CN113916183B - PBA structure deformation risk prediction system and application method thereof - Google Patents
PBA structure deformation risk prediction system and application method thereof Download PDFInfo
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B21/00—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
- G01B21/32—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring the deformation in a solid
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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- G06N3/004—Artificial life, i.e. computing arrangements simulating life
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Abstract
The invention discloses a prediction system of PBA structure deformation risk and a use method thereof, comprising the following steps: the device comprises a data acquisition unit, a data transmission unit and a data processing unit; the data acquisition unit is used for acquiring the soil pressure at the arch crown, the arch shoulder and the arch waist of the tunnel and the change monitoring data of the steel bar stress at the arch crown and the arch waist; and the data processing unit is used for predicting the soil pressure and the steel bar stress in the construction process. The method can overcome the defects of high risk, low frequency and large error of manual measurement, further realize real-time automatic accurate monitoring of the soil pressure and the steel bar stress of the tunnel, and provide guarantee for the safe construction of the tunnel; meanwhile, the fixing of the collecting box and the transmission box, the waterproof and anti-collision protection of the instrument are considered, and the service lives of the collecting box and the transmission box are prolonged; the short-distance wireless transmission system and the GPRS information transmission assembly are used for replacing long-distance signal data lines, so that the monitoring cost is effectively reduced, and the applicability of the equipment is improved.
Description
Technical Field
The invention relates to the technical field of tunnel data monitoring and parameter identification, in particular to a prediction system for PBA structure deformation risk and a use method thereof.
Background
The stability of the surrounding rock is important in the subway construction process. At present, stability of tunnel surrounding rock is judged mainly through a surrounding rock displacement mode of manual measurement, and the manual measurement method is high in risk, low in frequency and large in human error. Compared with manual measurement, the automatic monitoring has higher precision and more encryption set monitoring frequency, but the automatic monitoring applied in the current tunnel engineering takes one monitoring fracture surface as a monitoring target, belongs to the category of two-dimensional monitoring, causes that the monitoring data cannot embody the whole stable state of the engineering space, and has certain limitation.
The detection equipment in the current subway tunnel engineering is greatly influenced by environmental factors of the subway engineering, and mainly has the following problems: (1) The measuring target is in a two-dimensional plane, the distance between the current monitoring position and the face is ignored, and the space effect caused by the forward movement of the face cannot be determined; (2) The monitoring equipment needs workers to measure on site and cannot automatically monitor. (3) In the subway construction process, the environment is complex and severe (such as broken stone splashing and water seepage caused by blasting), and a corresponding instrument protection device and a corresponding fixing device are not designed. (4) A few automated collection facilities can only use a data line of a considerable length to connect to outside of the tunnel due to signal problems in the tunnel, and the cost is high, and the data line in the tunnel is easily damaged.
Disclosure of Invention
The invention provides a prediction system and a prediction method for PBA structure deformation risk so as to overcome the problems.
The system of the invention comprises: the device comprises a data acquisition unit, a data transmission unit and a data processing unit;
the data acquisition unit is used for acquiring the soil pressure at the arch crown, the arch shoulder and the arch waist of the tunnel and the change monitoring data of the steel bar stress at the arch crown and the arch waist;
the data transmission unit is used for integrating the soil pressure and the steel bar stress data acquired by the data acquisition unit to form time sequence monitoring data and transmitting the time sequence monitoring data to the data processing unit;
the data processing unit is used for establishing a nonlinear mapping relation between the monitoring data and the soil pressure and the steel bar stress of the tunnel through a fitting algorithm based on a least square support vector machine, and predicting the soil pressure and the steel bar stress in the construction process by adopting a bacterial foraging algorithm based on a time sequence.
Further, the data acquisition unit includes: the device comprises a data monitoring part, a data acquisition part, a protection structure part and a support structure part;
the data monitoring part is used for monitoring and acquiring change monitoring data of the soil pressure at the arch crown, the arch shoulder and the arch waist and the steel bar stress at the arch crown and the arch waist;
The data acquisition unit is used for collecting and sending the data monitoring unit to the data transmission unit;
the protection structure part is used for accommodating each data acquisition part;
the support structure portion is used to secure the protection structure portion to the tunnel sidewall.
Further, the data monitoring section includes: soil pressure vault measuring module, soil pressure arch waist measuring module, reinforcing steel bar stress vault measuring module and reinforcing steel bar stress arch waist measuring module;
the earth pressure vault measuring module is used for acquiring sampling data corresponding to the vault monitoring direction;
the soil pressure arch shoulder measuring module is used for acquiring sampling data corresponding to the arch shoulder monitoring direction;
the soil pressure arch measuring module is used for acquiring sampling data corresponding to the arch monitoring direction;
the steel bar stress vault measuring module is used for acquiring sampling data corresponding to the vault monitoring direction;
the steel bar stress arch measuring module is used for acquiring sampling data corresponding to the arch monitoring direction;
further, the data acquisition section includes: the data automatic acquisition module and the data automatic transmission module;
the data automatic acquisition module is used for monitoring the data monitoring part in real time.
The data automatic transmission module is connected with the data automatic acquisition module through a data line, so that the acquired data is sent to the data transmission unit in real time.
Further, the data acquisition unit includes: the device comprises a data receiving part, a data transmitting part, a protecting structure part and a supporting structure part;
the data receiving part is used for receiving the data transmitted by the data acquisition unit in a wireless way;
the data transmission part is used for transmitting the data received by the data receiving part to the data processing unit;
the protection structure part accommodates each data acquisition part;
the support structure portion is capable of securing the protective structure portion to the tunnel sidewall.
Further, the data receiving section includes: the data wireless receiver is connected with the transmission box signal receiving antenna through a data line;
the transmission box signal receiving antenna can realize short-distance data wireless transmission.
The method comprises the following steps:
s1, the data processing unit receives monitoring data transmitted by the data transmission unit and establishes sample data based on a time sequence, wherein the sample data comprises: values for the arch crown soil pressure, arch shoulder soil pressure, arch crown reinforcement stress on day i;
s2, the data processing unit learns time sequence data by utilizing a least square support vector machine theory to obtain a nonlinear relation between a soil pressure value and a reinforcement stress value change sequence;
S3, the data processing unit determines a mapping relation corresponding to a training set formed by means of time sequence monitoring data to obtain a nonlinear prediction model, and optimizes the nonlinear mapping model through trend operation, copying operation and migration operation of a bacterial foraging algorithm based on the nonlinear prediction model to realize rolling prediction of structural deformation risks.
