Acoustic emission monitoring and early warning method for collapse and damage of bridge construction support

Abstract

The invention discloses an acoustic emission monitoring and early warning method for collapse and damage of a bridge construction support, and relates to the field of bridge construction support damage monitoring.The method comprises the steps that firstly, buckling instability acoustic emission signals of a support steel pipe test piece under the axial loading condition are collected, and the change trends of different acoustic emission parameters in the whole buckling instability process are analyzed; the characteristic change trend range of parameters such as acoustic emission energy and amplitude is quantitatively determined, and three early warning models based on energy, accumulated energy and accumulated amplitude are provided based on MATLAB; the early warning model provided by the invention can realize early warning of buckling instability failure of the bridge construction support vertical rod in advance, can be effectively applied to engineering application of collapse failure of the construction support, can be applied to actual engineering to reduce actual engineering economic and personnel loss caused by support instability collapse, and has very important engineering application significance.

Description

Technical Field

[0001] The invention relates to the field of bridge construction support damage monitoring, and in particular to an acoustic emission monitoring and early warning method for support collapse damage caused by buckling damage of a bridge construction support pole. Background Art

[0002] The full-house construction scaffold is a temporary facility often used in engineering construction, which is responsible for bearing the upper load during construction. In recent years, reports on major engineering accidents caused by the collapse of the construction scaffold support system are common. Some accidents are caused by the collapse of the scaffold support system, some are caused by the sudden collapse of the formwork scaffold during the concrete pouring process of the building under construction, and some are caused by the collapse of the scaffold when pouring concrete during the construction process; among these construction accidents, improper design, construction errors, chaotic construction management, and unqualified material selection are the main causes of scaffold collapse. By examining the damage form of scaffold collapse, the present invention finds that the upright pole of the construction scaffold is the main rod for scaffold damage, and the buckling instability damage caused by the upright pole when bearing the upper axial load is the main cause of the collapse of the construction scaffold. Therefore, it is necessary to conduct special research on the buckling instability of the upright pole of the construction scaffold.

[0003] At present, the commonly used means for engineering monitoring include mechanical parameter means such as displacement sensors and axial force gauges, as well as non-destructive monitoring means such as acoustic emission technology, ultrasonic monitoring and infrared thermal imaging technology. The application of displacement sensors and axial force gauges is often too idealistic to reflect the deformation of the actual structure. Ultrasonic monitoring has limitations in monitoring structural defects, and infrared thermal imaging technology is difficult to apply to large structures such as bridge supports. Acoustic emission technology is a non-destructive monitoring technology widely used in the field of civil engineering structural health monitoring. It has high sensitivity in monitoring structural damage, and the non-destructive monitoring process is not greatly affected by external structures. It has the advantages of passive monitoring, real-time monitoring and remote monitoring. The parameters extracted from the acoustic emission signal, such as ring count, energy, amplitude, etc., can specifically describe the damage state of the material and realize long-term real-time monitoring of the structure. According to the collected signal characteristics, scholars can often use acoustic emission technology to conduct good research on engineering structure disaster prevention and early warning. Combined with its advantages of passive real-time monitoring, its long-term monitoring of engineering structures has great cost advantages.

[0004] In view of the above, the present invention provides a method for monitoring and early warning of acoustic emission of collapse and damage of bridge construction supports to solve the above problems. Summary of the invention

[0005] Aiming at the monitoring and early warning problem of the collapse of construction support, the present invention proposes an acoustic emission monitoring and early warning method for the collapse and damage of bridge construction support, by conducting an acoustic emission monitoring test on the axial pressure of the steel pipe of the bridge construction support upright pole, obtaining the acoustic emission monitoring signal of the whole process of the axial pressure buckling instability damage of the steel pipe, selecting different acoustic emission parameters, analyzing the characteristic change trend before the instability damage occurs, studying its relationship with the buckling instability of the steel pipe, conducting a correlation study on the analyzed acoustic emission parameters, selecting several correlation parameters to quantify the range of its change trend, using MATLAB to develop an early warning model based on the quantitative research results of the acoustic emission parameters, and determining its early warning effectiveness by analyzing the early warning time of the early warning model. The present invention can be applied to actual projects to reduce the actual engineering economic and personnel losses caused by the collapse of the support instability, and has a very important engineering application significance.

