LU500223B1 - Method for disease evaluation of existing tunnel lining structure based on analytic hierarchy process-extenics analysis - Google Patents
Method for disease evaluation of existing tunnel lining structure based on analytic hierarchy process-extenics analysis Download PDFInfo
- Publication number
- LU500223B1 LU500223B1 LU500223A LU500223A LU500223B1 LU 500223 B1 LU500223 B1 LU 500223B1 LU 500223 A LU500223 A LU 500223A LU 500223 A LU500223 A LU 500223A LU 500223 B1 LU500223 B1 LU 500223B1
- Authority
- LU
- Luxembourg
- Prior art keywords
- evaluation
- disease
- tunnel lining
- lining structure
- existing tunnel
- Prior art date
Links
- 238000011156 evaluation Methods 0.000 title claims abstract description 248
- 201000010099 disease Diseases 0.000 title claims abstract description 99
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 title claims abstract description 99
- 238000000034 method Methods 0.000 title claims abstract description 51
- 238000004458 analytical method Methods 0.000 title claims abstract description 15
- 239000011159 matrix material Substances 0.000 claims abstract description 37
- 239000013598 vector Substances 0.000 claims abstract description 23
- 238000005314 correlation function Methods 0.000 claims abstract description 16
- 238000013077 scoring method Methods 0.000 claims abstract description 10
- 238000010276 construction Methods 0.000 claims description 47
- 239000011435 rock Substances 0.000 claims description 17
- 238000005516 engineering process Methods 0.000 claims description 9
- 239000000463 material Substances 0.000 claims description 9
- 230000015572 biosynthetic process Effects 0.000 claims description 8
- 238000005259 measurement Methods 0.000 claims description 8
- 230000008520 organization Effects 0.000 claims description 8
- 238000011160 research Methods 0.000 claims description 8
- 238000012876 topography Methods 0.000 claims description 8
- 238000013461 design Methods 0.000 claims description 7
- 238000012423 maintenance Methods 0.000 claims description 7
- 238000012549 training Methods 0.000 claims description 7
- 238000010606 normalization Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 description 5
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008094 contradictory effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000009897 systematic effect Effects 0.000 description 2
- 241000784713 Cupido Species 0.000 description 1
- 239000008186 active pharmaceutical agent Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
Classifications
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21D—SHAFTS; TUNNELS; GALLERIES; LARGE UNDERGROUND CHAMBERS
- E21D9/00—Tunnels or galleries, with or without linings; Methods or apparatus for making thereof; Layout of tunnels or galleries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/08—Construction
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Mining & Mineral Resources (AREA)
- General Health & Medical Sciences (AREA)
- Marketing (AREA)
- Geochemistry & Mineralogy (AREA)
- Geology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Economics (AREA)
- Environmental & Geological Engineering (AREA)
- Human Resources & Organizations (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Lining And Supports For Tunnels (AREA)
Abstract
The present disclosure provides a method for disease evaluation of an existing tunnel lining structure based on an analytic hierarchy process (AHP)-Extenics analysis. The method includes: constructing a disease evaluation index system for the existing tunnel lining structure based on the AHP; constructing a judgment matrix for each type of evaluation indexes in the evaluation index system by using an expert scoring method; calculating a weight vector of each type of evaluation indexes in the evaluation index system according to the judgment matrix, and checking a consistency; determining an evaluation matter element and an evaluation grade for the disease of the existing tunnel lining structure based on an Extenics theory; constructing classical domains, nodal domains and an evaluation matter element for the disease evaluation of the existing tunnel lining structure according to the evaluation grade; constructing a correlation function between each type of evaluation indexes in the evaluation index system and the evaluation grade, and calculating a correlation degree; and determining an evaluation grade for the disease of the existing tunnel lining structure according to the correlation degree. The present disclosure makes the disease evaluation of the existing tunnel lining structure more comprehensive and objective.
Description
METHOD FOR DISEASE EVALUATION OF EXISTING TUNNEL LINING STRUCTURE BASED ON ANALYTIC HIERARCHY PROCESS-EXTENICS ANALYSIS LU500223
TECHNICAL FIELD The present disclosure relates to the technical field of tunnel engineering in civil engineering, in particular to a method for disease evaluation of an existing tunnel lining structure based on an analytic hierarchy process (AHP)-Extenics analysis.
