Hollow slab beam mechanical property degradation prediction method and system based on vibration frequency

Abstract

The invention discloses a hollow slab beam mechanical property degradation prediction method and system based on vibration frequency, and relates to the technical field of hollow slab beam mechanical property degradation prediction.The hollow slab beam mechanical property degradation prediction method comprises the steps that vibration data of a hollow slab beam are collected, and the collected data are preprocessed to obtain standard data; performing mechanical property parameter extraction on the standard data, and establishing a mechanical property degradation model of the hollow slab girder according to a mechanical property parameter extraction result; comprehensively constructing a prediction model between the vibration data and the mechanical property degradation according to the influence of different degradation states of the mechanical property parameters on the vibration data of the hollow slab beam; and monitoring vibration data of the hollow slab girder in real time, and outputting the mechanical property degradation condition of the hollow slab girder in combination with the prediction model. According to the method, the relationship between the vibration frequency and the mechanical property of the cracked hollow slab beam is established, and a prediction model between vibration data and mechanical property degradation is constructed; and monitoring vibration data of the hollow slab beam in real time, analyzing the mechanical property degradation condition of the hollow slab beam, and performing early warning on the mechanical property degradation of the hollow slab beam.

Description

Technical Field

[0001] The invention relates to the technical field of hollow slab beam mechanical performance degradation prediction, and more particularly to a method and system for predicting hollow slab beam mechanical performance degradation based on vibration frequency. Background Art

[0002] Bridges are important nodes and throats for highways, railways and urban roads, and they play a pivotal role in economic development. Among them, hollow slab beam bridges account for about 50% of small and medium span bridges. The existing RC hollow slab beam bridges have been affected by reciprocating loads and environmental factors for a long time, and a large number of transverse and longitudinal cracks, hinge joint damage (single plate stress) and other problems have appeared. Further deterioration will lead to serious consequences such as reduced structural durability and failure of bridge structure. In order to meet the needs of economic development, whether the bearing capacity of existing highway bridges can meet the load standards after reconstruction and expansion and load level upgrade, and whether the remaining service life of the beams and slabs removed from the old bridges can meet the standard requirements after the reconstruction and expansion of the highway are key issues that need to be solved in today's highway reconstruction and expansion and bridge operation management.

[0003] However, due to the complex distribution characteristics of defects in RC hollow slab beam bridges in service and the randomness and uncertainty of environmental effects, the existing research on the theory and method of RC beam vibration testing based on dynamic characteristics and the research on the degradation of mechanical properties of cracked RC hollow slab beams are still insufficient in the following aspects: (1) The vertical bending vibration frequency response characteristics of RC hollow slab beams after service degradation are still unclear. Although there are many studies on the cracking mechanism and bearing capacity of hollow slab beams, the research on the vertical bending response frequency of hollow slab beams under different cracking states is not very sufficient, especially for RC beams that have been in service for many years. (2) The degradation characteristics and evolution laws of the vertical bending vibration frequency of RC hollow slab beams are still unclear. There are relatively few studies on the relationship between the vibration frequency of RC beams and their bearing capacity, stiffness, deflection, etc., and the degradation characteristics and evolution laws of the vibration frequency of RC beams are still unclear.

[0004] Therefore, how to propose a method and system for predicting the degradation of the mechanical properties of hollow slab beams based on vibration frequency, establish the relationship between the vibration frequency and the mechanical properties of cracked RC hollow slab beams, and predict the degradation of the mechanical properties of hollow slab beams is an urgent problem that technicians in this field need to solve. Summary of the invention

[0005] In view of this, the present invention provides a method and system for predicting the degradation of mechanical properties of hollow slab beams based on vibration frequency, establishes the relationship between vibration frequency and mechanical properties of cracked RC hollow slab beams, and predicts the degradation of mechanical properties of hollow slab beams. In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0006] The prediction method of mechanical performance degradation of hollow slab beam based on vibration frequency includes:

[0007] Sensors are deployed on hollow slab beams to collect vibration data in real time, and the collected data are pre-processed to obtain standard data;

[0008] Extract mechanical property parameters from standard data, and establish a mechanical property degradation model of hollow slab beams based on the mechanical property parameter extraction results;

[0009] According to the influence of different degradation states of mechanical performance parameters on the vibration data of hollow slab beams, a prediction model between vibration data and mechanical performance degradation is comprehensively constructed;

[0010] Real-time monitoring of the vibration data of hollow slab beams is carried out, and the degradation of the mechanical properties of hollow slab beams is output in combination with the prediction model, and early warning is issued.

[0011] Optionally, the deploying of sensors on the hollow slab beam to collect its vibration data in real time includes: deploying accelerometers and vibration sensors on the hollow slab beam to collect its vibration data in real time, wherein the vibration data includes vibration frequency.

[0012] Optionally, the preprocessing of the collected data to obtain standard data includes:

[0013] Obtaining a degraded data set and a normal data set, and extracting a degraded waveform and a normal waveform;

[0014] Collect vibration data on hollow slab beams in real time and pre-process the collected vibration data;

[0015] Apply discrete Fourier transform to the preprocessed signal to analyze the spectral characteristics of the vibration data;

[0016] Extract positively correlated waveform features according to the degradation type;

[0017] Preset a first similarity threshold and a second similarity threshold, perform similarity calculation based on deep learning, and compare the extracted waveform with the degraded waveform and the normal waveform for waveform similarity;

[0018] When the similarity between the waveform extracted twice in succession and the normal waveform is lower than the first similarity threshold, and the similarity between the waveform extracted twice in succession and the degraded waveform is higher than the second similarity threshold, it is considered to meet the corresponding degradation type.

[0019] Optionally, the establishing of the mechanical property degradation model of the hollow slab beam includes:

[0020] Establishing a mathematical simulation model of the hollow slab beam, and calculating and obtaining the mechanical performance parameters and performance specificity of the hollow slab beam according to the mathematical simulation model of the hollow slab beam, wherein the mechanical performance parameters include fatigue damage, crack propagation, and material degradation;

[0021] Positively correlated mechanical performance parameters are selected from the mechanical performance parameters, and a performance-specific model corresponding to all positively correlated mechanical performance parameters of the hollow slab beam is established. Then, the mechanical performance degradation model of the hollow slab beam is established by combining the error model in the mathematical simulation model of the hollow slab beam.

[0022] Optionally, the mechanical performance parameters and performance specificity of the hollow slab beam obtained by calculation include:

[0023] After deconstructing the performance of the hollow slab beam according to the kinematic model in the mathematical simulation model of the hollow slab beam, the mechanical performance parameters of the hollow slab beam are obtained;

[0024] After decomposing the mixed error sources of the hollow slab beam according to the error model and the probabilistic error model in the mathematical simulation model of the hollow slab beam, all the positively correlated mechanical performance parameters of the hollow slab beam are obtained;

[0025] The influence of all positively correlated mechanical performance parameters of hollow slab beams on the hollow slab beams is calculated to obtain the performance specificity of the hollow slab beams.

[0026] Optionally, the mathematical simulation model of the hollow slab beam includes a kinematic model, an error model and a probabilistic error model of the hollow slab beam.

[0027] Optionally, the step of comprehensively constructing a prediction model between vibration data and mechanical property degradation according to the influence of different degradation states of mechanical property parameters on the vibration data of the hollow slab beam comprises:

[0028] According to the influence of different degradation states of mechanical performance parameters on the vibration data of hollow slab beams, a first relationship model between vibration data and mechanical performance parameters is established, and the monitored vibration data is correlated with the mechanical performance degradation model. The vertical bending frequency data is extracted from the vibration data, and a second relationship model is constructed based on the extracted vertical bending frequency and mechanical performance parameters, and the extracted vertical bending frequency is correlated with the mechanical performance degradation model. A prediction model between vibration data and mechanical performance degradation is comprehensively constructed.