Further, S2 includes:
s21, acquiring soil pressure and steel bar stress data of a monitoring section of the tunnel face based on the determined distance data of the monitoring position from the tunnel face;
s22, predicting the nonlinear change sequence, namely obtaining the relation between the soil pressure and the steel bar stress at the time p+1 and the soil pressure and the steel bar stress at the time p before;
s23, training the relation between the soil pressure and the steel bar stress change through time sequence data by utilizing a least square support vector machine theory to obtain a nonlinear relation between the soil pressure and the steel bar stress change sequence:
wherein y (x) p+1 ) The soil pressure and the steel bar stress value at the p+1 time are obtained; b is the offset;as a kernel function, K (x, x k )＝exp{||x-x i || 2 /σ 2 }，σ 2 Is the square bandwidth pass in the gaussian RBF kernel.
Further, S3 includes:
s31, utilizing a nonlinear relation between a soil pressure value and a reinforcement stress value change sequence to establish a nonlinear prediction model, wherein the nonlinear prediction model is used for predicting data of a current position p+1 through the previous P pieces of historical data;
S32, adding groundwater level influence parameters into an input time sequence, and correcting each prediction process:
s321, trending operation: any possible increase or decrease change in the set range is carried out on the data value for a set of time series monitoring data, so as to obtain a set of new time series monitoring data, and the operation is repeated;
s322, copy operation: and when the trend operation reaches the set critical times, all the time series monitoring data are orderly arranged from large to small by taking the absolute value of the predicted value distance monitoring value as an evaluation index, deleting 50% of the data, and copying the data of the previous 50%.
S323, migration operation: when the migration operation occurs in the process of predicting the next data, the execution reference point is the copying operation, and after the copying operation is carried out to a set limit step, if the data meets the requirements, the data is reserved; and if the data does not meet the requirements, deleting the data.
S33, performing corresponding rolling prediction on the sampling data obtained by the data acquisition unit based on a nonlinear prediction model optimized by a bacterial foraging algorithm.
The method can overcome the defects of high risk, low frequency and large error of manual measurement, further realize real-time automatic accurate monitoring of the soil pressure and the steel bar stress of the tunnel, and provide guarantee for the safe construction of the tunnel; meanwhile, the fixing of the collecting box and the transmission box, the waterproof and anti-collision protection of the instrument are considered, and the service lives of the collecting box and the transmission box are prolonged; the short-distance wireless transmission system and the GPRS information transmission assembly are used for replacing long-distance signal data lines, so that the monitoring cost is effectively reduced, and the applicability of the equipment is improved. The data transmission unit can upload data to the cloud, and can realize real-time acquisition of multiple terminals; and the data processing unit can train a nonlinear prediction model according to the monitoring data of the cloud to realize rolling prediction of the structural deformation risk.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, a brief description will be given below of the drawings required for the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is an overall flow chart of the present invention;
FIG. 2 is a schematic view of the structure of the soil pressure box according to the present invention;
fig. 3 is a schematic structural view of the reinforcement bar gauge according to the present invention;
FIG. 4 is a schematic view of the structure of the collection box according to the present invention;
FIG. 5 is a schematic view of the structure of the transfer box according to the present invention;
FIG. 6 is a schematic view of the structure of the horizontal supporting frame according to the present invention;
FIG. 7 is a schematic diagram of a monitoring profile arrangement in one embodiment;
FIG. 8 is a schematic diagram of monitoring points of a monitored section in one embodiment;
FIG. 9 is an overall schematic diagram of a monitoring cross-section apparatus arrangement in one embodiment;
FIG. 10 is a flowchart of a PBA structure deformation risk prediction method based on the intelligent response surface method according to the present invention;
FIG. 11 is a block diagram of the operation implementation of the acquisition box according to the present invention;
FIG. 12 is a block diagram of a transport case work implementation flow according to the present invention;
FIG. 13 is a formation map in one embodiment;
FIG. 14 is a plot of data processing unit soil pressure time series data in one embodiment.
Reference numerals illustrate:
1. a soil pressure box; 2. a soil pressure box bracket; 3. a soil pressure cell signal output line; 4. reinforcing steel bars; 5. a rebar meter; 6. a signal output line of the reinforcement meter; 7. a signal transmitting antenna of the collection box; 8. a collection box; 9. a data wireless transmitter; 10. a bracket expansion screw; 11. a bracket; 12. a power line; 13. a soil pressure cell signal input line; 14. a signal input line of the reinforcement meter; 15. a collection box power supply; 16. an automated collector; 17. the water-proof outer edge of the collecting box; 18. a transmission box signal receiving antenna, 19, a transmission box; 20. a waterproof outer edge of the transmission box, 21 and a GPRS signal transmitter; 22. a GPRS signal transmitting antenna; 23. a transmission box power line; 24. a transmission box power supply; 25. a data wireless receiver; 26. monitoring a section at 40 m; 27. monitoring a section at 75 m; 28. monitoring a section at 110 m; 29. vault soil pressure monitoring points; 30. arch shoulder soil pressure monitoring points; 31. a arching soil pressure monitoring point; 32. vault reinforcement stress monitoring points; 33. and (5) a monitoring point of the arch steel bar stress.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by one of ordinary skill in the art without undue burden on the person of ordinary skill in the art based on the embodiments of the present invention, are within the scope of the present invention.
Example 1
Aiming at the problems that the risk of manual measurement is high, the frequency is low, the error is large and the surrounding rock deformation risk cannot be early-warned in the current tunnel soil pressure and steel bar stress monitoring process, in the embodiment, the PBA structure deformation risk prediction method based on the intelligent response surface method is developed, and has the following characteristics: (1) The collecting box and the transmitting box can be arranged on the primary lining wall surface of the tunnel through the bracket, are of a closed structure, have the functions of preventing water and placing concrete and spraying flystones, and can adapt to complex and severe engineering environments; (2) The method can simultaneously acquire the soil pressure at the arch crown, the arch shoulder and the arch waist of the tunnel and the change monitoring data of the steel bar stress at the arch crown and the arch waist, and can make up errors caused by the space effect of tunnel excavation; (3) The information acquisition unit is matched with the acquisition box and the transmission box formed by the data transmission unit to replace the traditional wired transmission by using data wireless transmission, so that the practicability of the device is improved; (4) The data processing unit can construct a prediction model by using a vector machine and a bacterial foraging algorithm, and predicts in real time according to the monitored soil pressure and steel bar stress data, so as to provide more visual data reference for an engineer. Meanwhile, the device is convenient to install and detach, can be recycled, and has great cost advantage.
