CN108256191B - Tunnel excavation progress simulation method considering construction abnormity - Google Patents
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Abstract
The invention discloses a tunnel excavation progress simulation method considering construction abnormity, which decomposes tunnel excavation circulation into three working units of drilling, charging and blasting waiting, counts and summarizes the distribution form and parameter rule of each working unit, independently samples the three working units of the same excavation circulation by using a Monte Carlo simulation method and then performs coupling calculation, thereby establishing a progress simulation model which is more accurate than the traditional simulation model. The example proves that the method can better depict the influence of the construction abnormal event on the progress, has a simpler algorithm and better robustness compared with the traditional progress simulation method, and provides scientific basis for the progress simulation of tunnel excavation.
Description
Technical Field
The invention relates to the technical field of water conservancy and hydropower construction progress simulation, in particular to a tunnel excavation progress simulation method considering construction abnormity.
Background
The tunnel excavation process is various, the construction progress is greatly influenced by uncertain factors such as environmental factors, personnel equipment and process interference, for example, the factors such as geology and environment have space variability, and the factors such as ventilation, personnel investment, equipment faults and process interference have time variability. When the contractor compiles the schedule plan, the estimation is usually carried out according to an enterprise experience base, but the actual schedule often deviates from the schedule greatly under the influence of various factors.
Scholars propose a plurality of improved methods, such as Gezhegming [1] which adopts a parallel activity comprehensive expectation method to analyze progress, is more accurate than the expectation analysis of a single project, but does not consider the influence of construction uncertainty; wanzhu et al [2] consider uncertainty of working duration, uncertainty of effective construction days, and uncertainty of progress plan on the basis of PERT method, further adopt Monte Carlo simulation to obtain completion risk ratio of construction progress plan, but are difficult to adapt to variable construction process. The technical scheme includes that a cyclic network simulation technology is provided by Zhang Hua and the like [3], excavation cycle is decomposed into working units which are related to each logic, construction parameters of progress simulation are dynamically adjusted according to actual conditions, Bayesian theory is introduced on the basis of the cyclic network simulation technology by Zhang Cheng et al [4], posterior estimation is further updated according to dynamic update progress prior information of field construction parameters to predict progress, and various construction variation characteristics are well considered by the two progress simulation schemes. However, the actual construction process is extremely unstable, the duration of partial abnormity has strong time domain discreteness, although the occurrence frequency is low, the influence on the whole progress is large and can not be ignored, the construction abnormity is not distinguished, and the distribution parameters which are not calculated through statistical test are not suitable for progress simulation, so that special research on the characteristics of the construction abnormity parameters is necessary.
Reference documents:
[1] general expectation time analysis method for random activities in Gezheming progress control [ J ] application of systematic engineering theory method, 1998(02):48-51.
[2] Wangzhoufu, Chendengxing, Risk analysis of Water conservancy and hydropower construction progress plan [ J ]. proceedings of Hippocampus university (Nature science edition), 1999(04) 83-87.
[3] Stannus, Liu Queen, prediction and control of underground cavern group construction progress based on real-time simulation [ J ] Tianjin university bulletin 2007(06): 721-.
[4] Zhang Mianrong, Ducheng wave, Savingqi, etc. the Bayesian theory-based underground cavern group time-varying construction progress risk prediction method [ J ] system simulation academic newspaper 2014(05) 1131-.
Disclosure of Invention
Based on the defects of the prior art, the invention provides a tunnel excavation progress simulation method considering construction abnormity, which combines a circulation network simulation technology to decompose excavation circulation into three working units of drilling, charging and blasting waiting, utilizes a box diagram theory to divide the working units into normal parameter units and abnormal parameter units, adopts a mathematical statistics theory to analyze the distribution characteristics of the parameter units, combines a flexible network plan simulation method to provide a progress completion risk rate model considering construction difference, and provides theoretical support for uncertainty analysis of construction progress.
In order to achieve the purpose, the invention designs a tunnel excavation progress simulation method considering construction abnormity, which is characterized by comprising the following steps:
A) the tunnel excavation circulating process is divided into a drilling working unit, a charging working unit and a blasting waiting working unit;
B) separating the normal duration and the abnormal duration of each working unit of the tunnel excavation from the drilling working unit, the charging working unit and the blasting waiting working unit by using a box diagram theory,
C) deducing the distribution forms and the distribution parameters of the normal duration and the abnormal duration of the drilling working unit, the charging working unit and the blasting waiting working unit by using mathematical statistics;
D) independently sampling the drilling working unit, the charging working unit and the blasting waiting working unit in the same excavation cycle by using a Monte Carlo simulation method, and then performing coupling calculation;
E) and establishing a progress simulation experience model considering construction difference, and coupling triangular distribution of abnormal construction duration with normal distribution of normal construction duration by using abnormal frequency to obtain a probability density distribution curve corresponding to the total excavation footage in a simulation period.