[0029] Optionally, before acquiring the degraded data set and the normal data set, the step further includes:

[0030] Transform the vibration power spectrum density into an amplitude spectrum;

[0031] Filling the amplitude spectrum with zeros according to a sampling frequency range;

[0032] Performing inverse Fourier transform on the amplitude spectrum after zero padding and the uniformly distributed phase generated according to the congruential method to determine multiple single-frame pseudo-random signals of the vibration power spectrum density;

[0033] Processing the plurality of single-frame pseudo-random signals by quadratic B-spline weighting and three-layer superposition merging method to determine a true random vibration signal of a preset duration;

[0034] Degraded data sets and normal data sets are constructed from true random vibration signals of preset duration by intercepting multiple frames, and degraded waveforms and normal waveforms are extracted.

[0035] Optionally, establishing a first relationship model between vibration data and mechanical performance parameters includes:

[0036] Establish a multi-objective comprehensive optimization model for vibration data and mechanical performance parameters;

[0037] Establish a vibration data and mechanical performance parameter database, record the vibration frequency as the first correlation function, record multiple mechanical performance parameters as multiple characteristic constraints, and obtain the vibration data and mechanical performance parameter database;

[0038] Analyze and compare according to the database, analyze its operating rules, re-weight the parameters to adjust the optimization function, fit the optimization function calibrated by the database to obtain the fitting data, compare the fitting data with the real-time data, so that the monitored real-time vibration data, mechanical performance parameters and fitting data are close to the set value.

[0039] Optionally, the comprehensive construction of a prediction model between vibration data and mechanical performance degradation includes comparing the prediction results of the first relationship model with the prediction results of the second relationship model, converting the prediction results of the second relationship model and the prediction results of the first relationship model into vector form, calculating the Euclidean distance between the two, iterating the calculation process until the Euclidean distance between the two is less than a preset threshold, and outputting the prediction results of the first relationship model.

[0040] Optional, hollow slab beam mechanical performance degradation prediction system based on vibration frequency, including:

[0041] Acquisition module: used to deploy sensors on hollow slab beams to collect vibration data in real time and pre-process the collected data to obtain standard data;

[0042] Mechanical performance degradation model construction module: used to extract mechanical performance parameters from standard data and establish a mechanical performance degradation model for hollow slab beams based on the mechanical performance parameter extraction results;

[0043] Prediction model building module: used to comprehensively build a prediction model between vibration data and mechanical performance degradation according to the influence of different degradation states of mechanical performance parameters on the vibration data of hollow slab beams;

[0044] Prediction module: used to monitor the vibration data of hollow slab beams in real time, output the degradation of mechanical properties of hollow slab beams in combination with the prediction model, and issue early warning.

[0045] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a method and system for predicting the mechanical performance degradation of hollow slab beams based on vibration frequency, which has the following beneficial effects:

[0046] The present invention proposes a method for predicting the degradation of mechanical properties of hollow slab beams based on vibration frequency, including: deploying sensors on hollow slab beams to collect their vibration data in real time, preprocessing the collected data to obtain standard data; extracting mechanical property parameters from the standard data, and establishing a mechanical property degradation model of the hollow slab beam according to the mechanical property parameter extraction results; comprehensively constructing a prediction model between vibration data and mechanical property degradation according to the influence of different degradation states of mechanical property parameters on the vibration data of hollow slab beams; real-time monitoring of the vibration data of hollow slab beams, combining the prediction model to output the degradation of mechanical properties of hollow slab beams, and issuing an early warning. The present invention aims at the fact that as the structural damage develops, the structural stiffness will change, and the vibration frequency can characterize the change of structural characteristics. Establishing the relationship between the vibration frequency and the mechanical properties of cracked RC hollow slab beams can provide a basis for predicting the degradation of mechanical properties of cracked RC hollow slab beams based on vibration frequency; as the service age increases, the cracking has a more significant impact on the vertical bending response frequency of the RC beam. Therefore, it is necessary to study the influence of the cracking state on the vertical bending vibration frequency response characteristics of the RC hollow slab beam after service degradation. The relationship between vibration frequency and mechanical properties of cracked RC hollow slab beams is established, and the degradation of mechanical properties of hollow slab beams is predicted. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0048] Figure 1 A schematic flow chart of a method for predicting mechanical property degradation of hollow slab beams based on vibration frequency provided by the present invention.

