CN112100846B - Online intelligent early warning method for deformation and damage of steel frame steel column - Google Patents

Online intelligent early warning method for deformation and damage of steel frame steel column Download PDF

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CN112100846B
CN112100846B CN202010959995.XA CN202010959995A CN112100846B CN 112100846 B CN112100846 B CN 112100846B CN 202010959995 A CN202010959995 A CN 202010959995A CN 112100846 B CN112100846 B CN 112100846B
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付长凤
韩连福
李晓丽
卢召红
刘超
姜继玉
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JIANGSU JIANYAN CONSTRUCTION ENGINEERING QUALITY SAFETY IDENTIFICATION CO.,LTD.
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Abstract

The invention belongs to the technical field of civil engineering measurement, and particularly relates to an online intelligent early warning method for deformation damage of a steel frame steel column. 1. The method comprises the following steps that a resistance strain gauge group is arranged on a steel column, and a resistance strain gauge is adopted to measure short-time deformation quantity of each steel column along with time change in the axial direction and the radial direction; 2. predicting long-time deformation of the steel column along with time change in the axial direction and the radial direction by adopting a grey system theory, and taking the long-time deformation as a critical damage threshold of the steel-structure strut; 3. arranging a resistance strain gauge on the installed steel column to measure the actual axial strain sigma hf of the steel column along with the change of timetAnd actual radial strain σ rftCorrecting strain measurement abnormal values caused by factors such as vibration and the like by adopting a grey system theory; 4. and establishing an online early warning model for deformation and damage of the steel frame structure steel column, and performing online early warning judgment on the deformation and damage of the steel frame structure steel column. The long-time deformation variable of the steel column changing along with time in the axial direction and the radial direction is used as a critical damage threshold value of the steel structure strut, dynamic measurement of the steel column is achieved, and early warning accuracy is improved.

Description

Online intelligent early warning method for deformation and damage of steel frame steel column
The technical field is as follows:
the invention belongs to the technical field of civil engineering measurement, and particularly relates to an online intelligent early warning method for deformation damage of a steel frame steel column.
Background art:
the steel framework is widely applied to modern buildings due to simple and rapid construction and strong bearing capacity, and generally comprises steel beams, steel columns, steel trusses and other members. The steel column is a core component of the steel framework, plays a role in supporting the whole steel beam, the steel truss and other accessory products, and the mechanical strength of the steel column directly determines the stability and the safety of the steel framework building. The mechanical strength of the installed steel column is determined by the axial deformation and the radial deformation, once the deformation of a certain direction exceeds the deformation limit, the whole structure of the steel frame building is damaged, and the inestimable result is caused, so that early warning needs to be carried out on the deformation damage of the steel frame building to avoid unnecessary disasters.
At present, the following three methods are mainly used in the aspect of early warning treatment of deformation and damage of steel columns of steel frame structures: (1) the steel column is directly installed after checking the design strength of the steel column, and deformation damage detection is not carried out; (2) performing static deformation detection, namely detecting the mechanical strength of the steel column before the steel column is installed; (3) and detecting the deformation of the steel column according to the fixed quantity so as to judge whether deformation damage is possible. The designed strength and the actual bearing strength of the steel column may have a certain difference, so the mode (1) is unreliable; the steel column may cause mechanical strength change due to collision and the like during installation, so the mode (2) is unreliable; the amount of deformation of the steel column is not a fixed parameter and therefore the mode (3) is also unreliable. The deformation of the steel structure support is not completed instantly, but a releasing process of the energy of the load bearing weight is provided, so that the critical damage threshold of the steel structure support is not reached at a certain moment, and the critical damage threshold cannot be reached without representing the change along with time.
The invention content is as follows:
the invention aims to overcome the defects that the existing structural steel column deformation damage early warning method is unreliable and the like, and provides an online intelligent early warning method for deformation damage of a steel structural steel column.
