CN117471071B - Port infrastructure structure durability safety early warning system and port infrastructure structure durability safety early warning method - Google Patents
Port infrastructure structure durability safety early warning system and port infrastructure structure durability safety early warning method Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 42
- VEXZGXHMUGYJMC-UHFFFAOYSA-M Chloride anion Chemical compound [Cl-] VEXZGXHMUGYJMC-UHFFFAOYSA-M 0.000 claims abstract description 282
- 230000005540 biological transmission Effects 0.000 claims abstract description 122
- 239000004567 concrete Substances 0.000 claims abstract description 103
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- JEIPFZHSYJVQDO-UHFFFAOYSA-N iron(III) oxide Inorganic materials O=[Fe]O[Fe]=O JEIPFZHSYJVQDO-UHFFFAOYSA-N 0.000 description 3
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Abstract
The invention discloses a port infrastructure structure durability safety early warning system and method, which are used for constructing a port concrete structure chloride ion dynamic transmission model based on real-time monitoring data by the real-time monitoring data of port concrete structure chloride ion concentration distribution data, and are suitable for multi-sea area port concrete chloride ion concentration threshold and structure durability early warning and constructing a port infrastructure concrete structure durability safety early warning platform. The system provided by the invention is based on the multi-module interconnection and intercommunication functions of chloride ion concentration distribution data monitoring, chloride ion transmission parameter dynamic inversion analysis and structure durability early warning, and the developed port infrastructure concrete structure durability safety early warning platform realizes automatic real-time prediction and early warning of the service life of the port infrastructure structure. The limitations of conventional lossy detection methods and indoor test methods are overcome.
Description
Technical Field
The invention relates to the technical field of port infrastructure monitoring, in particular to a port infrastructure structure durability safety early warning system and a port infrastructure structure durability safety early warning method.
Background
The damage and cracking mechanism of active harbors and breakwater infrastructures is affected by various factors, but the dulling and rust removal of the steel bars under the influence of chloride ion invasion is a main factor affecting the durability of harbor infrastructures. For a specific port component, the characteristic of obvious partition of chloride ion invasion is that the typical partition is a splash zone, an underwater zone and an atmosphere zone, the chloride ion transmission time-varying rules of different zones are different, and the sampling time chloride ion distribution rule can only be obtained through the lossy detection at present.
In the prior art, aiming at a newly built port, the initial mix proportion of newly built port components is generally researched, and the total life durability of the components is predicted by combining the double time-varying parameters of chloride ion transmission and the service environment through indoor experimental research; aiming at the in-service port, the concrete chloride ion concentration distribution of different partitions is obtained through on-site drilling coring detection at different time, and finally the port durability is predicted by a chloride ion concentration prediction model obtained through conventional mathematical processing. The conventional method has poor adaptability to the specific service environment of the newly built port, and can not effectively consider the chloride ion concentration change caused by the emergency in the service environment into the durability prediction, and the conventional method is essentially to consider that the concrete environment in the service environment is consistent with the simulated environment in a laboratory, which is obviously neglected on the influence of unpredictable factors in the service. The conventional method is time-consuming and labor-consuming in the detection of the port in service, has objective inoperability for the long-time continuous detection of the structure for the lossy detection, and is most important that the data measured by drilling and coring are sparse data points for the service life of the port component, the durability prediction model obtained by processing the durability of the port component through the sparse data points is also difficult to convince, and the unpredictable factors in the service process are ignored in the conventional method.
The micro-array concrete chloride ion sensor and the measuring data correction method described in patent CN116381023a can only collect local chloride ion concentration data, and can not realize large-area analysis of the chloride ion invasion state of the structure.
Therefore, the existing port infrastructure (breakwater) durability prediction and early warning method cannot complete automatic real-time prediction and early warning of the service life of the port infrastructure structure, and the conventional monitoring method adopted for the in-service port and the newly-built port cannot adapt to the national requirements for real-time monitoring and early warning.
Disclosure of Invention
In view of the above, the present invention aims to provide a port infrastructure structural durability safety early warning system, which can perform in-situ, long-term, nondestructive observation and real-time prediction on the concrete durability of newly-built and in-service ports, so that a port management department can sense the operation state of the port infrastructure, the intervention action is based, and the extreme state is early warned.
In order to achieve the above purpose, the present invention provides the following technical solutions:
The invention provides a port infrastructure structure durability safety early warning system, which comprises a port concrete structure chloride ion concentration data monitoring module, a machine learning-based chloride ion transmission parameter module, a port infrastructure chloride ion dynamic transmission model and a port infrastructure structure durability safety early warning platform;
The port concrete structure chloride ion concentration data monitoring module is used for acquiring port concrete structure chloride ion concentration data in real time;
the chloride ion transmission parameter module based on machine learning is used for receiving and processing chloride ion concentration data to obtain chloride ion transmission parameters;
the port infrastructure chloride ion dynamic transmission model is used for receiving chloride ion transmission parameters and constructing transmission modes of concrete structure chloride ion parameters under different states;
The port infrastructure structure durability safety early warning platform is used for generating structure durability safety early warning information according to the chloride ion parameters of the concrete structure.