[0049] Figure 2 This is a structural framework diagram of the hollow slab beam mechanical performance degradation prediction system based on vibration frequency provided by the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] The embodiment of the present invention discloses a method for predicting the mechanical performance degradation of a hollow slab beam based on vibration frequency, such as Figure 1 As shown, including:

[0052] Sensors are deployed on hollow slab beams to collect vibration data in real time, and the collected data are pre-processed to obtain standard data;

[0053] Extract mechanical property parameters from standard data, and establish a mechanical property degradation model of hollow slab beams based on the mechanical property parameter extraction results;

[0054] According to the influence of different degradation states of mechanical performance parameters on the vibration data of hollow slab beams, a prediction model between vibration data and mechanical performance degradation is comprehensively constructed;

[0055] Real-time monitoring of the vibration data of hollow slab beams is carried out, and the degradation of the mechanical properties of hollow slab beams is output in combination with the prediction model, and early warning is issued.

[0056] Furthermore, the deploying of sensors on the hollow slab beam for collecting vibration data thereof in real time includes: deploying accelerometers and vibration sensors on the hollow slab beam to collect vibration data thereof in real time, wherein the vibration data includes vibration frequency.

[0057] Furthermore, the preprocessing of the collected data to obtain standard data includes:

[0058] Obtaining a degraded data set and a normal data set, and extracting a degraded waveform and a normal waveform;

[0059] Collect vibration data on hollow slab beams in real time and pre-process the collected vibration data;

[0060] Apply discrete Fourier transform to the preprocessed signal to analyze the spectral characteristics of the vibration data;

[0061] Extract positively correlated waveform features according to the degradation type;

[0062] Preset a first similarity threshold and a second similarity threshold, perform similarity calculation based on deep learning, and compare the extracted waveform with the degraded waveform and the normal waveform for waveform similarity;

[0063] When the similarity between the waveform extracted twice in succession and the normal waveform is lower than the first similarity threshold, and the similarity between the waveform extracted twice in succession and the degraded waveform is higher than the second similarity threshold, it is considered to meet the corresponding degradation type.

[0064] Specifically, the waveform similarity comparison includes: performing correlation calculations on the real-time waveform with the normal waveform and the degraded waveform respectively, and the formula is:

[0065] Rxy(Φ)=x(t)y(t+Φ)dt; wherein x(t) is the waveform function of the normal data set and the degraded data set, y(t) is the real-time waveform function calculated by real-time sampling, Φ is the time offset, and Rxy(Φ) is the cross-correlation function; the collected vibration data is preprocessed, including filtering, noise removal, normalization and baseline drift removal.

[0066] Specifically, the obtaining of the degraded data set and the normal data set includes:

[0067] Collect data, sample the data systematically, select discrete vibration signals with fixed periods and time intervals; perform discrete Fourier transform on discrete vibration signals, the formula is:

[0068] Where: G(r) represents the transformed spectrum signal, r represents the frequency component, u is the time domain point index, and i is the imaginary unit;

[0069] Perform spectrum analysis on the discrete Fourier transform results, including amplitude and phase, and save the spectrum analysis results as normal data sets and degraded data sets respectively.

