Method, system, terminal and medium for testing thickness of concrete reinforcement protective layer
1. A method for inspecting the thickness of a concrete reinforcement protective layer is characterized by comprising the following steps:
scanning a plurality of concrete reinforcing bars with different protective layer thicknesses to obtain corresponding standard oscillograms;
extracting the distribution characteristics of the standard oscillogram;
training and learning the distribution characteristics by using a machine learning algorithm supporting Bagging to obtain a protective layer thickness predicted value of the concrete reinforcement;
and evaluating the predicted protective layer thickness value by using the evaluation index of the machine learning algorithm to obtain a target protective layer thickness value of the concrete reinforcement.
2. The method for inspecting the thickness of a concrete reinforcing bar protective layer according to claim 1, wherein the distribution characteristics of the standard oscillogram include an average value, a peak value, a root mean square, a standard value, a skewness, a frequency band power and a crest factor of the standard oscillogram.
3. The method for inspecting the thickness of the protective layer of the concrete reinforcement according to claim 2, wherein the training and learning of the distribution characteristics by using a machine learning algorithm supporting Bagging to obtain the predicted value of the thickness of the protective layer of the concrete reinforcement comprises:
establishing a training sample set according to the average value, the peak value, the root mean square, the frequency band power and the crest factor of the standard oscillogram;
dividing the training sample set into a plurality of training sample subsets by adopting a random put-back sampling mode;
performing machine learning on the training sample subset by using a classification tree model to obtain a plurality of training results;
weighting and integrating the training results to obtain a mapping relation between the scanning signal intensity and the thickness of the steel bar protective layer, and obtaining a protective layer thickness predicted value of the concrete steel bar according to the mapping relation.
4. A method for inspecting a thickness of a protective layer of a concrete reinforcing bar according to claim 1 or 3, wherein the evaluating the predicted value of the protective layer thickness using the evaluation index of the machine learning algorithm to obtain a target value of the protective layer thickness of the concrete reinforcing bar includes:
obtaining a calculation formula of the evaluation index:
in the formula, y is a predicted value of the thickness of the steel bar protection layer, y' is an actual value of the thickness of the steel bar protection layer, and n is the total number of the predicted values;
and when the RMSE is less than 5, taking the predicted protective layer thickness value as the target protective layer thickness value.
5. A concrete reinforcement protective layer thickness inspection system, comprising:
the oscillogram acquisition unit is used for scanning a plurality of concrete reinforcing steel bars with different protective layer thicknesses to obtain corresponding standard oscillograms;
the characteristic extraction unit is used for extracting the distribution characteristics of the standard oscillogram;
the prediction unit is used for training and learning the distribution characteristics by using a machine learning algorithm supporting Bagging to obtain a predicted value of the thickness of the protective layer of the concrete reinforcement;
and the evaluation unit is used for evaluating the protective layer thickness predicted value by using the evaluation index of the machine learning algorithm to obtain a protective layer thickness target value of the concrete reinforcement.
6. The concrete reinforcing bar protective layer thickness inspection system of claim 5, wherein the distribution characteristics of the standard oscillogram include a mean, a peak, a root mean square, a standard value, a skewness, a frequency band power, and a crest factor of the standard oscillogram.
7. The concrete reinforcing bar protective layer thickness inspection system of claim 6, wherein the prediction unit is further configured to:
establishing a training sample set according to the average value, the peak value, the root mean square, the frequency band power and the crest factor of the standard oscillogram;
dividing the training sample set into a plurality of training sample subsets by adopting a random put-back sampling mode;
performing machine learning on the training sample subset by using a classification tree model to obtain a plurality of training results;
weighting and integrating the training results to obtain a mapping relation between the scanning signal intensity and the thickness of the steel bar protective layer, and obtaining a protective layer thickness predicted value of the concrete steel bar according to the mapping relation.
8. A concrete reinforcing bar protective layer thickness inspection system according to claim 5 or 7, wherein the evaluation unit is further configured to:
obtaining a calculation formula of the evaluation index:
in the formula, y is a predicted value of the thickness of the steel bar protection layer, y' is an actual value of the thickness of the steel bar protection layer, and n is the total number of the predicted values;
and when the RMSE is less than 5, taking the predicted protective layer thickness value as the target protective layer thickness value.