Based on the above design gist, it can be known that the PBA structure deformation risk system based on the intelligent response surface method according to the present embodiment includes:
(1) The data acquisition unit can simultaneously acquire the soil pressure at the arch crown, the arch shoulder and the arch waist of the tunnel and the change monitoring data of the steel bar stress at the arch crown and the arch waist, and more comprehensively reflect the soil pressure and the steel bar stress change process of the tunnel under the influence of the space effect in the tunnel excavation process; meanwhile, the data acquisition unit is used for processing tunnel guniting construction, blasting construction and rich water environment, and the protection structure can realize the functions of preventing water and flying stones from sputtering; the data acquisition unit is also provided with an independent supporting and fixing structure, so that a supporting structure which is stable in support and convenient to detach can be provided for the longitudinal extension characteristic of the tunnel engineering in space, and after the construction of the current area is finished, the device can be detached and moved and used for the next target position, and the utilization efficiency of the structure is improved;
(2) The data transmission unit can collect and remotely transmit the soil pressure and the steel bar stress monitoring information which are output by the data acquisition unit and are obtained through the soil pressure box and the steel bar meter. The data transmission unit sends the data to the data processing unit through the GPRS module. The early warning information realizes the synchronous release of data of a plurality of terminals such as a webpage, a mobile phone, a software platform and the like through a server, meets the data cognition requirements of different construction participation departments, and provides more visual and comprehensive surrounding rock state data for engineering management staff;
(3) The data processing unit can receive the data transmitted by the data transmission unit; establishing a nonlinear mapping relation between monitoring data and tunnel soil pressure and steel bar stress through a fitting algorithm based on a least square support vector machine, and predicting the soil pressure and the steel bar stress in the construction process by adopting a bacterial foraging algorithm based on a time sequence, and sending out monitoring and early warning if the predicted value exceeds a warning value three times in a day; the early warning information realizes synchronous release of data of multiple terminals such as a webpage, a mobile phone, a software platform and the like through the server, meets the data cognition requirements of different construction participation departments, and provides more visual and comprehensive soil pressure and steel bar stress state data for engineering management staff.
The PBA structure deformation risk prediction method based on the intelligent response surface method, which is composed of the three units, can realize the distribution of soil pressure and steel bar stress monitoring data and risk early warning information in the forward construction process of the tunnel face, and the combined working form and specific data flow of each unit are shown in the figures 10, 11 and 12.
As shown in fig. 1-7, 13 and 14, based on the above scheme, the present invention first develops a PBA structural deformation risk prediction system based on an intelligent response surface method, which is applied to a monitoring process of soil pressure at tunnel section vaults, shoulders and arches and steel bar stress at arches and arches, while in a conventional structural deformation risk prediction early warning process, no importance is attached to monitoring of the distance between a section and a tunnel surface. However, monitoring the change of the distance between the section and the tunnel face will affect the data of soil pressure and reinforcing steel bar stress, which represents the influence of the space effect of tunnel excavation, along with the tunnel excavation, rocks which originally support the upper stratum are excavated, the supporting balance of surrounding rocks is destroyed, the soil pressure and the reinforcing steel bar stress of the primary lining will establish new balance, and the balance will bring about the displacement of the surrounding rocks; as the excavated section advances, the old balance is continuously broken, the new balance is continuously established, and the displacement continuously generates new displacement. And a soil pressure and steel bar stress prediction model is established in the data processing unit by using a vector machine and a bacterial foraging algorithm, rolling prediction of the engineering is realized, prediction and early warning data are uploaded to a cloud, and real-time acquisition of multi-terminal data can be realized.
The data acquisition unit mainly comprises four parts: 1. a data monitoring section; 2. a data acquisition section; 3. A protective structure portion; 4. a support structure portion. The data monitoring part 1 can monitor the soil pressure at the arch crown, the arch shoulder and the arch waist of the tunnel section and the steel bar stress at the arch crown and the arch waist, collect the corresponding tunnel space characteristic information, and preferably adopt a soil pressure box and a steel bar meter; the data acquisition part 2 is connected with a monitoring instrument through a data line, collects corresponding monitoring data and transmits the monitoring data through an antenna in a wireless mode.
The data monitoring part 1 and the data acquisition part 2 are accommodated in the protection structure part 3, the protection structure part 2 can realize the protection of instruments, and the main protection targets are the water outlet environment, the high dust particle environment, the engineering equipment collision and the like in the tunnel; the support structure portion 4 is capable of fixing the data monitoring portion 1, the data acquisition portion 2 and the protection structure portion 3 to the side wall of the tunnel. The working flow chart of the collection box is shown in fig. 12.
In a specific embodiment, as shown in fig. 2 and 3, the data monitoring part includes: the soil pressure box 1, the pressure box bracket 2, the soil pressure box signal output line 3, the steel bar 4, the steel bar meter 5, the steel bar meter signal output line 6, the arch crown soil pressure monitoring point 29, the arch shoulder soil pressure monitoring point 30, the arch crown soil pressure monitoring point 31, the arch crown steel bar stress monitoring point 32 and the arch crown steel bar stress monitoring point 33; the soil pressure box 1 is fixed on a vault soil pressure monitoring point 29, a vault soil pressure monitoring point 30 and a vault soil pressure monitoring point 31 through a pressure box bracket 2 welded on a primary lining steel bar and is used for acquiring soil pressure monitoring data of corresponding vaults, vaults and vaults; the steel bar meter 5 is used for acquiring steel bar stress monitoring data corresponding to the arch crown and the arch crown by welding two ends of the steel bar 4 on primary lining steel bars of the arch crown steel bar stress monitoring point 32 and the arch crown steel bar stress monitoring point 33;
In a more specific embodiment, after the soil pressure box 1 and the rebar meter 5 are fixed at the vault soil pressure monitoring point 29, the vault soil pressure monitoring point 30, the vault soil pressure monitoring point 31, the vault rebar stress monitoring point 32 and the vault rebar stress monitoring point 33 as shown in fig. 8, the positions of the soil pressure box signal output line 3 and the rebar meter signal output line 6 need to be adjusted to ensure that the signal output line is not damaged in the field, and further ensure that a sufficient length is lined outside a tunnel.
In a more specific embodiment, the present case can define specific types and properties of the monitoring instrument, namely, the soil pressure box 1 and the rebar meter 5 adopt vibrating wire type instruments, so as to facilitate the collection of the data collection equipment.