[0070] Furthermore, the establishment of the mechanical property degradation model of the hollow slab beam includes:

[0071] Establishing a mathematical simulation model of the hollow slab beam, and calculating and obtaining the mechanical performance parameters and performance specificity of the hollow slab beam according to the mathematical simulation model of the hollow slab beam, wherein the mechanical performance parameters include fatigue damage, crack propagation, and material degradation;

[0072] Positively correlated mechanical performance parameters are selected from the mechanical performance parameters, and a performance-specific model corresponding to all positively correlated mechanical performance parameters of the hollow slab beam is established. Then, the mechanical performance degradation model of the hollow slab beam is established by combining the error model in the mathematical simulation model of the hollow slab beam.

[0073] Furthermore, the mechanical performance parameters and performance specificity of the hollow slab beam obtained by calculation include:

[0074] After deconstructing the performance of the hollow slab beam according to the kinematic model in the mathematical simulation model of the hollow slab beam, the mechanical performance parameters of the hollow slab beam are obtained;

[0075] After decomposing the mixed error sources of the hollow slab beam according to the error model and the probabilistic error model in the mathematical simulation model of the hollow slab beam, all the positively correlated mechanical performance parameters of the hollow slab beam are obtained;

[0076] The influence of all positively correlated mechanical performance parameters of hollow slab beams on the hollow slab beams is calculated to obtain the performance specificity of the hollow slab beams.

[0077] Furthermore, a performance-specific model corresponding to all positively correlated mechanical performance parameters of the hollow slab beam is established, and then a mechanical performance degradation model of the hollow slab beam is established by combining the error model in the mathematical simulation model of the hollow slab beam;

[0078] The formula for the performance-specific model for each positively correlated mechanical property parameter is:

[0079] D g =u(t,λ i ), where u(t,λ i ) is the performance-specific function of each positively correlated mechanical property parameter with respect to time t and the number of acquisitions i experienced, t is the time of operation of the hollow slab beam, λ i is the performance-specific factor after i acquisitions.

[0080] Furthermore, the Weibull distribution parameter estimation method is used to obtain a performance-specific model of positively correlated mechanical property parameters: the historical performance-specific model of positively correlated mechanical property parameters is analyzed, and the actual performance specificity is fitted by the Weibull distribution parameter estimation method to obtain the probability error distribution, thereby constructing a performance-specific model.

[0081] The formula of the mechanical property degradation model of hollow slab beam is:

[0082] D h =J error ·y(D g1 ,D g1 ,...,D gn ), where y(D g1 ,D g1 ,...,D gn ) is a function of the performance-specific model Epartdeg for each positively correlated mechanical property parameter.

[0083] Furthermore, the prediction model between vibration data and mechanical property degradation is comprehensively constructed according to the influence of different degradation states of mechanical property parameters on the vibration data of hollow slab beams, including:

[0084] According to the influence of different degradation states of mechanical performance parameters on the vibration data of hollow slab beams, a first relationship model between vibration data and mechanical performance parameters is established, and the monitored vibration data is correlated with the mechanical performance degradation model. The vertical bending frequency data is extracted from the vibration data, and a second relationship model is constructed based on the extracted vertical bending frequency and mechanical performance parameters, and the extracted vertical bending frequency is correlated with the mechanical performance degradation model. A prediction model between vibration data and mechanical performance degradation is comprehensively constructed.

[0085] Furthermore, before obtaining the degraded data set and the normal data set, the step further includes:

[0086] Transform the vibration power spectrum density into an amplitude spectrum;

[0087] Filling the amplitude spectrum with zeros according to a sampling frequency range;

[0088] Performing inverse Fourier transform on the amplitude spectrum after zero padding and the uniformly distributed phase generated according to the congruential method to determine multiple single-frame pseudo-random signals of the vibration power spectrum density;

[0089] Processing the plurality of single-frame pseudo-random signals by quadratic B-spline weighting and three-layer superposition merging method to determine a true random vibration signal of a preset duration;

[0090] Degraded data sets and normal data sets are constructed from true random vibration signals of preset duration by intercepting multiple frames, and degraded waveforms and normal waveforms are extracted.