[0006] A bridge construction support collapse damage acoustic emission monitoring and early warning method, characterized by comprising the following steps:

[0007] Step 1: Conduct an axial pressure acoustic emission monitoring test on the steel pipe of the bridge construction support upright to obtain the acoustic emission monitoring signal of the whole process of axial pressure buckling instability failure of the steel pipe;

[0008] Step 2: Analyze the acoustic emission signal, select different acoustic emission parameters, analyze its characteristic change trend before the instability failure occurs, and study its relationship with the buckling instability of the steel pipe;

[0009] Step 3: Conduct a correlation study on the analyzed acoustic emission parameters and select several correlation parameters to quantify their change trend range;

[0010] Step 4: Use MATLAB to develop three warning coefficients based on the quantitative research results of acoustic emission parameters, analyze the warning time, and determine its warning effectiveness;

[0011] Compared with the prior art, the present invention has the following beneficial effects:

[0012] (1) The acoustic emission monitoring and early warning method for support collapse caused by buckling failure of bridge construction support poles proposed in the present invention performs a scaled test by welding steel plates at both ends of a steel pipe, uses a hinged hydraulic jack support with an upper end hinged to simulate the boundary constraint of the upper end hinge of an actual support pole, and the close fit of the lower end steel plate to the ground simulates the boundary constraint of the lower end anchoring of the support pole in an actual project, which can well simulate the boundary constraint and stress condition of the support pole in an actual project;

[0013] (2) By analyzing the characteristics of the acoustic emission signal parameters in the steel pipe, the characteristic warning trends of parameters such as energy, ring count, amplitude, RMS and ring count in the buckling instability failure stage of the T-beam bridge vertical pole steel pipe under axial loading were clarified, and the acoustic emission warning effect of the above acoustic emission parameters on the buckling instability failure of the steel pipe was clarified;

[0014] (3) Through quantitative analysis of the weakly correlated acoustic emission parameter characteristic warning trends of the steel pipe buckling instability failure process, three early warning models were developed using MATLAB. These models can not only realize low-cost and effective monitoring of support collapse accidents caused by bridge support buckling instability, but also reduce a large amount of engineering economic losses and meet the actual needs of the project. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a flow chart of the present invention;

[0016] Figure 2 The force-stress-strain composite diagram of the steel pipe axial compression test conducted by the present invention;

[0017] Figure 3 This is a graph showing changes in the acoustic emission ringing count during the buckling instability process of the steel pipe specimen of the present invention;

[0018] Figure 4 This is a diagram showing the variation of the acoustic emission energy during the buckling instability process of the steel pipe specimen of the present invention;

[0019] Figure 5 This is a diagram showing the variation of the acoustic emission amplitude during the buckling instability process of the steel pipe specimen of the present invention;

[0020] Figure 6 This is a diagram showing the change of the acoustic emission RMS during the buckling instability process of the steel pipe specimen of the present invention;

[0021] Figure 7 It is a three-dimensional correlation diagram of acoustic emission parameters RMS, energy and ringing count;

[0022] Figure 8 It is the three-dimensional correlation diagram of acoustic emission parameters RMS, energy and amplitude;

[0023] Fig. 9 It is a quantitative early warning trend diagram of acoustic emission energy before instability and damage;

[0024] Fig.10 It is a schematic diagram of the arrangement position of the strain gauges in the cross section of the steel pipe from a top view;

[0025] Fig.11 This is a schematic diagram of the arrangement of strain gauges on the steel pipe from a front view angle. DETAILED DESCRIPTION

[0026] The following is a clear and complete description of the technical solution of the present invention in combination with the embodiments and the accompanying drawings. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art before making creative work are within the scope of protection of the present invention.

[0027] The purpose of the present invention is to provide an acoustic emission monitoring and early warning method for the collapse and damage of bridge construction supports. By analyzing the changing trend of the acoustic emission parameter characteristics of the axial compression buckling instability of the steel pipe, an early warning method is developed based on the acoustic emission characteristics to determine the collapse and damage of the bridge support.