In a specific embodiment, as shown in fig. 4, the data acquisition part includes: the system comprises a collecting box signal transmitting antenna 7, a collecting box 8, a data wireless transmitter 9, a power line 12, a soil pressure box signal input line 13, a reinforcement meter signal input line 14, a collecting box power supply (220 v to 24 v) 15 and an automatic collector 16; the data wireless transmitter 9, the power line 12, the acquisition box power supply (220 v to 24 v) 15 and the automatic acquisition device 16 are all arranged in the acquisition box 8, and the acquisition box power supply (220 v to 24 v) 15 is responsible for supplying power to the data wireless transmitter 9 and the automatic acquisition device 16; the automatic collector 16 is connected with the reinforcement meter signal input line 14 through the soil pressure box signal input line 13 so as to collect soil pressure at the arch crown, the arch shoulder and the arch waist and reinforcement stress at the arch crown and the arch waist; the data wireless transmitter 9 is connected with the automatic acquisition device 16 through a data line, and realizes wireless transmission of data of the automatic acquisition device 16 through an acquisition box signal transmitting antenna 7 outside an acquisition box.
In a more specific embodiment, the soil pressure cell signal input line 13 and the reinforcement meter signal input line 14 are respectively connected to the soil pressure cell signal output line 3 and the reinforcement meter signal output line 6, and the signal lines can only be connected in 1 to 1 mode.
In a specific embodiment, as shown in fig. 4, the protection structure part comprises a box structure formed by a collecting box 8 and a water-proof outer edge 17 of the collecting box; the whole collecting box 8 is formed by welding 3mm thick 304 stainless steel, and one side of the cover plate is fixed on the collecting box 8 through a rotating shaft so that the cover plate can rotate around the rotating shaft; the top of the collection box 8 is provided with a box waterproof outer edge 17, and water dripping in the hole is prevented. The stainless steel with the thickness of 304 being 3mm has a certain impact resistance, and can prevent the flying stone generated by tunnel guniting from sputtering to damage the instrument.
In a more specific embodiment, as shown in fig. 4 and 6, the supporting structure part includes water bracket expansion screws 10, wherein the bracket expansion screws 10 are fixed in the lining of the tunnel, and the bracket 11 is fixed on the side wall of the tunnel through the bracket expansion screws 10, so that a certain height of the collection box from the bottom of the tunnel is ensured.
The soil pressure and steel bar stress data measured by the data acquisition unit are transmitted to the data transmission unit in a wireless mode through the acquisition box signal transmitting antenna.
The data transmission unit mainly comprises four parts: 1. a data receiving section; 2. a data transmission section; 3. A protective structure portion; 4. a support structure portion. The data receiving part 1 can receive the data transmitted by the acquisition box in a wireless way and transmit the data to the data transmission part through a data line; the data transmission part 2 transmits the monitoring data to a remote data processing unit by a GPRS signal transmitter and a wireless antenna. The data receiving part 1 and the data transmitting part 2 are contained in the protection structure part 3, the protection structure part 3 can realize the protection of instruments, and the main protection targets are the water outlet environment, the high dust particle environment, the engineering equipment collision and the like in the tunnel; the support structure portion 4 is capable of fixing the data receiving portion 1, the data transmitting portion 2 and the protection structure portion 3 to the side wall of the tunnel. The transmission workflow is shown in fig. 12.
In a specific embodiment, as shown in fig. 5, the data receiving part includes: a transmission case signal receiving antenna 18, a transmission case 19, a transmission case power line 23, a transmission case power (220 v to 24 v) 24, and a data wireless receiver 25; wherein, the transmission box power supply (220 v to 24 v) 24 and the data wireless receiver 25 are arranged inside the transmission box 19; the transmission box signal receiving antenna 18 is used for receiving the soil pressure and steel bar stress monitoring data wirelessly transmitted by the acquisition box signal transmitting antenna 7 and transmitting the monitoring data to the data wireless receiver 25; the data wireless receiver 25 is powered by a box power supply (220 v to 24 v) 24;
In a specific embodiment, as shown in fig. 5, the data transmission part includes: the transmission box 19, the GPRS signal transmitter 21, the GPRS signal transmitting antenna 22, the transmission box power line 23 and the transmission box power (220 v to 24 v) 24; wherein, the GPRS signal transmitter 21 and the transmission box power supply (220 v-24 v) 24 are arranged in the transmission box 19, and the transmission box power supply (220 v-24 v) 24 is responsible for supplying power to the GPRS signal transmitter 21; the GPRS signal transmitter 21 receives the signal of the data radio receiver 25 via a data line and transmits the received monitoring data to a remote data processing unit via a GPRS signal transmitting antenna 22.
In a specific embodiment, as shown in the figure, the protection structure part comprises a box structure consisting of a transmission box 19 and a waterproof outer edge 20 of the transmission box; the whole transmission box 19 is formed by welding 3mm thick 304 stainless steel, and one side of the cover plate is fixed on the transmission box 19 through a rotating shaft so that the cover plate can rotate around the rotating shaft; the top of the transfer box 19 has a waterproof outer edge 20 of the transfer box, which prevents dripping in the hole. The stainless steel with the thickness of 304 being 3mm has a certain impact resistance, and can prevent the flying stone generated by tunnel guniting from sputtering to damage the instrument.
In a more specific embodiment, as shown in fig. 5 and 6, the support structure portion includes: the water bracket expansion screw 10, wherein the bracket expansion screw 10 is fixed in the lining of the tunnel, the bracket 11 is fixed on the side wall of the tunnel through the bracket expansion screw 10, and a certain height of the transmission box 19 from the bottom of the tunnel is ensured.
In a specific embodiment, the method of the present invention comprises:
s1, establishing sample data { x ] based on time sequence i }＝{x 1 ,x 2 ,…,x n -the sample data comprises dome earth pressure, shoulder earth pressure, arch waist earth pressure, dome rebar stress, arch waist rebar stress;
s2, according to the least square support vector machine theory, the nonlinear deformation relation can acquire the nonlinear relation between the soil pressure value and the reinforcement stress value change sequence through learning the acquired actual measured soil pressure value and reinforcement stress value by the support vector machine;
s3, determining a mapping relation corresponding to the training set to obtain a nonlinear mapping model, and optimizing the nonlinear mapping model through trend operation, copy operation and migration operation of a bacterial foraging algorithm based on the nonlinear mapping model to realize rolling prediction of structural deformation risks.
Optionally, in one embodiment, the S2 includes:
s21, acquiring soil pressure and steel bar stress data of a monitoring section corresponding to each forward movement of the face under one product based on the determined distance data of the monitoring position from the face;
s22, predicting the nonlinear change sequence, namely searching the soil pressure and the steel bar stress at the time p+1 and the soil pressure and the steel bar stress x at the previous time p 1 ,x 2 ,…,x p The relation of (a), x p+1 ＝f(x 1 ,x 2 ,…,x p ) For the learning function, the nonlinear relation between the soil pressure and the reinforcement stress variation sequence is represented.
