A reinforced concrete structure model construction method, device and medium

Abstract

The present application relates to a kind of reinforced concrete structure model construction method, device and medium, wherein the method comprises: according to the geometric parameter of bridge basic structure to establish first finite element model;According to the material constitutive relation of bridge structure obtained from reinforced concrete structure tensile test;Determine the first crack width calculation formula based on the section model construction of fiber beam column unit;Material constitutive relation and the first crack width calculation formula are embedded in first finite element model;Input structure load action, utilize first finite element model to obtain structure performance degradation parameter and structure crack parameter;The present application realizes the technology of more accurate reinforced concrete structure simulation based on fiber beam column unit, solves the technical problem that reinforced concrete structure is difficult to simulate steel crack.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of real-time simulation of bridge structure, and particularly relates to a reinforced concrete structure model construction method, device and medium. BACKGROUND

[0002] The number of bridges is increasing, and the traffic flow is also increasing. The operation and maintenance supervision of bridges is becoming increasingly important. How to ensure the safe operation of bridges is an area that needs to be researched and explored in the future. In fact, not only bridges, but also other transportation facilities, such as tunnels and slopes, are facing such problems.

[0003] The safety monitoring of bridge facilities during the operation period is an important field, which has developed to a certain extent in recent years. However, the current technology is still limited to the alarm of monitoring threshold and the trend analysis. In addition, the simulation model of the bridge during the operation period is only established in the monitoring scheme design stage, and can only simulate the single present situation.

[0004] How to realize real-time online simulation of bridge structure is an important research direction. The current technology mainly uses software for finite element simulation analysis, but this method still has many problems, such as:

[0005] 1) The simple finite element model based on elastic analysis is difficult to predict the whole process response of the structure after updating the model;

[0006] 2) The updating based on dynamic response is much more than the static response, and generally only the low-order modal is identified, but the low-order modal information mainly reflects the overall behavior of the structure, and is more sensitive to environmental and boundary conditions than to local damage; the high-order modal that can reflect local damage is difficult to be excited in the actual structure;

[0007] 3) There are many crack detection and identification technologies, but there is no method for applying crack damage information to model updating. SUMMARY

[0008] In view of the problems in the above background art, the present application provides a reinforced concrete structure model construction method, device and medium, and the specific technical scheme is as follows:

[0009] A reinforced concrete structure model construction method, comprising the following steps:

[0010] S1: establishing a first finite element model according to the geometric parameters of the basic structure of the bridge;

[0011] S2: obtaining the material constitutive relationship of the bridge structure itself according to the reinforced concrete structure tensile experiment, wherein the formula of the material constitutive relationship is:

[0012]

[0013] ε t = f t / E c

[0014] wherein σ c is the structural stress, ε is the structural strain, ε t is the crack strain of concrete, E c is the structural elastic modulus, f t is the compressive strength of concrete;

[0015] S3: based on the section model construction of the fiber beam column element, a first crack width calculation formula is determined, and the first crack width calculation formula is:

[0016]

[0017] wherein ω is the first crack width, L m is the average crack space length;

[0018] S4: the material constitutive relation and the first crack width calculation formula are embedded into the first finite element model;

[0019] S5: inputting a structural load action, the structural load action is an applied force, and using the first finite element model to obtain a structural performance degradation parameter and a structural crack parameter;

[0020] S6: according to the structure type of the bridge, a single load working condition is determined, and the single load working condition includes a concentrated force and a uniform load;

[0021] S7: according to the first finite element model, key material performance parameters of the fiber beam column model are selected, and the key material performance parameters include the elastic modulus E r of the steel bar in the reinforced concrete and the elastic model E c of the concrete;

[0022] S8: the single load working condition and the key material performance parameters are input into the first finite element model, and a structural response is obtained through simulation calculation of the first finite element model, and the structural response includes the displacement of the main beam, the stress of the main beam and the crack width of the crack;

[0023] S9: using mean square error as a loss function to train an artificial neural network, and obtaining an artificial neural network proxy model, and the formula of the loss function is:

[0024]

