CN111582564A - Method for predicting TBM rock mass condition-related utilization rate - Google Patents
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Abstract
The invention provides a method for predicting the condition-related utilization rate of a TBM rock mass, which comprises the following steps: determining a concept and a calculation model of the TBM rock mass condition related utilization rate; building a TBM predictive analysis database; analyzing the influence mechanism of CAI values of different lithological rocks in different dry and wet states; determining the correlation among the RMR value of a rock mass grading system, the rock wear resistance CAI and the TBM rock mass condition correlation utilization rate; establishing a prediction model of the TBM rock mass condition related utilization rate; and searching for the optimal arrangement of parameters in the tunneling process by comparing and analyzing the predicted value and the measured value of the TBM rock mass condition related utilization rate. The invention comprehensively utilizes the RMR value and the CAI values in different water-containing states to establish a TBM rock mass condition related utilization rate prediction model, and solves the technical problem that the TBM tunneling performance cannot be accurately predicted in TBM construction.
Description
Technical Field
The invention belongs to the technical field of TBM tunneling performance prediction, and particularly relates to a method for predicting TBM rock mass condition related utilization rate.
Background
The TBM tunnel construction has the advantages of high tunneling efficiency, good tunneling quality, safety, environmental protection, high automation degree and the like, and becomes a preferential selection method for long tunnel construction in China. But the compliance of the TBM construction to geological conditions is obvious, and the TBM tunneling performance under different geological conditions is obviously different. When extreme unfavorable geological conditions occur, the adaptability of the TBM is extremely poor, so that the tunneling efficiency is low, even accidents such as blocking occur, the construction cost is greatly increased, and the construction period is delayed. Therefore, the method can accurately predict the tunneling performance of the TBM under different geological conditions, and has great significance for tunnel construction period prediction, cost control and the like. The TBM utilization rate is taken as one of important factors influencing the tunneling performance of the TBM, the engineering period and economic risk can be reduced by accurately evaluating and predicting the TBM utilization rate, and therefore the maximization of the TBM construction benefit is achieved.
However, since the utilization rate of the TBM is affected by many factors such as geological conditions, mechanical parameters, tunneling parameters, construction management, etc., it is very difficult to construct a prediction model similar to the constitutive relation of rock and soil theoretically to describe the correlation between the utilization rate of the TBM and various influencing factors thereof completely and accurately. In addition, the fluctuation range of the TBM utilization rate is large, and the TBM utilization rates of different projects are greatly different. The occupied time of mechanical maintenance, support and step change and the like caused by mechanical factors is relatively fixed, and the fluctuation influence on the utilization rate of the TBM is small; due to the complexity of rock-machine interaction, geological conditions are uncertain in the TBM tunneling process, and the variable of geological factors is large, so that the time for the TBM to stop is short, the fluctuation range of the TBM utilization rate is seriously influenced, and the accurate prediction of the TBM utilization rate is very difficult.
Therefore, aiming at the problem of uncertainty of geological conditions in the tunneling process, only the influence of downtime caused by geological factors is considered, and the method for reducing the prediction range is used for providing the TBM utilization rate U related to rock mass conditionsrThe concept of (2) establishes rock mass classification system RMR value and rocks in different dry and wet states according to field multi-source measured dataThe model for predicting the rock mass condition-related utilization rate of the TBM with the wear resistance CAI value has an important guiding effect on TBM tunneling construction.
Disclosure of Invention
The invention provides a method for predicting TBM utilization rate, which provides a concept of rock mass condition related utilization rate to reduce a prediction range, establishes a TBM rock mass condition related utilization rate prediction model based on a rock mass grading system RMR value and a rock mass wear resistance CAI value, and solves the technical problem that TBM tunneling performance cannot be accurately predicted in TBM construction.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a method for predicting the condition-related utilization rate of a TBM rock mass comprises the following steps:
the method comprises the following steps: determining a concept and a calculation model of the TBM rock mass condition related utilization rate;
step two: and (3) comprehensively utilizing the RMR value of the rock mass grading system and the CAI values of rock wear resistance in different dry and wet states to establish a prediction model of rock mass condition related utilization rate.
