CN115563827A - Concrete temperature monitoring method and system - Google Patents
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Abstract
The invention provides a concrete temperature monitoring method and a system, comprising the steps of obtaining monitoring data of concrete; the monitoring data at least comprises the temperature inside the concrete; determining a first predicted temperature based on the monitored data; determining a maximum critical value and a minimum critical value of the concrete based on the first predicted temperature; determining a second predicted temperature based on the monitored data; when the second predicted temperature is greater than the maximum critical value, determining to cool the concrete; when the second predicted temperature is smaller than the minimum critical value, the concrete is determined to stop being cooled, so that a set of complete concrete temperature monitoring system is formed, the monitoring module cooperates with the early warning module and the judging module to regulate and control the cooling module to cool the concrete, and timely and automatic measures are realized.
Description
Technical Field
The invention relates to the technical field of concrete construction, in particular to a concrete temperature monitoring method and system.
Background
Cementitious materials such as cement in the bulky concrete can release a large amount of heats at the hydration in-process, but the concrete heat conductivity is relatively poor, and the heat accumulation is difficult for giving off in inside, and surface cooling is very fast, can form the great difference in temperature on concrete inside and top layer like this. And because expansion and contraction, internal and external temperature difference and restraint promote tensile stress development in the concrete, when the stress is greater than the tensile strength of the concrete, hot cracks or through cracks are generated inside or on the surface of the structure, so that the stability, the applicability and the durability of the concrete structure are influenced. Because the cooling in the concrete is slow, if the surface cooling rate is difficult to control, then accelerate the cooling in the concrete through the mode of at the inside cooling water pipe that disposes of concrete. However, when the temperature difference of the concrete is found to be large, the cooling is carried out, so that certain delay exists between the taking and stopping of the temperature control measures of the cooling water, and the field design and construction cannot be guided.
In view of the above, in order to improve the effectiveness of concrete hydration heat temperature monitoring, some embodiments of the present disclosure provide a concrete temperature monitoring method and system.
Disclosure of Invention
The invention aims to provide a concrete temperature monitoring method, which comprises the steps of obtaining monitoring data of concrete; the monitoring data at least comprises the temperature inside the concrete; determining a first predicted temperature based on the monitored data; determining a maximum critical value and a minimum critical value of concrete based on the first predicted temperature; determining a second predicted temperature based on the monitored data; when the second predicted temperature is greater than the maximum critical value, determining to cool the concrete; and when the second predicted temperature is smaller than the minimum critical value, determining to stop cooling the concrete.
Further, the determining the first predicted temperature includes that finite element software obtains the software predicted temperature by simulating the state of concrete; adjusting parameters of the finite element software by a genetic algorithm based on the software predicted temperature and the monitoring data; and obtaining the first predicted temperature based on the simulation result of the adjusted finite element software.
Further, the step of predicting the temperature and the monitoring data based on the software and adjusting the parameters of the finite element software through a genetic algorithm comprises the steps of establishing a finite element model and constructing a first database based on the output of the finite element model and the monitoring data; selecting partial parameters of the finite element model for multiple times to obtain individuals and populations; determining fitness values for individuals based on the output of the finite element model and the monitoring data; selecting individuals of which the fitness value accords with a first preset threshold value, and performing cross and variation operation on the selected individuals to obtain new individuals and a new population; judging whether the fitness value of the individual meets a first fitness condition or not; if so, taking the parameters corresponding to the individuals meeting the first fitness condition as the parameters of the finite element model; if not, repeatedly screening and processing the individuals until the individuals meeting the first fitness condition are obtained.
Further, the determining of the maximum critical value and the minimum critical value of the concrete includes obtaining an index value of the temperature difference between the inside and the outside of the concrete; judging whether the temperature difference between the first predicted temperature and the external temperature of the concrete is larger than the index value or not; if yes, judging whether the first predicted temperature is larger than the external temperature; if so, taking the first predicted temperature as the maximum critical value; if not, the first predicted temperature is taken as the minimum critical value.
Further, the determining the second predicted temperature includes adjusting parameters of the temperature prediction model through a genetic algorithm based on the model predicted temperature output by the temperature prediction model and the monitoring data to obtain an adjusted temperature prediction model; and inputting the monitoring data into the adjusted temperature prediction model, and outputting the second predicted temperature by the model.
