediction result, combining a high-pile wharf BIM model, analyzing a region with the greatest influence in a wharf region, and marking the region as an influence region;
Analyzing the positions of the monitoring points based on the influence area to generate a monitoring scheme;
the monitoring scheme comprises the number and position information of wharf monitoring sensors;
according to the monitoring scheme, the high pile wharf BIM model is combined to analyze the wharf structure health position and area, and a wharf monitoring and sensing network is generated based on the operation period influence factors.
In this scheme, based on the pier monitoring perception network, obtain the pier monitoring dataset of operation period and carry out data preprocessing, preprocessing includes removing noise, missing value processing, normalized data, data cleaning, extraction eigenvalue processing.
In this scheme, importing the dock monitoring dataset into a preset neural network model for internal force distribution and comprehensive dock health state assessment of the whole dock structure based on data analysis and prediction, and generating prediction result data, including:
acquiring actual monitoring data of a preset time period through a monitoring sensing network;
dividing the actual monitoring data into verification data and test data;
according to a field test and through an external load mode, verifying deviation between field actual measurement data and a predicted value of the multi-layer feedforward neural network model, using verification data to evaluate performance of the multi-layer feedforward neural network model, performing super-parameter tuning, and then using test data to evaluate generalization capability of the model.
In this scheme, importing the pier monitoring dataset into a preset neural network model for internal force distribution and comprehensive pier health state assessment of the whole pier structure based on data analysis and prediction, and generating prediction result data, further comprising:
importing the wharf monitoring data set into a multi-layer feedforward neural network model to perform wharf structural overall internal force distribution and comprehensive wharf health state assessment based on data analysis prediction, so as to obtain wharf internal force distribution prediction data and health assessment data;
the prediction result data comprises intra-dock force distribution prediction data and health assessment data.
In this scheme, the data mapping is performed on the prediction result data and the high pile wharf BIM model, and the prediction result data and the high pile wharf BIM model are displayed through a preset terminal device, specifically:
performing data mapping and position display mapping between the dock internal force distribution prediction data and a high pile dock BIM model, and performing result display through preset terminal equipment based on mapping results;
acquiring actual requirement information of wharf operation, and setting threshold information of internal force, displacement and deformation for different parts of a target high-pile wharf according to the requirement information;
and analyzing and comparing the threshold information with the predicted result data, and generating wharf early warning information based on the comparison result.
The second aspect of the invention also provides a system for monitoring the structural health of a high pile wharf in operation and maintenance period, which comprises: the system comprises a storage and a processor, wherein the storage comprises a high-pile wharf operation and maintenance period structure health monitoring program, and the high-pile wharf operation and maintenance period structure health monitoring program realizes the following steps when being executed by the processor:
in the operation and maintenance period, obtaining design data of a target pile wharf, and constructing a corresponding high pile wharf BIM model based on the design data;
constructing a finite element numerical calculation model of a target pile wharf, establishing different working condition information based on an operation period influence factor, analyzing internal force change and deformation characteristic data of a corresponding wharf structural member according to the working condition information, and taking the internal force change and deformation characteristic data as model training data;
the model training data is imported into a preset neural network model for training, and a weight relation between the parameters of the high-pile wharf and the predicted result is obtained based on the preset neural network model;
according to the weight relation between the parameters of the high-pile wharf and the prediction result, carrying out wharf structure health position and area analysis by combining a high-pile wharf BIM model, and generating a wharf monitoring and sensing network based on an operation period influence factor;
Acquiring a wharf monitoring data set in an operation period based on the wharf monitoring sensing network and preprocessing data;
importing a wharf monitoring data set into a preset neural network model to perform wharf structure integral internal force distribution and comprehensive wharf health state assessment based on data analysis prediction, and generating prediction result data;
and carrying out data mapping on the prediction result data and the high pile wharf BIM model, and displaying the prediction result data through preset terminal equipment.
The invention discloses a method and a system for monitoring structural health of a high-pile wharf in an operation and maintenance period, wherein a high-pile wharf BIM model is built in the operation and maintenance period; importing model training data into a preset neural network model for training, and obtaining a weight relation between the parameters of the high-pile wharf and the prediction result based on the preset neural network model; according to the weight relation between the parameters of the high-pile wharf and the prediction result, the high-pile wharf BIM model is combined to analyze the wharf structure health position and area, and a wharf monitoring and sensing network is generated based on the operation period influence factors; acquiring a wharf monitoring data set in an operation period based on the wharf monitoring sensing network and preprocessing data; importing a wharf monitoring data set into a preset neural network model to perform wharf structure integral internal force distribution and comprehensive wharf health state assessment based on data analysis prediction, and generating prediction result data; and carrying out data mapping on the prediction result data and the high pile wharf BIM model, and displaying the prediction result data through preset terminal equipment.