Further, the purpose of the related utilization rate of the rock mass conditions of the TBM is to eliminate uncertainty of prediction of the conventional TBM utilization rate, and only considering influence of the related factors of the rock mass conditions, the concept of the related utilization rate of the rock mass conditions of the TBM is as follows:
on the basis of the traditional TBM utilization rate, only the influence of the rock mass condition-related downtime is considered, and the influence of other factors-related downtime is not considered, namely, the part of the TBM utilization rate, which is only related to the rock mass condition, is called the TBM rock mass condition-related utilization rate.
Further, the calculation model of the concept of the TBM rock mass condition related utilization rate is as follows:
TBM rock mass condition related utilization rate Ur＝tAdvancing/tGeneral assembly·(tGeneral assembly-tGRRD)/tGeneral assembly＝U·(1-GRRD)；
Wherein, tAdvancingIndicates the tunneling time tGeneral assemblyIndicates the duration of the item, tGRRDRepresenting the rock mass condition-related shutdown time, U representing the TBM utilization rate, GRRD representing the rock mass condition-related shutdown timeThe percentage of the components is as follows;
the rock mass condition-dependent downtime is GRRD Dc+Ds+Di；
Wherein DcThe percentage of the cutter replacement time to the total construction time, DsThe percentage of the shutdown time of the surrounding rock support to the total construction time DiThe percentage of downtime for treatment of poor geological conditions to total time of construction.
Further, the establishment of the prediction model of the TBM rock mass condition-related utilization rate comprises the following steps:
the method comprises the following steps: building a TBM predictive analysis database;
step two: analyzing an influence mechanism of rock wear resistance CAI values of different lithological rocks in different dry and wet states;
step three: determining the correlation between the RMR value and the CAI value of rock wear resistance of the rock mass grading system and the related utilization rate of the TBM rock mass condition;
step four: and establishing a prediction model of the TBM rock mass condition related utilization rate.
Further, the TBM predictive analytics database comprises parameters:
RMR values of different geological units, CAI values of rock wear resistance under different dry and wet states, various downtime ratios and TBM utilization rate in the TBM tunneling process.
Further, the method for exploring the influence mechanism of the CAI values of different lithologic rocks in different water-containing states comprises the following work:
preparing standard samples of quartzite, granite, sandstone and andesite in different water-containing states respectively, and performing a surrounding rock abrasion test to obtain an average value of loss of a needle point, namely a CAI value;
summarizing the results of different lithologic rocks to obtain the CAI in a dry statedryValue and CAI under saturationsatA comparison graph of values;
and analyzing the influence mechanism of different dry and wet states under different lithologic rocks on the CAI value.
Further, the method for determining the correlation among the RMR value of the rock mass grading system, the CAI value of rock wear resistance and the related utilization rate of the TBM rock mass conditions comprises the following steps:
the method comprises the following steps: calculating TBM rock mass condition related utilization rate U of different geological unitsr；
Step two: respectively taking the RMR value and the CAI value of the rock mass grading system as abscissa and the rock mass condition related utilization rate UrDrawing a scattered point distribution diagram for a vertical coordinate;
step three: determining TBM rock mass condition related utilization rate U according to numerical value scatter distributionrCorrelation with RMR and CAI values.
Further, the method for establishing the prediction model of the TBM rock mass condition related utilization rate is based on the RMR value of a rock mass grading system and the CAI values of rock wear resistance in different dry and wet states, and comprises the following steps:
setting a predictive regression analysis function of the related utilization rate of the TBM rock mass as follows: u shaper＝a·RMR2+b·RMR+c·CAI+d；
Wherein RMR represents a rock mass grading system value, CAI represents a rock wear resistance value, and a, b, c and d represent regression coefficients;
TBM rock mass condition related utilization rate U of different rock mass units by using professional data fitting software SPSSrFitting with the RMR value and the CAI value of the rock mass grading system according to the relation to obtain UrThe predictive model of (1).
Further, the TBM rock mass condition related utilization rate prediction model is based on the TBM rock mass condition related utilization rate UrEstablishing a correlation between the RMR value and the CAI value, wherein the RMR value is related to rock mass classification, and the CAI value is related to the close relation with the abrasion of the hob; and the rock wear resistance value adopts different CAI values according to different dry and wet states of the rock in the tunneling process, so that the defect that the CAI value under the dry state is only adopted in the traditional TBM utilization rate prediction is overcome.