[0025] wherein PF is the loss function, MSE is the mean square error, T is the sample size of the training set, and ei the network error for the i-th data point in the training set, t i the true value in the training set, a i the predicted value generated by the artificial neural network agent model;

[0026] S10: obtaining the test output parameters of the structure to be evaluated, and calculating the updated structure performance parameters of the model through inverse normalization according to the artificial neural network agent model;

[0027] S11: inputting the updated structure performance parameters of the model into the first finite element model to obtain a second finite element model that can truly reflect the state of the structure;

[0028] S12: calculating a second crack width ω' according to the second finite element model, and judging the performance state of the bridge reinforced concrete according to the second crack width ω'.

[0029] In a preferred implementation, the material constitutive relation and the first crack width calculation formula are embedded in the first finite element model, comprising:

[0030] The material constitutive relation is embedded into the pre-processing program of the first finite element model as a subprogram of the first finite element model;

[0031] The first crack width calculation formula is embedded into the post-processing program of the first finite element model as a subprogram of the first finite element model.

[0032] In a preferred implementation, the S7 further comprises setting upper and lower limit values of the key material performance parameters, and generating a data set of the key material performance parameters by taking 1% of the upper and lower limit values as intervals.

[0033] In a preferred implementation, the artificial neural network is trained using mean square error as a loss function, specifically comprising:

[0034] The single load case and the structure response are respectively taken as input and output training data, and are brought into the loss function to adaptively and iteratively train the artificial neural network. When the loss value decreases to an allowable value, the training is automatically stopped, and an artificial neural network agent model is output.

[0035] In a preferred implementation, the test output parameters include static response data and dynamic response data, the static response data is the output parameter of static test, the dynamic response data is the output parameter of dynamic test, and the load action of the test output parameter is consistent with the single load case.

[0036] In a preferred implementation, the judging the performance state of the bridge reinforced concrete structure according to the second crack width ω' specifically comprises:

[0037] comparing the second crack width ω' with the structural crack limit ω limit

[0038]

[0039] wherein η ω is a performance state parameter of the bridge reinforced concrete structure, when η ω is less than 0.8, the performance state of the bridge reinforced concrete structure is a safe state, when η ω is between 0.8-0.1, the performance state of the bridge reinforced concrete structure is a dangerous approaching state, and when η ω is greater than 1, the performance state of the bridge reinforced concrete structure is a dangerous state.

[0040] A reinforced concrete structure model construction device, comprising:

[0041] at least one processor; and

[0042] a memory in communication connection with the at least one processor; wherein

[0043] the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to:

[0044] establish a first finite element model according to geometric parameters of a bridge basic structure;

[0045] obtain a material constitutive relation of the bridge structure according to a reinforced concrete structure tensile experiment, wherein a formula of the material constitutive relation is:

[0046]

[0047] ε t = f t / E c

[0048] wherein σ c is a structural stress, ε is a structural strain, ε t is a concrete crack strain, E c is a structural elastic modulus, and f t is a concrete compressive strength;

[0049] based on a section model construction of a fiber beam column element, a first crack width calculation formula is determined, and the first crack width calculation formula is:

[0050] ​

[0051] wherein ω is a first crack width, L m is an average crack space length;

[0052] The material constitutive relationship and the first crack width calculation formula are embedded into the first finite element model;

[0053] A structure load action is input, and a structure performance degradation parameter and a structure crack parameter are obtained by using the first finite element model, wherein the structure load action is an applied force;

[0054] According to the structure type of the bridge, a single load working condition is determined, and the single load working condition includes a concentrated force and a uniform load;

[0055] According to the first finite element model, key material performance parameters of a fiber beam column model are selected, and the key material performance parameters include an elastic modulus E r of steel in reinforced concrete, and an elastic modulus E c of concrete;

[0056] The single load working condition and the key material performance parameters are input into the first finite element model, and a structure response is obtained by simulation calculation of the first finite element model, wherein the structure response includes a displacement of a main beam, a stress of the main beam, and a crack width of a crack;