According to the invention, through the effective sensor number and data processing method, various characteristics of structural reaction are analyzed to identify the dock structural state, so that the safety, applicability and durability of dock facilities are improved, the comprehensive evaluation of the dock health state is realized, and the monitoring safety risk is reduced.
Through continuous iterative training, the method can learn complex nonlinear relations and hiding rules from big data monitored on site, so that more accurate prediction and analysis are realized.
Drawings
FIG. 1 shows a flow chart of a method for monitoring structural health during operation and maintenance of a high pile wharf according to the present invention;
FIG. 2 shows a flow chart of the dock monitoring awareness network construction of the present invention;
fig. 3 shows a block diagram of a high pile wharf operation and maintenance period structure health monitoring system of the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
Fig. 1 shows a flow chart of a method for monitoring the health of a structure in the operation and maintenance period of a high pile wharf according to the invention.
As shown in fig. 1, a first aspect of the present invention provides a method for monitoring structural health of a high pile wharf during operation and maintenance, including:
s102, in an operation and maintenance period, obtaining design data of a target pile wharf, and constructing a corresponding high pile wharf BIM model based on the design data;
s104, constructing a finite element numerical calculation model of the target pile wharf, establishing different working condition information based on an operation period influence factor, analyzing internal force change and deformation characteristic data of a corresponding wharf structural member according to the working condition information, and taking the internal force change and deformation characteristic data as model training data;
s106, importing the model training data into a preset neural network model for training, and obtaining a weight relation between the parameters of the high-pile wharf and the predicted result based on the preset neural network model;
s108, analyzing the health position and the area of the wharf structure by combining the high-pile wharf BIM model according to the weight relation between the high-pile wharf parameters and the prediction result, and generating a wharf monitoring and sensing network based on the operation period influence factors;
S110, acquiring an operation period wharf monitoring data set and preprocessing data based on the wharf monitoring sensing network;
s112, importing a wharf monitoring data set into a preset neural network model to perform wharf structure integral internal force distribution and comprehensive wharf health state assessment based on data analysis prediction, and generating prediction result data;
and S114, mapping the predicted result data with the high-pile wharf BIM model data and displaying the data through preset terminal equipment.
The visual carrier is presented as various information, data and the final result obtained after processing the data.
According to the embodiment of the invention, a finite element numerical calculation model of the target pile wharf is constructed, different working condition information is established based on the operation period influence factors, the internal force change and deformation characteristic data of the corresponding wharf structural member are analyzed according to the working condition information, the internal force change and deformation characteristic data are used as model training data, the working condition information comprises a plurality of types, and each working condition corresponds to different operation period influence factors.
The finite element numerical calculation model is a model with very wide application, and has application in the fields of engineering, physics, computer science and the like. In engineering, the finite element method can be used for structural analysis and design of mechanical structures, architectural structures and the like. In physical terms, finite element methods can be used to simulate various physical phenomena, such as electromagnetic fields, fluid dynamics, and the like. In terms of computer science, finite element methods can be used for computer aided design and analysis (CAD/CAE).
And analyzing the internal force change and deformation characteristic data of the corresponding wharf structural member according to the working condition information, wherein the internal force distribution and displacement change of different pile foundations, transverse longitudinal beams and panels are included after the impact force of the ship acts on different wharf bent frames. And taking internal force and displacement calculation (result) of the wharf components under different working conditions as training samples of the follow-up artificial intelligent model, and training by adopting a multilayer feedforward neural network model.
According to the embodiment of the invention, the model training data is imported into a preset neural network model for training, and the weight relation between the parameters of the high pile wharf and the predicted result is obtained based on the preset neural network model, specifically:
ST1 builds a multilayer feedforward neural network model;
ST2, initializing neural network model parameters, wherein the initialized parameters comprise wharf equipment external force parameters, natural external force parameters, ship impact force parameters and wharf self attribute parameters during the operation of a high-pile wharf;
ST3 inputs model training data into a neural network model, and transmits the data from an input layer to an output layer through forward propagation to generate a prediction result;
ST4 defines a Mean Square Error (MSE) loss function to measure the gap between the predicted result and the actual numerical calculation result;
ST5 calculates the gradient of the loss function to the network parameters through a back propagation algorithm, adjusts the network parameters according to the gradient to gradually reduce the loss function value, and updates the parameters of the neural network by adopting a gradient descent method to enable the loss function to be converged;
repeating the steps ST 3-ST 5 to update the training model and parameters until the value of the loss function meets the preset requirement;
and obtaining the weight relation between the parameters of the high pile wharf and the predicted result through the trained multilayer feedforward neural network model.