Further, the method for predicting the TBM rock mass condition related utilization rate further comprises the following steps:
and comparing and analyzing the predicted value of the related utilization rate of the rock mass condition of the TBM with the calculated value of the actually measured data of the TBM tunneling field, forming a feedback mechanism according to the error in the comparison result, and guiding the construction process to improve the tunneling parameters.
The beneficial effects produced by the invention are as follows:
1. the invention provides a concept and a calculation method of the TBM rock mass condition related utilization rate for the first time, can avoid considering influences except rock mass condition factors during the tunneling performance prediction, and is an effective method for carrying out TBM utilization rate prediction research.
2. The invention firstly proposes the influence of the water-containing state on the CAI value aiming at different lithologic rocks, and provides the correlation between the RMR value and the CAI value and the related utilization rate of the TBM rock mass condition.
3. According to the method, the RMR value and the CAI values in different dry and wet states are comprehensively utilized for the first time to establish the prediction model of the TBM rock mass condition related utilization rate, analysis is carried out, the consideration on related influence factors is more comprehensive, and the defect that the conventional prediction model cannot accurately predict the TBM utilization rate is overcome.
Drawings
FIG. 1 is a prediction flow diagram of the present invention;
FIG. 2 is a geological profile of a tunnel section project according to the present invention;
FIG. 3 is a diagram showing the ratio of the downtime of the surrounding rocks of different grades in the invention;
FIG. 4 is a CAI value comparison graph of different dry and wet states of different lithologic rocks in the invention;
FIG. 5 is a diagram showing the relationship between the TBM rock mass condition-related utilization rate and the RMR in the invention;
FIG. 6 is a diagram showing the relationship between the TBM rock mass condition-related utilization rate and the CAI in the invention;
FIG. 7 is a diagram showing the relationship between the actual value and the predicted value of the TBM rock mass condition-related utilization rate obtained by applying the method of the present invention to an embodiment;
FIG. 8 is a diagram showing the relationship between the actual value and the predicted value of the TBM rock mass condition-related utilization rate obtained by applying the method to another TBM tunneling engineering construction section.
Detailed Description
The invention will be further described with reference to the accompanying drawings.
Example 1
A method for predicting the condition-related utilization rate of a TBM rock mass comprises the following steps:
the method comprises the following steps: providing a TBM rock mass condition related utilization rate UrThe concept of (1) establishing the related utilization rate U of the rock mass condition of the TBMrThe computational model of (2), comprising the steps of;
the rock mass condition-related downtime only considers the influence of geological factors related to rock mass conditions, but is not related to other factors, and the rock mass condition-related downtime calculation model obtained according to the related definition is as follows:
GRRD＝Dc+Ds+Di
wherein DcThe percentage of the cutter replacement time to the total construction time, DsThe percentage of the shutdown time of the surrounding rock support to the total construction time DiThe percentage of downtime for treatment of poor geological conditions to total time of construction.
Because only the influence of the rock mass condition related factors is considered, the influence of other related factors, namely U, is ignoredrOnly a part of U is used, the relation between the rock mass condition related utilization rate of the TBM and the traditional TBM utilization rate is deduced according to the proposed new concept, and then the calculation model is as follows:
Ur＝tadvancing/tGeneral assembly·(tGeneral assembly-tGRRD)/tGeneral assembly＝U·(1-GRRD)
Wherein, tAdvancingIndicates the tunneling time tGeneral assemblyIndicates the duration of the item, tGRRDThe method comprises the following steps of representing rock mass condition-related downtime, representing the TBM utilization rate by U, and representing the percentage of the rock mass condition-related downtime by GRRD.
Step two: constructing a TBM predictive analysis database, wherein the database comprises the following parameters:
the RMR values of different geological units and the CAI values of rock wear resistance in different dry and wet states in the TBM tunneling process, various downtime and utilization rates, and related specific implementation conditions are as follows:
the geological section of the tunnel engineering is shown in fig. 2, and is divided into different geological units according to different tunneling sections or different lithologies, and the tunneling section rock stratum of the embodiment is divided into 40 geological units according to three conditions of lithology, strength, geological structure and underground water.