S23, learning time sequence data by utilizing a least square support vector machine theory: for n-p deformation sequences x i ,x i+1 ,…,x i+p Learning (i=1, 2, …, n-p) to obtain the nonlinear relation between the soil pressure and the steel bar stress change sequence,
in which y (x) p+1 ) Is the soil pressure and the reinforcement stress value x at the p+1 time p+1 Is the stress value x of the first p soil pressures and the reinforcing steel bars at the time p+1 p+1 ＝f(x 1 ,x 2 ,…,x p+1 ) For the first p soil pressure and reinforcement stress values at the p+k moment, x k ＝(x k ,x k+1 ,…,x k+p+1 )。
Optionally, in one embodiment, the step S3 includes:
s31, under the condition, the mapping form of the learning sample is set as { x } 1 ,x 2 ,…,x p }→{x p+1 }， {x 2 ,x 3 ,…,x p+1 }→{x p+2 }…{x n-p ,x n-p+1 ,…,x n-1 }→{x n }. The result of the vector machine learning is that the data of the current position can be predicted by P pieces of history data before the prediction point, such as x is needed to be predicted n+1 Only input, { x n-p+1 ,x n-p+2 ,…,x n Obtaining a prediction result; then predict the obtained x n+1 As a known quantity, { x } n-p+2 ,x n-p+3 ,…,x n+1 As a new timing pair x n+2 And (5) predicting.
S32, however, some errors exist in each prediction step in the prediction process, and as the number of the prediction steps increases, the errors are accumulated continuously, and finally, the prediction result can not accurately express the real working condition. To reduce the effect of such errors, Q impact parameters that can be accurately determined are added to the input timing sequence, and each prediction process is effectively modified. The bacterial foraging optimization algorithm BFAA is utilized to search the optimal parameters through trending operation, copying operation and migration operation, and the problem that the nonlinear prediction algorithm of the least square support vector machine has high dependence on the optimal historical step number p and the influence factor Q is solved in an optimized mode.
S33, based on the optimized nonlinear prediction model, corresponding rolling prediction is carried out on the sampling data obtained by the data acquisition unit.
Example 2
Step one, monitoring device installation and measurement data acquisition:
firstly, installing a soil pressure box and a reinforcing steel bar meter to corresponding measuring points (as shown in fig. 8) and protecting corresponding soil pressure box signal output lines and reinforcing steel bar meter signal output lines during tunnel construction; the method comprises the steps of installing (shown in figure 6) a collecting box bracket at a tunnel side wall at a proper position near a monitoring broken surface (shown in figure 7), installing a transmission box bracket at a tunnel inlet, fixing the bracket on the side wall by using an expansion bolt, and after the collecting box and the transmission box are placed on the bracket, passing steel wires through holes at the bottom of a box body to ensure firm installation of the bracket; secondly, respectively connecting a soil pressure box signal input line and a reinforcement meter signal input line of the collecting box with a soil pressure box signal output line and a reinforcement meter signal output line exposed outside the position lining, debugging an instrument, and ensuring that an automatic collector can collect data; and thirdly, adjusting the positions of the signal transmitting antenna of the acquisition box, the signal receiving antenna of the transmission box and the GPRS signal transmitting antenna, ensuring that the transmission box can receive the signal of the acquisition box, enabling a remote computer to receive the signal of the transmission box, and finally, connecting the data acquisition unit, the data transmission unit and the data processing unit.
Step two, monitoring data transmission and data integration:
firstly, collecting vibration string values of a soil pressure box and a reinforcing steel bar meter, and utilizing a formulaCalculating soil pressure and steel bar stress; secondly, the data wireless transmitter transmits the soil pressure and the steel bar stress measured by the automatic collector to the transmission box through the signal transmitting antenna of the collection box; and secondly, the data wireless receiver of the transmission box receives monitoring data through the signal receiving antenna of the transmission box, and then the monitoring data is transmitted to the data processing unit through the GPRS signal transmitting antenna by using the GPRS signal transmitter, and the data processing unit firstly processes the data into event sequence monitoring data.
Thirdly, calculating time sequence monitoring data based on a vector machine and a bacterial foraging algorithm to obtain an optimized prediction model, and performing rolling prediction on soil pressure and steel bar stress: the creation process of the rolling prediction comprises the following steps:
s1, establishing sample data { x ] based on time sequence i }＝{x 1 ,x 2 ,…,x n -the sample data comprises dome earth pressure, shoulder earth pressure, arch waist earth pressure, dome rebar stress, arch waist rebar stress;
s2, according to the least square support vector machine theory, the nonlinear deformation relation can acquire the nonlinear relation between the soil pressure value and the reinforcement stress value change sequence through learning the acquired actual measured soil pressure value and reinforcement stress value by the support vector machine; the step S2 comprises the following steps:
S21, acquiring soil pressure and steel bar stress data of a monitoring section corresponding to each forward movement of the face under one product based on the determined distance data of the monitoring position from the face;
s22, predicting the nonlinear change sequence, namely searching soil pressure and steel bar stress at the time p+1 and soil at the previous time pPressure and steel bar stress x 1 ,x 2 ,…,x p The relation of (a), x p+1 ＝f(x 1 ,x 2 ,…,x p ) For the learning function, the nonlinear relation between the soil pressure and the reinforcement stress variation sequence is represented.
S23, utilizing a least square support vector machine theory, for a given N training samples { x } i ,y i } i＝1…N (wherein x i ∈R n Training input sample y for n dimensions i ∈R n To train output samples), the objective optimization function isLearning the target optimization parameters and time sequence data: for n-p deformation sequences x i ,x i+1 ,…,x i+p Learning (i=1, 2, …, n-p) to obtain a nonlinear relationship (LSSVM regression function) between soil pressure and rebar stress variation sequence: />Wherein: k (x, x) k )＝exp{||x-x i || 2 /σ 2 And (kernel function using radial basis kernel function).
In which y (x) p+1 ) Is the soil pressure and the reinforcement stress value x at the p+1 time p+1 Is the stress value x of the first p soil pressures and the reinforcing steel bars at the time p+1 p+1 ＝f(x 1 ,x 2 ,…,x p+1 ) For the first p soil pressure and reinforcement stress values at the p+k moment, x k ＝(x k ,x k+1 ,…,x k+p+1 )。
S3, determining a mapping relation corresponding to the training set to obtain a nonlinear mapping model, and optimizing the nonlinear mapping model through trend operation, copy operation and migration operation of a bacterial foraging algorithm based on the nonlinear mapping model to realize rolling prediction of structural deformation risks.