It should be noted that the preset neural network model is a multi-layer feedforward neural network model. The influence of different wharf parameters on the whole wharf operation can be further confirmed through the weight relation between the high-pile wharf parameters and the prediction results, so that accurate monitoring and regulation and control on wharf health assessment and wharf operation are realized.
Fig. 2 shows a flow chart of the dock monitoring aware network construction of the present invention.
According to the embodiment of the invention, according to the weight relation between the parameters of the high-pile wharf and the predicted result, the high-pile wharf BIM model is combined to analyze the health position and the area of the wharf structure, and based on the operation period influence factors, a wharf monitoring and sensing network is generated, specifically:
S202, according to the weight relation between the parameters of the high-pile wharf and the prediction result, combining a BIM model of the high-pile wharf, analyzing a region with the greatest influence in a wharf region, and marking the region as an influence region;
s204, analyzing the positions of the monitoring points based on the influence area, and generating a monitoring scheme;
s206, the monitoring scheme comprises the number and position information of wharf monitoring sensors;
s208, according to a monitoring scheme, the high pile wharf BIM model is combined to analyze the wharf structure health position and area, and a wharf monitoring and sensing network is generated based on the operation period influence factors.
The wharf monitoring sensor comprises a hydrological sensor, a road condition sensor, an object displacement deformation sensor and the like. In the dock monitoring and sensing network, corresponding sensors are arranged according to the characteristics of influencing factors (factors), a sensing network during operation and maintenance of the high-pile dock is established, deformation information such as displacement and sedimentation of a dock pile foundation and a transverse beam and natural environment information such as wind power and waves are monitored, and a dock monitoring data set in an operation period can be formed.
According to the embodiment of the invention, the operation period wharf monitoring data set is acquired and data preprocessing is performed based on the wharf monitoring sensing network, wherein the preprocessing comprises noise removal, missing value processing, normalization data, data cleaning and characteristic value extraction.
According to an embodiment of the present invention, the importing the dock monitoring data set into the preset neural network model to perform the internal force distribution of the dock structure and the overall dock health status assessment based on the data analysis prediction, and generate the prediction result data includes:
acquiring actual monitoring data of a preset time period through a monitoring sensing network;
dividing the actual monitoring data into verification data and test data;
according to a field test and through an external load mode, verifying deviation between field actual measurement data and a predicted value of the multi-layer feedforward neural network model, using verification data to evaluate performance of the multi-layer feedforward neural network model, performing super-parameter tuning, and then using test data to evaluate generalization capability of the model.
It should be noted that the preset time period is a historical time period.
According to an embodiment of the present invention, the importing the dock monitoring data set into the preset neural network model performs the internal force distribution of the dock structure and the overall dock health status assessment based on the data analysis prediction, and generates the prediction result data, and further includes:
importing the wharf monitoring data set into a multi-layer feedforward neural network model to perform wharf structural overall internal force distribution and comprehensive wharf health state assessment based on data analysis prediction, so as to obtain wharf internal force distribution prediction data and health assessment data;
The prediction result data comprises intra-dock force distribution prediction data and health assessment data.
It should be noted that the health evaluation data is obtained by analyzing the predicted data based on the force distribution in the wharf and the current monitoring data (wharf monitoring data set).
According to the embodiment of the invention, the data mapping is performed on the predicted result data and the high pile wharf BIM model and the predicted result data is displayed through a preset terminal device, specifically:
performing data mapping and position display mapping between the dock internal force distribution prediction data and a high pile dock BIM model, and performing result display through preset terminal equipment based on mapping results;
acquiring actual requirement information of wharf operation, and setting threshold information of internal force, displacement and deformation for different parts of a target high-pile wharf according to the requirement information;
and analyzing and comparing the threshold information with the predicted result data, and generating wharf early warning information based on the comparison result.
It should be noted that, according to the actual requirement of dock operation, thresholds of internal force, displacement and deformation are set for different parts of the dock, if the result of analysis and prediction is greater than the threshold, the predicted function of the system is triggered, information is sent to related personnel, and warning indication (early warning) is carried out in the BIM model.
Fig. 3 shows a block diagram of a high pile wharf operation and maintenance period structure health monitoring system of the present invention.