Preferably, the specific steps of step E) include:
1) setting simulation times N and a simulation period T, and initializing a simulation time count N and a footage cycle count i;
2) drill work unit cycles were performed for a single sample:
21) the normal drilling speed of the single drilling machine of the drilling work unit meets normal distribution, and single sampling value t 'with normal duration in the circulation of the drilling work unit is obtained according to Monte Carlo simulation and a drilling work unit duration calculation formula'id；
22) The abnormal drilling speed of a single drilling machine of the drilling working unit meets the triangular distribution, and a single sampling value e of abnormal duration in the circulation of the drilling working unit is obtained according to the Monte Carlo simulation and the calculation formula of the duration of the drilling working unitid；
23) Obtaining single sampling value t of cycle duration of drilling working unit by Monte Carlo simulationidComprises the following steps:
wherein p is2Abnormal frequency P of drilling speed for single drilling machine of drilling unit2A single sampling of (a);
3) the charge work unit cycle is performed for a single sample:
31) the normal duration of the charging working unit meets normal distribution, and single sampling value t 'of the normal duration in the circulation of the charging working unit is obtained according to Monte Carlo simulation'im；
32) The abnormal duration of the charging working unit meets the triangular distribution, and a single sampling value e of the abnormal duration in the cycle of the charging working unit is obtained according to Monte Carlo simulationim；
33) Obtaining single sampling value t of circulation duration of charging working unit by Monte Carlo simulationimComprises the following steps:
wherein p is3Abnormal frequency P of working unit for charging3A single sampling of (a);
4) a blast-waiting unit cycle is performed for a single sample:
41) the normal duration of the blasting waiting unit meets normal distribution, and single sampling value t 'of the normal duration in the circulation of the charging working unit is obtained according to Monte Carlo simulation'iw；
42) The abnormal duration of the blasting waiting unit meets the triangular distribution, and a single sampling value e of the abnormal duration in the circulation of the charging working unit is obtained according to Monte Carlo simulationiw；
43) Obtaining single sampling value t of blasting waiting unit circulation duration by adopting Monte Carlo simulationiwComprises the following steps:
wherein p is4For abnormal frequency P of blast waiting units4A single sampling of (a);
5) calculating the cyclic duration value t of single excavation of the tunnelic：
tic＝tid+tim+tiw
6) Repeating the steps 2) to 5) until the accumulated excavation cycle duration value sigma ticEqual to or more than the simulation period T, according to the footage cycle count i and the design excavation cycle footage L0Obtaining the total excavation footage L in the simulation period TT＝L0×i；
7) And repeating the steps 2) to 6) until the simulation times count N is equal to the simulation times N to obtain N excavation footage sequences, and then generating a probability distribution map of the accumulated excavation footage in the simulation period T for progress evaluation, decision and risk analysis.
Preferably, said steps 2), 3), 4) are performed simultaneously.
Preferably, the drilling work unit duration calculation formula in the step 21) is as follows:
in the formula: t isdFor the duration of a drilling work unit, v is the drilling speed m/h of a single drilling machine, m is the number of drilling machines, n is the number of drilled holes on the face, L0And r is the depth of the ultra-drilling for designing the excavation circulating footage.
Preferably, the normal drilling speed of the single drilling machine of the drilling work unit in the step 21) meets normal distribution, and the expected value and the standard deviation of the normal drilling speed of the single drilling machine of the drilling work unit are v_2＝7.230m/h，σ_2＝1.317m/h。
Preferably, the triangular distribution of the abnormal drilling speed of the drilling unit single drilling machine in the step 22) is vd∈T(1.06v_2,2.75v_2,1.52v_2)。
Preferably, the abnormal frequency P of the drilling speed of the drilling unit single drilling machine in the step 23)2＝0.02～0.07。
Preferably, the normal duration of the charging work units in the step 31) meets a normal distribution, and the expected value and the standard deviation of the normal duration of the charging work units are respectively mu_3＝1.067h，σ_3＝0.25h。
Preferably, the triangular distribution of abnormal durations of the pharmaceutical work units of step 32) is em∈T(1.47μ_3,2.81μ_3,1.78μ_3) (ii) a The abnormal frequency P of the medicine-loading working unit in the step 33)3＝0.04～0.05。
Preferably, the normal duration of the blasting-waiting unit in the step 41) satisfies a normal distribution, and the expected value and the standard deviation of the normal duration of the blasting-waiting unit are respectively
Preferably, the triangular distribution of the abnormal duration of the blast waiting unit in the step 42) is ew∈T(2.26μ_4,11.11μ_4,4.45μ_4) (ii) a The abnormal frequency P of the blasting waiting unit in the step 43)4＝0.11～0.15。
The invention has the following characteristics:
1. the tunnel excavation cycle is divided into three working units of drilling, charging and blasting waiting, the distribution form and parameter rules of all the working units are counted and summarized, the three working units of the same excavation cycle are independently sampled by using a Monte Carlo simulation method and then are subjected to coupling calculation, and a progress simulation model which is more accurate than a traditional simulation model is established.