S31, under the condition, the mapping form of the learning sample is set as { x } 1 ,x 2 ,…,x p }→{x p+1 }， {x 2 ,x 3 ,…,x p+1 }→{x p+2 }…{x n-p ,x n-p+1 ,…,x n-1 }→{x n }. The result of the vector machine learning is that the data of the current position can be predicted by P pieces of history data before the prediction point, such as x is needed to be predicted n+1 Only input, { x n-p+1 ,x n-p+2 ,…,x n Obtaining a prediction result; then predict the obtained x n+1 As a known quantity, { x } n-p+2 ,x n-p+3 ,…,x n+1 As a new timing pair x n+2 And (5) predicting.
S32, however, some errors exist in each prediction step in the prediction process, and as the number of the prediction steps increases, the errors are accumulated continuously, and finally, the prediction result can not accurately express the real working condition. To reduce the effect of such errors, Q impact parameters that can be accurately determined are added to the input timing sequence, and each prediction process is effectively modified. The bacterial foraging optimization algorithm BFAA is utilized to search the optimal parameters through trending operation, copying operation and migration operation, and the problem that the nonlinear prediction algorithm of the least square support vector machine has high dependence on the optimal historical step number p and the influence factor Q is solved in an optimized mode.
S321, tendering operation, wherein θ (i, g, n, m) represents the position information of the individual i after g chemotactic operations, n replication operations and m migration operations for a community with a population size S, C (i) represents the step size, and Δ represents [ -1, 1) ]A random vector of units is added to the random vector,indicating the randomly adjusted direction, then the positional formula of the chemotactic cycle can be expressed as: />
S322, copy operation: the copying operation follows the natural selection rule of the winner and the winner, when the trend operation reaches the critical frequency, all data are orderly arranged from big to small by taking the adaptive value as the evaluation index, and the total data number is recorded as 2S r The method comprises the steps of carrying out a first treatment on the surface of the S with smaller adaptation value to be arranged in the latter half r The data undergo an extinction operation before S is reserved r Data with larger adaptation values; the remaining good individuals are replicated to obtain data with the same foraging capacity as the good individuals. The copy operation is completed once.
S323, migration operation: when the migration operation occurs in the change of the resource environment, the execution reference point is N for the copy operation re After the step. Migration operations mean two different outcomes: the data is migrated entirely to another area or the set of data is canceled.
S33, based on the optimized nonlinear prediction model, corresponding rolling prediction is carried out on the sampling data obtained by the data acquisition unit.
Fourth, predicting early warning results to be released: and formally using the obtained optimized prediction early-warning model pair, installing the model pair to a data acquisition processing module, integrating and calculating real-time acquired data, obtaining a prediction early-warning result, and sending the prediction early-warning result to a cloud end to realize real-time access inquiry of multiple terminals to the calculation result.
By implementing the embodiment of the invention, firstly, a PBA structure deformation risk prediction method based on an intelligent response surface method is developed on the basis of automatic acquisition of the Internet of things so as to overcome the defects of high risk, low frequency and large error of manual measurement, further realize real-time automatic accurate monitoring of the soil pressure and the steel bar stress of the tunnel and provide guarantee for the safe construction of the tunnel; meanwhile, the fixation of the collecting box and the transmission box, the waterproof and anti-collision protection of instruments are considered, and the service lives of the collecting box and the transmission box are prolonged; the short-distance wireless transmission system and the GPRS information transmission assembly are used for replacing long-distance signal data lines, so that the monitoring cost is effectively reduced, and the applicability of the equipment is improved. The data transmission unit can upload data to the cloud, and can realize real-time acquisition of multiple terminals; and the data processing unit can train a nonlinear prediction model according to the monitoring data of the cloud to realize rolling prediction of the structural deformation risk. The method is suitable for the dynamic design process of tunnel construction and improves the intelligent process of tunnel engineering.
Example 3
The data acquisition unit can simultaneously acquire the soil pressure at the arch crown, the arch shoulder and the arch waist of the tunnel and the change monitoring data of the stress of the reinforcing steel bars at the arch crown and the arch waist;
The data transmission unit can integrate the soil pressure and the steel bar stress data acquired by the data acquisition unit to form time sequence monitoring data and send the time sequence monitoring data to the data processing unit;
and the data processing unit establishes a nonlinear mapping relation between the monitoring data and the soil pressure and the steel bar stress of the tunnel through a fitting algorithm based on a least square support vector machine, predicts the soil pressure and the steel bar stress in the construction process by adopting a bacterial foraging algorithm based on a time sequence, and sends out monitoring and early warning if the predicted value exceeds the warning value three times in a day.
Optionally, in one embodiment, the data acquisition unit includes:
the measuring structure is used for monitoring and acquiring displacement change monitoring data in three directions of vault settlement, hole circumference convergence and distance between a monitoring position and a tunnel face;
a protective structure, the protective structure housing each of the measurement structures;
and a support structure capable of securing the protective structure to a tunnel sidewall.
Optionally, in one embodiment, the data acquisition unit includes:
the data monitoring part is used for monitoring and acquiring change monitoring data of the soil pressure at the arch crown, the arch shoulder and the arch waist and the steel bar stress at the arch crown and the arch waist;
The data acquisition unit is used for collecting and sending the data monitoring unit to the data transmission unit;
a protective structure portion accommodating each of the data collection portions;
a support structure portion capable of securing the protective structure portion to a tunnel sidewall.
Optionally, in one embodiment, the data monitoring portion includes: soil pressure vault measuring module, soil pressure arch waist measuring module, reinforcing steel bar stress vault measuring module and reinforcing steel bar stress arch waist measuring module; the soil pressure vault measuring module is fixed on the vertical supporting frame through a bracket and is used for acquiring sampling data corresponding to the vault monitoring direction; the soil pressure arch shoulder measuring module is fixed on the vertical support frame through a bracket and used for acquiring sampling data corresponding to the arch shoulder monitoring direction; the soil pressure arch measuring module is fixed on the vertical support frame through a bracket and used for acquiring sampling data corresponding to the arch monitoring direction; the steel bar stress vault measuring module is fixed on the vertical support frame through welding and is used for acquiring sampling data corresponding to the vault monitoring direction; the steel bar stress arch measuring module is fixed on the vertical support frame through welding and used for acquiring sampling data corresponding to the arch monitoring direction;
Optionally, in one embodiment, the data acquisition portion
Comprising the following steps: the data automatic acquisition module and the data automatic transmission module; the automatic data acquisition module is characterized in that a soil pressure box signal input line of the automatic data acquisition module is connected with a soil pressure box signal output line, and the automatic data acquisition module is connected with a rebar meter signal output line through a rebar meter signal input line to realize real-time monitoring of the data monitoring part. The data automatic transmission module is connected with the data automatic acquisition module through a data line, so that the acquired data can be sent to the data transmission unit in real time.