[0091] Furthermore, the establishing of the first relationship model between the vibration data and the mechanical performance parameters includes:

[0092] Establish a multi-objective comprehensive optimization model for vibration data and mechanical performance parameters;

[0093] Establish a vibration data and mechanical performance parameter database, record the vibration frequency as the first correlation function, record multiple mechanical performance parameters as multiple characteristic constraints, and obtain the vibration data and mechanical performance parameter database;

[0094] Analyze and compare according to the database, analyze its operating rules, re-weight the parameters to adjust the optimization function, fit the optimization function calibrated by the database to obtain fitting data, compare the fitting data with the real-time data, make the monitored real-time vibration data, mechanical performance parameters and fitting data close to the set value, and obtain a multi-objective comprehensive optimization model of vibration data and mechanical performance parameters.

[0095] Furthermore, a second relationship model between vertical bending frequency and mechanical performance parameters is established, including:

[0096] Establish a multi-objective comprehensive optimization model of vertical bending frequency and mechanical performance parameters;

[0097] A vertical bending frequency and mechanical performance parameter database is established, the vibration frequency is recorded as the second correlation function, and multiple mechanical performance parameters are recorded as multiple characteristic constraints to obtain a vertical bending frequency and mechanical performance parameter database;

[0098] According to the database, analysis and comparison are carried out to analyze its operating rules, the parameters are re-weighted to adjust the optimization function, the optimization function calibrated by the database is fitted to obtain fitting data, and the fitting data is compared with the real-time data. The monitored real-time vertical bending frequency, mechanical performance parameters and fitting data are close to the set value, and a multi-objective comprehensive optimization model of vertical bending frequency and mechanical performance parameters is obtained.

[0099] Furthermore, the comprehensive construction of the prediction model between vibration data and mechanical performance degradation includes comparing the prediction results of the first relationship model with the prediction results of the second relationship model, converting the prediction results of the second relationship model and the prediction results of the first relationship model into vector form, calculating the Euclidean distance between the two, iterating the calculation process until the Euclidean distance between the two is less than a preset threshold, and outputting the prediction results of the first relationship model.

[0100] In a specific implementation, the method further includes: evaluating the correctness of the prediction result through a third relationship model, including:

[0101] Collecting a horizontal vibration acceleration signal, the obtained vibration acceleration signal includes a vibration acceleration value and a time corresponding thereto, wherein it is assumed that the vibration acceleration value constitutes a signal time domain amplitude sequence, and the corresponding time constitutes a signal time sequence;

[0102] The impact signal is a signal with a short action time and a very large instantaneous physical value. Therefore, in the monitored vibration acceleration signal, the maximum value in the signal must be a transient impact response, so the maximum value in the vibration acceleration signal is selected as the starting point; the data sequence of the transient impact is taken out from the signal time domain amplitude sequence and the signal time sequence, that is, the maximum value point of the signal time domain amplitude sequence is found, and the maximum value point is used as the starting point, and the N data points thereafter are taken out to form the signal time domain amplitude sequence of the transient impact, and the signal time sequence corresponding to the same position is taken out and the starting point is used as the time zero point, and N data points are obtained to form the signal time sequence of the transient impact;

[0103] Based on the free decay vibration dynamics model, nonlinear data fitting is performed on transient impact data to obtain the optimal impact response parameters; based on the free decay vibration dynamics model of a single degree of freedom system with damping, the maximum likelihood method is used to perform nonlinear data fitting to obtain the optimal impact response parameters;

[0104] The prediction results are split into amplitude and damping coefficient, and the degradation of mechanical performance parameters is evaluated based on the size of the optimal impact response parameters. The evaluation thresholds are set according to historical data, including setting an amplitude threshold and a coefficient threshold. The amplitude threshold is set, and when it exceeds the threshold, it indicates performance degradation; the coefficient threshold is set, and when the relative damping coefficient exceeds the threshold, it indicates performance degradation.