The second aspect of the present invention also provides a system 3 for monitoring structural health during operation and maintenance of a high pile wharf, the system comprising: the storage 31 and the processor 32, wherein the storage comprises a high-pile wharf operation and maintenance period structure health monitoring program, and the high-pile wharf operation and maintenance period structure health monitoring program realizes the following steps when being executed by the processor:
in the operation and maintenance period, obtaining design data of a target pile wharf, and constructing a corresponding high pile wharf BIM model based on the design data;
constructing a finite element numerical calculation model of a target pile wharf, establishing different working condition information based on an operation period influence factor, analyzing internal force change and deformation characteristic data of a corresponding wharf structural member according to the working condition information, and taking the internal force change and deformation characteristic data as model training data;
the model training data is imported into a preset neural network model for training, and a weight relation between the parameters of the high-pile wharf and the predicted result is obtained based on the preset neural network model;
according to the weight relation between the parameters of the high-pile wharf and the prediction result, carrying out wharf structure health position and area analysis by combining a high-pile wharf BIM model, and generating a wharf monitoring and sensing network based on an operation period influence factor;
Acquiring a wharf monitoring data set in an operation period based on the wharf monitoring sensing network and preprocessing data;
importing a wharf monitoring data set into a preset neural network model to perform wharf structure integral internal force distribution and comprehensive wharf health state assessment based on data analysis prediction, and generating prediction result data;
and carrying out data mapping on the prediction result data and the high pile wharf BIM model, and displaying the prediction result data through preset terminal equipment.
The visual carrier is presented as various information, data and the final result obtained after processing the data.
According to the embodiment of the invention, a finite element numerical calculation model of the target pile wharf is constructed, different working condition information is established based on the operation period influence factors, the internal force change and deformation characteristic data of the corresponding wharf structural member are analyzed according to the working condition information, the internal force change and deformation characteristic data are used as model training data, the working condition information comprises a plurality of types, and each working condition corresponds to different operation period influence factors.
The finite element numerical calculation model is a model with very wide application, and has application in the fields of engineering, physics, computer science and the like. In engineering, the finite element method can be used for structural analysis and design of mechanical structures, architectural structures and the like. In physical terms, finite element methods can be used to simulate various physical phenomena, such as electromagnetic fields, fluid dynamics, and the like. In terms of computer science, finite element methods can be used for computer aided design and analysis (CAD/CAE).
And analyzing the internal force change and deformation characteristic data of the corresponding wharf structural member according to the working condition information, wherein the internal force distribution and displacement change of different pile foundations, transverse longitudinal beams and panels are included after the impact force of the ship acts on different wharf bent frames. And taking internal force and displacement calculation (result) of the wharf components under different working conditions as training samples of the follow-up artificial intelligent model, and training by adopting a multilayer feedforward neural network model.
According to the embodiment of the invention, the model training data is imported into a preset neural network model for training, and the weight relation between the parameters of the high pile wharf and the predicted result is obtained based on the preset neural network model, specifically:
ST1 builds a multilayer feedforward neural network model;
ST2, initializing neural network model parameters, wherein the initialized parameters comprise wharf equipment external force parameters, natural external force parameters, ship impact force parameters and wharf self attribute parameters during the operation of a high-pile wharf;
ST3 inputs model training data into a neural network model, and transmits the data from an input layer to an output layer through forward propagation to generate a prediction result;
ST4 defines a Mean Square Error (MSE) loss function to measure the gap between the predicted result and the actual numerical calculation result;
ST5 calculates the gradient of the loss function to the network parameters through a back propagation algorithm, adjusts the network parameters according to the gradient to gradually reduce the loss function value, and updates the parameters of the neural network by adopting a gradient descent method to enable the loss function to be converged;
repeating the steps ST 3-ST 5 to update the training model and parameters until the value of the loss function meets the preset requirement;
and obtaining the weight relation between the parameters of the high pile wharf and the predicted result through the trained multilayer feedforward neural network model.
It should be noted that the preset neural network model is a multi-layer feedforward neural network model. The influence of different wharf parameters on the whole wharf operation can be further confirmed through the weight relation between the high-pile wharf parameters and the prediction results, so that accurate monitoring and regulation and control on wharf health assessment and wharf operation are realized.
According to the embodiment of the invention, according to the weight relation between the parameters of the high-pile wharf and the predicted result, the high-pile wharf BIM model is combined to analyze the health position and the area of the wharf structure, and based on the operation period influence factors, a wharf monitoring and sensing network is generated, specifically:
according to the weight relation between the high-pile wharf parameters and the prediction result, combining a high-pile wharf BIM model, analyzing a region with the greatest influence in a wharf region, and marking the region as an influence region;
Analyzing the positions of the monitoring points based on the influence area to generate a monitoring scheme;
the monitoring scheme comprises the number and position information of wharf monitoring sensors;
according to the monitoring scheme, the high pile wharf BIM model is combined to analyze the wharf structure health position and area, and a wharf monitoring and sensing network is generated based on the operation period influence factors.