[0028] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0029] Step 1: Conduct an axial pressure acoustic emission monitoring test on the steel pipe of the bridge construction support upright to obtain the acoustic emission monitoring signal of the whole process of axial pressure buckling instability failure of the steel pipe;

[0030] like Fig.10 , Fig.11 As shown, 8 strain gauges are arranged at a quarter section near the upper end of the specimen in a "T" shape and distributed at 90°. A hinged hydraulic jack is used at the upper end of the specimen, and the lower end is directly placed on the ground, simulating the upper end hinged and lower end anchored in actual engineering. High-strength rubber pads are used to support the upper and lower ends of the specimen, which can reduce friction and noise interference while obtaining test signals.

[0031] Step 2: Analyze the acoustic emission signal, select different acoustic emission parameters, analyze its characteristic change trend before the instability failure occurs, and study its relationship with the buckling instability of the steel pipe;

[0032] The acoustic emission energy, ringing count, RMS, amplitude, and their corresponding cumulative curves were extracted, and axial pressure tests on steel pipes were carried out to study the characteristic warning change trends of acoustic emission parameters before the steel pipe buckling instability failure, including the sudden increase and decrease of acoustic emission parameter values, and the abnormal change of the slope of the cumulative curve of acoustic emission parameters.

[0033] Step 3: Conduct a correlation study on the analyzed acoustic emission parameters and select weakly correlated parameters to quantify their change trend range;

[0034] Draw a three-dimensional graph of the acoustic emission parameters in step 2. To prevent repeated warnings with strong correlation, select weakly correlated acoustic emission parameters, such as energy, cumulative energy curve, amplitude and cumulative amplitude curve, and quantify the characteristic warning trend range of its acoustic emission parameters.

[0035] Step 4: Use MATLAB to develop three warning coefficients based on the quantitative research results of acoustic emission parameters, analyze the warning time, and determine its warning effectiveness;

[0036] According to the quantitative characteristic warning trend range of the weakly correlated acoustic emission parameters in step 3, three different warning models were developed using MATLAB code, which were substituted into the experimental group and the control group to determine the warning time of the warning model and verify its warning effectiveness.

[0037] Mechanical results of the axial pressure acoustic emission monitoring test of the support steel pipe are as follows Figure 2 As shown in the figure, in the unstable failure stage, when the strain and load reach the peak value, the axial load suddenly drops to about 70% of the peak load, and the strain data suddenly drops and increases, indicating that the specimen has undergone buckling instability deformation.

[0038] The characteristic warning trends of acoustic emission ring count, energy, amplitude and RMS signal in the axial pressure acoustic emission monitoring test of the support steel pipe are as follows: Figure 3 , Figure 4 , Figure 5 and Figure 6 As shown in the figure: the acoustic emission ring count and energy fall rapidly after the sudden increase to the peak value, the cumulative ring curve and the cumulative energy curve have a small-scale sudden increase and a short-term 0 growth, showing an inverted "L" type change trend, the slope of the cumulative curve increases suddenly, and the cumulative energy is larger than the cumulative ring count, which shows that the cumulative energy is more sensitive to the buckling instability of the specimen steel pipe than the cumulative ring count; before the specimen is damaged by buckling instability, the acoustic emission amplitude rises to more than 80dB, the cumulative amplitude curve has a short horizontal section, and the slope of the curve drops sharply; RMS also increases rapidly with the increase of load, and reaches a peak value when the load reaches the peak value. Before the specimen is subjected to the ultimate load and buckling instability is damaged, the RMS value shows a rapid decrease trend.

[0039] like Figure 7 , Figure 8 As shown, the present invention studies the correlation of the acoustic emission parameters in step 2, and finds that there is a linear correlation between RMS, ring count and energy, but a weak linear relationship between amplitude and energy, RMS. In order to prevent the duplication and redundant warning of strong correlation coefficients, the present invention selects energy, cumulative energy and cumulative amplitude to establish an acoustic emission warning model for axial compression instability of bridge construction support.