[0105] In a specific implementation, a hollow slab beam mechanical performance degradation prediction system based on vibration frequency, such as Figure 2 As shown, including:

[0106] Acquisition module: used to deploy sensors on hollow slab beams to collect vibration data in real time and pre-process the collected data to obtain standard data;

[0107] Mechanical performance degradation model construction module: used to extract mechanical performance parameters from standard data and establish a mechanical performance degradation model for hollow slab beams based on the mechanical performance parameter extraction results;

[0108] Prediction model building module: used to comprehensively build a prediction model between vibration data and mechanical performance degradation according to the influence of different degradation states of mechanical performance parameters on the vibration data of hollow slab beams;

[0109] Prediction module: used to monitor the vibration data of hollow slab beams in real time, output the degradation of mechanical properties of hollow slab beams in combination with the prediction model, and issue early warnings to detect potential problems in time and take corresponding maintenance and remedial measures.

[0110] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0111] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting mechanical performance degradation of hollow slab beams based on vibration frequency, characterized in that: include: Sensors are deployed on hollow slab beams to collect vibration data in real time, and the collected data are preprocessed to obtain standard data; Extract mechanical property parameters from standard data, and establish a mechanical property degradation model of hollow slab beams based on the mechanical property parameter extraction results; According to the influence of different degradation states of mechanical performance parameters on the vibration data of hollow slab beams, a prediction model between vibration data and mechanical performance degradation is comprehensively constructed; Real-time monitoring of the vibration data of hollow slab beams is carried out, and the degradation of the mechanical properties of hollow slab beams is output in combination with the prediction model, and early warning is issued.

2. The method for predicting mechanical property degradation of hollow slab beams based on vibration frequency according to claim 1 is characterized in that: The deploying of sensors on the hollow slab beam for collecting vibration data thereof in real time comprises: deploying accelerometers and vibration sensors on the hollow slab beam for collecting vibration data thereof in real time, wherein the vibration data comprises vibration frequency.

3. The method for predicting mechanical property degradation of hollow slab beams based on vibration frequency according to claim 1 is characterized in that: The preprocessing of the collected data to obtain standard data comprises: Obtaining a degraded data set and a normal data set, and extracting a degraded waveform and a normal waveform; Collect vibration data on hollow slab beams in real time and pre-process the collected vibration data; Apply discrete Fourier transform to the preprocessed signal to analyze the spectral characteristics of the vibration data; Extract positively correlated waveform features according to the degradation type; Preset a first similarity threshold and a second similarity threshold, perform similarity calculation based on deep learning, and compare the extracted waveform with the degraded waveform and the normal waveform for waveform similarity; When the similarity between the waveform extracted twice in succession and the normal waveform is lower than the first similarity threshold, and the similarity between the waveform extracted twice in succession and the degraded waveform is higher than the second similarity threshold, it is considered to meet the corresponding degradation type.

4. The method for predicting mechanical property degradation of hollow slab beams based on vibration frequency according to claim 1 is characterized in that: The method for establishing the mechanical property degradation model of the hollow slab beam includes: Establishing a mathematical simulation model of the hollow slab beam, and calculating and obtaining the mechanical performance parameters and performance specificity of the hollow slab beam according to the mathematical simulation model of the hollow slab beam, wherein the mechanical performance parameters include fatigue damage, crack propagation, and material degradation; Positively correlated mechanical performance parameters are selected from the mechanical performance parameters, and a performance-specific model corresponding to all positively correlated mechanical performance parameters of the hollow slab beam is established. Then, the mechanical performance degradation model of the hollow slab beam is established by combining the error model in the mathematical simulation model of the hollow slab beam.

5. The method for predicting mechanical property degradation of hollow slab beams based on vibration frequency according to claim 4 is characterized in that: The mechanical performance parameters and performance specificity of the hollow slab beam obtained by calculation include: After deconstructing the performance of the hollow slab beam according to the kinematic model in the mathematical simulation model of the hollow slab beam, the mechanical performance parameters of the hollow slab beam are obtained; After decomposing the mixed error sources of the hollow slab beam according to the error model and the probabilistic error model in the mathematical simulation model of the hollow slab beam, all the positively correlated mechanical performance parameters of the hollow slab beam are obtained; The influence of all positively correlated mechanical performance parameters of hollow slab beams on the hollow slab beams is calculated to obtain the performance specificity of the hollow slab beams.