Further, the port concrete structure chloride ion concentration data monitoring module comprises a measuring point position layout module, a data acquisition, transmission and interpretation module, a chloride ion in-situ long-term database and real-time optimized transmission parameters;
the measuring point position layout module is used for obtaining the measured chloride ion concentration value to be stored in the chloride ion in-situ long-term database;
the data acquisition, transmission and interpretation are carried out, and concrete double time-varying parameters are obtained through inversion calculation;
The chloride ion in-situ long-term database is used as a basic database for interpretation of transmission parameters and is used for supporting data transmission interpretation and displaying chloride ion concentration cloud pictures in real time;
The real-time optimized transmission parameters are used for dynamic inversion calculation, real-time chloride ion concentration evaluation and concrete durability prediction.
Further, the chloride ion transmission parameter module based on machine learning comprises document searching and sorting, supervised training set database construction, machine learning model optimization and important transmission parameter acquisition;
The literature searching and sorting device is used for collecting data such as the concrete mix proportion of the port to be monitored in service, collecting and sorting historical chloride ion concentration data of the port to be monitored, and constructing a machine learning supervision training set database;
The supervision training set database is constructed and used for machine learning an original database and selecting a plurality of machine learning methods to carry out machine learning training to expand the original database;
the optimized machine learning model is used for constructing a machine learning model of a machine learning optimization algorithm;
And acquiring the important transmission parameters, and performing inversion calculation by adopting the expanded port original chloride ion database to be monitored to acquire concrete double-time-varying parameters taking the reinforcement blocking effect coefficient into consideration based on Fick second law, so as to construct a port infrastructure chloride ion dynamic transmission model.
Further, the preferred machine learning model is constructed specifically as follows:
The method comprises the steps of firstly, processing raw data to obtain chloride ion data of a port in years, extracting two variables of service year and concrete sampling depth from a supervision training set database to serve as characteristic variables, actually measuring the chloride ion concentration of a sample of the service year and concrete sampling depth to serve as target variables, and dividing the total data into two parts of training data and test data;
Training the algorithm by a second step, selecting an epsilon-SVR type SVM, setting a kernel function type as an RBF kernel, performing multi-group parameter training trial, and recording training parameters and training effects;
thirdly, step parameters are adjusted, and training parameters and training effects recorded in the algorithm training step are preferably selected as training parameters;
fourthly, evaluating the model, namely using the training completion model for test data verification calculation, and observing the accuracy of verification data to perform the next step if the accuracy of the verification data can meet the requirements;
and fifthly, predicting and expanding the model, and performing original data expansion calculation according to the dynamic chloride ion transmission model of the port infrastructure.
Further, the port infrastructure structure durability safety early warning platform comprises a dynamic transmission model, a concentration threshold system, a safety early warning mechanism, a typical structure chloride ion simulation model and a structure durability safety early warning platform;
the dynamic transmission model is used for evaluating the distribution of chloride ions in the reinforced concrete in real time;
the concentration threshold system is used for judging the service state of the structure in the corrosion induction period, the corrosion period, the accelerated corrosion period and the corrosion destruction period according to the distribution of chloride ions in the reinforced concrete estimated by the dynamic transmission model;
the safety early warning mechanism is used for setting early warning reference signals corresponding to different states of the structure;
the typical structure chloride ion simulation model is used for displaying the chloride ion concentration distribution of the structure, and displaying the chloride ion concentration distribution cloud image of the structure in real time on the basis of a typical port three-dimensional model;
The structural durability safety early warning platform is used for displaying a port infrastructure chloride ion dynamic transmission model, a concentration threshold system and a safety early warning mechanism, and displaying the port real-time chloride ion concentration on a typical structural chloride ion simulation model.
Further, the early warning reference signals under different states of the structure are determined according to the chloride ion concentration threshold C cr and according to the following modes:
When the chloride ion concentration threshold is less than 0.4C cr, the structure is in a green safety state;
When the chloride ion concentration threshold is 0.4C cr≤C＜0.6Ccr, the structural object is orange early warning;
When the chloride ion concentration threshold is 0.6C cr≤C＜0.8Ccr, the structural object is blue early warning;
when the chloride ion concentration threshold is more than or equal to 0.9C cr, the structure is red early warning.
The early warning method for the port infrastructure structure durability safety early warning system comprises the following steps:
constructing a structural chloride ion concentration data monitoring module for acquiring the chloride ion concentration data of the port concrete structure in real time;
Constructing a chloride ion transmission parameter module for receiving and processing chloride ion concentration data, wherein the chloride ion transmission parameter module obtains chloride ion transmission parameters through machine learning training;
constructing a dynamic chloride ion transmission model of a port infrastructure provided with concrete double time-varying parameters, wherein the dynamic chloride ion transmission model of the port infrastructure is used for transmitting concrete structure chloride ion parameters in different states;
And constructing a port infrastructure structure durability safety early warning platform for outputting safety early warning information, wherein the port infrastructure structure durability safety early warning platform receives the concrete structure chloride ion parameters transmitted by the port infrastructure chloride ion dynamic transmission model and generates structure durability safety early warning information according to the concrete structure chloride ion parameters.