9. A terminal device, comprising:
one or more processors;
a memory coupled to the processor for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of checking the thickness of a concrete reinforcing bar protective layer as claimed in any one of claims 1 to 4.
10. A computer-readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the method for inspecting a thickness of a concrete reinforcing bar protective layer according to any one of claims 1 to 4.
Background
The concrete reinforcing steel bar protective layer of the concrete member generally refers to the distance from the outer edge of the stressed longitudinal bar to the outer connecting edge of the concrete of the member, and mainly plays a role in protecting reinforcing steel bars and preventing materials such as the reinforcing steel bars from being corroded by water vapor, so that the stress performance of the reinforced concrete and the service life and durability of the bridge are protected from being influenced. The accurate detection result of the thickness of the steel bar protection layer is not only the basis of engineering acceptance and quality evaluation, but also can better promote the improvement and development of the construction control process of the steel bar protection layer, so that the safety and the durability of the concrete member can be better ensured. However, most of the conventional methods for detecting the thickness of the protective layer of the fabricated concrete member are sampling damage detection, and the whole batch of nondestructive detection of the prefabricated member is lacked, thereby causing quality problems and safety problems during the construction of the concrete protective layer.
Disclosure of Invention
The invention aims to provide a method, a system, a terminal and a medium for inspecting the thickness of a concrete reinforcement protective layer, and the method can solve the technical problems of incompleteness, incapability of guaranteeing the accuracy of a detection result and low construction safety in the conventional sampling detection.
In order to overcome the defects in the prior art, the invention provides a method for inspecting the thickness of a concrete reinforcement protective layer, which comprises the following steps:
scanning a plurality of concrete reinforcing bars with different protective layer thicknesses to obtain corresponding standard oscillograms;
extracting the distribution characteristics of the standard oscillogram;
training and learning the distribution characteristics by using a machine learning algorithm supporting Bagging to obtain a protective layer thickness predicted value of the concrete reinforcement;
and evaluating the predicted protective layer thickness value by using the evaluation index of the machine learning algorithm to obtain a target protective layer thickness value of the concrete reinforcement.
Further, the distribution characteristics of the standard oscillogram include an average value, a peak value, a root mean square, a standard value, a skewness, a frequency band power and a crest factor of the standard oscillogram.
Further, the training and learning of the distribution characteristics by using a machine learning algorithm supporting Bagging to obtain a predicted value of the thickness of the protective layer of the concrete reinforcement comprises:
establishing a training sample set according to the average value, the peak value, the root mean square, the frequency band power and the crest factor of the standard oscillogram;
dividing the training sample set into a plurality of training sample subsets by adopting a random put-back sampling mode;
performing machine learning on the training sample subset by using a classification tree model to obtain a plurality of training results;
weighting and integrating the training results to obtain a mapping relation between the scanning signal intensity and the thickness of the steel bar protective layer, and obtaining a protective layer thickness predicted value of the concrete steel bar according to the mapping relation.
Further, the evaluating the predicted protection layer thickness value by using the evaluation index of the machine learning algorithm to obtain a protection layer thickness target value of the concrete reinforcement bar includes:
obtaining a calculation formula of the evaluation index:
in the formula, y is a predicted value of the thickness of the steel bar protection layer, y' is an actual value of the thickness of the steel bar protection layer, and n is the total number of the predicted values;
and when the RMSE is less than 5, taking the predicted protective layer thickness value as the target protective layer thickness value.
The invention also provides a system for inspecting the thickness of the concrete reinforcement protective layer, which comprises the following components:
the oscillogram acquisition unit is used for scanning a plurality of concrete reinforcing steel bars with different protective layer thicknesses to obtain corresponding standard oscillograms;
the characteristic extraction unit is used for extracting the distribution characteristics of the standard oscillogram;
the prediction unit is used for training and learning the distribution characteristics by using a machine learning algorithm supporting Bagging to obtain a predicted value of the thickness of the protective layer of the concrete reinforcement;
and the evaluation unit is used for evaluating the protective layer thickness predicted value by using the evaluation index of the machine learning algorithm to obtain a protective layer thickness target value of the concrete reinforcement.
Further, the distribution characteristics of the standard oscillogram include an average value, a peak value, a root mean square, a standard value, a skewness, a frequency band power and a crest factor of the standard oscillogram.