The wharf monitoring sensor comprises a hydrological sensor, a road condition sensor, an object displacement deformation sensor and the like. In the dock monitoring and sensing network, corresponding sensors are arranged according to the characteristics of influencing factors (factors), a sensing network during operation and maintenance of the high-pile dock is established, deformation information such as displacement and sedimentation of a dock pile foundation and a transverse beam and natural environment information such as wind power and waves are monitored, and a dock monitoring data set in an operation period can be formed.
According to the embodiment of the invention, the operation period wharf monitoring data set is acquired and data preprocessing is performed based on the wharf monitoring sensing network, wherein the preprocessing comprises noise removal, missing value processing, normalization data, data cleaning and characteristic value extraction.
According to an embodiment of the present invention, the importing the dock monitoring data set into the preset neural network model to perform the internal force distribution of the dock structure and the overall dock health status assessment based on the data analysis prediction, and generate the prediction result data includes:
Acquiring actual monitoring data of a preset time period through a monitoring sensing network;
dividing the actual monitoring data into verification data and test data;
according to a field test and through an external load mode, verifying deviation between field actual measurement data and a predicted value of the multi-layer feedforward neural network model, using verification data to evaluate performance of the multi-layer feedforward neural network model, performing super-parameter tuning, and then using test data to evaluate generalization capability of the model.
It should be noted that the preset time period is a historical time period.
According to an embodiment of the present invention, the importing the dock monitoring data set into the preset neural network model performs the internal force distribution of the dock structure and the overall dock health status assessment based on the data analysis prediction, and generates the prediction result data, and further includes:
importing the wharf monitoring data set into a multi-layer feedforward neural network model to perform wharf structural overall internal force distribution and comprehensive wharf health state assessment based on data analysis prediction, so as to obtain wharf internal force distribution prediction data and health assessment data;
the prediction result data comprises intra-dock force distribution prediction data and health assessment data.
It should be noted that the health evaluation data is obtained by analyzing the predicted data based on the force distribution in the wharf and the current monitoring data (wharf monitoring data set).
According to the embodiment of the invention, the data mapping is performed on the predicted result data and the high pile wharf BIM model and the predicted result data is displayed through a preset terminal device, specifically:
performing data mapping and position display mapping between the dock internal force distribution prediction data and a high pile dock BIM model, and performing result display through preset terminal equipment based on mapping results;
acquiring actual requirement information of wharf operation, and setting threshold information of internal force, displacement and deformation for different parts of a target high-pile wharf according to the requirement information;
and analyzing and comparing the threshold information with the predicted result data, and generating wharf early warning information based on the comparison result.
It should be noted that, according to the actual requirement of dock operation, thresholds of internal force, displacement and deformation are set for different parts of the dock, if the result of analysis and prediction is greater than the threshold, the predicted function of the system is triggered, information is sent to related personnel, and warning indication (early warning) is carried out in the BIM model.
The invention discloses a method and a system for monitoring structural health of a high-pile wharf in an operation and maintenance period, wherein a high-pile wharf BIM model is built in the operation and maintenance period; importing model training data into a preset neural network model for training, and obtaining a weight relation between the parameters of the high-pile wharf and the prediction result based on the preset neural network model; according to the weight relation between the parameters of the high-pile wharf and the prediction result, the high-pile wharf BIM model is combined to analyze the wharf structure health position and area, and a wharf monitoring and sensing network is generated based on the operation period influence factors; acquiring a wharf monitoring data set in an operation period based on the wharf monitoring sensing network and preprocessing data; importing a wharf monitoring data set into a preset neural network model to perform wharf structure integral internal force distribution and comprehensive wharf health state assessment based on data analysis prediction, and generating prediction result data; and carrying out data mapping on the prediction result data and the high pile wharf BIM model, and displaying the prediction result data through preset terminal equipment.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (6)
1. The utility model provides a high pile pier operation maintenance period structure health monitoring method which is characterized in that the method comprises the following steps:
in the operation and maintenance period, obtaining design data of a target pile wharf, and constructing a corresponding high pile wharf BIM model based on the design data;
constructing a finite element numerical calculation model of a target pile wharf, establishing different working condition information based on an operation period influence factor, analyzing internal force change and deformation characteristic data of a corresponding wharf structural member according to the working condition information, and taking the internal force change and deformation characteristic data as model training data;
the model training data is imported into a preset neural network model for training, and a weight relation between the parameters of the high-pile wharf and the predicted result is obtained based on the preset neural network model;
according to the weight relation between the parameters of the high-pile wharf and the prediction result, carrying out wharf structure health position and area analysis by combining a high-pile wharf BIM model, and generating a wharf monitoring and sensing network based on an operation period influence factor;