Further, the obtaining of the adjusted temperature prediction model includes constructing a temperature prediction model and a second database based on the monitoring data; selecting part of nodes and the monitoring data from the second database for multiple times to obtain individuals and populations; the nodes are points on the concrete where sensors are arranged; determining a fitness value for the individual based on the output of the temperature prediction model and the monitoring data; selecting individuals of which the fitness value accords with a second preset threshold value, and performing cross and variation operation on the selected individuals to obtain new individuals and a new population; judging whether the fitness value of the individual meets a second fitness condition or not; if so, taking the parameters corresponding to the individuals meeting the second fitness condition as the parameters of the temperature prediction model; if not, repeatedly screening and processing the individuals until the individuals meeting the second fitness condition are obtained.
The invention aims to provide a concrete temperature monitoring system which comprises a monitoring module, an early warning module, a judging module and a cooling module, wherein the early warning module is used for monitoring the temperature of concrete; the monitoring module is used for acquiring monitoring data of the concrete; the monitoring data at least comprises the temperature inside the concrete; the early warning module is used for determining a first predicted temperature based on the monitoring data; determining a maximum critical value and a minimum critical value of concrete based on the first predicted temperature; the judging module is used for determining a second predicted temperature based on the monitoring data; judging the magnitude relation between the second predicted temperature and the maximum critical value and the minimum critical value; the cooling module is used for determining to cool the concrete when the second predicted temperature is greater than the maximum critical value; and when the second predicted temperature is smaller than the minimum critical value, determining to stop cooling the concrete.
Further, the determining the first predicted temperature includes that finite element software obtains the software predicted temperature by simulating the state of concrete; adjusting parameters of the finite element software by a genetic algorithm based on the software predicted temperature and the monitoring data; obtaining the first predicted temperature based on the adjusted finite element software simulation result
Further, the determining the maximum critical value and the minimum critical value of the concrete comprises obtaining an index value of the temperature difference between the inside and the outside of the concrete; judging whether the temperature difference between the first predicted temperature and the external temperature of the concrete is larger than the index value or not; if so, judging whether the first predicted temperature is greater than the external temperature; if so, taking the first predicted temperature as the maximum critical value; if not, the first predicted temperature is used as the minimum critical value.
Further, the determining the second predicted temperature includes adjusting parameters of the temperature prediction model through a genetic algorithm based on the model predicted temperature output by the temperature prediction model and the monitoring data to obtain an adjusted temperature prediction model; and inputting the monitoring data into the adjusted temperature prediction model, and outputting the second predicted temperature by the model.
The technical scheme of the embodiment of the invention at least has the following advantages and beneficial effects:
a set of complete concrete temperature monitoring system is formed, the monitoring module cooperates with the early warning module and the judgment module to regulate and control the cooling module to cool the concrete, and timely and automatic measures are realized.
The finite element software can update the prediction result according to the actual monitoring data, and the temperature prediction model can accurately predict the temperature change, so that the temperature prediction accuracy is greatly improved.
And optimizing BP neural network training by using a genetic algorithm, and realizing the effect of predicting the temperature change of each node at the next time period according to the measured data.
The finite element software is combined with the genetic algorithm to optimize the finite element calculation parameters, and the prediction result can be updated according to the measured data.
Drawings
FIG. 1 is an exemplary flow chart of a method for monitoring concrete temperature according to some embodiments of the present invention;
FIG. 2 is an exemplary diagram of adjusting parameters of finite element software according to some embodiments of the present invention;
FIG. 3 is an exemplary diagram of an adjusted temperature prediction model provided by some embodiments of the present invention;
fig. 4 is a block diagram illustrating an exemplary system for monitoring concrete temperature according to some embodiments of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Fig. 1 is an exemplary flowchart of a concrete temperature monitoring method according to some embodiments of the present invention. In some embodiments, the process illustrated in FIG. 1 may be performed by the system illustrated in FIG. 4. As shown in fig. 1, the flow of the concrete temperature monitoring method includes the following steps:
and step 110, acquiring monitoring data of the concrete. In some embodiments, step 110 may be performed by monitoring module 410.
The monitored data may be monitored data relating to the temperature of the concrete. For example, the monitoring data may include one or more of a temperature inside the concrete, a temperature outside, a rate of temperature increase, a rate of temperature decrease, and a difference between internal and external temperatures, among others.