[0057] A mean square error is used as a loss function to train an artificial neural network, and an artificial neural network proxy model is obtained, wherein a formula of the loss function is:

[0058]

[0059] wherein PF is a loss function, MSE is a mean square error, T is a sample size of a training set, e i is a network error of an i-th data point in the training set, t i is a true value in the training set, α i is a predicted value automatically generated by the artificial neural network proxy model;

[0060] A test output parameter of a structure to be evaluated is obtained, and a model updated structure performance parameter is obtained by inverse normalization calculation according to the artificial neural network proxy model;

[0061] The model updated structure performance parameter is input into the first finite element model, and a second finite element model capable of truly reflecting a structure state is obtained;

[0062] A second crack width ω' is calculated according to the second finite element model, and a performance state of reinforced concrete of the bridge is determined according to the second crack width ω'.

[0063] A non-volatile computer storage medium, the non-volatile computer storage medium stores a computer program, the computer program is executed by a processor to realize the steel reinforced concrete structure model construction method.

[0064] The beneficial effects of the present application are:

[0065] (1) The present application realizes more accurate simulation of reinforced concrete structure based on fiber beam column unit, solves the technical problem of difficult simulation of steel crack in reinforced concrete structure;

[0066] (2) The present application realizes the technology of only needing to test the structure response to correct the model, solves the technical problem of traditional need to test load condition and structure response at the same time;

[0067] (3) The present application realizes the technology of evaluating the structure performance based on the crack state of the structure, solves the technical problem of traditional finite element difficult to evaluate the structure by simulating the crack state of the structure. BRIEF DESCRIPTION OF DRAWINGS

[0068] Figure 1 The flowchart of the construction method in the present application. DETAILED DESCRIPTION

[0069] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0070] In the description of the present application, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.

[0071] In addition, the terms "first", "second", "third", etc. are used herein only to describe different instances, and do not imply or suggest relative importance or a number of the technical features indicated. Therefore, the features defined as "first", "second", etc. can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly specified and limited.

[0072] In the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connecting", "fixing" and the like should be understood in a broad sense, for example, can be connected, or can be detachable, or integrated; can be mechanically connected, or electrically connected; can be directly connected, or indirectly connected through an intermediate medium, or the internal communication of two elements.

[0073] As shown in Figure 1 , the present application discloses a reinforced concrete structure model construction method, comprising the following steps:

[0074] S1: establishing a first finite element model according to the geometric parameters of the basic structure of the bridge;

[0075] S2: obtaining the material constitutive relation of the bridge structure itself according to the reinforced concrete structure tensile experiment, wherein the formula of the material constitutive relation is:

[0076]

[0077] ε t =f t / E c

[0078] Wherein, σ c is the structure stress, ε is the structure strain, ε t is the crack strain of concrete, E c is the structure elastic modulus, f t is the compressive strength of concrete;

[0079] S3: based on the section model construction of fiber beam column unit, the first crack width calculation formula is determined, and the first crack width calculation formula is:

[0080]

[0081] Wherein, ω is the first crack width, L m is the average crack space length;

[0082] S4: embedding the material constitutive relation and the first crack width calculation formula into the first finite element model;

[0083] Specifically, embedding the material constitutive relation and the first crack width calculation formula into the first finite element model includes:

[0084] The material constitutive relation is used as a subroutine of the first finite element model and embedded into the preprocessing program of the first finite element model.

[0085] The formula for calculating the first crack width is used as a subroutine of the first finite element model and embedded into the post-processing program of the first finite element model.

[0086] This invention embeds the material constitutive relation and the crack width calculation formula into the preprocessing module and postprocessing module respectively, thereby improving the functionality of the first finite element model and laying the computational foundation for subsequent calculations.

[0087] S5: Input the structural load, which is the applied force, and use the first finite element model to obtain the structural performance degradation parameters and structural crack parameters.

[0088] S6: Determine a single load case based on the structural type of the bridge. The single load case includes concentrated force and uniformly distributed load.