Optionally, in one embodiment, the automatic data transmission module includes an automatic collector and a collection box signal transmitting antenna, and the automatic collector is connected with the collection box signal transmitting antenna through a data line; the collecting box signal transmitting antenna can realize short-distance data wireless transmission.
Optionally, in one embodiment, the protection structure part includes a box structure formed by a top plate, a side plate and a bottom plate, and a waterproof outer edge of the collection box; the front side plate is fixed on the side plate through a rotating shaft, so that the front side plate can rotate around the rotating shaft, and the box body structure is opened for installation and adjustment; and the waterproof outer edge of the collecting box is positioned at the outer edge of the top plate, so that dripping water in a hole can be prevented from penetrating into the collecting box.
Optionally, in one embodiment, the support structure portion includes a bracket bolt, wherein the bracket is fixed to the tunnel sidewall by the bracket bolt, and there are two brackets under one collection box.
Optionally, in one embodiment, the data acquisition unit includes:
a data receiving section for receiving data wirelessly transmitted by the data acquisition unit described in claim 2;
a data transmission part for transmitting the data received by the data receiving part to the data processing unit;
a protective structure portion accommodating each of the data collection portions;
a support structure portion capable of securing the protective structure portion to a tunnel sidewall.
Optionally, in one embodiment, the data receiving part includes a data wireless receiver and a transmission box signal receiving antenna, and the data wireless receiver is connected to the transmission box signal receiving antenna through a data line; the transmission box signal receiving antenna can realize short-distance data wireless transmission.
Optionally, in one embodiment, the data transmission part includes a GPRS signal transmitter and a GPRS signal transmitting antenna, where the GPRS signal transmitter is connected to the data wireless receiver through a data line and is used for receiving data; the GPRS signal transmitter is connected with the GPRS signal transmitting antenna through a data line, and transmits data to the data processing unit through the GPRS signal transmitting antenna.
Optionally, in one embodiment, the structural deformation risk prediction and early warning based on the vector machine and the bacterial foraging algorithm corresponding to the data processing unit includes:
s1, establishing sample data based on time sequence{x i }＝{x 1 ,x 2 ,…,x n -the sample data comprises dome earth pressure, shoulder earth pressure, arch waist earth pressure, dome rebar stress, arch waist rebar stress;
s2, according to the least square support vector machine theory, the nonlinear deformation relation can acquire the nonlinear relation between the soil pressure value and the reinforcement stress value change sequence through learning the acquired actual measured soil pressure value and reinforcement stress value by the support vector machine;
s3, determining a mapping relation corresponding to the training set to obtain a nonlinear mapping model, and optimizing the nonlinear mapping model through trend operation, copy operation and migration operation of a bacterial foraging algorithm based on the nonlinear mapping model to realize rolling prediction of structural deformation risks.
Optionally, in one embodiment, the S2 includes:
s21, acquiring soil pressure and steel bar stress data of a monitoring section corresponding to each forward movement of the face under one product based on the determined distance data of the monitoring position from the face;
S22, predicting the nonlinear change sequence, namely searching the soil pressure and the steel bar stress at the time p+1 and the soil pressure and the steel bar stress x at the previous time p 1 ,x 2 ,…,x p The relation of (a), x p+1 ＝f(x 1 ,x 2 ,…,x p ) For the learning function, the nonlinear relation between the soil pressure and the reinforcement stress variation sequence is represented.
S23, learning time sequence data by utilizing a least square support vector machine theory: for n-p deformation sequences x i ,x i+1 ,…,x i+p Learning (i=1, 2, …, n-p) to obtain the nonlinear relation between the soil pressure and the steel bar stress change sequence,
in which y (x) p+1 ) Is the soil pressure and the reinforcement stress value x at the p+1 time p+1 Is the stress value x of the first p soil pressures and the reinforcing steel bars at the time p+1 p+1 ＝f(x 1 ,x 2 ,…,x p+1 ) For the first p soil pressure and reinforcement stress values at the p+k moment, x k ＝(x k ,x k+1 ,…,x k+p+1 )。
Optionally, in one embodiment, the step S3 includes:
s31, under the condition, the mapping form of the learning sample is set as { x } 1 ,x 2 ,…,x p }→{x p+1 }， {x 2 ,x 3 ,…,x p+1 }→{x p+2 }…{x n-p ,x n-p+1 ,…,x n-1 }→{x n }. The result of the vector machine learning is that the data of the current position can be predicted by P pieces of history data before the prediction point, such as x is needed to be predicted n+1 Only input, { x n-p+1 ,x n-p+2 ,…,x n Obtaining a prediction result; then predict the obtained x n+1 As a known quantity, { x } n-p+2 ,x n-p+3 ,…,x n+1 As a new timing pair x n+2 And (5) predicting.
S32, however, some errors exist in each prediction step in the prediction process, and as the number of the prediction steps increases, the errors are accumulated continuously, and finally, the prediction result can not accurately express the real working condition. To reduce the effect of such errors, Q impact parameters that can be accurately determined are added to the input timing sequence, and each prediction process is effectively modified. The bacterial foraging optimization algorithm BFAA is utilized to search the optimal parameters through trending operation, copying operation and migration operation, and the problem that the nonlinear prediction algorithm of the least square support vector machine has high dependence on the optimal historical step number p and the influence factor Q is solved in an optimized mode.
S33, based on the optimized nonlinear prediction model, corresponding rolling prediction is carried out on the sampling data obtained by the data acquisition unit.
The beneficial effects are that:
1. the method overcomes the defects of high risk, low frequency and large error of manual measurement, further realizes real-time automatic accurate monitoring of the soil pressure and the steel bar stress of the tunnel, and provides guarantee for the safe construction of the tunnel; meanwhile, the fixing of the collecting box and the transmission box, the waterproof and anti-collision protection of the instrument are considered, and the service lives of the collecting box and the transmission box are prolonged; the short-distance wireless transmission system and the GPRS information transmission assembly are used for replacing long-distance signal data lines, so that the monitoring cost is effectively reduced, and the applicability of the equipment is improved. The data transmission unit can upload data to the cloud, and can realize real-time acquisition of multiple terminals; and the data processing unit can train a nonlinear prediction model according to the monitoring data of the cloud to realize rolling prediction of the structural deformation risk.
2. A set of internet of things monitoring system suitable for a hole pile method (PBA) underground excavation station is constructed, real-time monitoring and automatic uploading of monitoring data of soil pressure and reinforcing steel bar stress are realized, and the defects of high risk, low frequency and large error of manual measurement are overcome.
3. The short-distance wireless transmission system and the GPRS information transmission assembly are used for replacing long-distance signal data lines, so that the monitoring cost is effectively reduced, and the applicability of the equipment is improved.