Further, the real-time acquisition of the chloride ion concentration data of the port concrete structure is obtained by acquiring micro-array type sensing chloride ion sensors arranged at measuring point positions of a typical port splash zone, an underwater zone and an atmosphere zone through a structural chloride ion concentration data monitoring module.
Further, the port infrastructure chloride ion dynamic transmission model is carried out according to the following steps:
Collecting concrete mix proportion data of an in-service port to be monitored and historical chloride ion concentration data of the port to be monitored to obtain an original chloride ion database;
Expanding an original chloride ion database by machine learning training by utilizing the original database;
and obtaining concrete double time-varying parameters by inversion calculation through an original chloride ion database, and constructing a port infrastructure chloride ion dynamic transmission model by utilizing the concrete double time-varying parameters.
Further, the method also comprises the step of constructing a typical structure chloride ion simulation model, wherein the construction process of the typical structure chloride ion simulation model is as follows:
reasonably simplifying the model according to the actual condition of the research test piece and establishing a three-dimensional numerical model;
corresponding attributes are given according to the actual characteristics of each phase of material of the test piece;
determining a boundary condition of a test piece;
setting a concrete double-time-varying parameter of a chloride ion transmission model in concrete;
performing simulation calculation by comsol numerical simulation calculation software;
and outputting the calculation result and evaluating the result.
The invention has the beneficial effects that:
According to the port infrastructure structure durability safety early warning system and method provided by the invention, the port concrete structure chloride ion dynamic transmission model based on the real-time monitoring data is constructed through the real-time monitoring data of the port concrete structure chloride ion concentration distribution data, and the port infrastructure structure durability safety early warning system and method are suitable for multi-sea area port concrete chloride ion concentration threshold and structure durability early warning and construct a port infrastructure concrete structure durability safety early warning platform.
The system provided by the invention is based on the multi-module interconnection and intercommunication functions of chloride ion concentration distribution data monitoring, chloride ion transmission parameter dynamic inversion analysis and structure durability early warning, and the developed port infrastructure concrete structure durability safety early warning platform realizes automatic real-time prediction and early warning of the service life of the port infrastructure structure. The limitations of conventional lossy detection methods and indoor test methods are overcome.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objects and other advantages of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out in the specification.
Drawings
In order to make the objects, technical solutions and advantageous effects of the present invention more clear, the present invention provides the following drawings for description:
Fig. 1 is a schematic diagram of the cooperative operation of a port infrastructure durability safety precaution system.
FIG. 2 shows the concrete chloride ion concentration threshold value, the early warning mechanism and the wharf simulation model in the multi-sea area.
Fig. 3 is a simulation method of the transmission value of chloride ions in reinforced concrete.
Fig. 4 is a flow chart of a method for simulating the transmission of chloride ions in reinforced concrete.
Fig. 5 is a port infrastructure durability safety precaution method.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and specific examples, which are not intended to limit the invention, so that those skilled in the art may better understand the invention and practice it.
Example 1
As shown in fig. 1, the durability safety early warning system for the port infrastructure structure provided in this embodiment includes a chloride ion concentration data monitoring module for a port concrete structure, a chloride ion transmission parameter module based on machine learning, a dynamic chloride ion transmission model for the port infrastructure, and a durability safety early warning platform for the port infrastructure structure;
The port concrete structure chloride ion concentration data monitoring module is used for acquiring port concrete structure chloride ion concentration data in real time;
the chloride ion transmission parameter module based on machine learning is used for receiving and processing chloride ion concentration data to obtain chloride ion transmission parameters;
the port infrastructure chloride ion dynamic transmission model is used for receiving chloride ion transmission parameters and constructing transmission modes of concrete structure chloride ion parameters under different states;
The port infrastructure structure durability safety early warning platform is used for generating structure durability safety early warning information according to chloride ion parameters of the concrete structure;
The safety early warning system provided by the embodiment realizes real-time automatic prediction early warning on newly-built and in-service port infrastructures through the cooperative work of the components.