In some embodiments, the monitoring data may be obtained by arranging temperature sensors at corresponding locations of the concrete. For example, the sensors may be arranged in the interior of the concrete, the surface, the walls of the cooling water pipes and the concrete between adjacent water pipes. The sensor is an automatic monitoring sensor. The sensor is connected with the temperature acquisition device through a wire, and then the data is transmitted to the statistical module through the wireless communication module arranged in the acquisition device, and the statistical module is one of the central processing units of the temperature monitoring system.
For example, the positioning may be arranged at the edge, corner, middle, etc. of the concrete; when the concrete casting body is even in thickness, the positioning distance is 10m-15m, and the positioning quantity of the variable cross-section part can be increased. Two representative intersecting vertical sections are selected for temperature measurement, and the intersecting position of the vertical sections is suitable to pass through the middle area of the foundation. In the test area, the positions and the number of the monitoring points can be determined according to the distribution condition of a temperature field in the concrete casting body and the requirement of temperature control.
Based on the monitored data, a first predicted temperature is determined, step 120. In some embodiments, step 120 may be performed by the early warning module 420.
The first predicted temperature may refer to a predicted temperature for a future time period. In some embodiments, the first predicted temperature may be obtained by way of software analytical modeling.
In some embodiments, the early warning module 420 may simulate the state of the concrete through finite element software to obtain the first predicted temperature, including the following:
the finite element software obtains the software predicted temperature by simulating the state of the concrete.
The software predicted temperature can refer to the current temperature of the concrete predicted by the finite element software.
The state of the concrete may include, but is not limited to, unit parameters, monitorable nodes, aggregate parameters, material parameters, etc. of the concrete. For example, based on concrete unit parameters, monitorable nodes, aggregate parameters, material parameters and the like, a finite element model of the concrete is established through MIDAS FEANX finite element software, and the software predicted temperature is obtained through calculation by applying boundary conditions and load working conditions.
And (4) adjusting parameters of the finite element software through a genetic algorithm based on the predicted temperature and the monitoring data of the software. In some embodiments, the finite element parameter optimization uses a genetic algorithm that, through successive iterations, selects the parameter that simulates the data closest to the actual monitored data. In other embodiments, after the pre-warning triggers the corresponding action, the finite element software may factor in the corresponding action and calculate the predicted value based on the optimized parameter. The corresponding measures can comprise temperature reduction measures, such as water cooling and the like. See fig. 2 and its associated description for more details on tuning the finite element software.
And obtaining a first predicted temperature based on the simulation result of the adjusted finite element software.
In some embodiments in this specification, parameters of the finite element software are optimized by using a genetic algorithm, so that simulation of the finite element software can be as close to reality as possible, and a prediction result is more accurate.
Some embodiments in this specification, on the basis of the optimized finite element software, may take into account the corresponding cooling measures, and may stop the corresponding measures in time when the concrete is cooled to the corresponding temperature.
Based on the first predicted temperature, a maximum critical value and a minimum critical value of the concrete are determined 130. In some embodiments, step 130 may be performed by the early warning module 420.
The maximum critical value may refer to a maximum temperature value at which the temperature inside the concrete is higher than the temperature outside the concrete. The minimum critical value may refer to a minimum temperature value at which the temperature inside the concrete is lower than the temperature outside the concrete.
In some embodiments, the early warning module 420 may determine the maximum critical temperature and the minimum critical temperature based on the first predicted temperature and the indicator value, including the following:
and obtaining an index value of the temperature difference between the inside and the outside of the concrete.
The index value may refer to index data of concrete cooling determined according to experience or construction requirements. For example, the index value may include an index such as an inside-outside temperature difference value of concrete, a cooling rate of concrete, and the like.
And judging whether the temperature difference between the first prediction temperature and the external temperature of the concrete is larger than the index value.
For example, the index value may be that the difference between the internal temperature and the external temperature of the concrete does not exceed 100 ℃, and the absolute value of the difference between the first predicted temperature and the external temperature of the concrete is compared with the index value to determine whether the difference is greater than the index value.
In some embodiments, the index value may also be related to a rate of temperature increase, a rate of temperature decrease, and the like.
If not, determining that the internal and external temperature difference of the concrete meets the index, not processing the concrete, and continuing to monitor.