2. The abnormal duration of each working unit of the tunnel excavation is separated by utilizing a box line diagram theory, the distribution form and the distribution parameters of the abnormal duration are deduced by utilizing mathematical statistics, the influence of the abnormal duration on the tunnel excavation duration is quantified, and the construction abnormal duration is coupled into a progress simulation model by utilizing a Monte Carlo simulation method so as to accurately reflect the actual situation.
3. According to the application conditions of different nonparametric hypothesis testing methods, the Lilliefors principle (K-S improvement method), the Mann-Whitney and Moses two independent sample nonparametric testing principle, the Median and Jonkheere-Terpstra multiple independent sample nonparametric testing principle and the 0-1 distribution nonparametric hypothesis testing principle are comprehensively adopted to carry out statistical induction on the distribution form and the parameter rule of the normal duration and the abnormal duration in each working unit in the claim 1 to obtain the empirical simulation model considering the construction abnormity, and the empirical simulation model shows that the empirical simulation model can better predict the actual situation and has simpler algorithm and better robustness compared with the traditional simulation model.
4. The method comprises the steps of carrying out sensitivity analysis on related factors of an empirical simulation model considering construction abnormity to obtain the conclusion that the maximum influence on the total excavation progress is the duration frequency of abnormity of a blasting waiting unit, the drilling depth and the drilling quantity, in the field construction management process, paying attention to whether technical schemes and guarantee measures related to the three key parameters are arranged in place or not, and optimizing and adjusting related aspects of the three key parameters on the site on the premise of ensuring construction quality and safety when the actual progress deviates from the planned progress so as to improve the configuration efficiency of construction resources.
Drawings
FIG. 1 is a schematic view of a network model of tunnel excavation construction circulation according to the present invention.
FIG. 2 is a classification matrix diagram of the construction anomaly of the present invention.
FIG. 3 is a logic diagram of the information processing of the excavation duration of the present invention.
FIG. 4 is a diagram of a process simulation model architecture according to the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and specific embodiments.
The invention provides a tunnel excavation progress simulation method considering construction abnormity, which comprises the following steps:
A) the tunnel excavation circulating process is divided into a drilling working unit, a charging working unit and a blasting waiting working unit;
B) separating the normal duration and the abnormal duration of each working unit of the tunnel excavation from the drilling working unit, the charging working unit and the blasting waiting working unit by using a box diagram theory,
C) deducing the distribution forms and the distribution parameters of the normal duration and the abnormal duration of the drilling working unit, the charging working unit and the blasting waiting working unit by using mathematical statistics;
D) independently sampling the drilling working unit, the charging working unit and the blasting waiting working unit in the same excavation cycle by using a Monte Carlo simulation method, and then performing coupling calculation;
E) and establishing a progress simulation experience model considering construction difference, and coupling triangular distribution of abnormal construction duration with normal distribution of normal construction duration by using abnormal frequency to obtain a probability density distribution curve corresponding to the total excavation footage in a simulation period.
The research process of the invention is as follows:
1. establishing excavation circulation model
And circularly decomposing the tunnel excavation into three working units of drilling, charging and blasting waiting.
The tunnel excavation cycle time can be expressed as:
Tc＝Td+Tm+Tw
wherein, TcRepresenting the cycle time, T, of a single footage for tunnel excavationdRepresenting the duration of the drilling unit, TmRepresenting the duration of the charge unit, TwRepresenting the duration of the blast waiting unit.
The duration of the drilling unit is related to the hole depth, the hole number, the equipment investment and the like of the excavated tunnel face. The duration of the charging unit is related to factors such as the size of a construction face, personnel and the like. The blasting waiting unit consists of a slag discharging and system supporting sub-cycle working unit and other multiple processes, the duration distribution of each sub-cycle working unit changes along with the change of construction conditions, the mutual interference among the processes is large, if each process is disassembled for progress simulation, the accumulated error is increased, the model practicability is greatly reduced due to excessive input parameters, the model is regarded as a working unit, the duration distribution parameters are dynamically updated according to the field progress condition, and the target of real-time simulation can be achieved.