[0089] S7: Based on the first finite element model, select the key material performance parameters for the fiber beam-column model. These key material performance parameters include the elastic modulus E of the reinforcing steel in reinforced concrete. r Elastic model of concrete E c ;

[0090] Specifically, upper and lower limits are set for the key material performance parameters, and a dataset of the key material performance parameters is generated by taking 1% of the upper and lower limits as the interval.

[0091] S8: Input the single load condition and the key material performance parameters into the first finite element model, and obtain the structural response through the simulation calculation of the first finite element model. The structural response includes the displacement of the main beam, the stress of the main beam, and the crack width.

[0092] S9: The mean squared error is used as the loss function to train the artificial neural network, resulting in an artificial neural network surrogate model. The formula for the loss function is:

[0093]

[0094] Where PF is the loss function, MSE is the mean squared error, T is the sample size of the training set, and e i For the network error of the i-th data point in the training set, t i For the true values ​​in the training set, α i Predictions automatically generated for artificial neural network surrogate models.

[0095] Specifically, it includes:

[0096] The single load condition and the structural response are used as input and output training data, respectively. These are then fed into the loss function to perform adaptive iterative training on the artificial neural network. Training automatically stops when the loss value decreases to an acceptable value, which is set to 0.001. The artificial neural network surrogate model is then output.

[0097] S10: Obtain the test output parameters of the structure to be evaluated, and calculate the updated structural performance parameters of the model through inverse normalization based on the artificial neural network proxy model;

[0098] In a preferred implementation, the test output parameters include static response data and dynamic response data, wherein the static response data is the output parameter of the static test, the dynamic response data is the output parameter of the dynamic test, and the load effect of the test output parameters is consistent with the single load condition.

[0099] S11: Input the updated structural performance parameters of the model into the first finite element model to obtain a second finite element model that can truly reflect the structural state;

[0100] S12: Calculate the second crack width ω′ based on the second finite element model, and determine the performance status of the bridge reinforced concrete based on the second crack width ω′.

[0101] Specifically, the second crack width ω′ is compared with the structural crack limit ω. limit Comparison:

[0102]

[0103] Where, η ω For the structural state parameters of reinforced concrete bridges, when η ω When η is between 0.5 and 0.8, the structural performance of reinforced concrete bridges is in a safe state. ω When η is between 0.8 and 0.1, the structural state of the reinforced concrete of the bridge is in a near-critical state. ω When the value is greater than 1, the structural performance of the reinforced concrete of the bridge is in a dangerous state.

[0104] A reinforced concrete structure model building device, comprising:

[0105] At least one processor; and,

[0106] A memory communicatively connected to the at least one processor; wherein,

[0107] The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to:

[0108] The first finite element model is established based on the geometric parameters of the basic bridge structure.

[0109] The constitutive relation of the bridge structure obtained from the tensile test of reinforced concrete structure is as follows:

[0110]

[0111] ε t =f t / E c

[0112] Where, σ c Let ε be the structural stress and ε be the structural strain. t E represents the strain of concrete cracks. c f is the structural elastic modulus. t It refers to the compressive strength of concrete;

[0113] Based on the construction of the cross-sectional model using fiber beam-column elements, the formula for calculating the first crack width is determined. The formula for calculating the first crack width is as follows:

[0114]

[0115] Where ω is the width of the first crack, and L m This represents the average crack space length.

[0116] The material constitutive relation and the first crack width calculation formula are embedded into the first finite element model;

[0117] Input the structural load, and use the first finite element model to obtain the structural performance degradation parameters and structural crack parameters. The structural load is the applied force.

[0118] Based on the structural type of the bridge, a single load condition is determined, which includes concentrated force and uniformly distributed load.

[0119] Based on the first finite element model, key material performance parameters for the fiber beam-column model are selected, including the elastic modulus E of the steel reinforcement in reinforced concrete. r Elastic model of concrete E c ;

[0120] The single load condition and the key material performance parameters are input into the first finite element model, and the structural response is obtained through simulation calculation of the first finite element model. The structural response includes the displacement of the main beam, the stress of the main beam, and the crack width.