Further, the prediction unit is further configured to:
establishing a training sample set according to the average value, the peak value, the root mean square, the frequency band power and the crest factor of the standard oscillogram;
dividing the training sample set into a plurality of training sample subsets by adopting a random put-back sampling mode;
performing machine learning on the training sample subset by using a classification tree model to obtain a plurality of training results;
weighting and integrating the training results to obtain a mapping relation between the scanning signal intensity and the thickness of the steel bar protective layer, and obtaining a protective layer thickness predicted value of the concrete steel bar according to the mapping relation.
Further, the evaluation unit is further configured to:
obtaining a calculation formula of the evaluation index:
in the formula, y is a predicted value of the thickness of the steel bar protection layer, y' is an actual value of the thickness of the steel bar protection layer, and n is the total number of the predicted values;
and when the RMSE is less than 5, taking the predicted protective layer thickness value as the target protective layer thickness value.
The present invention also provides a terminal device, including:
one or more processors;
a memory coupled to the processor for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of checking the thickness of a concrete reinforcing bar protective layer as described in any one of the above.
The invention also provides a computer-readable storage medium, on which a computer program is stored, the computer program being executed by a processor to implement the method for inspecting the thickness of a concrete reinforcing bar protective layer as described in any one of the above.
Compared with the prior art, the invention has the beneficial effects that:
the invention discloses a method for inspecting the thickness of a concrete reinforcement protective layer, which comprises the steps of scanning a plurality of concrete reinforcements with different protective layer thicknesses to obtain corresponding standard oscillograms; extracting the distribution characteristics of the standard oscillogram; training and learning the distribution characteristics by using a machine learning algorithm supporting Bagging to obtain a protective layer thickness predicted value of the concrete reinforcement; and evaluating the predicted protective layer thickness value by using the evaluation index of the machine learning algorithm to obtain a target protective layer thickness value of the concrete reinforcement. The invention overcomes the incomplete sampling problem in the existing sampling detection, can quickly and accurately detect the thickness of the protective layer of the concrete reinforcement on the premise of no damage to the component, and has the advantages of easy implementation, high safety and strong reliability. Meanwhile, the method can improve the qualification rate of the precast concrete member, more effectively improve, guide and control the construction quality, and more obviously prolong the safe service life and the durability service life of the fabricated concrete structure.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for inspecting a thickness of a concrete reinforcing bar protective layer according to an embodiment of the present invention;
FIG. 2 is a flow chart illustrating the sub-steps of step S30 in FIG. 1;
fig. 3 is a scanning test diagram of a steel bar scanner according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a scanning waveform signal according to an embodiment of the present invention;
FIG. 5 is a flow chart of machine learning provided by an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a system for checking a thickness of a concrete reinforcing bar protective layer according to an embodiment of the present invention.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be understood that the step numbers used herein are for convenience of description only and are not used as limitations on the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.
In a first aspect:
referring to fig. 1, an embodiment of the present invention provides a method for checking a thickness of a concrete reinforcement protective layer, including:
s10, scanning a plurality of concrete reinforcing bars with different protective layer thicknesses to obtain corresponding standard oscillograms;
it should be noted that, in this step, a steel bar scanning test is mainly performed on the prefabricated concrete component to obtain a large number of waveform signal diagrams of the standard component, and then a corresponding standard waveform diagram is obtained. Generally, the acquisition period can be set according to actual needs, and a quantitative acquisition mode is adopted. Specifically, S10 includes the following steps:
A1) prefabricating concrete standard components with different protective layer thicknesses in a factory;
A2) scanning the precast concrete standard component by using a steel bar scanner;
A3) acquiring waveform signal diagrams of precast concrete standard components corresponding to different protective layer thicknesses;
A4) repeating the steps A1) -A3) to obtain a large number of waveform signal diagrams corresponding to the known standard steel bar distribution and the known protective layer thickness, and finally obtaining the standard waveform diagram.
S20, extracting the distribution characteristics of the standard oscillogram;
in this step, a mathematical statistics method is mainly used, the waveform signal diagram is calculated in the time domain to generate characteristic values such as an average value, a peak value, a root mean square, a standard value, and the like, the distribution of the signal in the frequency domain is obtained based on the frequency domain convolution theorem of fourier transform, and the characteristic values such as the peak value, skewness, and the like are calculated and generated in the frequency domain. And finally, extracting the distribution characteristics of the standard oscillogram.