The port concrete structure chloride ion concentration data monitoring module provided by the embodiment comprises a measuring point position layout module, a data acquisition, transmission and interpretation module and a chloride ion in-situ long-term database, wherein transmission parameters are optimized in real time;
The measuring point position layout module is used for obtaining the measured chloride ion concentration value and taking the measured chloride ion concentration value as an important component of a chloride ion in-situ long-term database;
The data acquisition, transmission and interpretation are carried out, and concrete double-time-varying parameters considering the steel bar blocking effect coefficient based on Fick's second law are obtained through inversion calculation;
the chloride ion in-situ long-term database is used as a basic database for transmission parameter interpretation and is used for supporting data interpretation and finally displaying chloride ion concentration cloud pictures on a durability platform in real time;
the real-time optimized transmission parameters are used for dynamic inversion calculation, real-time chloride ion invasion evaluation and concrete durability prediction by supporting a durability platform;
The chloride ion transmission parameter module based on machine learning comprises literature searching and sorting, construction of a supervision training set database, optimization of a machine learning model and acquisition of important transmission parameters;
The literature searching and sorting device is used for collecting data such as the concrete mix proportion of the port to be monitored in service, collecting and sorting historical chloride ion concentration data of the port to be monitored, and constructing a machine learning supervision training set database;
The supervision training set database is constructed and used for machine learning an original database and selecting a plurality of machine learning methods to carry out machine learning training to expand the original database;
The optimized machine learning model is used for calculating and comparing the accuracy of various machine learning methods such as BP neural network learning, SVM (support vector machine) and the like to determine which machine learning method is finally adopted;
the important transmission parameters are obtained, and the expanded original chloride ion database of the port to be monitored is adopted to carry out inversion calculation to obtain concrete double-time-varying parameters which are based on Fick second law and consider the steel bar blocking effect coefficient, so as to construct a dynamic chloride ion transmission model of the port infrastructure;
The main content of the optimized machine learning model provided by the embodiment is chloridion transmission parameter interpretation based on machine learning, the machine learning module builds a support of a chloridion in-situ long-term database which mainly comprises two parts of content, and one part is chloridion in-situ long-term database basic data obtained by methods of reference, numerical analysis, indoor test and the like; and part of the real-time updated port concrete structure chloride ion concentration data is obtained after the micro array type chloride ion sensor is embedded.
The machine learning model construction in this embodiment needs to take on-site actual data as main basic data and takes Fick second law equation as auxiliary data, and the two time-varying parameters of chloride ion transmission are determined by the optimized machine learning model: dynamic regression calculation is performed on the surface chloride ion concentration Cs (t) and the apparent chloride ion diffusion coefficient Da (t).
And finally determining to use an SVM (support vector machine) method to expand the original data through a preferable machine learning model. The specific implementation process is as follows:
The first step of raw data processing is to take chloride ion data of a port in years as an example, extract two variables of service year and concrete sampling depth from a supervision training set database as characteristic variables, and actually measure the chloride ion concentration of samples of the service year and the concrete sampling depth as target variables. The total data are divided into two parts of training data and test data, wherein the training data are 70% of the total data.
And training by a second step of algorithm, selecting an epsilon-SVR type SVM, setting a kernel function type as an RBF kernel, performing multi-group parameter training trial, and recording training parameters and training effects.
The third step is to adjust parameters, and the training parameters and the training effect recorded by the algorithm training step are preferably as follows: the epsilon-SVR penalty is 2.2, the number of features in the kernel function is 2.8, and the loss function e is 0.01.
And fourthly, evaluating the model, namely using the training completion model for test data verification calculation, and observing the accuracy of verification data to perform the next step if the accuracy of the verification data can meet the requirement.
And fifthly, predicting and expanding the model, and performing original data expansion calculation according to the dynamic chloride ion transmission model of the port infrastructure.
The port infrastructure structure durability safety precaution platform in fig. 1 comprises a dynamic transmission model, a concentration threshold system, a safety precaution mechanism, a typical structure chloride ion simulation model and a structure durability safety precaution platform;
the dynamic transmission model is used for evaluating the distribution of chloride ions in the reinforced concrete in real time;
The concentration threshold system is used for judging the service state of the structure in the corrosion induction period, the corrosion period, the accelerated corrosion period and the corrosion destruction period according to the distribution of chloride ions in the reinforced concrete estimated by the dynamic transmission model;
the safety early warning mechanism is used for sending early warning timely, and giving proper early warning on the structural durability platform to provide references for the intervention time and intervention decision (maintenance or stop use and the like) of management personnel;
The chloride ion simulation model with the typical structure is used for a chloride ion concentration distribution display platform of the structure, and a chloride ion concentration distribution cloud picture of the structure is displayed in real time on the basis of the three-dimensional model of the typical port.
The structural durability safety early warning platform is used for integrating a port infrastructure chloride ion dynamic transmission model, a concentration threshold system and a safety early warning mechanism, displaying the port real-time chloride ion concentration on a typical structural chloride ion simulation model, and providing scientific and reliable management basis for management staff through the structural durability safety early warning platform management staff to obtain port service state, health condition and residual life information;
The structural durability safety early warning platform comprises a service state, a health state and a residual life;
the service state; the structural object chloride ion concentration distribution used for evaluating according to the port infrastructure chloride ion dynamic transmission model is based on a concentration threshold system and a safety early warning mechanism to display the durable service state of the structural object;
The state of health; the method comprises the steps of judging and displaying the health state of a structure based on a concentration threshold system according to the structure chloride ion concentration distribution estimated by a port infrastructure chloride ion dynamic transmission model;
The remaining life; the method comprises the steps of dynamically predicting and displaying the residual life of a structure on the basis of a concentration threshold system according to the chloride ion concentration distribution of the structure estimated by a port infrastructure chloride ion dynamic transmission model;
The chloride ion concentration data monitoring module for on-site monitoring, the chloride ion transmission parameter module based on machine learning, the chloride ion dynamic transmission module and the structural durability safety early warning platform in the embodiment work cooperatively; the safety state for monitoring the in-service structure in real time is formed.