The technical scheme adopted by the invention is as follows: an online intelligent early warning method for deformation damage of a steel frame structure steel column comprises the following steps:
the method comprises the following steps: randomly extracting N steel column sample pieces which are not installed, loading the force borne by the design from the upper part of a steel column, arranging a resistance strain gauge group on the steel column, and measuring the short-time deformation quantity of each steel column along with the change of time in the axial direction and the radial direction by adopting the resistance strain gauges;
the method for measuring the short-time deformation of each steel column along with the change of time in the axial direction and the radial direction by adopting the resistance strain gauge is characterized by comprising the following steps:
welding the lower end of the steel column to a steel plate, and uniformly distributing 8 resistance strain gauges on the same circumference at a position 1 m away from the bottom of the steel column, wherein 4 resistance strain gauges are adhered along the axial direction, the other 4 resistance strain gauges are adhered along the radial direction, and the axial adhesion and the radial adhesion are alternately carried out;
applying designed bearing force F on a steel column supporting point, discretizing time, and measuring the axial strain sigma h of the steel column at intervals in time ntAnd radial strain σ rtAnd t is the deformation time of the steel column, and timing is started from the moment when the steel column applies the bearing force F. Axial strain σ htAnd radial strain σ rtThe expression of (a) is as follows:
Figure GDA0003021201070000021
in the formula, σ htfThe strain value of the f-th resistance strain gauge is shown, and f is the serial number of axial strain;
σrtgthe strain value of the g-th resistance strain gauge is shown, and g is the serial number of radial strain;
step two: according to the short-time deformation quantity of the steel column changing along with time in the axial direction and the radial direction, predicting the long-time deformation quantity of the steel column changing along with time in the axial direction and the radial direction by adopting a gray system theory, and taking the long-time deformation quantity as a critical damage threshold value of the steel structure strut;
the method for predicting the long-time deformation of the steel column along with the time change in the axial direction and the radial direction by adopting the gray system theory comprises the following steps:
because the data forms of the short-time deformation quantity of the steel column changing along with time in the axial deformation and the radial deformation are various and unfixed, and the data forms required by the GM (1,1) model, the DGM (1,1) model and the NDGM (1,1) model are different, namely the predicted values obtained by the GM (1,1) model, the DGM (1,1) model and the NDGM (1,1) model are different for the same data, so that the single GM (1,1) model, the DGM (1,1) model and the NDGM (1,1) model can not adapt to the data form of the short-time deformation quantity, the GM (1,1) model, the DGM (1,1) model and the NDGM (1,1) model are fused together by adopting a method of prediction weight coefficients, and the following long-time deformation quantity models changing along with time in the axial direction and the radial direction are established:
Figure GDA0003021201070000031
in the formula (I), the compound is shown in the specification,
Figure GDA0003021201070000032
respectively the long-time deformation quantity of the steel column changing along with time in the axial direction and the radial direction, namely the critical damage threshold value of the steel framework strut;
Figure GDA0003021201070000033
respectively axial strain σ htLong-term prediction value of GM (1,1) model, long-term prediction value of DGM (1,1) model, and long-term prediction value of NDGM (1,1) model, lambdah、γhAre respectively as
Figure GDA0003021201070000034
And
Figure GDA0003021201070000035
the prediction weight coefficient of (a);
Figure GDA0003021201070000036
respectively radial strain σ rtLong-term prediction value of GM (1,1) model, long-term prediction value of DGM (1,1) model, and long-term prediction value of NDGM (1,1) model, lambdar、γrAre respectively as
Figure GDA0003021201070000037
And
Figure GDA0003021201070000038
the prediction weight coefficient of (a);
Figure GDA0003021201070000039
and
Figure GDA00030212010700000310
the expression of (a) is as follows:
Figure GDA0003021201070000041
in the formula of alphah、αrRespectively axial strain GM (1,1) and radial strain GM (1,1) gray expansion coefficients, μh、μrThe ash action amount of the axial strain GM (1,1) and the ash action amount of the radial strain GM (1, 1); alpha is alphah、αrAnd muh、μrThe calculation method of (2) is as follows:
Figure GDA0003021201070000042
wherein, BhAnd BrRespectively an axial strain GM (1,1) gray matrix and a radial strain GM (1,1) gray matrix, BhAnd BrAre respectively:
Figure GDA0003021201070000043