[0121] The mean squared error is used as the loss function to train the artificial neural network, resulting in an artificial neural network surrogate model. The formula for the loss function is:

[0122]

[0123] Where PF is the loss function, MSE is the mean squared error, T is the sample size of the training set, and e i For the network error of the i-th data point in the training set, t i For the true values ​​in the training set, α i Predictions automatically generated for artificial neural network surrogate models;

[0124] Obtain the test output parameters of the structure to be evaluated, and calculate the updated structural performance parameters of the model through inverse normalization based on the artificial neural network proxy model.

[0125] The updated structural performance parameters of the model are input into the first finite element model to obtain a second finite element model that can truly reflect the structural state.

[0126] The second crack width ω′ is calculated based on the second finite element model, and the performance status of the bridge reinforced concrete is determined based on the second crack width ω′.

[0127] A non-volatile computer storage medium storing a computer program, wherein the computer program, when executed by a processor, implements any of the methods for constructing reinforced concrete structure models.

[0128] This invention realizes a technology for more accurate simulation of reinforced concrete structures based on fiber beam-column elements, and solves the technical problem of difficulty in simulating steel bar cracks in reinforced concrete structures.

[0129] This invention realizes a technology that only requires testing the structural response to make model corrections, solving the technical problem of traditional methods that require simultaneous measurement of load conditions and structural response.

[0130] This invention realizes a technology for structural performance evaluation based on the crack state of the structure, and solves the technical problem that traditional finite element method is difficult to use to evaluate the structure by simulating the crack state of the structure.

[0131] The above description is merely a preferred embodiment of the present invention, and while it is quite specific and detailed, it should not be construed as a limitation on the scope of the invention patent. It should be noted that several improvements and modifications can be made without departing from the concept of the present invention, and these all fall within the protection scope of the present invention.

Claims

1. A method for constructing a reinforced concrete structure model, characterized in that, Includes the following steps: S1: Establish the first finite element model based on the geometric parameters of the basic bridge structure; S2: The constitutive relation of the bridge structure obtained from the tensile test of reinforced concrete structure, wherein the formula of the constitutive relation is: e t =f t / E c Where, σ c Let ε be the structural stress and ε be the structural strain. t E represents the strain of concrete cracks. c f is the structural elastic modulus. t It refers to the compressive strength of concrete; S3: Based on the cross-sectional model of the fiber beam-column element, determine the formula for calculating the first crack width. The formula for calculating the first crack width is as follows: Where ω is the width of the first crack, and L m This represents the average crack space length. S4: Embed the material constitutive relation and the first crack width calculation formula into the first finite element model; S5: Input the structural load, which is the applied force, and use the first finite element model to obtain the structural performance degradation parameters and structural crack parameters. S6: Determine a single load case based on the structural type of the bridge. The single load case includes concentrated force and uniformly distributed load. S7: Based on the first finite element model, select the key material performance parameters for the fiber beam-column model. These key material performance parameters include the elastic modulus E of the reinforcing steel in reinforced concrete. r Elastic model of concrete E c ; S8: Input the single load condition and the key material performance parameters into the first finite element model, and obtain the structural response through simulation calculation of the first finite element model. The structural response includes the displacement of the main beam, the stress of the main beam, and the crack width. S9: The mean squared error is used as the loss function to train the artificial neural network, resulting in an artificial neural network surrogate model. The formula for the loss function is: Where PF is the loss function, MSE is the mean squared error, T is the sample size of the training set, and e i For the network error of the i-th data point in the training set, t i For the true values ​​in the training set, α i Predictions automatically generated for artificial neural network surrogate models; S10: Obtain the test output parameters of the structure to be evaluated, and calculate the updated structural performance parameters of the model through inverse normalization based on the artificial neural network proxy model; S11: Input the updated structural performance parameters of the model into the first finite element model to obtain a second finite element model that can truly reflect the structural state; S12: Calculate the second crack width ω′ based on the second finite element model, and determine the performance status of the bridge reinforced concrete based on the second crack width ω′.