TABLE 1 different geological unit TBM rock mass condition related utilization rate calculation model related parameters
The TBM construction geological daily report is sorted to obtain geological information of the surrounding rock, indoor experimental data made by field sampling are combined, a rock mass grading system RMR value and rock wear resistance CAI values under different dry and wet states are calculated, and the downtime of each embodiment is determined, as shown in Table 1:
the method comprises the steps of obtaining the occupation time and the shutdown reason of each construction process of the TBM according to the collected TBM construction records, and determining the shutdown time in the embodiment, wherein the shutdown time related to rock mass conditions is the shutdown time caused by geological condition factors and mainly comprises three types of cutter replacement shutdown time, surrounding rock support shutdown time and unfavorable geological condition processing time; other factors the downtime is caused by the factors of TBM blocking, muck transportation and unloading, daily maintenance of the TBM and the like, the downtime is obtained, and different working time proportion graphs are drawn, as shown in figure 3, wherein the related utilization rate of other factors is U0＝U-Ur；
The RMR gives corresponding grade values through tables according to basic parameters such as uniaxial compressive strength of the rock, rock condition indexes, discontinuous surface conditions, water inrush rate, included angles between tunnel axes and structural surfaces and the like, and finally calculates the sum to obtain the product.
Two samples in dry and saturated states are respectively prepared for four different lithological rocks of quartzite, granite, sandstone and andesite in the embodiment, the samples are cylinders with the diameter of 50mm and the height of 40mm, and then a rock Cerchar abrasion test is carried out, and the measured average value of the loss of the needle point is the CAI value.
Step three: analyzing the influence mechanism of the CAI values of different lithological rocks in different dry and wet states, comprising the following works:
as shown in FIG. 4, the CAI of different lithologic rocks in dry state is plotteddryAnd CAI under saturated conditionsatThe comparison graph is as follows: discovery of CAIsatValue is mostly greater than CAIdryThe value is lower (8.54% -27.49%), i.e. the water content of rock has a great influence on the CAI value, in order to eliminate the CAI value caused by the adoption of CAI in practical engineeringdryValue replacement of CAIsatThe method considers the actual dry and saturated states of the rock of the tunneling section when the TBM utilization rate is predicted, and distinguishes and selects the CAI value;
step four: determining the correlation among the RMR value and the CAI value of rock wear resistance of a rock mass grading system and the related utilization rate of the rock mass condition of the TBM, and comprising the following steps:
drawing the related utilization rate U of the RMR value of the rock mass grading system and the rock mass condition of the TBM according to various on-site measured data and calculated values in the table 1rThe relationship diagram is shown in figure 5, and the rock wear resistance CAI value and the related utilization rate U of the rock mass condition of the TBMrThe relationship graph is shown in FIG. 6:
can know UrApproximate quadratic function relationship (R) with RMR value2＝0.788)，UrApproximately linear function of CAI value (R)2＝0.813)；
Step five: establishing a prediction model of TBM rock mass condition related utilization rate
As the RMR value is not greatly related to the CAI value and the RMR value and the CAI value are closely related to the TBM rock mass condition related utilization rate, wherein the RMR value is related to rock mass grading, and the CAI value is closely related to the abrasion of the hob cutter. When a prediction model is established, the RMR value related to rock mass grading is considered, and the CAI value closely related to the abrasion of the hob is combined, and the rock abrasion resistance value adopts different CAI values according to different dry and wet states of the rock in the tunneling process, so that the defect that the CAI value under the dry state is only adopted in the conventional TBM utilization rate prediction is overcome; therefore, the prediction regression analysis function for setting the related utilization rate of the TBM rock mass by comprehensively utilizing the two parameters is as follows:
TBM rock mass stripRelative utilization rate U of partsr＝a·RMR2+b·RMR+c·CAI+d
Wherein the rock wear resistance value adopts different CAI values according to different dry and wet states of the rock in the tunneling process;
TABLE 2UrAnd RMR2Partial correlation test of RMR, CAI
Data in Table 1 for UrAnd RMR2RMR and CAI were subjected to partial correlation tests, respectively, and the results are shown in Table 2, thus a in the above formula<0,b>0 and c<0。
TBM rock mass condition related utilization rate U of 40 groups of different rock mass units by using professional data fitting software SPSSrFitting with the RMR value and the CAI value of the rock mass grading system according to the assumed relationship, and obtaining the following results:
Ur(％)＝-0.008RMR2+0.987RMR±2.259CAI-3.912(R2＝0.891)
step six: and carrying out comparison analysis on the predicted value and the measured value of the TBM rock mass related utilization rate, verifying the effectiveness of the TBM rock mass condition related utilization rate prediction model and feeding back to guide construction.