in the formula, zh (1)(t)、zr (1)(t) axial strain GM (1,1) background sequence and radial strain GM (1,1) background sequence, Y, respectivelyhAnd YrRespectively an axial strain GM (1,1) original matrix and a radial strain GM (1,1) original matrix; z is a radical ofh (1)(t)、zr (1)(t) the expression is as follows:
Figure GDA0003021201070000051
Yh、Yrthe expression of (a) is as follows:
Figure GDA0003021201070000052
Figure GDA0003021201070000053
and
Figure GDA0003021201070000054
the expression of (a) is as follows:
Figure GDA0003021201070000055
in the formula, betah1、βh2、βh3、βh4Respectively, a multiple coefficient of an axial strain NDGM (1,1) model, a linear coefficient of the axial strain NDGM (1,1) model, an intercept of the axial strain NDGM (1,1) model and an initial correction coefficient of the axial strain NDGM (1,1) model; beta is ar1、βr2、βr3、βr4The coefficient of multiple of the model of the radial strain NDGM (1,1), the linear coefficient of the model of the radial strain NDGM (1,1), the intercept of the model of the radial strain NDGM (1,1) and the initial correction coefficient of the model of the radial strain NDGM (1,1) are respectively;
βh1、βh2、βh3、βh4the solving method is as follows:
(βh1，βh2，βh3)T＝(ATA)-1ATM
wherein A is an axial strain NDGM (1,1) gray matrix, M is an axial strain NDGM (1,1) model background sequence, and the expression is as follows:
Figure GDA0003021201070000061
βh4can be predicted by solving the axial strain
Figure GDA0003021201070000062
And the actual value error least squares sum, expressed as follows:
Figure GDA0003021201070000063
in the formula, j is an axial temporary statistical coefficient;
βr1、βr2、βr3、βr4the solving method comprises the following steps:
(βr1，βr2，βr3)T＝(ETE)-1ETN
wherein E is a radial strain NDGM (1,1) model gray matrix, N is a radial strain NDGM (1,1) model background matrix, and the expression is as follows:
Figure GDA0003021201070000071
βr4can be predicted by solving the radial strain
Figure GDA0003021201070000072
And the actual value error least squares sum, expressed as follows:
Figure GDA0003021201070000073
in the formula, 1 is a radial temporary statistical coefficient;
Figure GDA0003021201070000074
and
Figure GDA0003021201070000075
the expression of (a) is as follows:
Figure GDA0003021201070000076
in the formula, thetah1、θh2Respectively axial strain
Figure GDA0003021201070000077
A first coefficient and a second coefficient of the prediction value; thetar1、θr2Respectively radial strain
Figure GDA0003021201070000078
A first coefficient and a second coefficient of the prediction value;
θh1and thetah2The calculation method of (2) is as follows:
(θh1，θh2)T＝(Qh TQh)-1QhPh
in the formula, QhIs axial strain DGM (1,1) model gray matrix, PhThe axial strain DGM (1,1) model background matrix is expressed as follows:
Figure GDA0003021201070000081
θr1and thetar2The calculation method of (2) is as follows:
(θr1，θr2)T＝(Qr TQr)-1QrPr
in the formula, QrIs a radial strain DGM (1,1) model gray matrix, PrThe radial strain DGM (1,1) model background matrix is expressed as follows:
Figure GDA0003021201070000082
establishing a weight λh、γh、λr、γrThe unconstrained optimization solution model is as follows:
Figure GDA0003021201070000091
obtaining a modeling weight lambda by derivationh、γh、λr、γrA value of (d);
step three: arranging a resistance strain gauge on the installed steel column to measure the actual axial strain sigma hf of the steel column along with the change of timetAnd actual radial strain σ rftAnd correcting abnormal strain measurement values caused by vibration and other factors by adopting a gray system theory, wherein the corrected axial strain and radial strain are respectively sigma xhftAnd σ xrft；
The method for measuring the strain of the steel column along with time by arranging the resistance strain gauge on each installed steel column and eliminating and correcting the strain measurement abnormal value caused by vibration and other factors by adopting the gray system theory comprises the following steps:
the actual axial strain and the radial strain of the steel frame structure steel column after installation are respectively sigma hftAnd σ rftBecause various interferences exist in the actual use process of the steel frame steel column, the actual axial strain and the actual radial strain of the steel frame steel column are respectively sigma hftAnd σ fhrThe method must contain a measurement abnormal value, and the existence of the measurement abnormal value can cause false alarm, so the measurement abnormal value needs to be corrected; the method for eliminating and correcting the strain measurement abnormal value caused by vibration and other factors by adopting the gray system theory has the following characteristics:
if the c-th actual axial strain amount σ hfcSatisfy the requirement of
Figure GDA0003021201070000092
Then σ hf is consideredcTo measure abnormal values, in the formula
Figure GDA0003021201070000093
Is σ hfcAnd predicting the sigma hf by the GM (1,1) model of (1)cBy using
Figure GDA0003021201070000094
Correcting;
if the w-th actual radial strain amount σ rfwSatisfy the requirement of
Figure GDA0003021201070000101
Then σ rf is consideredwMeasuring an abnormal value, wherein
Figure GDA0003021201070000102
Is sigma rfwAnd predicting the sigma rf by the GM (1,1) model of (1)wBy using
Figure GDA0003021201070000103
Correcting; in the formula