6. The method for predicting mechanical property degradation of hollow slab beams based on vibration frequency according to claim 1 is characterized in that: According to the influence of different degradation states of mechanical property parameters on the vibration data of hollow slab beams, a prediction model between vibration data and mechanical property degradation is comprehensively constructed, including: According to the influence of different degradation states of mechanical performance parameters on the vibration data of hollow slab beams, a first relationship model between vibration data and mechanical performance parameters is established, and the monitored vibration data is correlated with the mechanical performance degradation model. The vertical bending frequency data is extracted from the vibration data, and a second relationship model is constructed based on the extracted vertical bending frequency and mechanical performance parameters, and the extracted vertical bending frequency is correlated with the mechanical performance degradation model. A prediction model between vibration data and mechanical performance degradation is comprehensively constructed.

7. The method for predicting mechanical property degradation of hollow slab beams based on vibration frequency according to claim 3 is characterized in that: Before obtaining the degraded data set and the normal data set, the method further includes: Transform the vibration power spectrum density into an amplitude spectrum; Filling the amplitude spectrum with zeros according to a sampling frequency range; Performing inverse Fourier transform on the amplitude spectrum after zero padding and the uniformly distributed phase generated according to the congruential method to determine multiple single-frame pseudo-random signals of the vibration power spectrum density; Processing the plurality of single-frame pseudo-random signals by quadratic B-spline weighting and three-layer superposition merging method to determine a true random vibration signal of a preset duration; Degraded data sets and normal data sets are constructed from true random vibration signals of preset duration by intercepting multiple frames, and degraded waveforms and normal waveforms are extracted.

8. The method for predicting mechanical property degradation of hollow slab beams based on vibration frequency according to claim 6 is characterized in that: The first relationship model between vibration data and mechanical performance parameters is established including: Establish a multi-objective comprehensive optimization model for vibration data and mechanical performance parameters; Establish a vibration data and mechanical performance parameter database, record the vibration frequency as the first correlation function, record multiple mechanical performance parameters as multiple characteristic constraints, and obtain the vibration data and mechanical performance parameter database; Analyze and compare according to the database, analyze its operating rules, re-weight the parameters to adjust the optimization function, fit the optimization function calibrated by the database to obtain the fitting data, compare the fitting data with the real-time data, so that the monitored real-time vibration data, mechanical performance parameters and fitting data are close to the set value.

9. The method for predicting mechanical property degradation of hollow slab beams based on vibration frequency according to claim 6 is characterized in that: The comprehensive construction of the prediction model between vibration data and mechanical performance degradation includes comparing the prediction results of the first relationship model with the prediction results of the second relationship model, converting the prediction results of the second relationship model and the prediction results of the first relationship model into vector form, calculating the Euclidean distance between the two, iterating the calculation process until the Euclidean distance between the two is less than a preset threshold, and outputting the prediction results of the first relationship model.

10. A hollow slab beam mechanical performance degradation prediction system based on vibration frequency, characterized in that: include: Acquisition module: used to deploy sensors on hollow slab beams to collect vibration data in real time and pre-process the collected data to obtain standard data; Mechanical performance degradation model construction module: used to extract mechanical performance parameters from standard data and establish a mechanical performance degradation model for hollow slab beams based on the mechanical performance parameter extraction results; Prediction model building module: used to comprehensively build a prediction model between vibration data and mechanical performance degradation according to the influence of different degradation states of mechanical performance parameters on the vibration data of hollow slab beams; Prediction module: used to monitor the vibration data of hollow slab beams in real time, output the degradation of mechanical properties of hollow slab beams in combination with the prediction model, and issue early warning.

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