acquiring a wharf monitoring data set in an operation period based on the wharf monitoring sensing network and preprocessing data;
importing a wharf monitoring data set into a preset neural network model to perform wharf structure integral internal force distribution and comprehensive wharf health state assessment based on data analysis prediction, and generating prediction result data;
Carrying out data mapping on the prediction result data and the high pile wharf BIM model and displaying the prediction result data through preset terminal equipment;
the model training data are imported into a preset neural network model for training, and a weight relation between the parameters of the high-pile wharf and the predicted result is obtained based on the preset neural network model, specifically:
ST1 builds a multilayer feedforward neural network model;
ST2, initializing neural network model parameters, wherein the initialized parameters comprise wharf equipment external force parameters, natural external force parameters, ship impact force parameters and wharf self attribute parameters during the operation of a high-pile wharf;
ST3 inputs model training data into a neural network model, and transmits the data from an input layer to an output layer through forward propagation to generate a prediction result;
ST4 defines a mean square error loss function to measure the difference between the predicted result and the actual numerical calculation result;
ST5 calculates the gradient of the loss function to the network parameters through a back propagation algorithm, adjusts the network parameters according to the gradient to gradually reduce the loss function value, and updates the parameters of the neural network by adopting a gradient descent method to enable the loss function to be converged;
repeating the steps ST 3-ST 5 to update the training model and parameters until the value of the loss function meets the preset requirement;
Obtaining a weight relation between the parameters of the high pile wharf and the predicted result through the trained multilayer feedforward neural network model;
according to the weight relation between the parameters of the high-pile wharf and the predicted results, the high-pile wharf BIM model is combined to analyze the wharf structure health position and area, and a wharf monitoring and sensing network is generated based on the operation period influence factors, specifically:
according to the weight relation between the high-pile wharf parameters and the prediction result, combining a high-pile wharf BIM model, analyzing a region with the greatest influence in a wharf region, and marking the region as an influence region;
analyzing the positions of the monitoring points based on the influence area to generate a monitoring scheme;
the monitoring scheme comprises the number and position information of wharf monitoring sensors;
according to a monitoring scheme, a high pile wharf BIM model is combined to analyze the health position and the area of a wharf structure, and a wharf monitoring and sensing network is generated based on an operation period influence factor;
the data preprocessing method comprises the steps of acquiring a wharf monitoring data set in an operation period based on a wharf monitoring sensing network, and preprocessing the data, wherein the preprocessing comprises noise removal, missing value processing, normalized data, data cleaning and characteristic value extraction.
2. The method for monitoring the structural health of the high pile wharf in the operation and maintenance period according to claim 1, wherein the method is characterized in that the finite element numerical calculation model of the target pile wharf is constructed, different working condition information is established based on the operation period influence factors, the internal force change and deformation characteristic data of the corresponding wharf structural member are analyzed according to the working condition information, the internal force change and deformation characteristic data are used as model training data, the working condition information comprises a plurality of types, and each working condition corresponds to different operation period influence factors.
3. The method for monitoring the health of a high pile wharf operation and maintenance period structure according to claim 1, wherein the step of importing a wharf monitoring data set into a preset neural network model to evaluate internal force distribution and overall wharf health state of the wharf structure based on data analysis and prediction, and generating prediction result data comprises the following steps:
acquiring actual monitoring data of a preset time period through a monitoring sensing network;
dividing the actual monitoring data into verification data and test data;
according to a field test and through an external load mode, verifying deviation between field actual measurement data and a predicted value of the multi-layer feedforward neural network model, using verification data to evaluate performance of the multi-layer feedforward neural network model, performing super-parameter tuning, and then using test data to evaluate generalization capability of the model.
4. The method for monitoring the health of a high pile wharf operation and maintenance period structure according to claim 3, wherein the step of importing a wharf monitoring data set into a preset neural network model to evaluate the internal force distribution and the overall wharf health state of the whole wharf structure based on data analysis and prediction, and generating prediction result data, further comprises the steps of:
importing the wharf monitoring data set into a multi-layer feedforward neural network model to perform wharf structural overall internal force distribution and comprehensive wharf health state assessment based on data analysis prediction, so as to obtain wharf internal force distribution prediction data and health assessment data;
the prediction result data comprises intra-dock force distribution prediction data and health assessment data.
5. The method for monitoring the health of a high-pile wharf operation and maintenance period structure according to claim 4, wherein the data mapping between the predicted result data and the high-pile wharf BIM model is performed and displayed through a preset terminal device, specifically:
performing data mapping and position display mapping between the dock internal force distribution prediction data and a high pile dock BIM model, and performing result display through preset terminal equipment based on mapping results;
Acquiring actual requirement information of wharf operation, and setting threshold information of internal force, displacement and deformation for different parts of a target high-pile wharf according to the requirement information;
and analyzing and comparing the threshold information with the predicted result data, and generating wharf early warning information based on the comparison result.