Figure GDA0003021201070000106
Is sigma rftPredicted value of GM (1,1) model of (1)
Step four: establishing an online early warning model for deformation and damage of the steel frame structure steel column, and performing online early warning judgment on the deformation and damage of the steel frame structure steel column;
the method for establishing the online early warning model of the deformation and damage of the steel column of the steel structure and performing the online early warning and judgment on the deformation and damage of the steel column of the steel structure is characterized by comprising the following steps:
the axial or radial deformation amount of the steel framework steel column deformation exceeds a threshold value, and early warning is needed, namely, the steel framework steel column deformation damages the online early warning model:
Figure GDA0003021201070000104
Figure GDA0003021201070000105
further, in order to overcome the defects that the existing steel frame steel column deformation damage early warning method is unreliable and the like, a resistance strain gauge group is adopted to measure the short-time deformation quantity of the steel column along with the time change in the axial direction and the radial direction, the long-time deformation quantity of the steel column along with the time change in the axial direction and the radial direction is predicted by adopting a gray system theory, and the long-time deformation quantity is used as a critical damage threshold value of the steel frame strut; the abnormal value of strain measurement caused by factors such as vibration is eliminated and corrected by adopting a grey system theory, so that the influence of the factors such as vibration on the measurement result is reduced; and establishing an online early warning model for deformation and damage of the steel frame structure steel column, and performing online early warning judgment on the deformation and damage of the steel frame structure steel column. According to the method, the long-time deformation quantity of the steel column changing along with time in the axial direction and the radial direction is used as the critical damage threshold of the steel structure strut, dynamic measurement of the steel column is achieved, and early warning accuracy is improved.
The invention has the beneficial effects that: the critical damage threshold value of the deformation of the steel column after installation is predicted by adopting a grey system theory, the defect that the early warning value is taken as a fixed value in the existing online intelligent early warning method for the deformation damage of the steel column of the steel framework is overcome, and the online dynamic early warning of the deformation of the steel column of the steel framework is realized, so that the safety of the steel framework is ensured, and unnecessary financial and material resource losses are avoided.
Description of the drawings:
FIG. 1 is a schematic diagram of the distribution of resistance strain gage groups on steel columns of a steel frame according to one embodiment;
FIG. 2 shows the axial critical damage threshold and the radial critical damage threshold of the deformation of the steel column after installation according to the first embodiment;
FIG. 3 is axial strain data and radial strain data of an installed steel column containing a measurement anomaly in the first embodiment;
FIG. 4 is axial strain data and radial strain data of the installed steel column after correction in the first embodiment;
FIG. 5 is a graph of the warning rate of deformation and damage of a steel column in the prior art, wherein "1" represents that the warning is correct, and "0" represents that the warning is wrong;
in the first embodiment of fig. 6, the warning rate of the damage of the steel column is shown, wherein "1" represents correct warning, and "0" represents wrong warning.
The specific implementation mode is as follows:
example one
Referring to the figures, the online intelligent early warning method for deformation damage of the steel frame structure steel column comprises the following steps:
the method comprises the following steps: randomly extracting N steel column sample pieces which are not installed, loading the force borne by the design from the upper part of a steel column, arranging a resistance strain gauge group on the steel column, and measuring the short-time deformation quantity of each steel column along with the change of time in the axial direction and the radial direction by adopting the resistance strain gauges;
the method for measuring the short-time deformation of each steel column along with the change of time in the axial direction and the radial direction by adopting the resistance strain gauge is characterized by comprising the following steps:
welding the lower end of the steel column to a steel plate, and uniformly distributing 8 resistance strain gauges on the same circumference at a position 1 m away from the bottom of the steel column, wherein 4 resistance strain gauges are adhered along the axial direction, the other 4 resistance strain gauges are adhered along the radial direction, and the axial adhesion and the radial adhesion are alternately carried out;