As shown in FIG. 2, FIG. 2 shows a concrete chloride ion concentration threshold, an early warning mechanism and a wharf simulation model in a multi-sea area, the abscissa of an image in FIG. 2 shows structural service time and the ordinate shows structural performance, four characteristic chloride ion concentration values appear on a curve according to JTJ302-2006 technical Specification for detecting and evaluating Port hydraulic buildings, the curve is divided into four characteristic points which affect structural bearing capacity, affect normal use of the structure, rust expansion and cracking of a protective layer and rust corrosion of a steel bar from large to small, the four characteristic points divide the curve into five areas according to the abscissa, and the four characteristic points are respectively a corrosion induction period, a corrosion period and an accelerated corrosion period from large to small and are collectively called as a corrosion destruction period below the characteristic points affecting normal use of the structure.
Different early warning states are given in different periods of the structure, the green safety state corresponds to the corrosion induction period, the orange early warning corresponds to the corrosion period, the blue early warning corresponds to the accelerated corrosion period, and the red early warning corresponds to the corrosion damage period.
The chloride ion concentration threshold C cr determines different early warning states according to the following modes;
When the chloride ion concentration threshold is less than 0.4C cr, the structure is in a green safety state;
When the chloride ion concentration threshold is 0.4C cr≤C＜0.6Ccr, the structural object is orange early warning;
when the chloride ion concentration threshold is 0.6C cr≤C≤0.8Ccr, the structural object is blue early warning;
When the chloride ion concentration threshold is more than or equal to 0.9C cr, the structural object is red early warning;
The concrete concentration threshold value of the multi-sea area and the early warning mechanism drawing are the research foundation of the structural durability safety early warning platform, and the structural durability safety early warning platform is based on the basis that the processed measured chloride ion concentration sends early warning according to the dynamic chloride ion transmission model of the port infrastructure.
The chloride ion in-situ long-term database has two parts of contents, namely typical port historical data obtained by reference and measured chloride ion concentration value obtained by actual measurement of a chloride ion sensor, the two parts of data form data in the supervision dataset database together, and when the reference is actually referred, the information such as sampling year, sampling depth, sampling position and the like of the typical port historical data cannot be completely kept consistent, so that the interpretation method of the homologous heterogeneous data such as linear interpolation is needed to convert the data into isomorphic data;
the real-time optimized transmission parameters and the parameters in the important parameter acquisition and the real-time optimized transmission parameters are concrete double-time-varying parameters, the data based on the important parameter acquisition are typical port chloride ion concentration duration data obtained through literature reference in a concrete in-situ long-term database, the data based on the real-time optimized transmission parameters are actual measured chloride ion concentrations obtained through embedded chloride ion sensors, and the partial data are expanded continuously along with the monitoring duration and optimize the concrete double-time-varying parameters continuously; the dynamic chloride ion transmission model of the port infrastructure is a chloride ion transmission model established based on Fick's second law, two most important parameters in the model are concrete double time-varying parameters, and the parameters are obtained by calculating a concrete in-situ long-term database comprising chloride ion concentration historical data and chloride ion actual measurement data.
As shown in FIG. 3, FIG. 3 is a simulation diagram of the transmission value of chloride ions in reinforced concrete, wherein a random three-dimensional throwing algorithm is adopted to simulate the numerical simulation of the invasion value of chloride ions of a standard test piece with a certain mixing ratio of 100mm multiplied by 100mm of concrete under the condition that the side of the five-sided epoxy resin coating constraint is exposed to the concentration of chloride ions in the ocean environment for 180 days, the color of the figure is from blue to red to show that the concentration of the invasion chloride ions is gradually increased, and the figure shows that the chloride ions only invade the surface of the test piece in a short period of time.
The chloride ion concentration data monitoring module of the port concrete structure in the embodiment comprises a field real-time monitoring module and a historical data module; the on-site real-time monitoring module is used for monitoring chloride ion concentration data of a multi-sea-area wharf and a breakwater in real time, and the historical data module can obtain representative service environments and material factors of the port concrete structure according to on-site investigation, literature review and other methods, select ocean dry-wet alternating areas with the most serious corrosion of chloride ions, and develop an indoor physical test for natural corrosion of the chloride ions of the port concrete structure under the artificial simulated ocean environment. In the indoor test, a real-time sampling method of a micro array type sensing chloride ion sensor and a traditional grinding sampling means are adopted to obtain the distribution data of the chloride ion concentration in the parallel sample of the test piece.
The embedded method of the newly-built and in-service port micro array type sensing chloride ion sensor is different, and specifically comprises the following steps: the miniature array type chloride ion sensor of the newly built port is buried along with construction;
The miniature array type sensing chloride ion sensor in the in-service port is required to manufacture an embedded part, the miniature array type sensing chloride ion sensor is embedded in the embedded part in advance, the chloride ion concentration of the miniature array type sensing chloride ion sensor reaches the same chloride ion concentration as the position of the selected measuring point in the in-service port by adopting methods such as an indoor acceleration test, and then the embedded part is placed at the measuring point position for in-situ monitoring.
