CN111365051A - Method for estimating stress of carbonaceous rock tunnel anchor rod based on transfer function of feedback algorithm - Google Patents
Method for estimating stress of carbonaceous rock tunnel anchor rod based on transfer function of feedback algorithm Download PDFInfo
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- CN111365051A CN111365051A CN202010150302.2A CN202010150302A CN111365051A CN 111365051 A CN111365051 A CN 111365051A CN 202010150302 A CN202010150302 A CN 202010150302A CN 111365051 A CN111365051 A CN 111365051A
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21D—SHAFTS; TUNNELS; GALLERIES; LARGE UNDERGROUND CHAMBERS
- E21D21/00—Anchoring-bolts for roof, floor in galleries or longwall working, or shaft-lining protection
- E21D21/02—Anchoring-bolts for roof, floor in galleries or longwall working, or shaft-lining protection having means for indicating tension
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F17/00—Methods or devices for use in mines or tunnels, not covered elsewhere
- E21F17/18—Special adaptations of signalling or alarm devices
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Abstract
The invention discloses a carbonaceous rock tunnel anchor rod stress estimation method based on a feedback algorithm transfer function, which comprises the following steps of 1, selecting a tunnel monitoring section; step 2, laying monitoring equipment; step 3, collecting monitoring data; step 4, establishing an artificial neural network model; step 5, training an artificial neural network model; step 6, estimating and predicting the stress of the anchor rod; and 7, selecting the diameter of the anchor rod according to the grade of the surrounding rock. According to the method, the stress of the anchor rod is estimated by using three parameters of surrounding rock pressure, osmotic pressure and surrounding rock deformation through a feedback artificial neural network analysis method, and the tunnel anchor rod is designed according to the stress of the anchor rod, so that the problem of resource waste caused by the fact that the anchor rod is designed by a similar engineering method or a semi-empirical semi-theoretical method is solved.
Description
Technical Field
The invention relates to the field of tunnel engineering monitoring, in particular to a carbonaceous rock tunnel anchor rod stress estimation method based on a feedback algorithm transfer function.
Background
With the vigorous development of infrastructure in China, a large number of expressways and expressways are newly built, China belongs to a country with a plurality of mountains, a plurality of tunnels can be built when the expressways and the expressways are built, and when the tunnels are in danger of collapse, reasonable supporting measures are necessary.
The "carbon rock" is a special rock which is easily weathered in the open air, is easy to soften after absorbing water, is easy to disintegrate and break after being disturbed when meeting water and has great influence on engineering properties along with the environment. The carbonaceous rock is widely distributed in China, such as Guangxi, Guizhou, Yunnan and other areas. The Guangxi typical carbonaceous rock mainly comprises mineral components such as quartz, illite and the like, contains 25-45% of carbon elements by mass, has a natural density of 2.66-2.77 g/cm3 and a natural water content of 1-1.5%, is mainly distributed in three areas such as a Liuzhou area, a Baihuan area and a river basin area in Guangxi, and has the engineering characteristics of softening property, expansibility, environmental sensitivity, disintegration and the like. When a tunnel is built in an area with 'carbon rocks', the tunnel is supported more importantly.
The anchor rod is an indispensable supporting method in construction based on the new Austrian's method, and plays a certain role in hoisting of weak surrounding rock of tunnel roof, composite beam construction and arch center reinforcement. In addition, the effectiveness of anchor bolt support is proved based on the support theory and the strength strengthening theory of the rock loosening zone. Therefore, when the tunnel is at risk of collapse, it is necessary to adopt a reasonable bolting system as soon as possible. Although many scholars have studied the principle and effect of bolting up to now, many engineering projects rely on similar engineering methods or semi-empirical semi-theoretical methods. Bolt parameters are sometimes not reasonable depending on the results of these methods, resulting in waste of material and increased costs. And if the diameter of the anchor rod cannot be selected according to the change of the grade of the surrounding rock.
Therefore, reasonable anchor rod parameters are determined according to the evaluation parameters of the tunnel field surrounding rock body, the method has important significance in the method, and the influence of different parameters of the general Hooke-Brown criterion on the collapse section can be analyzed.
Disclosure of Invention
The invention aims to solve the technical problem of the prior art and provides a carbonaceous rock tunnel anchor rod stress estimation method based on a feedback algorithm transfer function. And finally, designing an anchor bolt support according to the estimated anchor bolt stress, wherein the reliability and the rationality of the method are verified by the effectiveness of the anchor bolt support. Therefore, the method has the advantages of high precision, high efficiency, low cost and the like, and has good social and economic benefits and engineering application prospects.