applying designed bearing force F on a steel column supporting point, and measuring the axial strain sigma h of the steel column at intervals in discrete time ntAnd radial strain σ rtWherein t is the deformation time of the steel column, and the moment F of applying the bearing force on the steel column is measured to obtain the axial strain sigma htAnd radial strain σ rtThe expression of (a) is as follows:
Figure GDA0003021201070000121
in the formula, σ htfThe strain value of the f-th resistance strain gauge is shown, and f is the serial number of axial strain;
σrtgthe strain value of the g-th resistance strain gauge is shown, and g is the serial number of radial strain;
step two: according to the short-time deformation quantity of the steel column changing along with time in the axial direction and the radial direction, predicting the long-time deformation quantity of the steel column changing along with time in the axial direction and the radial direction by adopting a gray system theory, and taking the long-time deformation quantity as a critical damage threshold value of the steel structure strut;
the method for forming the critical damage threshold of the steel structure strut is characterized by comprising the following steps:
fusing a GM (1,1) model, a DGM (1,1) model and an NDGM (1,1) model together by adopting a method of predicting weight coefficients, and establishing a long-time deformation model with axial and radial time changes as follows:
Figure GDA0003021201070000122
in the formula (I), the compound is shown in the specification,
Figure GDA0003021201070000123
respectively the long-time deformation quantity of the steel column changing along with time in the axial direction and the radial direction, namely the critical damage threshold value of the steel framework strut;
Figure GDA0003021201070000124
respectively axial strain σ htLong-term prediction value of GM (1,1) model, long-term prediction value of DGM (1,1) model, and long-term prediction value of NDGM (1,1) model, lambdah、γhAre respectively as
Figure GDA0003021201070000125
And
Figure GDA0003021201070000126
the prediction weight coefficient of (a);
Figure GDA0003021201070000127
respectively radial strain σ rtLong-term prediction value of GM (1,1) model, long-term prediction value of DGM (1,1) model, and long-term prediction value of NDGM (1,1) model, lambdar、γrAre respectively as
Figure GDA0003021201070000131
And
Figure GDA0003021201070000132
the prediction weight coefficient of (a);
Figure GDA0003021201070000133
and
Figure GDA0003021201070000134
the expression of (a) is as follows:
Figure GDA0003021201070000135
in the formula of alphah、αrRespectively axial strain GM (1,1) and radial strain GM (1,1) gray expansion coefficients, μh、μrThe ash action amount of the axial strain GM (1,1) and the ash action amount of the radial strain GM (1, 1); alpha is alphah、αrAnd muh、μrThe calculation method of (2) is as follows:
Figure GDA0003021201070000136
wherein, BhAnd BrRespectively an axial strain GM (1,1) gray matrix and a radial strain GM (1,1) gray matrix, BhAnd BrAre respectively:
Figure GDA0003021201070000137
in the formula, zh (1)(t)、zr (1)(t) axial strain GM (1,1) background sequence and radial strain GM (1,1) background sequence, Y, respectivelyhAnd YrRespectively an axial strain GM (1,1) original matrix and a radial strain GM (1,1) original matrix; z is a radical ofh (1)(t)、zr (1)(t) the expression is as follows:
Figure GDA0003021201070000141
Yh、Yrthe expression of (a) is as follows:
Figure GDA0003021201070000142
Figure GDA0003021201070000143
and
Figure GDA0003021201070000144
the expression of (a) is as follows:
Figure GDA0003021201070000145
in the formula, betah1、βh2、βh3、βh4Respectively, a multiple coefficient of an axial strain NDGM (1,1) model, a linear coefficient of the axial strain NDGM (1,1) model, an intercept of the axial strain NDGM (1,1) model and an initial correction coefficient of the axial strain NDGM (1,1) model; beta is ar1、βr2、βr3、βr4The coefficient of multiple of the model of the radial strain NDGM (1,1), the linear coefficient of the model of the radial strain NDGM (1,1), the intercept of the model of the radial strain NDGM (1,1) and the initial correction coefficient of the model of the radial strain NDGM (1,1) are respectively;
βh1、βh2、βh3、βh4the solving method is as follows:
(βh1，βh2，βh3)T＝(ATA)-1ATM
wherein A is an axial strain NDGM (1,1) gray matrix, M is an axial strain NDGM (1,1) model background sequence, and the expression is as follows:
Figure GDA0003021201070000151
βh4can be predicted by solving the axial strain
Figure GDA0003021201070000152
And the actual value error least squares sum, expressed as follows:
Figure GDA0003021201070000153
in the formula, j is an axial temporary statistical coefficient;
βr1、βr2、βr3、βr4the solving method comprises the following steps:
(βr1，βr2，βr3)T＝(ETE)-1ETN
wherein E is a radial strain NDGM (1,1) model gray matrix, N is a radial strain NDGM (1,1) model background matrix, and the expression is as follows:
Figure GDA0003021201070000161
βr4can be predicted by solving the radial strain
Figure GDA0003021201070000162
And the actual value error least squares sum, expressed as follows:
Figure GDA0003021201070000163
in the formula, 1 is a radial temporary statistical coefficient;