The real-time monitoring data is acquired and transmitted to the upper computer in real time by utilizing the singlechip and the communication module, so that the real-time transmission of the chloride ion monitoring data is realized, and the on-line long time sequence real-time monitoring method for the chloride ion concentration distribution data of the port concrete structure is formed.
The method is based on a real-time monitoring method of chloride ion concentration distribution data, and a chloride ion concentration distribution data monitoring module is constructed to realize real-time collection, transmission, storage and display of the chloride ion concentration distribution data in the port concrete structure.
As shown in fig. 4, fig. 4 is a flow chart of a simulation method for the transmission value of chloride ions in reinforced concrete, and the specific process is as follows:
reasonably simplifying the model according to the actual condition of the research test piece and establishing a three-dimensional numerical model;
corresponding attributes are given according to the actual characteristics of each phase of material of the test piece;
Determining boundary conditions of the test piece, such as material properties of the surface of the test piece and the like;
setting a concrete double-time-varying parameter of a chloride ion transmission model in concrete;
performing simulation calculation by comsol numerical simulation calculation software;
and outputting the calculation result and evaluating the result.
Example 2
As shown in fig. 5, the port infrastructure durability safety precaution method in this embodiment includes the following steps:
Constructing a structural chloride ion concentration data monitoring module for acquiring the chloride ion concentration data of the port concrete structure in real time; in the embodiment, measuring point positions of a typical harbor splash zone, an underwater zone and an atmospheric zone are selected; arranging a micro array type sensing chloride ion sensor; the method is used for acquiring real-time chloride ion concentration data;
Constructing a chloride ion transmission parameter module for receiving and processing chloride ion concentration data, wherein the chloride ion transmission parameter module can acquire chloride ion transmission parameters through machine learning training;
Constructing a dynamic chloride ion transmission model of a port infrastructure provided with concrete double time-varying parameters, wherein the dynamic chloride ion transmission model of the port infrastructure is used for transmitting concrete structure chloride ion parameters in different states; and evaluating the structure status;
Constructing a port infrastructure structure durability safety early warning platform for outputting safety early warning information, wherein the port infrastructure structure durability safety early warning platform in the embodiment receives concrete structure chloride ion parameters transmitted by a port infrastructure chloride ion dynamic transmission model and generates structure durability safety early warning information according to the concrete structure chloride ion parameters;
In the embodiment, the chloride ion concentration cloud image can be displayed in a field variable form through a three-dimensional model of the structural durability platform by the chloride ion concentration acquired in real time; according to actual conditions, historical concentration data of typical ports can be collected widely;
Meanwhile, the chloride ion transmission parameters can be used for expanding the original data through a machine learning method; acquiring chloride ion concentration historical data and actually measured chloride ion concentration data, and inputting the chloride ion concentration historical data and the actually measured chloride ion concentration data into an in-situ long-term chloride ion concentration library; calculating concrete double time-varying parameters in real time and setting a dynamic chloride ion transmission model of a port infrastructure;
The chloride ion dynamic transmission model can evaluate and analyze the structure according to the acquired chloride ion transmission parameters to respectively obtain the service state, the health state, the residual life and other information of the structure; the information is sent to a port infrastructure structure durability safety early warning platform for display; meanwhile, the port infrastructure structure durability safety early warning platform can be displayed on the structure durability platform according to a chloride ion concentration threshold system and a safety early warning mechanism.
In the embodiment, the dynamic chloride ion transmission model of the concrete structure for a numerical simulation test is established by acquiring the surface chloride ion concentration, the apparent chloride ion diffusion coefficient time-varying characteristic and the steel bar blocking effect coefficient;
The dynamic chloride ion transmission model of the concrete structure is corrected by acquiring field monitoring/detection test data of the concrete structure subject to long-term erosion of chloride ions of a representative port in a multi-sea area of China, which are reported in relevant literature data, so as to form a dynamic inversion analysis module based on the measured data of chloride ion concentration distribution;
evaluating and predicting the chloride ion concentration distribution characteristics of the port concrete structure in real time and the chloride ion concentration value on the surface of the steel bar;
The structural durability prediction early warning module provided by the embodiment is mainly developed for a port and dock and breakwater concrete structural durability safety early warning platform, the structural durability safety early warning platform is used for real-time displaying of chloride ion concentration cloud pictures by constructing a three-dimensional model of a port, and the residual service life, the durability service state and the health condition of a representative port infrastructure concrete structure in China are estimated, predicted and displayed in real time by combining a chloride ion dynamic transmission model through the port infrastructure concrete structural durability safety early warning platform, so that the prediction early warning of the durability of the port concrete structure is realized.
The above-described embodiments are merely preferred embodiments for fully explaining the present invention, and the scope of the present invention is not limited thereto. Equivalent substitutions and modifications will occur to those skilled in the art based on the present invention, and are intended to be within the scope of the present invention. The protection scope of the invention is subject to the claims.