S30, training and learning the distribution characteristics by using a Bagging-supporting machine learning algorithm to obtain a protective layer thickness prediction value of the concrete reinforcement;
it should be noted that, in this step, based on a learning method supporting a Bagging machine, on the basis of the known steel bar distribution and the known protective layer thickness, the distribution characteristics of the training learning waveform signal diagram are learned:
further, in a specific embodiment, S30 further includes the following sub-steps, as shown in fig. 2:
s301, establishing a training sample set according to the average value, the peak value, the root mean square, the frequency band power and the crest factor of the standard oscillogram;
s302, dividing the training sample set into a plurality of training sample subsets by adopting a random putting back sampling mode;
s303, performing machine learning on the training sample subset by using a classification tree model to obtain a plurality of training results;
s304, weighting and integrating the training results to obtain a mapping relation between the scanning signal strength and the thickness of the steel bar protective layer, and obtaining a protective layer thickness predicted value of the concrete steel bar according to the mapping relation.
And S40, evaluating the protective layer thickness predicted value by using the evaluation index of the machine learning algorithm to obtain a protective layer thickness target value of the concrete reinforcement.
In the step, a common machine learning model evaluation index RMES is selected to measure the prediction accuracy of machine learning, and the smaller the error measurement index RMES value is, the higher the accuracy of model prediction is. Wherein, the calculation model of RMES is:
in the formula, y is a predicted value of the thickness of the steel bar protection layer, y' is an actual value of the thickness of the steel bar protection layer, and n is the total number of the predicted values;
further, when the RMSE is less than 5, the current predicted protective layer thickness value is used as the target protective layer thickness value.
The method provided by the embodiment of the invention can quickly and accurately detect the thickness of the protective layer of the concrete reinforcement on the premise of no damage to the component, and has the advantages of easiness in implementation, high safety and strong reliability. The method not only improves the qualification rate of the precast concrete member, but also can effectively improve, guide and control the construction quality, and can obviously prolong the safe service life and the durability service life of the fabricated concrete structure.
In a second aspect:
in a specific embodiment, the whole scheme comprises the following processes:
1) acquiring a waveform signal diagram of the standard component, and obtaining a standard waveform diagram:
1.1) prefabricating concrete standard components with different protective layer thicknesses in a factory;
1.2) scanning the precast concrete standard component by using a steel bar scanner. Wherein, the scanning test chart of the steel bar scanner is shown in fig. 3;
1.3) acquiring a waveform signal diagram of the precast concrete standard component, as shown in FIG. 4.
1.4) replacing precast concrete standard components with different protective layer thicknesses, repeating the steps 1.1) to 1.3), obtaining a large number of waveform signal diagrams corresponding to the known standard steel bar distribution and the protective layer thickness, and finally obtaining a standard waveform diagram.
2) Extracting the distribution characteristics of the standard oscillogram:
calculating a waveform signal diagram in a time domain according to the formula (2-4) to generate characteristic values such as an average value, a root mean square, a peak value, a standard value and the like, obtaining the distribution of the signal in a frequency domain by using a frequency domain convolution formula (5) of Fourier transform, and then calculating and generating characteristic values such as frequency band power, skewness and the like in a frequency domain according to the formula (6-7).
XP＝max{|xi|} (4)
Wherein x isiIs the signal strength in the waveform plot;is an average over the time domain; xRMSRoot mean square over time domain; xPIs a peak in the time domain; s is the band power in the spectral domain, w1And w2Minimum and maximum frequencies, respectively; c is skewness in the frequency spectrum domain; n is the total number of predicted values.
Through the steps, the characteristic values such as the distribution characteristic average value, the root mean square, the peak value, the frequency band power, the crest factor and the like of the waveform signal diagram are extracted.
3) Training and learning the distribution characteristics by using a machine learning algorithm supporting Bagging to obtain a protective layer thickness predicted value of the concrete reinforcement; the flow chart of machine learning is shown in fig. 5:
3.1) taking characteristic values such as average value, root mean square, peak value, frequency band power, crest factor and the like as an independent variable X of the Bagging machine learning input layer; taking the thickness of the steel bar protection layer as an output variable Y;
3.2) dividing the training set into N independent training subsets by random put-back sampling;
3.3) using the classification tree model to carry out machine learning on the N independent training subset pairs;
and 3.4) weighting the thickness predicted values of the steel bar protection layers obtained by the N training to obtain final predicted values, integrating to obtain a complex mapping relation between scanning signals and the thickness of the steel bar protection layers under the big data, and finally obtaining the thickness predicted value of the protection layer of the concrete steel bar.