Statistical analysis is carried out on the field records of the rest geological units in the tunnel engineering to obtain the relevant parameters of the TBM rock mass condition relevant utilization rate calculation model of 10 groups of geological units shown in the table 3, then the predicted value is calculated according to the result obtained in the step five and compared with the actual recorded value, and the U is drawnrActual value and UrComparison of predicted values, as in fig. 7:
TABLE 3 residual geological unit TBM rock mass condition related utilization rate calculation model related parameters
U calculated by the modelrThe predicted value is well matched with the field recorded value, the maximum absolute error is 4.90 percent, and the average absolute error is 2.78 percent. Illustrating U derived based on the predictive model of the present inventionrThe prediction result is reasonable, and U can be accurately predictedr. However, it can be seen that the over-prediction occurs because there are other factors. Therefore, the tunneling parameters are properly adjusted according to the related prediction results so as to better adapt to tunneling work, and the prediction model is fed back and corrected to obtain the optimal prediction parameter combination.
Example 2
Further, the method for obtaining the TBM rock mass condition related utilization rate prediction model is applied to another engineering embodiment, the model effectiveness is verified, the field actual measurement data in the other engineering embodiment is adopted, the related parameters are shown in the table 4, the fifth step and the sixth step in the embodiment 1 are repeated, and the prediction model established at the moment is as follows:
Ur(％)＝-0.006RMR2+0.9963RMR-1.365CAI-6.153(R2＝0.889)
TABLE 4. parameters related to calculation model for rock mass condition-dependent utilization rate of geological unit TBM in another engineering embodiment
While plotting U against the remaining data of Table 5rActual value and UrComparison of predicted values, as in fig. 8:
TABLE 5 parameters associated with the residual geocellular TBM rock mass condition-dependent availability calculation model of another engineering embodiment
As can be seen, the U calculated by the modelrThe predicted value is well matched with the field recorded value, the maximum absolute error is 10.7 percent, and the average absolute error is 2.25 percent, thereby proving that the established absolute error is goodThe relationship can accurately predict Ur. The adjustment of the tunneling parameters is carried out through analysis, so that the utilization rate of the TBM can be improved, and the error between the predicted value and the true value is reduced. It should be noted that, when performing the regression analysis, different engineering data may be used to generate different regression coefficients. These differences may be due to different hob arrangements, different hob materials and different wear patterns.
It should be noted that the above-mentioned embodiments illustrate rather than limit the technical solutions of the present invention, and that equivalent substitutions or other modifications made by persons skilled in the art according to the prior art are included in the scope of the claims of the present invention as long as they do not exceed the spirit and scope of the technical solutions of the present invention.
Claims (9)
1. A method for predicting the condition-dependent utilization rate of a TBM rock mass is characterized by comprising the following steps:
the method comprises the following steps: determining a concept and a calculation model of the TBM rock mass condition related utilization rate;
step two: and (3) comprehensively utilizing the RMR value of the rock mass grading system and the CAI values of rock wear resistance in different dry and wet states to establish a prediction model of rock mass condition related utilization rate.
2. The method for predicting the condition-dependent utilization rate of the TBM rock mass according to claim 1, wherein the concept of the condition-dependent utilization rate of the TBM rock mass is as follows:
on the basis of the traditional TBM utilization rate, only the influence of the rock mass condition-related downtime is considered, and the influence of other factors-related downtime is not considered, namely, the part of the TBM utilization rate, which is only related to the rock mass condition, is called the TBM rock mass condition-related utilization rate.