[0040] like Fig. 9 , Fig.10As shown in the figure, the growth rate of the energy peak value in the unstable failure stage relative to the energy value before the peak and the growth rate of the energy peak value in the time period before the peak are calculated. It can be seen that before the specimen buckling instability, the energy peak signal increased by an average of 96.2% relative to the previous signal intensity, and the growth rate of the signal peak before the peak signal reached an average of 70.73%; considering that the acoustic emission signal before the maximum value of the acoustic emission parameter has a strong randomness, we prefer the latter for quantitative analysis, as shown in formula (1):

[0041]

[0042] Among them, A is the first warning model, a 1 is the peak value of the energy in the time period before the peak moment, a is the energy value before the peak moment, and its MATLAB code is shown in Table 1;

[0043] Considering the safety of early warning, we set the safety factor to increase the safety redundancy. In the Chinese standard specification "Safety Technical Specification for Clamped Steel Pipe Scaffolding in Building Construction" (JGJ130-2011), in the calculation of the relevant bearing capacity of the support, the value range of the safety factor is 0.8~0.9. Therefore, this paper selects 0.8 as the safety factor of the early warning threshold. For energy, the early warning values ​​are 0.738×0.8=0.5904=59.04%, and 60% is selected as the integer. That is, when the energy A>60%, the early warning system is triggered, and the construction support will be in a dangerous state at this time.

[0044] Table 1 MATLAB code of the first warning model

[0045]

[0046]

[0047] For the cumulative energy curve, when the specimen buckles and loses stability, the slope of the curve tends to infinity. Based on this, the second early warning model is proposed, as shown in formula (2):

[0048] B=P (2)

[0049] Where: B is the second warning model. When B>14, a warning signal is generated. P is the slope of the cumulative energy curve. The MATLAB code is shown in Table 2.

[0050] Since the slope of the cumulative energy curve at the moment of instability is very large, when calculating the slope of the cumulative energy curve, considering that there is a gap of several orders of magnitude between the cumulative energy and the horizontal axis time, all the cumulative energy values ​​will be multiplied by 10 -4When calculating the slope, Origin2024 software was used to calculate the maximum slope value of the main part of the first stage of the cumulative energy curve of the four specimens. See Table 3. For safety considerations, a slope of 17 was selected, and 17 was rounded off as the maximum slope. A safety factor of 0.8 was introduced. When P>13.6 and P>14 was rounded off, a warning signal was generated.

[0051] Table 2 MATLAB code of the second warning model

[0052]

[0053]

[0054] Table 3 Slope of the cumulative energy curve at the instability stage

[0055] Test piece Slope SJ1 31.9 SJ2 24.5 SJ3 17.2

[0056] For the acoustic emission amplitude, Figure 5 It can be seen that the most obvious feature of the specimen buckling failure is the cumulative acoustic emission amplitude greater than 2.8×10 5 dB signal and the slope of the cumulative amplitude curve appears a horizontal section. Therefore, the third warning model C is formula (3):

[0057] C=α·K (3)

[0058] Where: C is the third warning model. When C>200, the construction support will be in a dangerous state. α is the judgment coefficient. When the cumulative amplitude of acoustic emission is greater than 2.8×10 5 dB, α = 1, when the cumulative amplitude of acoustic emission is less than 2.8×10 5 dB, α = 0; K is the reciprocal of the slope k of the acoustic emission cumulative amplitude curve. -5 The cumulative amplitude slopes of the four groups of specimens when buckling instability is calculated under the condition shown in Table 4. The warning range of k is selected as (0, 0.005), and the corresponding K value needs to be greater than 200. The MATLAB code is shown in Table 5.

[0059] Table 4 Slope of the cumulative amplitude curve in the unstable stage

[0060] Test piece Slope 1 0.0045 2 0.0038 3 0.0037

[0061] Table 5 MATLAB code of the third warning model

[0062]

[0063]

[0064] The buckling failure time of the construction support calculated using the three warning parameters is called the warning time (YT), and the actual buckling failure time of the construction support obtained from the mechanical test is called the failure time (PT). The difference between the two time parameters is called the advance time (TT) and the ratio of YT to PT is calculated. The relevant time parameters of the three warning parameters are shown in Table 6.