2. Establishing a time-lapse model that takes into account construction anomalies
The tunnel excavation influence factor is numerous, takes place occasionally when the construction is unusual, according to unusual emergence frequency, duration length can be divided into four types:
the first type is system anomaly, high occurrence frequency, short cycle duration, and small contribution to expectation and variance of duration distribution, such as unreasonable construction organization, improper allocation of labor force and construction machinery, unreasonable construction plane arrangement, unsmooth ventilation and smoke dissipation, and the like. The abnormity is closely related to the organization management level of a construction unit, and the abnormity can be reduced by means of optimizing a scheme, strengthening personnel and equipment management and the like.
The second type is random abnormity, the occurrence frequency is low, the cycle duration is short, and the expected contribution and variance contribution to duration distribution are small, such as temporary mechanical failure, temporary water and electricity interruption, untimely production arrangement, untimely temporary supply of initiating explosive devices, local fault fracture zone and small amount of water inrush condition, and reinforced support and drainage blocking measures are taken. The anomaly is construction-accompanying and related to the organization and management level of the construction unit, the site geological conditions and various external factors.
The third category is low-frequency special abnormity, low occurrence frequency, long cycle duration, large expectation and variance contribution to duration distribution, such as toxic gas, collapse, fault fracture zone in large range, and large water inrush condition, and reinforced support and drainage blocking measures are taken. The abnormal hydrology, rock conditions and the like are related, and the abnormal occurrence can be reduced to a certain extent through reasonable organization and production.
The fourth type is high-frequency special abnormity, the occurrence frequency is high, the cycle duration is long, and the occurrence of the abnormity can cause the construction process to be gradually away from a steady state, tends to diverge and disorder, and finally evolves to a dissipative state. The current engineering construction mode is mature, the probability of high-frequency special abnormity is greatly reduced by advanced management level and early scientific geological exploration, the first three types of abnormity are mainly researched, and fig. 2 is a classification matrix of construction abnormity.
In summary, the duration t of the tunnel excavation unit can be represented as:
t＝t0+e1+e2+e3
in the formula, t0Representing the average time of circulation of the excavation unit irrespective of the abnormal conditions of construction, e1For abnormal working hours of the first type of system in a unit cycle, e2Random abnormal man-hours of the second kind, e3The third category is low-frequency special abnormal working hours.
The first and second types of construction abnormal information have small expectation and variance contribution to duration distribution, can be regarded as 'background noise' of 'duration wave', and fuse the lossless duration and the 'background noise' into 'normal duration't ', namely t' ═ t0+e1+e2Abnormal calendar of the third kindThe time distribution has a large expected and variance contribution, called "abnormal duration" e3. At this time have
t＝t′+e3
The deviation between the abnormal duration and the expected value of the normal excavation duration is large, the original data needs to be preprocessed firstly, the waveform distortion is corrected, and the processed information directly reflects the construction progress condition without special abnormal interference.
3. Empirical distribution statistics of simulation parameters
Based on 1344 parts of quasi-drilling certificate, quasi-loading certificate and quasi-blasting certificate (also called blasting triplet) actually generated in tunnel excavation construction of a large hydropower station water diversion power generation system in a certain Tibetan region, relevant parameters of drilling duration, charging duration and blasting waiting duration of a plurality of excavation cycles are counted in different parts.
According to the boxplot theory, the low-frequency high-amplitude abnormal information can be intercepted, and the algorithm is as follows:
wherein: q1 is the first quartile, which is equal to the 25 th% of all values in the sample, arranged from small to large. Q3 is the third quartile, which is equal to the 75 th% of all values in the sample, arranged from small to large. And QR is the difference value of the third quartile and the first quartile, namely QR is Q3-Q1.
And stripping abnormal information from the original information by adopting a certain method, respectively carrying out distribution characteristic and parameter analysis on the abnormal construction duration and the normal construction duration, researching the probability of the abnormal construction duration, coupling the two durations by Monte Carlo simulation, and finally realizing the tunnel excavation progress simulation, which is shown in figure 3.
Because the real distribution characteristics of the excavation normal duration are unknown, according to the central limit theorem, when a sample of the excavation normal duration is large enough, the distribution of the random variable sequence approaches normal distribution, the assumption that the normal duration of the excavation three working units obeys the normal distribution is made, then the Lilliefors method (improved KS method) nonparametric hypothesis test method is adopted to judge the distribution of the sample, the normal duration of the excavation three working units of the tunnel meets specific normal distribution characteristics, and the law has universal significance.
Further, the non-parameter inspection principle of two independent samples of Mann-Whitney and Moses is adopted to judge that the expectation and the total distribution of standard deviation of the drilling speed of the drilling unit single drilling machine at different positions have no obvious difference, the average value of the expectation and the standard deviation of the speed of the single drilling machine at different positions is taken as the expectation and the standard deviation (shown in the following formula) of the total distribution of the drilling speed of the single drilling machine for cavity excavation, and the distribution parameters of the drilling duration of each position can be calculated according to the parameters of the number of drilling machines in different cavities, the number of drilling holes, the design footage and the like. The statistical value is suitable for the construction conditions of class III surrounding rock environments mainly comprising silty slates and equipment YT28 air-leg rock drills.
v_2＝7.230m/h
σ_2＝1.317m/h
Wherein v is_2And sigma_2Representing the expectation and standard deviation of the drilling speed of a single drilling machine of the drilling unit.