Figure GDA0003021201070000164
and
Figure GDA0003021201070000165
the expression of (a) is as follows:
Figure GDA0003021201070000166
in the formula, thetah1、θh2Respectively axial strain
Figure GDA0003021201070000167
A first coefficient and a second coefficient of the prediction value; thetar1、θr2Respectively radial strain
Figure GDA0003021201070000168
A first coefficient and a second coefficient of the prediction value;
θh1and thetah2The calculation method of (2) is as follows:
(θh1，θh2)T＝(Qh TQh)-1QhPh
in the formula, QhIs axial strain DGM (1,1) model gray matrix, PhThe axial strain DGM (1,1) model background matrix is expressed as follows:
Figure GDA0003021201070000171
θr1and thetar2The calculation method of (2) is as follows:
(θr1，θr2)T＝(Qr TQr)-1QrPr
in the formula, QrIs a radial strain DGM (1,1) model gray matrix, PrThe radial strain DGM (1,1) model background matrix is expressed as follows:
Figure GDA0003021201070000172
establishing a weight λh、γh、λr、γrThe unconstrained optimization solution model is as follows:
Figure GDA0003021201070000181
obtaining a modeling weight lambda by derivationh、γh、λr、γrA value of (d);
step three: arranging a resistance strain gauge on the installed steel column to measure the actual axial strain sigma hf of the steel column along with the change of timetAnd actual radial strain σ rftAnd correcting abnormal strain measurement values caused by vibration and other factors by adopting a gray system theory, wherein the corrected axial strain and radial strain are respectively sigma xhftAnd σ xrft；
The method for eliminating and correcting the strain measurement abnormal value caused by vibration and other factors by adopting the gray system theory has the following characteristics:
if the c-th actual axial strain amount σ hfcSatisfy the requirement of
Figure GDA0003021201070000182
Then σ hf is consideredcTo measure abnormal values, in the formula
Figure GDA0003021201070000183
Is σ hfcAnd predicting the sigma hf by the GM (1,1) model of (1)cBy using
Figure GDA0003021201070000184
Correcting; in the formula
Figure GDA0003021201070000185
Is σ hftThe predicted value of the GM (1,1) model of (1);
if the w-th actual radial strain amount σ rfwSatisfy the requirement of
Figure GDA0003021201070000186
Then σ rf is consideredwTo measure abnormal values, in the formula
Figure GDA0003021201070000187
Is sigma rfwAnd predicting the sigma rf by the GM (1,1) model of (1)wBy using
Figure GDA0003021201070000188
Correcting; in the formula
Figure GDA0003021201070000189
Is sigma hrtThe predicted value of the GM (1,1) model of (1);
step four: establishing an online early warning model for deformation and damage of the steel frame structure steel column, and performing online early warning judgment on the deformation and damage of the steel frame structure steel column;
the method for establishing the online early warning model of the deformation and damage of the steel column of the steel structure and performing the online early warning and judgment on the deformation and damage of the steel column of the steel structure is characterized by comprising the following steps:
Figure GDA0003021201070000191
Figure GDA0003021201070000192
as shown in FIG. 1, a resistance strain gauge group is arranged on a steel column of a steel frame structure, and the resistance strain gauge group is composed of 8 resistance strain gauges, 4 resistance strain gauges are distributed along the radial direction of the steel column, 4 resistance strain gauges are distributed along the axial direction of the steel column, and the resistance strain gauges are uniformly distributed at intervals along the radial direction and the axial direction.
And (3) loading a weight of 20 tons from the upper part of the steel column, recording and calculating the axial strain sigma h of the steel columntAnd radial strain σ rtAnd predicting the long-time deformation of the steel column along the axial direction and the radial direction along with the time
Figure GDA0003021201070000193
Namely, the axial critical damage threshold and the radial critical damage threshold of the deformation of the steel column after installation as shown in fig. 2.
Arranging a resistance strain gauge on the installed steel column to measure the actual axial strain sigma hf of the steel column along with the change of timetAnd radial strain σ rftThe measured data is shown in FIG. 3, the abnormal value of strain measurement caused by vibration and other factors is eliminated and corrected by adopting the gray system theory, and the corrected axial strain and radial strain are respectively sigma xhftAnd σ xrftThe corrected data are shown in fig. 4.
Continuously applying force, carrying out experimental verification by adopting the method and the existing method, and carrying out 15 times of experiments in total, wherein the existing method alarms 7 times correctly, the method alarms 15 times correctly, and 100% alarm is achieved, as shown in fig. 5 and 6, so that the alarm accuracy of the method is far higher than that of the existing method. This application has realized that high accuracy is reported to the police, has improved the warning precision, has avoided because the manpower financial resources loss that the steelframe constructed.