2. The method for constructing a reinforced concrete structure model according to claim 1, characterized in that, Embedding the material constitutive relation and the first crack width calculation formula into the first finite element model includes: The material constitutive relation is used as a subroutine of the first finite element model and embedded into the preprocessing program of the first finite element model. The formula for calculating the first crack width is used as a subroutine of the first finite element model and embedded into the post-processing program of the first finite element model.

3. The method for constructing a reinforced concrete structure model according to claim 1, characterized in that, S7 further includes: setting upper and lower limits for the key material performance parameters, and using 1% of the upper and lower limits as an interval to generate a dataset of the key material performance parameters.

4. The method for constructing a reinforced concrete structure model according to claim 1, characterized in that, The specific steps of using mean squared error as the loss function to train the artificial neural network include: The single load condition and the structural response are used as input and output training data, respectively. These are then fed into the loss function to perform adaptive iterative training on the artificial neural network. Training automatically stops when the loss value decreases to an acceptable level, and an artificial neural network surrogate model is output.

5. The method for constructing a reinforced concrete structure model according to claim 4, characterized in that, The test output parameters include static response data and dynamic response data. The static response data is the output parameter of the static test, and the dynamic response data is the output parameter of the dynamic test. The load effect of the test output parameters is consistent with the single load condition.

6. The method for constructing a reinforced concrete structure model according to claim 1, characterized in that, The determination of the structural performance of the bridge reinforced concrete based on the second crack width ω′ specifically includes: The second crack width ω′ is compared with the structural crack limit ω. limit Comparison: Where, η ω For the structural state parameters of reinforced concrete bridges, when η ω When η is less than 0.8, the structural performance of the reinforced concrete bridge is in a safe state. ω When η is between 0.8 and 0.1, the structural state of the reinforced concrete of the bridge is in a near-critical state. ω When the value is greater than 1, the structural performance of the reinforced concrete of the bridge is in a dangerous state.

7. A reinforced concrete structure model building device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to: The first finite element model is established based on the geometric parameters of the basic bridge structure. The constitutive relation of the bridge structure obtained from the tensile test of reinforced concrete structure is as follows: e t =f t / E c Where, σ c Let ε be the structural stress and ε be the structural strain. t E represents the strain of concrete cracks. c f is the structural elastic modulus. t It refers to the compressive strength of concrete; Based on the construction of the cross-sectional model using fiber beam-column elements, the formula for calculating the first crack width is determined. The formula for calculating the first crack width is as follows: Where ω is the width of the first crack, and L m This represents the average crack space length. The material constitutive relation and the first crack width calculation formula are embedded into the first finite element model; Input the structural load, and use the first finite element model to obtain the structural performance degradation parameters and structural crack parameters. The structural load is the applied force. Based on the structural type of the bridge, a single load condition is determined, which includes concentrated force and uniformly distributed load. Based on the first finite element model, key material performance parameters for the fiber beam-column model are selected, including the elastic modulus E of the steel reinforcement in reinforced concrete. r Elastic model of concrete E c ; The single load condition and the key material performance parameters are input into the first finite element model, and the structural response is obtained through simulation calculation of the first finite element model. The structural response includes the displacement of the main beam, the stress of the main beam, and the crack width. The mean squared error is used as the loss function to train the artificial neural network, resulting in an artificial neural network surrogate model. The formula for the loss function is: Where PF is the loss function, MSE is the mean squared error, T is the sample size of the training set, and e i For the network error of the i-th data point in the training set, t i For the true values ​​in the training set, α i Predictions automatically generated for artificial neural network surrogate models; Obtain the test output parameters of the structure to be evaluated, and calculate the updated structural performance parameters of the model through inverse normalization based on the artificial neural network proxy model. The updated structural performance parameters of the model are input into the first finite element model to obtain a second finite element model that can truly reflect the structural state. The second crack width ω′ is calculated based on the second finite element model, and the performance status of the bridge reinforced concrete is determined based on the second crack width ω′.

8. A non-volatile computer storage medium, characterized in that, The non-volatile computer storage medium stores a computer program, which, when executed by a processor, implements the reinforced concrete structure model construction method as described in any one of claims 1-6.

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