Claims (8)
1. Port infrastructure structure durability safety precaution system, its characterized in that: the system comprises a chloride ion concentration data monitoring module of a port concrete structure, a chloride ion transmission parameter module based on machine learning, a chloride ion dynamic transmission model of port infrastructure and a durability safety early warning platform of the port infrastructure structure;
The chloride ion concentration data monitoring module is used for acquiring the chloride ion concentration data of the port concrete structure in real time;
the chloride ion transmission parameter module based on machine learning is used for receiving and processing chloride ion concentration data to obtain chloride ion transmission parameters;
the dynamic chloride ion transmission model of the port infrastructure is used for receiving chloride ion transmission parameters and constructing transmission modes of concrete structure chloride ion parameters under different states;
The durability safety early warning platform of the port infrastructure structure is used for generating structure durability safety early warning information according to chloride ion parameters of the concrete structure;
the chloride ion transmission parameter module based on machine learning comprises document searching and sorting, construction of a supervision training set database, optimization of a machine learning model and important transmission parameter acquisition;
The literature searching and sorting device is used for collecting data such as the concrete mix proportion of the port to be monitored in service, collecting and sorting historical chloride ion concentration data of the port to be monitored, and constructing a machine learning supervision training set database;
The supervision training set database is constructed and used for machine learning an original database and selecting a plurality of machine learning methods to carry out machine learning training to expand the original database;
the optimized machine learning model is used for constructing a machine learning model of a machine learning optimization algorithm;
The important transmission parameters are obtained, and the expanded original chloride ion database of the port to be monitored is adopted to carry out inversion calculation to obtain concrete double-time-varying parameters which are based on Fick second law and consider the steel bar blocking effect coefficient, so as to construct a chloride ion dynamic transmission model of port infrastructure;
the optimized machine learning model is specifically constructed according to the following steps:
The method comprises the steps of firstly, processing raw data to obtain chloride ion data of a port in years, extracting two variables of service year and concrete sampling depth from a supervision training set database to serve as characteristic variables, actually measuring the chloride ion concentration of a sample of the service year and concrete sampling depth to serve as target variables, and dividing the total data into two parts of training data and test data;
Training the algorithm by a second step, selecting an epsilon-SVR type SVM, setting a kernel function type as an RBF kernel, performing multi-group parameter training trial, and recording training parameters and training effects;
thirdly, step parameters are adjusted, and training parameters and training effects recorded in the algorithm training step are preferably selected as training parameters;
fourthly, evaluating the model, namely using the training completion model for test data verification calculation, and observing the accuracy of verification data to perform the next step if the accuracy of the verification data can meet the requirements;
and fifthly, predicting and expanding the model, and performing original data expansion calculation according to the dynamic chloride ion transmission model of the port infrastructure.
2. The port infrastructure structural durability safety precaution system of claim 1, wherein: the chloride ion concentration data monitoring module of the port concrete structure comprises a measuring point position layout module, a data acquisition, transmission and interpretation module, a chloride ion in-situ long-term database and real-time optimized transmission parameters;
the measuring point position layout module is used for obtaining the measured chloride ion concentration value to be stored in the chloride ion in-situ long-term database;
the data acquisition, transmission and interpretation are carried out, and concrete double time-varying parameters are obtained through inversion calculation;
The chloride ion in-situ long-term database is used as a basic database for interpretation of transmission parameters and is used for supporting data transmission interpretation and displaying chloride ion concentration cloud pictures in real time;
The real-time optimized transmission parameters are used for dynamic inversion calculation, real-time chloride ion concentration evaluation and concrete durability prediction.
3. The port infrastructure structural durability safety precaution system of claim 1, wherein: the durability safety early warning platform of the port infrastructure structure comprises a dynamic transmission model, a concentration threshold system, a safety early warning mechanism, a chloride ion simulation model with a typical structure and a structure durability safety early warning platform;
the dynamic transmission model is used for evaluating the distribution of chloride ions in the reinforced concrete in real time;
the concentration threshold system is used for judging the service state of the structure in the corrosion induction period, the corrosion period, the accelerated corrosion period and the corrosion destruction period according to the distribution of chloride ions in the reinforced concrete estimated by the dynamic transmission model;
the safety early warning mechanism is used for setting early warning reference signals corresponding to different states of the structure;
the typical structure chloride ion simulation model is used for displaying the chloride ion concentration distribution of the structure, and displaying the chloride ion concentration distribution cloud image of the structure in real time on the basis of a typical port three-dimensional model;
The structural durability safety early warning platform is used for displaying a port infrastructure chloride ion dynamic transmission model, a concentration threshold system and a safety early warning mechanism, and displaying the port real-time chloride ion concentration on a typical structural chloride ion simulation model.
4. The port infrastructure structural durability safety precaution system of claim 3, wherein: the early warning reference signals under different states of the structure are based on chloride ion concentration threshold valuesAnd is determined in accordance with the following modes:
When the chloride ion concentration threshold is at When the structure is in a green safety state;
When the chloride ion concentration threshold is at When the structure is orange, early warning is carried out;
When the chloride ion concentration threshold is at When the structure is blue, early warning is carried out;
When the chloride ion concentration threshold is at And when the structure is red, early warning is carried out.