The non-parameter inspection principle of Median and Jonkheere-Terpstra multiple independent samples is adopted for judgment, the expected values of the explosive-starting duration of each part and the overall distribution of standard deviations do not have obvious difference, the average values of the expected values and the standard deviations of the explosive-starting duration of excavated explosive-charges of the related part are taken as the expected values and the standard deviations of the overall distribution of the explosive-starting duration of each tunnel, the statistical values are different according to the comprehensive effects of the size of a working face, the input of personnel and the like, and the statistical values can be regarded as fixed values for the same project.
μ_3＝1.067h
σ_3＝0.25h
Wherein, mu_3And sigma_3Representing the expected and standard deviation of the charge detonation unit duration.
Dynamically adjusting distribution parameters of blasting waiting duration according to the field construction condition, considering that the blasting waiting duration distribution conforms to normal distribution, selecting a waiting duration sample in the latest period to recalculate the latest overall distribution parameter when the boundary factors such as field ventilation condition, slag tapping condition and surrounding rock condition are changed greatly and systematically in the initial simulation or when the boundary factors such as field ventilation condition, slag tapping condition and surrounding rock condition are changed greatly, and recommending that the content of the selected samples is not less than 50 according to the relevant conditions of the Lilliefors test.
Wherein, mu_4And sigma_4Representing the expected and standard deviation of the blast-waiting unit duration.
The triangular distribution is adopted to fit the abnormal duration of each part excavation construction, the envelope curve of the relevant abnormal point value is given through the triangular distribution fitting, and the method is suitable for simulation of the abnormal duration of each medium and small tunnel excavation progress. The abnormal value is obtained by carrying out mathematical statistics on the abnormal value:
abnormal value v of drilling speed of drilling unitdThe following empirical distribution is satisfied:
vd∈T(1.06v_2,2.75v_2,1.52v_2)
duration outlier e of a charge unitmThe following empirical distribution is satisfied:
em∈T(1.47μ_3,2.81μ_3,1.78μ_3)
blasting waiting unit duration abnormal value ewThe following empirical distribution is satisfied:
ew∈T(2.26μ_4,11.11μ_4,4.45μ_4)
the statistical value varies according to factors such as surrounding rock conditions, equipment performance, management level and the like of different projects.
Assuming that the occurrence probability of abnormal duration in the construction process meets Bernoulli distribution (0-1 distribution), verifying the abnormal duration by adopting a 0-1 distribution nonparametric inspection principle, and giving out a distribution inspection probability range of each working unit in the excavation cycle
Abnormal frequency of drilling speed of single drilling machine of drilling unit:
P2＝0.02～0.07
abnormal duration frequency of charge units:
P3＝0.04～0.05
burst wait unit exception duration frequency:
P4＝0.11～0.15
the statistical value varies according to factors such as surrounding rock conditions, equipment performance, management level and the like of different projects.
4. Establishing progress simulation experience model considering construction difference
Coupling the triangular distribution of the abnormal construction duration with the normal distribution of the normal construction duration by using the abnormal frequency to obtain a probability density distribution curve corresponding to the total excavation footage in the simulation period, wherein the implementation steps are as follows (refer to fig. 4):
step 1: given the simulation times N, generally requiring more than 100 ten thousand times, the simulation times count N and the footage cycle count i are initialized.
Step 2: the simulation period T is set to be 7 days, 15 days, 30 days and the like according to actual requirements.
And step 3: starting simulation times counting n and footage cycle counting i.
And 4, step 4: a drilling work cell cycle is performed for a single sample. The calculation formula of the cycle duration of the drilling work unit is as follows:
in the formula: v is the drilling speed (m/h) of a single drilling machine, m is the number of drilling machines, L is the number of drilled holes on the face, L0For designing the excavation circulating footage, r is the depth of the super drill, and r belongs to [0.2,0.5 ] according to practical experience]。
According to the content, the normal drilling speed of the single drilling machine of the drilling work unit meets the normal distribution, and the expectation and the standard deviation meet the following conditions:
v_2＝7.230m/h，σ_2＝1.317m/h
the single sampling value t of the normal duration in the circulation of the drilling working unit can be obtained by adopting the Monte Carlo combined with the calculation formula of the duration of the drilling working uniti′d。
The sampling method using normal distribution is as follows:
for a normal distribution N (μ, σ), the sampling results are:
wherein the content of the first and second substances,
is a pair of random variables that obey a normal distribution, N ∈ (μ, σ).