Claims (1)

1. The utility model provides an online intelligent early warning method of deformation damage of steel frame structure steel column which characterized in that: the online intelligent early warning method comprises the following steps:
the method comprises the following steps: randomly extracting N steel column sample pieces which are not installed, loading the force borne by the design from the upper part of a steel column, arranging a resistance strain gauge group on the steel column, and measuring the short-time deformation quantity of each steel column along with the change of time in the axial direction and the radial direction by adopting the resistance strain gauges;
the method for measuring the short-time deformation of each steel column along with the change of time in the axial direction and the radial direction by adopting the resistance strain gauge is characterized by comprising the following steps:
welding the lower end of the steel column to a steel plate, and uniformly distributing 8 resistance strain gauges on the same circumference at a position 1 m away from the bottom of the steel column, wherein 4 resistance strain gauges are adhered along the axial direction, the other 4 resistance strain gauges are adhered along the radial direction, and the axial adhesion and the radial adhesion are alternately carried out;
applying designed bearing force F on a steel column supporting point, and measuring the axial strain sigma h of the steel column at intervals in discrete time ntAnd radial strain σ rtWherein t is the deformation time of the steel column, and the moment F of applying the bearing force on the steel column is measured to obtain the axial strain sigma htAnd radial strain σ rtThe expression of (a) is as follows:
Figure FDA0003021201060000011
in the formula, σ htfThe strain value of the f-th resistance strain gauge is shown, and f is the serial number of axial strain;
σrtgis the strain value of the g-th resistance strain gage, and g is the sequence of radial strainNumber;
step two: according to the short-time deformation quantity of the steel column changing along with time in the axial direction and the radial direction, predicting the long-time deformation quantity of the steel column changing along with time in the axial direction and the radial direction by adopting a gray system theory, and taking the long-time deformation quantity as a critical damage threshold value of the steel structure strut;
the method for forming the critical damage threshold of the steel structure strut is characterized by comprising the following steps:
fusing a GM (1,1) model, a DGM (1,1) model and an NDGM (1,1) model together by adopting a method of predicting weight coefficients, and establishing a long-time deformation model with axial and radial time changes as follows:
Figure FDA0003021201060000021
in the formula (I), the compound is shown in the specification,
Figure FDA0003021201060000022
respectively the long-time deformation quantity of the steel column changing along with time in the axial direction and the radial direction, namely the critical damage threshold value of the steel framework strut;
Figure FDA0003021201060000023
respectively axial strain σ htLong-term prediction value of GM (1,1) model, long-term prediction value of DGM (1,1) model, and long-term prediction value of NDGM (1,1) model, lambdah、γhAre respectively as
Figure FDA0003021201060000024
And
Figure FDA0003021201060000025
the prediction weight coefficient of (a);
Figure FDA0003021201060000026
respectively radial strain σ rtLong-term prediction value of GM (1,1) model, long-term prediction value of DGM (1,1) model, and long-term prediction value of NDGM (1,1) model, lambdar、γrAre respectively as
Figure FDA0003021201060000027
And
Figure FDA0003021201060000028
the prediction weight coefficient of (a);
Figure FDA0003021201060000029
and
Figure FDA00030212010600000210
the expression of (a) is as follows:
Figure FDA00030212010600000211
in the formula of alphah、αrRespectively axial strain GM (1,1) and radial strain GM (1,1) gray expansion coefficients, μh、μrThe ash action amount of the axial strain GM (1,1) and the ash action amount of the radial strain GM (1, 1); alpha is alphah、αrAnd muh、μrThe calculation method of (2) is as follows:
Figure FDA0003021201060000031
wherein, BhAnd BrRespectively an axial strain GM (1,1) gray matrix and a radial strain GM (1,1) gray matrix, BhAnd BrAre respectively:
Figure FDA0003021201060000032
in the formula, zh (1)(t)、zr (1)(t) axial strain GM (1,1) background sequence and radial strain GM (1,1) background sequence, Y, respectivelyhAnd YrRespectively axial strain GM (1,1) atomA starting matrix and a radial strain GM (1,1) original matrix; z is a radical ofh (1)(t)、zr (1)(t) the expression is as follows:
Figure FDA0003021201060000033
Yh、Yrthe expression of (a) is as follows:
Figure FDA0003021201060000041
Figure FDA0003021201060000042
and