If yes, judging whether the first prediction temperature is larger than the external temperature.
The external temperature is the temperature outside the concrete. In some embodiments, the temperature outside the concrete may be considered to be room temperature.
If yes, the first predicted temperature is used as the maximum critical value.
If not, the first predicted temperature is taken as the minimum critical value.
Some embodiments in this specification enable better timeliness of measures determined from minimum and maximum thresholds by setting the maximum and minimum thresholds for respective time periods.
In some embodiments, when the temperature monitoring system monitors that the predicted value of the next time period reaches the maximum critical value, an alarm is given, and corresponding measures are taken to reduce the temperature.
Based on the monitored data, a second predicted temperature is determined, step 140. In some embodiments, step 140 may be performed by decision module 430.
The second predicted temperature may refer to a temperature of the concrete predicted by the neural network model for a future period of time. The neural network model may be a BP neural network model.
In some embodiments, the determining module 430 may determine the second predicted temperature from the output of the model by training a neural network model, including the following:
and adjusting parameters of the temperature prediction model through a genetic algorithm based on model prediction temperature and monitoring data output by the temperature prediction model to obtain an adjusted temperature prediction model.
The temperature prediction model may be used to predict the temperature of the concrete over a future period of time. The decision module 430 may input the monitored data into a temperature prediction model that outputs a second predicted temperature. For more details on obtaining the adjusted temperature prediction model, refer to fig. 3 and its related description.
And inputting the monitoring data into the adjusted temperature prediction model, and outputting a second predicted temperature by the model.
And 150, when the second predicted temperature is greater than the maximum critical value, determining that the concrete is cooled. In some embodiments, step 150 may be performed by cool down module 440.
In some embodiments, the cooling module 440 may cool the interior of the concrete in various possible ways. For example, the concrete is cooled by providing a liquid-feeding pipe inside the concrete and injecting a cooling medium into the pipe.
And step 160, when the second predicted temperature is smaller than the minimum critical value, determining to stop cooling the concrete. In some embodiments, step 160 may be performed by cool down module 440.
In some embodiments, a sensor may be provided on the fluid conduit to obtain a conduit temperature, determine a temperature of the concrete for a future time period based on the conduit temperature and the monitored temperature, and determine whether to stop cooling based on the predicted temperature for the future time period and a minimum threshold.
Some embodiments in this specification reserve reaction time for implementation of a cooling measure by predicting temperature data of concrete in a future time period, and the cooling water module automatically supplies water or stops supplying water before a value monitored by a temperature monitoring system reaches a specification value or a design value, so that delay or excessive water supply caused by untimely monitoring of the measure is avoided.
Some embodiments in this specification may improve the accuracy of the predicted temperature by predicting the temperature of the concrete in a future time period through finite element software and a neural network model.
FIG. 2 is an exemplary diagram of adjusting parameters of finite element software according to some embodiments of the present invention. In some embodiments, the process illustrated in fig. 2 may be performed by the early warning module 420. As shown in FIG. 2, the process of adjusting the parameters of the finite element software comprises the following steps:
and establishing a finite element model, and constructing a first database based on the output and monitoring data of the finite element model. For example, the concrete is modeled by MIDAS FEANX finite element software, and the temperature of the concrete at each node and moment is predicted based on the model. The first database may be used to store monitoring data and simulation data for the nodes and nodes. For example, for node 1, the first database may include the monitored temperatures and simulated temperatures of node 1 at times t1, t2, and t 3.
And selecting partial parameters of the finite element model for multiple times to obtain individuals and populations.
For example, the early warning module 420 may extract a part of parameters constituting the finite element model in a certain proportion to obtain individuals, and may obtain the population through multiple extractions.
Numbering the parameters, setting the selected parameters to be 1 and setting the unselected parameters to be 0 according to the numbering sequence of the parameters; to encode individuals and populations for subsequent genetic algorithm processing.
Based on the output of the finite element model and the monitored data, fitness values of the individuals are determined.
The fitness value of an individual may be used to represent the degree to which the monitoring data is adapted to the finite-element model. In some embodiments, the individual fitness value may be a variance of the output of the finite element model and the monitoring data.
And selecting individuals with fitness values meeting a first preset threshold, and performing crossover and mutation operations on the selected individuals to obtain new individuals and a new population.