3. The method for predicting the TBM rock mass condition-related utilization rate according to claim 1, wherein the conceptual computational model of the TBM rock mass condition-related utilization rate is as follows:
TBM rock mass condition related utilization rate Ur＝tAdvancing/tGeneral assembly·(tGeneral assembly-tGRRD)/tGeneral assembly＝U·(1-GRRD)
Wherein, tAdvancingIndicates the tunneling time tGeneral assemblyIndicates the duration of the item, tGRRDThe method comprises the following steps of representing rock mass condition-related downtime, representing the TBM utilization rate by U, and representing the percentage of the rock mass condition-related downtime by GRRD.
GRRD＝Dc+Ds+Di
Wherein DcThe percentage of the cutter replacement time to the total construction time, DsThe percentage of the shutdown time of the surrounding rock support to the total construction time DiThe percentage of downtime for treatment of poor geological conditions to total time of construction.
4. The method for predicting the condition-dependent rock mass utilization rate of the TBM according to claim 1, wherein the establishment of the prediction model of the condition-dependent rock mass utilization rate comprises the following steps:
the method comprises the following steps: building a TBM predictive analysis database;
step two: analyzing an influence mechanism of rock wear resistance CAI values of different lithological rocks in different dry and wet states;
step three: determining the correlation among the RMR value of a rock mass grading system, the rock wear resistance CAI and the TBM rock mass condition correlation utilization rate;
step four: and establishing a prediction model of the TBM rock mass condition related utilization rate.
5. The method for predicting the condition-dependent utilization rate of the TBM rock mass according to claim 4, wherein the TBM predictive analysis database comprises the following parameters:
RMR values of different geological units, CAI values of rock wear resistance under different dry and wet states, various downtime ratios and TBM utilization rate in the TBM tunneling process.
6. The method for predicting the condition-dependent utilization rate of the TBM rock mass according to claim 4, wherein the influence mechanism of the rock wear resistance CAI value of different lithologic rocks in different dry and wet states is analyzed, and the method comprises the following steps:
the method comprises the following steps: preparing standard samples of quartzite, granite, sandstone and andesite in different water-containing states respectively;
step two: performing a surrounding rock abrasion test on the standard sample in the step one, wherein the measured average value of the loss of the needle point is a CAI value;
step three: summarizing the results of different lithologic rocks to obtain the CAI in a dry statedryValue and CAI under saturationsatA comparison graph of values;
step four: and analyzing the influence mechanism of different dry and wet states under different lithologic rocks on the CAI value.
7. The method for predicting the related utilization rate of the TBM rock mass condition according to claim 4, wherein the method for determining the related relationship among the RMR value of a rock mass grading system, the CAI value of rock wear resistance and the related utilization rate of the TBM rock mass condition comprises the following steps:
the method comprises the following steps: calculating TBM rock mass condition related utilization rate U of different geological unitsr；
Step two: respectively taking the RMR value and the CAI value of the rock mass grading system as abscissa and the rock mass condition related utilization rate UrDrawing a scattered point distribution diagram for a vertical coordinate;
step three: determining TBM rock mass condition related utilization rate U according to numerical value scatter distributionrCorrelation with RMR and CAI values.
8. The method for predicting the condition-dependent utilization rate of the TBM rock mass according to claim 4, wherein the prediction model is established based on the RMR value of a rock mass grading system and the CAI values of the rock wear resistance in different dry and wet states, and comprises the following steps:
setting a predictive regression analysis function of the related utilization rate of the TBM rock mass as follows: u shaper＝a·RMR2+b·RMR+c·CAI+d；
Wherein RMR represents a rock mass grading system value, CAI represents a rock wear resistance value, and a, b, c and d represent regression coefficients;
TBM rock mass condition related utilization rate U of different rock mass units by using professional data fitting software SPSSrFitting with the RMR value and the CAI value of the rock mass grading system according to the relation to obtain UrThe predictive model of (1).
9. The method for predicting the condition-dependent utilization rate of the TBM rock mass according to claim 4, is characterized by further comprising the following steps:
and comparing and analyzing the predicted value of the related utilization rate of the rock mass condition of the TBM with the calculated value of the actually measured data of the TBM tunneling field, forming a feedback mechanism according to the error in the comparison result, and guiding the construction process to improve the tunneling parameters.
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