Wherein ξ1，ξ2Is a pair of obeys [0, 1]Uniformly distributed random variables.
According to the invention, the drilling work unit drilling speed abnormal value vd satisfies the following triangular distribution:
vd∈T(1.06v_2,2.75v_2,1.52v_2)
the single sampling value e of the abnormal duration in the circulation of the drilling working unit can be obtained by adopting the Monte Carlo combined with the calculation formula of the duration of the drilling working unitid。
The sampling method of the triangular distribution is as follows:
for a triangular distribution T (a, b, c), the sampling results are:
where ξ is a random variable that follows a uniform distribution of [0, 1 ].
According to the invention, the probability of the occurrence of the construction abnormality in the drilling work unit satisfies Bernoulli distribution (0-1 distribution), and the abnormal frequency of the drilling speed of the single drilling machine of the drilling unit is as follows:
P2＝0.02～0.07
the single sample values for the duration of the drilling unit cycle using monte carlo simulation were:
wherein p is2Represents P2A single sample of the 0-1 distribution at 0.02 to 0.07.
And 5: a single sample of the charge work unit cycle duration is made.
According to the invention, the normal duration of the charging work units satisfies a normal distribution with expectations and standard deviations:
μ_3＝1.067h，σ_3＝0.25h
simulation of single sample value t 'of normal duration in cycles of active charge working units using Monte Carlo'id。
According to the invention, the charging work unit exception duration emThe following triangular distribution is satisfied:
em∈T(1.47μ_3,2.81μ_3,1.78μ_3)
simulation of single sample value e of abnormal duration in circulation of working unit of available charge by Monte Carloim。
According to the invention, the probability of construction abnormity in the charging working unit meets the Bernoulli distribution (0-1 distribution), and the abnormal duration frequency of the charging unit is as follows:
P3＝0.04～0.05
the single sample values for the duration of the cycle of the available charge working unit using monte carlo simulations were:
wherein p is3Represents P3A single sample of the 0-1 distribution at 0.04-0.05.
Step 6: a single sampling of the shot wait unit cycle duration is made.
According to the invention, the normal duration of the blast waiting unit satisfies a normal distribution with expectations and standard deviations:
single sample values t 'of normal duration in a cycle of blast hold units are available using Monte Carlo simulations'iw。
According to the invention, the blasting waiting unit is abnormal for a duration ewThe following triangular distribution is satisfied:
ew∈T(2.26μ_4,11.11μ_4,4.45μ_4)
single sampling value e of abnormal duration in blasting waiting unit circulation can be obtained by adopting Monte Carlo simulationiw。
According to the invention, the probability of occurrence of construction abnormality in the blasting-waiting unit satisfies the bernoulli distribution (0-1 distribution), and the frequency of abnormality duration of the blasting-waiting unit is:
P4＝0.11～0.15
the single sampling value of the cycle duration of the blasting waiting unit obtained by adopting Monte Carlo simulation is as follows:
wherein p is4Represents P40.11-0.15 times a single sample of the 0-1 distribution.
And 7: calculating the duration value of the single excavation cycle of the tunnel:
tic＝tid+tim+tiw
and 8: repeating the steps 3-7, and gradually calculating the excavation cycle duration until the accumulated excavation cycle duration sigma ticAnd stopping the cycle simulation when the simulation period is equal to or larger than the simulation period T. According to the footage cycle count i and the design excavation cycle footage L0Obtaining the total excavation footage L in the simulation period TT＝L0×i。
And step 9: and (5) repeating the steps 3 to 8 until the simulation times count N is equal to N, finishing simulation to obtain N excavation footage sequences, and then generating a probability distribution map of accumulated excavation footage in the simulation period T for assisting progress evaluation, decision and risk analysis.
In order to comprehensively consider the influence of construction abnormity on the duration of excavation, the Bernoulli distribution with abnormal probability is utilized to carry out simulation coupling on the triangular distribution with the duration distribution of normal working units, the recalculation of blasting waiting duration distribution parameters is triggered when the initial simulation condition or the site ventilation, slag discharge and surrounding rock conditions and the like are changed to a large extent systematically, the calculation cost can be greatly saved and the model is simplified by adopting a parameter triggering and updating strategy, the simulation precision is improved, and the dynamic change of the construction process is comprehensively reflected, as shown in figure 4. After the simulation times and the progress meter planning period are set, relevant experience distribution parameters are substituted, a probability distribution curve of the excavation accumulated footage in the planning period can be obtained, and the obtained result can be used for assisting site progress decision, review, risk analysis and the like.