The first preset threshold may be a preset value. In some embodiments, the first preset threshold may be set empirically, and may be selected as a new individual when the fitness value is greater than the first preset threshold, and not otherwise.
And judging whether the fitness value of the individual meets a first fitness condition.
The first fitness condition may be a condition that the preset optimal individual should satisfy. In some embodiments, the first fitness condition may be set empirically. For example, the first fitness condition may be that the fitness value of the individual is greater than 95.
If so, taking the parameters corresponding to the individuals meeting the first fitness condition as the parameters of the finite element model.
If not, repeatedly screening and processing the individuals until the individuals meeting the first fitness condition are obtained.
Fig. 3 is an exemplary diagram of an adjusted temperature prediction model according to some embodiments of the invention. In some embodiments, the flow illustrated in FIG. 3 may be performed by the decision module 430. As shown in fig. 2, the process of obtaining the adjusted temperature prediction model includes the following steps:
and constructing a temperature prediction model and a second database based on the monitoring data.
In some embodiments, the determining module 430 may determine the network topology of the neural network model based on the number of parameters, which are parameters for calculating the concrete temperature field. And then initializing the weight and the threshold of the neural network model based on experience to obtain a temperature prediction model.
And coding the weight value and the threshold value of the temperature prediction model to obtain the parameters of the coded temperature prediction model. For example, GA encodes the initial value for the weight threshold.
In some embodiments, the determining module 430 may select parameters according to actual conditions to calculate a temperature field of the concrete, and construct the second database by extracting the temperature and nodes in the temperature field.
Selecting part of nodes and monitoring data from the second database for multiple times to obtain individuals and populations; the nodes are points on the concrete where sensors are arranged.
For example, part of the nodes are extracted from the second database according to 60% of the total node number to form an individual; and extracting part of nodes from the second database for multiple times to obtain the population.
And coding the extracted individuals and populations to obtain the coded individuals and populations. For example, all nodes may be numbered, with selected nodes being set to 1 and unselected nodes being set to 0 in the order of the numbering.
Based on the output of the temperature prediction model and the monitored data, a fitness value for the individual is determined.
The individual fitness value may be used to represent the degree to which the monitored data adapts to the temperature prediction model. In some embodiments, the individual fitness value may be a variance of the output of the temperature prediction model and the monitored data.
And selecting individuals of which the fitness value accords with a second preset threshold value, and performing cross and variation operation on the selected individuals to obtain new individuals and a new population.
The second preset threshold may be a preset value. In some embodiments, the second predetermined threshold may be set empirically, and may be selected as a new individual when the fitness value is greater than the second predetermined threshold, and not otherwise.
And judging whether the fitness value of the individual meets a second fitness condition.
The second fitness condition may be a condition that the preset optimal individual should satisfy. In some embodiments, the second fitness condition may be set empirically. For example, the second fitness condition may be that the fitness value of the individual is greater than 95.
If so, taking the parameters corresponding to the individuals meeting the second fitness condition as the parameters of the temperature prediction model.
For example, the determining module 430 may classify the individuals meeting the second fitness condition into a current monitored temperature and a historical monitored temperature, where the current monitored temperature is a temperature to be predicted by the temperature prediction model, and the historical monitored temperature is a temperature before the current monitored temperature. And inputting the historical monitoring temperature into a temperature prediction model, and adjusting parameters of the temperature prediction model based on the difference between the output of the model and the current monitoring temperature to obtain the adjusted temperature prediction model.
If not, repeatedly screening and processing the individuals until the individuals meeting the second fitness condition are obtained.
Fig. 4 is a block diagram illustrating an exemplary system for monitoring concrete temperature according to some embodiments of the present invention. As shown in fig. 4, the concrete temperature monitoring system includes a monitoring module 410, an early warning module 420, a judging module 430 and a cooling module 440.
The monitoring module 410 is used for acquiring monitoring data of concrete; the monitoring data includes at least the temperature inside the concrete. The monitoring module 410 may include a data acquisition device, which is responsible for classifying and storing the data measured by each sensor and inputting the data into the central processing unit; the command transmission device is responsible for commanding the cooling module 440 according to the measures of the central processing unit. For more of the monitoring module 410, refer to fig. 1 and its associated description.