6. A high pile wharf operation and maintenance period structure health monitoring system, comprising: the system comprises a storage and a processor, wherein the storage comprises a high-pile wharf operation and maintenance period structure health monitoring program, and the high-pile wharf operation and maintenance period structure health monitoring program realizes the following steps when being executed by the processor:
in the operation and maintenance period, obtaining design data of a target pile wharf, and constructing a corresponding high pile wharf BIM model based on the design data;
constructing a finite element numerical calculation model of a target pile wharf, establishing different working condition information based on an operation period influence factor, analyzing internal force change and deformation characteristic data of a corresponding wharf structural member according to the working condition information, and taking the internal force change and deformation characteristic data as model training data;
the model training data is imported into a preset neural network model for training, and a weight relation between the parameters of the high-pile wharf and the predicted result is obtained based on the preset neural network model;
According to the weight relation between the parameters of the high-pile wharf and the prediction result, carrying out wharf structure health position and area analysis by combining a high-pile wharf BIM model, and generating a wharf monitoring and sensing network based on an operation period influence factor;
acquiring a wharf monitoring data set in an operation period based on the wharf monitoring sensing network and preprocessing data;
importing a wharf monitoring data set into a preset neural network model to perform wharf structure integral internal force distribution and comprehensive wharf health state assessment based on data analysis prediction, and generating prediction result data;
carrying out data mapping on the prediction result data and the high pile wharf BIM model and displaying the prediction result data through preset terminal equipment;
the model training data are imported into a preset neural network model for training, and a weight relation between the parameters of the high-pile wharf and the predicted result is obtained based on the preset neural network model, specifically:
ST1 builds a multilayer feedforward neural network model;
ST2, initializing neural network model parameters, wherein the initialized parameters comprise wharf equipment external force parameters, natural external force parameters, ship impact force parameters and wharf self attribute parameters during the operation of a high-pile wharf;
ST3 inputs model training data into a neural network model, and transmits the data from an input layer to an output layer through forward propagation to generate a prediction result;
ST4 defines a mean square error loss function to measure the difference between the predicted result and the actual numerical calculation result;
ST5 calculates the gradient of the loss function to the network parameters through a back propagation algorithm, adjusts the network parameters according to the gradient to gradually reduce the loss function value, and updates the parameters of the neural network by adopting a gradient descent method to enable the loss function to be converged;
repeating the steps ST 3-ST 5 to update the training model and parameters until the value of the loss function meets the preset requirement;
obtaining a weight relation between the parameters of the high pile wharf and the predicted result through the trained multilayer feedforward neural network model;
according to the weight relation between the parameters of the high-pile wharf and the predicted results, the high-pile wharf BIM model is combined to analyze the wharf structure health position and area, and a wharf monitoring and sensing network is generated based on the operation period influence factors, specifically:
according to the weight relation between the high-pile wharf parameters and the prediction result, combining a high-pile wharf BIM model, analyzing a region with the greatest influence in a wharf region, and marking the region as an influence region;
Analyzing the positions of the monitoring points based on the influence area to generate a monitoring scheme;
the monitoring scheme comprises the number and position information of wharf monitoring sensors;
according to a monitoring scheme, a high pile wharf BIM model is combined to analyze the health position and the area of a wharf structure, and a wharf monitoring and sensing network is generated based on an operation period influence factor;
the data preprocessing method comprises the steps of acquiring a wharf monitoring data set in an operation period based on a wharf monitoring sensing network, and preprocessing the data, wherein the preprocessing comprises noise removal, missing value processing, normalized data, data cleaning and characteristic value extraction.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311704418.6A CN117390938B (en) | 2023-12-13 | 2023-12-13 | High-pile wharf operation and maintenance period structure health monitoring method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311704418.6A CN117390938B (en) | 2023-12-13 | 2023-12-13 | High-pile wharf operation and maintenance period structure health monitoring method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117390938A CN117390938A (en) | 2024-01-12 |