In order to solve the technical problems, the invention adopts the technical scheme that:
a method for estimating the stress of a carbonaceous rock tunnel anchor rod based on a transfer function of a feedback algorithm comprises the following steps.
And 6, estimating and predicting the stress of the anchor rod: and (3) carrying out data monitoring on the stress, osmotic pressure and surrounding rock strain displacement of the surrounding rock on the carbonaceous rock tunnel needing stability evaluation, inputting the monitoring data into the optimal artificial neural network model determined in the step (5), and estimating the stress of the anchor rod so as to correspond to the stability of the carbonaceous rock tunnel.
And 7, selecting the diameter of the anchor rod: and (3) classifying the grade of the surrounding rock in the carbon rock tunnel under construction, monitoring the stress, osmotic pressure and strain displacement data of the surrounding rock at each grade of the surrounding rock, and inputting the monitoring data into the optimal artificial neural network model determined in the step 5, so as to obtain the anchor rod stress corresponding to each grade of the surrounding rock. And at each surrounding rock level, selecting the diameter of the anchor rod corresponding to the obtained stress of the corresponding anchor rod.
In step 4, for the established artificial neural network model, a sigmoid function is selected as a transfer function among network nodes in an input layer, a hidden layer and an output layer, wherein the expression of the sigmoid function is as follows:
in the formula (1), x is an input value of the network node, and s (x) is an output value of the network node.
In the step 5, in the training process of the artificial neural network model, different momentum factors are selected for training, so that a better artificial neural network structure model is found.
The different momentum factors are 0.001, 0.002 and 0.003, respectively.
In step 5, the correlation coefficient R is adopted in the training process of the artificial neural network model2And evaluating the artificial neural network model by the Mean Square Error (MSE) when the MSE is minimum and R is greater2And at the maximum, the best artificial neural network model is searched. The mean square error MSE is calculated according to the following formula:
in the formula (2), yiIs the measured value of the stress of the anchor rod,
and estimating the predicted value of the anchor rod stress according to the artificial neural network model, wherein N is the number of the training sample.
In step 5, in the training process of the artificial neural network model, the variance ratio VAF and the root mean square error RMSE are adopted to evaluate the artificial neural network model, and when VAF is 100% and RMSE is 0, the model is the best artificial neural network model to be searched. The calculation formulas of the variance ratio VAF and the root mean square error RMSE are respectively as follows:
in the formulae (3) and (4), var is the variance, yiIs the measured value of the stress of the anchor rod,
and estimating the predicted value of the anchor rod stress according to the artificial neural network model, wherein N is the number of the training sample.
In the step 1, three sections are selected as monitoring sections at the place with the highest grade of the surrounding rock of the carbonaceous rock tunnel, wherein the grade of the surrounding rock of two monitoring sections is V grade, and the grade of the surrounding rock of the other monitoring section is IV grade.
In the step 2, four monitoring points are distributed on each monitoring section along the arch direction, namely a left arch springing, a left arch shoulder, a right arch shoulder and a right arch springing.
The invention has the following beneficial effects:
1. according to the method, the stress of the anchor rod is estimated by using three parameters of surrounding rock pressure, osmotic pressure and surrounding rock deformation through a feedback artificial neural network analysis method, and the tunnel anchor rod is designed according to the stress of the anchor rod, so that the problem of resource waste caused by the fact that the anchor rod is designed by a similar engineering method or a semi-empirical semi-theoretical method is solved.
2. The stability of surrounding rocks of the high-stress carbonaceous rock tunnel engineering is very complex, the stress mechanism between the surrounding pressure and the supporting structure still needs to be deeply researched, and the stress state of the supporting structure is quantitatively evaluated. According to the invention, the stress characteristic of the wrong supporting rod of the carbonaceous rock tunnel is evaluated by two methods of on-site actual monitoring and artificial neural network calculation, the on-site monitoring can provide safety guarantee for tunnel construction, and the neural network prediction model can provide advanced prediction for engineering construction to a certain extent.
3. The method is based on a feedback algorithm transfer function analysis method, a calculation model for predicting the anchor rod stress is established, the model effectively avoids the influence of overfitting of a calculation result by adopting a cross validation technology, and the method has the characteristics of simple structure, high efficiency and accuracy in estimating the anchor rod stress.
Drawings
FIG. 1 shows a schematic layout of the monitoring device in step 2 of the present invention.
FIG. 2 is a diagram showing a model structure of an artificial neural network according to the present invention.