4) And evaluating the predicted protective layer thickness value by using the evaluation index of the machine learning algorithm to obtain the target protective layer thickness value of the concrete reinforcement.
In the step, a common machine learning model evaluation index RMES is selected to measure the prediction accuracy of machine learning, and the smaller the error measurement index RMES value is, the higher the accuracy of model prediction is. Wherein, the calculation model of RMES is:
in the formula, y is a predicted value of the thickness of the steel bar protection layer, y' is an actual value of the thickness of the steel bar protection layer, and n is the total number of the predicted values;
further, when the RMSE is less than 5, the current predicted protective layer thickness value is used as the target protective layer thickness value.
In a third aspect:
referring to fig. 6, an embodiment of the present invention further provides a system for checking a thickness of a concrete reinforcing bar protective layer, including:
the oscillogram obtaining unit 01 is used for scanning a plurality of concrete steel bars with different protective layer thicknesses to obtain corresponding standard oscillograms;
a feature extraction unit 02, configured to extract a distribution feature of the standard oscillogram;
the prediction unit 03 is used for training and learning the distribution characteristics by using a Bagging-supported machine learning algorithm to obtain a predicted value of the thickness of the protective layer of the concrete reinforcement;
and the evaluation unit 04 is used for evaluating the protective layer thickness predicted value by using the evaluation index of the machine learning algorithm to obtain a protective layer thickness target value of the concrete reinforcement.
Further, the distribution characteristics of the standard oscillogram include an average value, a peak value, a root mean square, a standard value, a skewness, a frequency band power and a crest factor of the standard oscillogram.
Further, the prediction unit is further configured to:
establishing a training sample set according to the average value, the peak value, the root mean square, the frequency band power and the crest factor of the standard oscillogram;
dividing the training sample set into a plurality of training sample subsets by adopting a random put-back sampling mode;
performing machine learning on the training sample subset by using a classification tree model to obtain a plurality of training results;
weighting and integrating the training results to obtain a mapping relation between the scanning signal intensity and the thickness of the steel bar protective layer, and obtaining a protective layer thickness predicted value of the concrete steel bar according to the mapping relation.
Further, the evaluation unit is further configured to:
obtaining a calculation formula of the evaluation index:
in the formula, y is a predicted value of the thickness of the steel bar protection layer, y' is an actual value of the thickness of the steel bar protection layer, and n is the total number of the predicted values;
and when the RMSE is less than 5, taking the predicted protective layer thickness value as the target protective layer thickness value.
The system provided by the embodiment of the invention can quickly and accurately detect the thickness of the protective layer of the concrete reinforcement on the premise of no damage to the component, and has the advantages of easiness in implementation, high safety and strong reliability. The method not only improves the qualification rate of the precast concrete member, but also can effectively improve, guide and control the construction quality, and can obviously prolong the safe service life and the durability service life of the fabricated concrete structure.
In a fourth aspect:
an embodiment of the present invention further provides a terminal device, including:
one or more processors;
a memory coupled to the processor for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method for inspecting a thickness of a concrete reinforcing bar protective layer as described above.
The processor is used for controlling the overall operation of the terminal equipment so as to complete all or part of the steps of the concrete reinforcement protective layer thickness checking method. The memory is used to store various types of data to support operation at the terminal device, and these data may include, for example, instructions for any application or method operating on the terminal device, as well as application-related data. The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
The terminal Device may be implemented by one or more Application Specific1 integrated circuits (AS 1C), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and is configured to perform the method for inspecting the thickness of the concrete steel bar protection layer according to any one of the embodiments described above, so AS to achieve the technical effects consistent with the above methods.
An embodiment of the present invention further provides a computer-readable storage medium including program instructions, which when executed by a processor, implement the steps of the method for inspecting the thickness of a concrete reinforcing bar protective layer according to any one of the embodiments. For example, the computer readable storage medium may be the above-mentioned memory including program instructions, which are executable by the processor of the terminal device to perform the method for inspecting the thickness of the concrete reinforcing bar protection layer according to any one of the above-mentioned embodiments, and achieve the technical effects consistent with the above-mentioned method.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