5. A method of early warning with the port infrastructure durability safety early warning system of any one of the preceding claims 1 to 4, characterized by: the method comprises the following steps:
constructing a chloride ion concentration data monitoring module of the port concrete structure, and acquiring the chloride ion concentration data of the port concrete structure in real time;
Constructing a chloride ion transmission parameter module for receiving and processing chloride ion concentration data, wherein the chloride ion transmission parameter module obtains chloride ion transmission parameters through machine learning training;
Constructing a chloride ion dynamic transmission model of a port infrastructure provided with concrete double time-varying parameters, wherein the chloride ion dynamic transmission model of the port infrastructure is used for transmitting concrete structure chloride ion parameters in different states;
And constructing a port infrastructure structure durability safety early warning platform for outputting safety early warning information, wherein the port infrastructure structure durability safety early warning platform receives the concrete structure chloride ion parameters transmitted by the port infrastructure chloride ion dynamic transmission model and generates structure durability safety early warning information according to the concrete structure chloride ion parameters.
6. The port infrastructure structural durability safety precaution method of claim 5, wherein: the real-time acquisition of the chloride ion concentration data of the port concrete structure is realized by acquiring the micro-array type sensing chloride ion sensors arranged at measuring point positions of a splash zone, an underwater zone and an atmosphere zone of a typical port through a structural chloride ion concentration data monitoring module.
7. The port infrastructure structural durability safety precaution method of claim 5, wherein: the port infrastructure chloride ion dynamic transmission model is carried out according to the following steps:
Collecting concrete mix proportion data of an in-service port to be monitored and historical chloride ion concentration data of the port to be monitored to obtain an original chloride ion database;
Expanding an original chloride ion database by machine learning training by utilizing the original database;
and obtaining concrete double time-varying parameters by inversion calculation through an original chloride ion database, and constructing a port infrastructure chloride ion dynamic transmission model by utilizing the concrete double time-varying parameters.
8. The port infrastructure structural durability safety precaution method of claim 5, wherein: the method also comprises the step of constructing a typical structure chloride ion simulation model, wherein the construction process of the typical structure chloride ion simulation model is as follows:
reasonably simplifying the model according to the actual condition of the research test piece and establishing a three-dimensional numerical model;
corresponding attributes are given according to the actual characteristics of each phase of material of the test piece;
determining a boundary condition of a test piece;
setting a concrete double-time-varying parameter of a chloride ion transmission model in concrete;
performing simulation calculation by comsol numerical simulation calculation software;
and outputting the calculation result and evaluating the result.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101122595A (en) * | 2007-09-04 | 2008-02-13 | 中交四航工程研究院有限公司 | Concrete structure residual life analysis system |
| KR20170111213A (en) * | 2016-03-25 | 2017-10-12 | 강원대학교산학협력단 | Method And Computer Program For Salt Damage Prediction of Reinforced Concrete |
| CN110567513A (en) * | 2019-08-22 | 2019-12-13 | 青岛理工大学 | intelligent sensing and deterioration early warning system and method for full-life performance of concrete structure |
| CN110737967A (en) * | 2019-09-10 | 2020-01-31 | 上海交通大学 | concrete structure durability prediction method, system and terminal |
| CN112016244A (en) * | 2020-08-07 | 2020-12-01 | 中国交通建设股份有限公司吉林省分公司 | Durable concrete multi-target mix proportion optimization method based on SVM and intelligent algorithm |
| KR20220088057A (en) * | 2020-12-18 | 2022-06-27 | 한국과학기술원 | Predictive method of steel corrosion of reinforced concrete structures exposed to high pressure immersion environment |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI385492B (en) * | 2008-12-16 | 2013-02-11 | Ind Tech Res Inst | A system for maintaining and analyzing manufacturing equipment and method therefor |
-
2023
- 2023-10-30 CN CN202311423344.9A patent/CN117471071B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101122595A (en) * | 2007-09-04 | 2008-02-13 | 中交四航工程研究院有限公司 | Concrete structure residual life analysis system |
| KR20170111213A (en) * | 2016-03-25 | 2017-10-12 | 강원대학교산학협력단 | Method And Computer Program For Salt Damage Prediction of Reinforced Concrete |
| CN110567513A (en) * | 2019-08-22 | 2019-12-13 | 青岛理工大学 | intelligent sensing and deterioration early warning system and method for full-life performance of concrete structure |
| CN110737967A (en) * | 2019-09-10 | 2020-01-31 | 上海交通大学 | concrete structure durability prediction method, system and terminal |
| CN112016244A (en) * | 2020-08-07 | 2020-12-01 | 中国交通建设股份有限公司吉林省分公司 | Durable concrete multi-target mix proportion optimization method based on SVM and intelligent algorithm |
| KR20220088057A (en) * | 2020-12-18 | 2022-06-27 | 한국과학기술원 | Predictive method of steel corrosion of reinforced concrete structures exposed to high pressure immersion environment |
Non-Patent Citations (1)
| Title |
|---|
| "钢筋对混凝土中氯离子扩散的阻挡效应预测模型";吴林键 等;《建筑材料学报》;20210430;第24卷(第2期);第296-303, 332页 * |
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