FIG. 3 is a graph showing the connection weight values of the best artificial neural network model after training in this example.
FIG. 4 shows a schematic diagram of performance evaluation indexes of the artificial neural network model.
Among them are: 1. an anchor rod stress measuring instrument; 2. an osmometer; 3. a soil pressure cell; 4. a strain gauge; 5. primary lining; 6. a second liner; 7. spraying and protecting; 8. an anchor rod.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific preferred embodiments.
In the description of the present invention, it is to be understood that the terms "left side", "right side", "upper part", "lower part", etc., indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and that "first", "second", etc., do not represent an important degree of the component parts, and thus are not to be construed as limiting the present invention. The specific dimensions used in the present example are only for illustrating the technical solution and do not limit the scope of protection of the present invention.
The present embodiment will be described in detail by taking the case of the baw tunnel from the guangxi river basin to the hundred-color highway.
A method for estimating the stress of a carbonaceous rock tunnel anchor rod based on a transfer function of a feedback algorithm comprises the following steps.
In A Bashi tunnel from A Guangxi river basin to A hectograph highway, one end of the Bashi tunnel is A carbon rock tunnel, and the grade of surrounding rocks is high, so that A V-grade surrounding rock section and an IV-grade surrounding rock section are selected at the carbon rock tunnel, and preferably three sections are selected as monitoring sections, namely an A section (ANN-A), A B section (ANN-B) and A C section (ANN-C). Wherein, the A section and the B section are selected in a V-level surrounding rock section and are respectively Z2K4+140 and Z2K4+155, and the C section is selected in an IV-level surrounding rock section and is Z2K4+ 170.
As shown in fig. 1, four monitoring points, namely a left arch foot M1, a left arch shoulder M2, a right arch shoulder M3 and a right arch foot M4, are preferably arranged in the arch direction in each monitoring section.
And a set of monitoring equipment is uniformly distributed at each monitoring point, and the monitoring equipment comprises a soil pressure box 3, a osmometer 2, a strain gauge 4 and an anchor rod stress measuring instrument 1. The soil pressure cell is used for measuring the surrounding rock stress of the corresponding monitoring point, the osmometer is used for measuring the osmotic pressure of the corresponding monitoring point, the strain gauge is used for measuring the surrounding rock strain displacement of the corresponding monitoring point, the anchor rod stress measuring instrument is used for measuring the real stress of the corresponding anchor rod, namely the stress measured value, and the model is preferably a GJ-16 vibrating wire type steel bar dynamometer.
The collected monitoring data comprises the stress pi and the osmotic pressure sigma of the surrounding rock1The surrounding rock strain displacement D and the anchor rod stress measured value.
And 4, establishing an artificial neural network model.
The established artificial neural network model comprises an input layer, a hidden layer and an output layer, as shown in fig. 2.
The input layer comprises three nodes of stress, osmotic pressure and strain displacement of surrounding rockThe parameters respectively correspond to P1、P2And P3。
The number of the hidden layers is one, two or more, and the number of the nodes of each hidden layer is four. In this example, the hidden layer has two, H1And H2。
The number of nodes of the output layer is one, corresponding to R1And the output node is the anchor rod stress.
For the established artificial neural network model, the feedback learning algorithm is preferably a Levenberg-Marquardt algorithm, and a sigmoid function is selected as a transfer function among network nodes in an input layer, a hidden layer and an output layer, wherein the expression of the sigmoid function is as follows:
in the formula (1), x is an input value of the network node, and s (x) is an output value of the network node.
And 5, training an artificial neural network model.
And (4) training the artificial neural network model established in the step (4) according to the monitoring data acquired in the step (3), and determining the optimal connection weight and the trained optimal artificial neural network model.
In the training process of the artificial neural network model, different momentum factors are selected for training, so that a better artificial neural network structure model is found. The different momentum factors are preferably 0.001, 0.002, 0.003, etc., respectively.
The above found better artificial neural network structure model is preferably evaluated by the following two methods, and the best artificial neural network model is found.
Using a correlation coefficient R2And evaluating the artificial neural network model by Mean Square Error (MSE). Coefficient of correlation R2The anchor rod stress prediction method is a correlation coefficient between an anchor rod stress measured value and an anchor rod stress predicted value.
When MSE is minimum and R is2At maximum, for the best artificial spirit soughtVia a network model. The mean square error MSE is calculated according to the following formula:
in the formula (2), yiIs the measured value of the stress of the anchor rod,
and estimating the predicted value of the anchor rod stress according to the artificial neural network model, wherein N is the number of the training sample.