And (3) sampling an empirical simulation model considering construction difference, a primary simulation model considering construction difference, a traditional simulation model and different statistical time periods of a sample sequence respectively for 100 ten thousand times by combining a Monte Carlo simulation method (Monte Carlo), and obtaining an excavation cumulative scale-in probability distribution curve of each simulation model. Calculating a correlation coefficient between the curves by adopting a Pearson correlation theory, calculating a standard deviation of difference values of the two curves respectively, quantifying the correlation and the dispersion degree of the curves corresponding to different simulation models and samples, and performing comparative analysis to obtain the following conclusion:
1. the primary simulation model considering the construction abnormity is always attached to the sample curve under different caverns and different construction abnormity conditions to show the best performance, and the simulation model considering the construction abnormity is proved to be superior to the traditional simulation model not specially considering the construction abnormity, so that the actual situation can be more accurately reflected.
2. The traditional simulation model can better fit a sample curve, but the robustness is poor. If the excavation accumulated footage result calculated for the part with larger construction abnormity is smaller, the excavation accumulated footage length calculated for the part with relatively smaller construction abnormity is larger. In addition, the traditional simulation model is required to be substituted into the statistical parameters of the actual samples, so that the simulation result is more accurate, the simulation parameters of the prediction samples are calculated by adopting complex algorithms such as a neural network, a Bayesian theory, a moving average method, deep learning and the like, and the operation difficulty is higher.
3. The empirical simulation model considering the construction abnormity can better fit a sample curve, but the calculated excavation accumulated footage result is slightly smaller.
4. The correlation degree of the experimental simulation model result considering the construction abnormity and the sample curve is higher, and the model is more consistent with the actual situation.
In summary, the empirical simulation model considering the construction abnormity can better simulate and predict the actual situation, has simpler algorithm and better robustness compared with the traditional simulation model, and is more suitable for the progress simulation modeling of excavation construction.
In addition, through sensitivity analysis of relevant factors of the model, the drilling depth which has the greatest influence on a simulation result directly reflects the excavation construction efficiency; secondly, the influence of the abnormal duration frequency of the blasting waiting unit on the simulation result is realized, and the larger the frequency is, the longer the pause time of the excavation influenced by the low-frequency special abnormality is; the number of boreholes has a secondary effect on the simulation results, and the value represents the magnitude of the excavation workload. The influence of other parameters on the simulation result is relatively small. Therefore, when the simulation model is used, the accuracy of three parameters, i.e., the abnormal duration frequency of the blasting-waiting unit, the drilling depth and the number of drilled holes, needs to be ensured so as to improve the accuracy of the simulation result. In addition, in the field construction management, whether technical schemes and guarantee measures related to the three key parameters are reasonable or not needs to be focused, and when the actual progress deviates from the planned progress, the related aspects of the three key parameters on the field and the three key parameters should be optimized and adjusted preferentially on the premise of ensuring the construction quality and safety, so that the configuration efficiency of construction resources is improved.
The foregoing is only a preferred embodiment of this invention and it should be noted that modifications can be devised by those skilled in the art without departing from the principles of the invention and these modifications should also be considered as within the scope of the invention.
Details not described in this specification are within the skill of the art that are well known to those skilled in the art.
Claims (9)
1. A tunnel excavation progress simulation method considering construction abnormity is characterized in that: the method comprises the following steps:
A) the tunnel excavation circulating process is divided into a drilling working unit, a charging working unit and a blasting waiting working unit;
B) separating the normal duration and the abnormal duration of each working unit of the tunnel excavation from the drilling working unit, the charging working unit and the blasting waiting working unit by using a box diagram theory,
C) deducing the distribution forms and the distribution parameters of the normal duration and the abnormal duration of the drilling working unit, the charging working unit and the blasting waiting working unit by using mathematical statistics;
D) independently sampling the drilling working unit, the charging working unit and the blasting waiting working unit in the same excavation cycle by using a Monte Carlo simulation method, and then performing coupling calculation;
E) establishing a progress simulation experience model considering construction difference, and coupling triangular distribution of abnormal duration of construction with normal distribution of normal duration by using abnormal frequency to obtain a probability density distribution curve corresponding to the total excavation footage in a simulation period;
the step E) comprises the following steps:
1) setting simulation times N and a simulation period T, and initializing a simulation time count N and a footage cycle count i;
2) drill work unit cycles were performed for a single sample:
21) the normal drilling speed of the single drilling machine of the drilling work unit meets normal distribution, and single sampling value t 'with normal duration in the circulation of the drilling work unit is obtained according to Monte Carlo simulation and a drilling work unit duration calculation formula'id；
22) The abnormal drilling speed of a single drilling machine of the drilling working unit meets the triangular distribution, and a single sampling value e of abnormal duration in the circulation of the drilling working unit is obtained according to the Monte Carlo simulation and the calculation formula of the duration of the drilling working unitid；
23) Obtaining single sampling value t of cycle duration of drilling working unit by Monte Carlo simulationidComprises the following steps:
wherein p is2Abnormal frequency P of drilling speed for single drilling machine of drilling unit2A single sampling of (a);
3) the charge work unit cycle is performed for a single sample:
31) the normal duration of the charging working unit meets normal distribution, and single sampling value t 'of the normal duration in the circulation of the charging working unit is obtained according to Monte Carlo simulation'im；
32) The abnormal duration of the charging working unit meets the triangular distribution, and a single sampling value e of the abnormal duration in the cycle of the charging working unit is obtained according to Monte Carlo simulationim；
33) Obtaining single sampling value t of circulation duration of charging working unit by Monte Carlo simulationimComprises the following steps:
wherein p is3Abnormal frequency P of working unit for charging3A single sampling of (a);
4) a blast-waiting unit cycle is performed for a single sample:
41) the normal duration of the blasting waiting unit meets normal distribution, and single sampling value t 'of the normal duration in the circulation of the charging working unit is obtained according to Monte Carlo simulation'iw；
42) The abnormal duration of the blasting waiting unit meets the triangular distribution, and a single sampling value e of the abnormal duration in the circulation of the charging working unit is obtained according to Monte Carlo simulationiw；
43) Obtaining single sampling value t of blasting waiting unit circulation duration by adopting Monte Carlo simulationiwComprises the following steps:
wherein p is4For abnormal frequency P of blast waiting units4A single sampling of (a);
5) calculating the cyclic duration value t of single excavation of the tunnelic：
tic＝tid+tim+tiw
6) Repeating the steps 2) to 5) until the accumulated excavation cycle duration value sigma ticEqual to or more than the simulation period T, according to the footage cycle count i and the design excavation cycle footage L0Obtaining the total excavation footage L in the simulation period TT＝L0×i；
7) And repeating the steps 2) to 6) until the simulation times count N is equal to the simulation times N to obtain N excavation footage sequences, and then generating a probability distribution map of the accumulated excavation footage in the simulation period T for progress evaluation, decision and risk analysis.
2. The tunnel excavation progress simulation method considering construction abnormalities as set forth in claim 1, wherein: the steps 2), 3) and 4) are carried out simultaneously.
3. The tunnel excavation progress simulation method considering construction abnormalities as set forth in claim 1, wherein: the drilling work unit duration calculation formula in the step 21) is as follows:
in the formula: t isdFor the duration of a drilling work unit, v is the drilling speed m/h of a single drilling machine, m is the number of drilling machines, L is the number of drilled holes on the face, L0And r is the depth of the ultra-drilling for designing the excavation circulating footage.
4. The tunnel excavation progress simulation method considering construction abnormalities as set forth in claim 1, wherein: the normal drilling speed of the single drilling machine of the drilling working unit in the step 21) meets normal distribution, and the expected value and the standard deviation of the normal drilling speed of the single drilling machine of the drilling working unit are v respectively_2＝7.230m/h、σ_21.317m/h, m/h represents meters per hour.
5. The tunnel excavation progress simulation method considering construction abnormalities as set forth in claim 4, wherein: the triangular distribution of the abnormal drilling speed of the single drilling machine of the drilling working unit in the step 22) is vd∈T(1.06v_2,2.75v_2,1.52v_2) (ii) a The abnormal frequency P of the drilling speed of the single drilling machine of the drilling unit in the step 23)2＝0.02～0.07。
6. The method for simulating the tunnel excavation progress considering construction abnormalities as claimed in claim 1, wherein: the normal duration of the charging working units in the step 31) meets a normal distribution, and the expected value and the standard deviation of the normal duration of the charging working units are respectively mu_3＝1.067h、σ_3H represents 0.25h, and h represents hour.
7. The method for simulating the progress of excavation of a tunnel considering construction abnormalities as set forth in claim 6, wherein the method is characterized in thatCharacterized in that: the step 32) comprises triangulating the abnormal duration of the pharmaceutical work units by em∈T(1.47μ_3,2.81μ_3,1.78μ_3) (ii) a The abnormal frequency P of the medicine-loading working unit in the step 33)3＝0.04～0.05。
8. The tunnel excavation progress simulation method considering construction abnormalities as set forth in claim 1, wherein: the normal duration of the blasting waiting unit in the step 41) meets normal distribution, and the expected value and the standard deviation of the normal duration of the blasting waiting unit are respectively
9. The method for simulating a tunnel excavation progress considering construction abnormalities as set forth in claim 8, wherein: the triangular distribution of the abnormal duration of the blasting waiting unit in the step 42) is ew∈T(2.26μ_4,11.11μ_4,4.45μ_4) (ii) a The abnormal frequency P of the blasting waiting unit in the step 43)4＝0.11～0.15。
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