The early warning module 420 is configured to determine a first predicted temperature based on the monitoring data; based on the first predicted temperature, a maximum threshold value and a minimum threshold value of the concrete are determined. For more on the early warning module 420, refer to fig. 1 and its associated description.
The determining module 430 is configured to determine a second predicted temperature based on the monitoring data; and judging the magnitude relation between the second predicted temperature and the maximum critical value and the minimum critical value. The determining module 430 may include a central processing unit, which is responsible for processing the calculation of the warning module 420 and selecting the measure command according to the warning and terminal command. For more details on the decision module 430, refer to fig. 1 and its associated description.
The cooling module 440 is configured to determine to cool the concrete when the second predicted temperature is greater than the maximum threshold; and when the second predicted temperature is smaller than the minimum critical value, determining to stop cooling the concrete. For more of the cooling module 440, see FIG. 1 and its associated description.
In some embodiments, the cooling module 440 may be a cooling water conditioning module. The temperature change and the speed of the mass concrete are regulated and controlled by the cooling water regulation and control module. When the early warning module gives an alarm, the cooling water regulation and control module starts or stops working. When the corresponding prediction value of the temperature prediction model to the concrete in the next time interval reaches the maximum critical value, the cooling water regulation and control module starts to supply water, so that the internal cooling rate of the concrete is increased, and the internal and external temperature difference of the concrete is reduced; and when the predicted value of the internal and external temperature difference is reduced to the minimum critical value, stopping water supply.
The cooling water regulation and control module consists of a pipeline, a valve and a water tank. The supply and discharge of the cooling water are controlled by valves.
The pipelines are arranged in an S shape, and the winding directions of the upper-layer cooling water pipe and the lower-layer cooling water pipe are mutually vertical; each layer of cooling water pipe is mutually independent and is provided with a water inlet pipe and a water outlet pipe, the water inlet pipe is connected with the water diversion device, and the water outlet pipe is connected with the water return device; according to the concrete construction condition, water is introduced into the water inlet pipe layer by layer.
The valve and the water pump are provided with a command receiving module which can receive and execute the measure command transmitted by the central processing unit transmitted by the command transmission device and regulate and control the delivery of the cooling water.
The monitoring module 410 and the early warning module 420 may input the acquired data into the determining module 430, and after determining, transmit a command to the cooling module 440.
In some embodiments, the system further comprises an information transmission module, which is responsible for transmitting the monitoring data, the early warning data and the corresponding measures to the cloud and inputting the terminal instruction into the central processing unit.
The terminal may be a terminal for a user to issue a control instruction or display a concrete state. For example, a terminal may include, but is not limited to, a computer, a tablet, and/or a mobile terminal, among others. When the data is transmitted to the central processing unit, the data is processed by the early warning module 420, and is uploaded and backed up to the cloud end through the information transmission module. And when the warning module 420 triggers a warning, the warning is uploaded to the cloud. And the data and the alarm are uploaded by the information transmission module. The information transmission module adopts a 4G cellular network.
In some embodiments, the terminal can carry software, log in the cloud end through software networking to acquire data and an alarm, and issue a command of starting or stopping water supply to the central processing unit through the cloud end and the information transmission device by the software.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A concrete temperature monitoring method is characterized by comprising the following steps,
acquiring monitoring data of concrete; the monitoring data at least comprises the temperature inside the concrete;
determining a first predicted temperature based on the monitored data;
determining a maximum critical value and a minimum critical value of the concrete based on the first predicted temperature;
determining a second predicted temperature based on the monitored data;
when the second predicted temperature is greater than the maximum critical value, determining to cool the concrete; and when the second predicted temperature is smaller than the minimum critical value, determining to stop cooling the concrete.
2. The concrete temperature monitoring method according to claim 1, wherein said determining a first predicted temperature comprises,
the finite element software obtains the predicted temperature of the software by simulating the state of the concrete;
adjusting parameters of the finite element software by a genetic algorithm based on the software predicted temperature and the monitoring data;
and obtaining the first predicted temperature based on the simulation result of the adjusted finite element software.