B. Evaluation method two
And (3) evaluating the artificial neural network model by using the variance ratio VAF and the root mean square error RMSE, and finding the optimal artificial neural network model when the VAF is 100% and the RMSE is 0. The calculation formulas of the variance ratio VAF and the root mean square error RMSE are respectively as follows:
in the formulae (3) and (4), var is the variance, yiIs the measured value of the stress of the anchor rod,
and estimating the predicted value of the anchor rod stress according to the artificial neural network model, wherein N is the number of the training sample.
In the basic tunnel from the Guangxi river basin to the hundred-color expressway, the connection weight value of the optimal artificial neural network model after training is shown in fig. 3 according to the monitoring data acquired in the step 3. Wherein pi is the stress of the surrounding rock, sigma1Osmotic pressure, D surrounding rock strain displacement and sigma tunnel anchor rod stress.
As can be seen from FIG. 4, the artificial neural network model used in the present application is very effective in predicting wrong rod stress because of their correlation coefficient R2Are all greater than0.65, the root mean square error RMSE is less than 0.65Map, and the variance ratio VAF is more than 80%. In addition, the test result is compared with the prediction result of a single neural network model, and the correlation coefficient R of the section A20.7201, correlation coefficient R of section B20.6741, correlation coefficient R of section C20.9291; the correlation coefficient R of the section A and the section B is lower than that of the section C because the surrounding rock grade of the section A and the surrounding rock grade of the section B are V-grade, the surrounding rock grade of the section C is IV-grade, the surrounding rock is more unstable when the surrounding rock grade is higher, factors influencing the stress of the anchor rod can be correspondingly changed, and the correlation coefficient R of the section A and the section B is lower than that of the section C; but from the prediction result, each neural network model predicts the anchor rod stress quite accurately.
And 6, estimating and predicting the stress of the anchor rod: and (3) carrying out data monitoring on the stress, osmotic pressure and surrounding rock strain displacement of the surrounding rock on the carbonaceous rock tunnel needing stability evaluation, inputting the monitoring data into the optimal artificial neural network model determined in the step (5), and estimating the stress of the anchor rod so as to correspond to the stability of the carbonaceous rock tunnel.
And 7, selecting the diameter of the anchor rod: and (3) classifying the grade of the surrounding rock in the carbon rock tunnel under construction, monitoring the stress, osmotic pressure and strain displacement data of the surrounding rock at each grade of the surrounding rock, and inputting the monitoring data into the optimal artificial neural network model determined in the step 5, so as to obtain the anchor rod stress corresponding to each grade of the surrounding rock. And at each surrounding rock level, selecting the diameter of the anchor rod corresponding to the obtained stress of the corresponding anchor rod.
Although the preferred embodiments of the present invention have been described in detail, the present invention is not limited to the details of the embodiments, and various equivalent modifications can be made within the technical spirit of the present invention, and the scope of the present invention is also within the scope of the present invention.
Claims (8)
1. A carbonaceous rock tunnel anchor rod stress estimation method based on a feedback algorithm transfer function is characterized by comprising the following steps: the method comprises the following steps:
step 1, selecting a tunnel monitoring section: selecting at least two sections as monitoring sections at the place with the highest grade of the surrounding rock of the carbonaceous rock tunnel;
step 2, laying monitoring equipment: distributing a plurality of monitoring points on each monitoring section selected in the step 1 along the arch direction; a set of monitoring equipment is uniformly distributed at each monitoring point, and the monitoring equipment comprises a soil pressure box, an osmometer, a strain gauge and an anchor rod stress measuring instrument; the device comprises a soil pressure box, an osmometer, a strain gauge, an anchor rod stress measuring instrument and a monitoring point, wherein the soil pressure box is used for measuring the surrounding rock stress of a corresponding monitoring point, the osmometer is used for measuring the osmotic pressure of the corresponding monitoring point, the strain gauge is used for measuring the surrounding rock strain displacement of the corresponding monitoring point, and the anchor rod stress measuring instrument is used for measuring the real stress of a corresponding anchor rod, namely a stress measured value;
step 3, collecting monitoring data: continuously monitoring and collecting data of not less than 30 days at each monitoring point of each selected monitoring section; the collected monitoring data comprise surrounding rock stress, osmotic pressure, surrounding rock strain displacement and anchor rod stress measured values;