| CN117390938B true CN117390938B (en) | 2024-03-08 |
Family
ID=89463544
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311704418.6A Active CN117390938B (en) | 2023-12-13 | 2023-12-13 | High-pile wharf operation and maintenance period structure health monitoring method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117390938B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112132264A (en) * | 2020-09-11 | 2020-12-25 | 合肥综合性国家科学中心人工智能研究院(安徽省人工智能实验室) | Regional exhaust emission prediction method and system based on space-time residual perception network |
| CN114894411A (en) * | 2022-05-24 | 2022-08-12 | 东营固泰尔建筑科技有限责任公司 | Bridge health monitoring method and system based on wireless sensor network |
| CN115063020A (en) * | 2022-07-07 | 2022-09-16 | 中国长江三峡集团有限公司 | Cascade hydropower station multidimensional safety scheduling device and method based on risk monitoring fusion |
| CN115795970A (en) * | 2022-12-10 | 2023-03-14 | 浙江省交通运输科学研究院 | Method for predicting lateral displacement of pile foundation of high-pile wharf |
| CN116975990A (en) * | 2023-09-25 | 2023-10-31 | 中交第四航务工程勘察设计院有限公司 | Management method and system for three-dimensional model of oil-gas chemical engineering wharf |
-
2023
- 2023-12-13 CN CN202311704418.6A patent/CN117390938B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112132264A (en) * | 2020-09-11 | 2020-12-25 | 合肥综合性国家科学中心人工智能研究院(安徽省人工智能实验室) | Regional exhaust emission prediction method and system based on space-time residual perception network |
| CN114894411A (en) * | 2022-05-24 | 2022-08-12 | 东营固泰尔建筑科技有限责任公司 | Bridge health monitoring method and system based on wireless sensor network |
| CN115063020A (en) * | 2022-07-07 | 2022-09-16 | 中国长江三峡集团有限公司 | Cascade hydropower station multidimensional safety scheduling device and method based on risk monitoring fusion |
| CN115795970A (en) * | 2022-12-10 | 2023-03-14 | 浙江省交通运输科学研究院 | Method for predicting lateral displacement of pile foundation of high-pile wharf |
| CN116975990A (en) * | 2023-09-25 | 2023-10-31 | 中交第四航务工程勘察设计院有限公司 | Management method and system for three-dimensional model of oil-gas chemical engineering wharf |
Non-Patent Citations (3)
| Title |
|---|
| 周世良 ; 关英俊 ; 古西召 ; 程烈 ; .整体卸荷式板桩挡土结构三维有限元数值模拟分析.水运工程.2012,(第06期),全文. * |
| 基于人工智能和数字孪生技术的码头全周期健康监测应用研究;崔国谨;珠江水运;20230930;第6-8页 * |
| 整体卸荷式板桩挡土结构三维有限元数值模拟分析;周世良;关英俊;古西召;程烈;;水运工程;20120625(第06期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117390938A (en) | 2024-01-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Frangopol et al. | Life-cycle of structural systems: recent achievements and future directions | |
| CN113744395B (en) | Monitoring and early warning method and system for building foundation settlement | |
| Hariri-Ardebili et al. | Simplified reliability analysis of multi hazard risk in gravity dams via machine learning techniques | |
| Zhang et al. | Efficient Bayesian FFT method for damage detection using ambient vibration data with consideration of uncertainty | |
| CN101382473B (en) | EWMA control chart method for bridge structure safety alarm | |
| CN110008096B (en) | Data monitoring method, device, electronic equipment and computer readable storage medium | |
| Su et al. | Dynamic non-probabilistic reliability evaluation and service life prediction for arch dams considering time-varying effects | |
| CN114580493B (en) | Heavy haul railway bridge health monitoring method based on AI | |
| Wang et al. | A Bayesian framework for estimating fragility curves based on seismic damage data and numerical simulations by adaptive neural networks | |
| CN115659729B (en) | Dam safety monitoring analysis method and system based on structural simulation calculation | |
| Mata et al. | Construction of decision rules for early detection of a developing concrete arch dam failure scenario. A discriminant approach | |
| CN104535346B (en) | Anti-slip stability detection method for concrete gravity dangerous dam | |
| CN117493815B (en) | BIM-based deep foundation pit monitoring method and system | |
| CN116248176B (en) | Optical fiber state monitoring and early warning method, system, equipment and medium | |
| CN111583067A (en) | Urban underground large space construction safety early warning and emergency decision-making method and system | |
| Rocher et al. | A two-scale probabilistic time-dependent fatigue model for offshore steel wind turbines | |
| CN115098561A (en) | Method for extracting alarm threshold of cultural relic body monitoring data | |
| Zhang et al. | Conjugate Bayesian updating of analytical fragility functions using dynamic analysis with application to corroded bridges | |
| Jensen | Fatigue damage estimation in non-linear systems using a combination of Monte Carlo simulation and the First Order Reliability Method | |
| CN117390938B (en) | High-pile wharf operation and maintenance period structure health monitoring method and system | |
| CN101382474A (en) | Multi-purpose evaluating method for bridge structure safety | |
| CN112598319A (en) | Intelligent bridge operation and maintenance management method and system based on BIM, computer equipment and storage medium | |
| CN111582634A (en) | Multi-factor safety grading method and system for underground large-space construction | |
| Oh et al. | Artificial intelligence-based damage localization method for building structures using correlation of measured structural responses | |
| Di-Sarno et al. | Seismic fragility relationships for structures |
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 |