4. The detection box and the transmission box are provided with corresponding waterproof and dustproof protective shells and placing supports, so that the practicability of the equipment in a complex limited space construction environment is improved.
5. The remote data processing unit forms a nonlinear prediction model trained according to monitoring data of the cloud by using a vector machine optimized by a bacterial foraging algorithm, can conduct advanced prediction on soil pressure and steel bar stress, and can conduct structural deformation risk early warning on data exceeding early warning values.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (6)
1. A system for predicting risk of deformation of a PBA structure, comprising: the device comprises a data acquisition unit, a data transmission unit and a data processing unit;
the data acquisition unit is used for acquiring the soil pressure at the arch crown, the arch shoulder and the arch waist of the tunnel and the change monitoring data of the steel bar stress at the arch crown and the arch waist;
the data transmission unit is used for integrating the soil pressure and the steel bar stress data acquired by the data acquisition unit to form time sequence monitoring data and transmitting the time sequence monitoring data to the data processing unit;
the data processing unit is used for establishing a nonlinear mapping relation between monitoring data and tunnel soil pressure and reinforcing steel bar stress through a fitting algorithm based on a least square support vector machine, and predicting the soil pressure and the reinforcing steel bar stress in the construction process by adopting a bacterial foraging algorithm based on a time sequence; the data acquisition unit includes: the device comprises a data monitoring part, a data acquisition part, a protection structure part and a support structure part;
the data monitoring part is used for monitoring and acquiring change monitoring data of soil pressure at the arch crown, the arch shoulder and the arch waist and the stress of the reinforcing steel bars at the arch crown and the arch waist;
the data acquisition part is used for collecting and sending the data monitoring unit to the data transmission unit; the data transmission unit includes a data receiving section; the data receiving section includes: the data wireless receiver is connected with the transmission box signal receiving antenna through a data line; the transmission box signal receiving antenna is used for wireless transmission of data;
The protection structure part is used for accommodating each data acquisition part; so as to realize water resistance, flying stone sputtering resistance, dust particle resistance and engineering equipment collision resistance;
the protection structure part comprises a box body structure formed by a top plate, side plates and a bottom plate and a waterproof outer edge of the collection box; the side plates comprise a front side plate and a side plate; the front side plate is fixed on the side plate through a rotating shaft, so that the front side plate can rotate around the rotating shaft, and the box body structure is opened for installation and adjustment; the waterproof outer edge of the collecting box is positioned at the outer edge of the top plate, so that dripping water in a hole can be prevented from penetrating into the collecting box;
the support structure portion is used to secure the protection structure portion to a tunnel sidewall.
2. A PBA structural deformation risk prediction system according to claim 1, wherein the data monitoring section comprises: a soil pressure vault measuring module, a soil pressure arch waist measuring module, a steel bar stress vault measuring module and a steel bar stress arch waist measuring module;
the soil pressure vault measuring module is used for acquiring sampling data corresponding to the vault monitoring direction;
the soil pressure arch shoulder measuring module is used for acquiring sampling data corresponding to the arch shoulder monitoring direction;
The soil pressure arch measuring module is used for acquiring sampling data corresponding to the arch monitoring direction;
the steel bar stress vault measuring module is used for acquiring sampling data corresponding to the vault monitoring direction;
the steel bar stress arch measuring module is used for acquiring sampling data corresponding to the arch monitoring direction.
3. A PBA structural deformation risk prediction system according to claim 1, wherein the data acquisition section comprises: the data automatic acquisition module and the data automatic transmission module;
the data automatic acquisition module is used for monitoring the data monitoring part in real time;
the data automatic transmission module is connected with the data automatic acquisition module through a data line, so that the acquired data can be sent to the data transmission unit in real time.
4. A method of using a PBA structural deformation risk prediction system according to any of claims 1-3, comprising:
s1, the data processing unit receives monitoring data transmitted by the data transmission unit and establishes sample data based on a time sequence, wherein the sample data comprises: values for the arch crown soil pressure, arch shoulder soil pressure, arch crown reinforcement stress on day i;
S2, the data processing unit learns time sequence data by utilizing a least square support vector machine theory to obtain a nonlinear relation between a soil pressure value and a reinforcement stress value change sequence;
s3, the data processing unit determines a mapping relation corresponding to a training set formed by means of time sequence monitoring data to obtain a nonlinear prediction model, and optimizes the nonlinear mapping model through trend operation, copy operation and migration operation of a bacterial foraging algorithm based on the nonlinear prediction model to realize rolling prediction of structural deformation risks.
5. The method of claim 4, wherein S2 comprises:
s21, acquiring soil pressure and steel bar stress data of a monitoring section of the tunnel face based on the determined distance data of the monitoring position from the tunnel face;
s22, predicting the nonlinear change sequence, namely obtaining the relation between the soil pressure and the steel bar stress at the time p+1 and the soil pressure and the steel bar stress at the time p before;
s23, training the relation between the soil pressure and the steel bar stress change through time sequence data by utilizing a least square support vector machine theory to obtain a nonlinear relation between the soil pressure and the steel bar stress change sequence:
Wherein y (x) p+1 ) The soil pressure and the steel bar stress value at the p+1 time are obtained; b is the offset;as a kernel function, K (x, x k )＝exp{||x-x i || 2 /σ 2 }，σ 2 Is the square bandwidth of the gaussian RBF kernel.
6. A method of predicting risk of deformation of a PBA structure according to claim 4, wherein said data processing unit, said S3 comprises:
s31, establishing a nonlinear prediction model by utilizing a nonlinear relation between a soil pressure value and a reinforcement stress value change sequence, wherein the nonlinear prediction model is used for predicting data of a current position p+1 through the previous P pieces of historical data;
s32, adding groundwater level influence parameters into an input time sequence, and correcting each prediction process:
s321, trending operation: any possible increase or decrease change in the set range is carried out on the data value for a set of time series monitoring data, so as to obtain a set of new time series monitoring data, and the operation is repeated;
s322, copy operation: sequentially arranging all time series monitoring data from large to small according to the absolute value of the predicted value distance monitoring value as an evaluation index when the trend operation reaches the set critical frequency, deleting 50% of data, and copying the data of the previous 50%;
s323, migration operation: when the migration operation occurs in the process of predicting the next data, the execution reference point is the copying operation, and after the copying operation is carried out to a set limit step, if the data meets the requirements, the data is reserved; if the data does not meet the requirements, deleting the data;
S33, performing corresponding rolling prediction on the sampling data obtained by the data acquisition unit based on a nonlinear prediction model optimized by a bacterial foraging algorithm.
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