3. The concrete temperature monitoring method of claim 2, wherein said adjusting parameters of said finite element software by a genetic algorithm based on said software predicted temperature and said monitored data comprises,
establishing a finite element model, and constructing a first database based on the output of the finite element model and the monitoring data;
selecting partial parameters of the finite element model for multiple times to obtain individuals and populations;
determining fitness values of individuals based on the output of the finite element model and the monitoring data;
selecting individuals with fitness values meeting a first preset threshold, and performing crossover and mutation operations on the selected individuals to obtain new individuals and new populations;
judging whether the fitness value of the individual meets a first fitness condition or not;
if so, taking the parameters corresponding to the individuals meeting the first fitness condition as the parameters of the finite element model;
if not, repeatedly screening and processing the individuals until the individuals meeting the first fitness condition are obtained.
4. The concrete temperature monitoring method of claim 1, wherein determining a maximum threshold value and a minimum threshold value for concrete comprises,
acquiring an index value of the temperature difference between the inside and the outside of the concrete;
judging whether the temperature difference between the first predicted temperature and the external temperature of the concrete is larger than the index value or not;
if yes, judging whether the first predicted temperature is larger than the external temperature;
if so, taking the first predicted temperature as the maximum critical value;
if not, the first predicted temperature is used as the minimum critical value.
5. The concrete temperature monitoring method according to claim 1, wherein said determining a second predicted temperature comprises,
adjusting parameters of the temperature prediction model through a genetic algorithm based on model prediction temperature and the monitoring data output by the temperature prediction model to obtain an adjusted temperature prediction model;
and inputting the monitoring data into the adjusted temperature prediction model, and outputting the second predicted temperature by the model.
6. The method of claim 5, wherein the obtaining the adjusted temperature prediction model comprises,
building a temperature prediction model and a second database based on the monitoring data;
selecting part of nodes and the monitoring data from the second database for multiple times to obtain individuals and populations; the nodes are points on the concrete where sensors are arranged;
determining a fitness value for the individual based on the output of the temperature prediction model and the monitoring data;
selecting individuals of which the fitness value accords with a second preset threshold value, and performing cross and variation operation on the selected individuals to obtain new individuals and a new population;
judging whether the fitness value of the individual meets a second fitness condition or not;
if so, taking the parameters corresponding to the individuals meeting the second fitness condition as the parameters of the temperature prediction model;
if not, repeatedly screening and processing the individuals until the individuals meeting the second fitness condition are obtained.
7. A concrete temperature monitoring system is characterized by comprising a monitoring module, an early warning module, a judging module and a cooling module;
the monitoring module is used for acquiring monitoring data of the concrete; the monitoring data at least comprises the temperature inside the concrete;
the early warning module is used for determining a first predicted temperature based on the monitoring data; determining a maximum critical value and a minimum critical value of concrete based on the first predicted temperature;
the judging module is used for determining a second predicted temperature based on the monitoring data; judging the magnitude relation between the second predicted temperature and the maximum critical value and the minimum critical value;
the cooling module is used for determining to cool the concrete when the second predicted temperature is greater than the maximum critical value; and when the second predicted temperature is smaller than the minimum critical value, determining to stop cooling the concrete.
8. The concrete temperature monitoring system of claim 7, wherein the determining a first predicted temperature comprises,
the finite element software obtains the predicted temperature of the software by simulating the state of the concrete;
adjusting parameters of the finite element software by a genetic algorithm based on the software predicted temperature and the monitoring data;
and obtaining the first predicted temperature based on the simulation result of the adjusted finite element software.
9. The concrete temperature monitoring system of claim 7, wherein the determining of the maximum and minimum critical values of concrete comprises,
acquiring an index value of the temperature difference between the inside and the outside of the concrete;
judging whether the temperature difference between the first predicted temperature and the external temperature of the concrete is larger than the index value or not;
if yes, judging whether the first predicted temperature is larger than the external temperature;
if so, taking the first predicted temperature as the maximum critical value;
if not, the first predicted temperature is used as the minimum critical value.
10. The concrete temperature monitoring system of claim 7, wherein said determining a second predicted temperature comprises,
adjusting parameters of the temperature prediction model through a genetic algorithm based on model prediction temperature and the monitoring data output by the temperature prediction model to obtain an adjusted temperature prediction model;
and inputting the monitoring data into the adjusted temperature prediction model, and outputting the second predicted temperature by the model.
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| CN116308217B (en) * | 2023-05-19 | 2023-08-01 | 中交第四航务工程勘察设计院有限公司 | Concrete monitoring platform management method and system based on Internet of things |
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