step 4, establishing an artificial neural network model: the established artificial neural network model comprises an input layer, a hidden layer and an output layer; the input layer comprises three node parameters of surrounding rock stress, osmotic pressure and surrounding rock strain displacement, the number of hidden layers is one, two or more, the number of nodes of each hidden layer is 4-10, the number of nodes is selected according to the size of a training database, the data volume is less than 70 groups, the number of nodes is controlled to be 4-7, the number of data groups is greater than 70 groups, the number of nodes is controlled to be 7-10, the number of nodes of the output layer is 1, and the output nodes are anchor rod stress;
step 5, training an artificial neural network model: training the artificial neural network model established in the step 4 according to the monitoring data acquired in the step 3, and determining an optimal connection weight and the trained optimal artificial neural network model;
and 6, estimating and predicting the stress of the anchor rod: monitoring the data of surrounding rock stress, osmotic pressure and surrounding rock strain displacement of the carbonaceous rock tunnel needing stability evaluation, inputting the monitoring data into the optimal artificial neural network model determined in the step 5, and estimating the anchor rod stress so as to correspond to the stability of the carbonaceous rock tunnel;
and 7, selecting the diameter of the anchor rod: classifying the grade of the surrounding rock of the carbon rock tunnel under construction, monitoring the stress, osmotic pressure and strain displacement data of the surrounding rock at each grade of the surrounding rock, and inputting the monitoring data into the optimal artificial neural network model determined in the step 5, so as to obtain the anchor rod stress corresponding to each grade of the surrounding rock; and at each surrounding rock level, selecting the diameter of the anchor rod corresponding to the obtained stress of the corresponding anchor rod.
2. The method for estimating the stress of the carbonaceous rock tunnel anchor rod based on the transfer function of the feedback algorithm according to claim 1, wherein the method comprises the following steps: in step 4, for the established artificial neural network model, a sigmoid function is selected as a transfer function among network nodes in an input layer, a hidden layer and an output layer, wherein the expression of the sigmoid function is as follows:
in the formula (1), x is an input value of the network node, and s (x) is an output value of the network node.
3. The method for estimating the stress of the carbonaceous rock tunnel anchor rod based on the transfer function of the feedback algorithm according to claim 1, wherein the method comprises the following steps: in the step 5, in the training process of the artificial neural network model, different momentum factors are selected for training, so that a better artificial neural network structure model is found.
4. The method for estimating the stress of the carbonaceous rock tunnel anchor rod based on the transfer function of the feedback algorithm according to claim 3, wherein the method comprises the following steps: the different momentum factors are 0.001, 0.002 and 0.003, respectively.
5. The method for estimating the stress of the carbonaceous rock tunnel anchor rod based on the transfer function of the feedback algorithm according to claim 3, wherein the method comprises the following steps: in step 5, the correlation coefficient R is adopted in the training process of the artificial neural network model2And mean square error MSEEvaluation of the artificial neural network model when MSE is minimum and R is2At maximum, the optimum artificial neural network model is searched; the mean square error MSE is calculated according to the following formula:
in the formula (2), yiIs the measured value of the stress of the anchor rod,
and estimating the predicted value of the anchor rod stress according to the artificial neural network model, wherein N is the number of the training sample.
6. The method for estimating the stress of the carbonaceous rock tunnel anchor rod based on the transfer function of the feedback algorithm according to claim 3, wherein the method comprises the following steps: in the step 5, in the training process of the artificial neural network model, the variance ratio VAF and the root mean square error RMSE are adopted to evaluate the artificial neural network model, and when the VAF is 100% and the RMSE is 0, the model is the best searched artificial neural network model; the calculation formulas of the variance ratio VAF and the root mean square error RMSE are respectively as follows:
in the formulae (3) and (4), var is the variance, yiIs the measured value of the stress of the anchor rod,
and estimating the predicted value of the anchor rod stress according to the artificial neural network model, wherein N is the number of the training sample.
7. The method for estimating the stress of the carbonaceous rock tunnel anchor rod based on the transfer function of the feedback algorithm according to claim 1, wherein the method comprises the following steps: in the step 1, three sections are selected as monitoring sections at the place with the highest grade of the surrounding rock of the carbonaceous rock tunnel, wherein the grade of the surrounding rock of two monitoring sections is V grade, and the grade of the surrounding rock of the other monitoring section is IV grade.
8. The method for estimating the stress of the carbonaceous rock tunnel anchor rod based on the transfer function of the feedback algorithm according to claim 1, wherein the method comprises the following steps: in the step 2, four monitoring points are distributed on each monitoring section along the arch direction, namely a left arch springing, a left arch shoulder, a right arch shoulder and a right arch springing.
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