Figure FDA0003021201060000043
the expression of (a) is as follows:
Figure FDA0003021201060000044
in the formula, betah1、βh2、βh3、βh4Respectively, a multiple coefficient of an axial strain NDGM (1,1) model, a linear coefficient of the axial strain NDGM (1,1) model, an intercept of the axial strain NDGM (1,1) model and an initial correction coefficient of the axial strain NDGM (1,1) model; beta is ar1、βr2、βr3、βr4The coefficient of multiple of the model of the radial strain NDGM (1,1), the linear coefficient of the model of the radial strain NDGM (1,1), the intercept of the model of the radial strain NDGM (1,1) and the initial correction coefficient of the model of the radial strain NDGM (1,1) are respectively;
βh1、βh2、βh3、βh4the solving method is as follows:
(βh1，βh2，βh3)T＝(ATA)-1ATM
wherein A is an axial strain NDGM (1,1) gray matrix, M is an axial strain NDGM (1,1) model background sequence, and the expression is as follows:
Figure FDA0003021201060000051
βh4can be predicted by solving the axial strain
Figure FDA0003021201060000052
And the actual value error least squares sum, expressed as follows:
Figure FDA0003021201060000053
in the formula, j is an axial temporary statistical coefficient;
βr1、βr2、βr3、βr4the solving method comprises the following steps:
(βr1，βr2，βr3)T＝(ETE)-1ETN
wherein E is a radial strain NDGM (1,1) model gray matrix, N is a radial strain NDGM (1,1) model background matrix, and the expression is as follows:
Figure FDA0003021201060000061
βr4can be predicted by solving the radial strain
Figure FDA0003021201060000062
And the actual value error least squares sum, expressed as follows:
Figure FDA0003021201060000063
in the formula, l is a radial temporary statistical coefficient;
Figure FDA0003021201060000064
and
Figure FDA0003021201060000065
the expression of (a) is as follows:
Figure FDA0003021201060000066
in the formula, thetah1、θh2Respectively axial strain
Figure FDA0003021201060000067
A first coefficient and a second coefficient of the prediction value;
θr1、θr2respectively radial strain
Figure FDA0003021201060000068
A first coefficient and a second coefficient of the prediction value;
θh1and thetah2The calculation method of (2) is as follows:
(θh1，θh2)T＝(Qh TQh)-1QhPh
in the formula, QhIs axial strain DGM (1,1) model gray matrix, PhThe axial strain DGM (1,1) model background matrix is expressed as follows:
Figure FDA0003021201060000071
θr1and thetar2The calculation method of (2) is as follows:
(θr1，θr2)T＝(Qr TQr)-1QrPr
in the formula, QrIs a radial strain DGM (1,1) model gray matrix, PrThe radial strain DGM (1,1) model background matrix is expressed as follows:
Figure FDA0003021201060000072
establishing a weight λh、γh、λr、γrThe unconstrained optimization solution model is as follows:
Figure FDA0003021201060000081
obtaining a modeling weight lambda by derivationh、γh、λr、γrA value of (d);
step three: arranging a resistance strain gauge on the installed steel column to measure the actual axial strain sigma hf of the steel column along with the change of timetAnd actual radial strain σ rftAnd correcting abnormal strain measurement values caused by vibration factors by adopting a gray system theory, wherein the corrected axial strain and radial strain are respectively sigma xhftAnd σ xrft；
The method for eliminating and correcting the strain measurement abnormal value caused by vibration and other factors by adopting the gray system theory has the following characteristics:
if the c-th actual axial strain amount σ hfcSatisfy the requirement of
Figure FDA0003021201060000082
Then σ hf is consideredcTo measure abnormal values, in the formula
Figure FDA0003021201060000083
Is σ hfcAnd predicting the sigma hf by the GM (1,1) model of (1)cBy using
Figure FDA0003021201060000084
Correcting; in the formula
Figure FDA0003021201060000085
Is σ hftThe predicted value of the GM (1,1) model of (1);
if the w-th actual radial strain amount σ rfwSatisfy the requirement of
Figure FDA0003021201060000086
Then σ rf is consideredwMeasuring an abnormal value, wherein
Figure FDA0003021201060000087
Is sigma rfwAnd predicting the sigma rf by the GM (1,1) model of (1)wBy using
Figure FDA0003021201060000088
Correcting; in the formula
Figure FDA0003021201060000089
Is sigma rftThe predicted value of the GM (1,1) model of (1);
step four: establishing an online early warning model for deformation and damage of the steel frame structure steel column, and performing online early warning judgment on the deformation and damage of the steel frame structure steel column;
the method for establishing the online early warning model of the deformation and damage of the steel column of the steel structure and performing the online early warning and judgment on the deformation and damage of the steel column of the steel structure is characterized by comprising the following steps:
Figure FDA0003021201060000091
Figure FDA0003021201060000092
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