[0065] It can be seen from Table 6 that the three early warning models can all give early warnings before the support buckles and becomes unstable. The average warning time of the three models is: 1.0s, 7.05s and 4.52s. The warning time of the control group is basically consistent with the warning time of the experimental group. The warning effects of the three early warning models are model 2>3>1 from large to small. The results fully show that the three early warning models proposed in this paper can realize the early warning of the buckling and instability accidents of the construction support and reduce the occurrence of safety accidents.

[0066] Table 6 Warning time of the three warning models in the experimental group and the control group

[0067]

[0068]

[0069] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A bridge construction support collapse damage acoustic emission monitoring and early warning method, characterized in that: The steps include: Step 1: Conduct an axial pressure acoustic emission monitoring test on the steel pipe of the bridge construction support upright to obtain the acoustic emission monitoring signal of the whole process of axial pressure buckling instability failure of the steel pipe; Step 2: Analyze the acoustic emission signal, select different acoustic emission parameters, analyze its characteristic change trend before the instability failure occurs, and study its relationship with the buckling instability of the steel pipe; Step 3: Conduct a correlation study on the analyzed acoustic emission parameters and select several correlation parameters to quantify their change trend range; Step 4: Use MATLAB to develop three warning coefficients based on the quantitative research results of acoustic emission parameters, analyze the warning time, and determine its warning effectiveness.

2. The method for monitoring and early warning of bridge construction support collapse and damage acoustic emission according to claim 1 is characterized in that: The monitoring test in step 1 includes arranging 8 strain gauges in a "T" shape and distributed at 90 degrees at a quarter section near the upper end of the specimen. A hinged hydraulic jack is used at the upper end of the specimen, and the lower end is directly placed on the ground to simulate the upper end hinged and lower end anchored in actual engineering. High-strength rubber pads are used to support the upper and lower ends of the specimen, which can reduce friction and noise interference while obtaining test signals.

3. The method for monitoring and early warning of bridge construction support collapse and damage acoustic emission according to claim 1 is characterized in that: The step 2 includes extracting acoustic emission energy, ring count, RMS, amplitude, and their corresponding cumulative curves, conducting axial pressure tests on steel pipes, and analyzing characteristic warning change trends of acoustic emission parameters before the steel pipe is buckled and unstable and fails, including sudden increases and decreases in acoustic emission parameter values ​​and abnormal changes in the slope of the cumulative curve of acoustic emission parameters.

4. The method for monitoring and early warning of bridge construction support collapse and damage acoustic emission according to claim 3 is characterized in that: The step 3 includes drawing a three-dimensional graph of the acoustic emission parameters associated with the step 2, selecting weakly correlated acoustic emission parameters such as energy, cumulative energy curve, amplitude and cumulative amplitude curve to quantify the characteristic warning trend range of the acoustic emission parameters in order to prevent repeated warnings with strong correlation.

5. The method for monitoring and early warning of bridge construction support collapse and damage acoustic emission according to claim 4 is characterized in that: The step 4 includes developing three different early warning models using MATLAB code according to the quantitative characteristic early warning trend range of the weakly correlated acoustic emission parameters in the step 3, substituting them into the experimental group and the control group to determine the early warning time of the early warning model and verify its early warning effectiveness.

6. The method for monitoring and early warning of bridge construction support collapse and damage acoustic emission according to claim 5, characterized in that: The three different early warning models include: The first early warning model is shown in formula (1): Among them, A is the first warning model, a1 is the peak value of energy in the time period before the peak moment, a is the energy value at the moment before the peak moment, and when the energy A>60%, the warning system is triggered, and the construction support will be in a dangerous state at this time; The second early warning model is shown in formula (2): B=P (2) Wherein, B is the second warning model. When B>14, a warning signal is generated. P is the slope of the cumulative energy curve. When P>13.6, the warning signal is generated when P>14 is rounded. The third early warning model C is formula (3): C=α·x (3) Where, C is the third warning model. When C>200, the construction support will be in a dangerous state. α is the judgment coefficient. When the cumulative amplitude of acoustic emission is greater than 2.8×10 5 dB, α = 1, when the cumulative amplitude of acoustic emission is less than 2.8×10 5 When α=0, K is the reciprocal of the slope k of the acoustic emission cumulative amplitude curve. The warning range of k is selected as (0, 0.005), and the corresponding K value needs to be greater than 200.

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