BACKGROUND At present, the methods for disease evaluation of the existing tunnel are simple, and the traditional fuzzy comprehensive evaluation method is mainly used. This method cannot solve the problem of duplication of evaluation information caused by the correlation between evaluation indexes, and it is complicated for calculation and subjective in the determination of the index weight vector. Meanwhile, the fuzzy comprehensive evaluation method does not take into account the index information amount of each evaluated object, which may affect the discrimination of the evaluation results. That is, when there are a large number of index sets, under the condition that the sum of weight vectors is 1, the weight coefficient of the relative membership degree is often small, and the weight vector does not match the fuzzy matrix R. As a result, there will be an ultra-fuzzy phenomenon, leading to a very poor resolution, making it impossible to discriminate between the membership degrees, and even causing the determination to fail. The pure AHP can be used for the systematic evaluation of unstructured characteristics and the systematic evaluation of multi-objectives, multi-criteria and multi-periods. Extenics is a unique theory based on the matter element theory and extension set theory to research and to solve contradictory problems and a formal tool established with primitives as logical cells to solve contradictory problems. Compared with classical mathematics and fuzzy mathematics, the essential difference of Extenics is that it is oriented to the problem itself, rather than oriented to data or spatial form. Compared with the mathematical processing methods that require strict data, Extenics has a wider range of applications.
SUMMARY In order to overcome the shortcomings of the existing evaluation method, the present disclosure provides a method for disease evaluation of an existing tunnel lining structure based on an analytic hierarchy process (AHP)-Extenics analysis. The method includes the following steps: S101: constructing a disease evaluation index system for the existing tunnel lining structure based on the AHP; S102: constructing a judgment matrix for each type of evaluation indexes in the evaluation index system by using an expert scoring method;
S103: calculating a weight vector of each type of evaluation indexes in the evaluation index LU500223 system according to the judgment matrix, and checking a consistency; S104: determining an evaluation matter element and an evaluation grade for the disease of the existing tunnel lining structure based on an Extenics theory; $105: constructing classical domains, nodal domains and an evaluation matter element for disease evaluation of the existing tunnel lining structure according to the evaluation grade; S106: constructing a correlation function between each type of evaluation indexes in the evaluation index system and the evaluation grade, and calculating a correlation degree; and S107: determining an evaluation grade for the disease of the existing tunnel lining structure according to the correlation degree.
Further, in step S101, the evaluation index system may include: an objective A of existing tunnel lining structure disease evaluation, where under the objective A, there are three types of indexes, namely three criteria: construction criterion B1, natural environment criterion B2 and engineering geology criterion Bs; the construction criterion Bs may include 6 types of evaluation indexes: construction process and technology C1, site survey and measurement C,, construction equipment and materials Cs, post-operation and maintenance C4, construction organization design Cs and operator education and training Ce; the natural environment criterion Ba may include 3 types of evaluation indexes: topography C7, climate and meteorology Cs and potential natural disasters Co; and the engineering geology criterion B3 may include 4 types of evaluation indexes: surrounding rock grade Cio, rock formation occurrence C1, geological structure C42 and poor geological conditions Crs.
Further, in step S102, the constructing a judgment matrix for each type of evaluation indexes in the evaluation index system by using an expert scoring method may specifically include: determining basic data through pairwise comparison of the evaluation indexes under the objective A, the criterion By, the criterion Bz and the criterion Bs by using a 1-9 scale method, quantifying relative importance between two evaluation indexes, obtaining importance of the three types of indexes under the objective A of existing tunnel lining structure disease evaluation through pairwise comparison by using an expert scoring method, and then deriving a judgment matrix corresponding to the objective A, where the basic data are elements in each judgment matrix; and similarly deriving determination matrices of the criteria B+ to Ba.
Further, in step S103, the calculating a weight vector of each type of evaluation indexes in the evaluation index system according to the judgment matrix, and checking a consistency may specifically include: calculating eigenvectors of the determination matrices corresponding to the objective A, the criterion B1, the criterion Ba and the criterion Bs respectively by using a square root method to obtain weight vectors of the 13 evaluation indexes in C+ to C43, and then subjecting the weight LU500223 vectors of the 13 evaluation indexes to normalization and consistency check to obtain weight w =(w w w )" vectors © Neots) where Yi denotes the weight of an i-th evaluation index ©; a larger Yi indicates a greater influence of the evaluation index Ci on the tunnel lining structure disease, i=1,2,...,13; if the consistency check fails, the determination matrices need to be reconstructed through expert scoring, and new weight vectors need to be calculated. Further, in step S104, the determining an evaluation matter element and an evaluation grade for the disease of the existing tunnel lining structure based on an Extenics theory may specifically include: determining research of the existing tunnel lining structure disease evaluation as a matter element R, dividing the 13 evaluation indexes under the matter element into 5 evaluation grades: ui, Uz, ..., Us, and obtaining an evaluation index set C={c1, Ca, ..., C13} and an evaluation grade set U={u1, uo, ..., Us}, combining historical data to determine a score range for the disease of the existing tunnel lining structure as [50,100], and then determining the evaluation grade set U={u1, u, ..., Us}={mild, light, moderate, heavy, severe}.
Further, in step S105: constructing classical domains, nodal domains and an evaluation matter element for disease evaluation of the existing tunnel lining structure according to the evaluation grade: the classical domains may include: mild (95-100), light (85-95), moderate (65-85), heavy (55-65) and severe (50-55); a nodal domain of the criterion B1 may be: U c <50,100> c, <50,100> c, <50,100> R(B,) = ce, <50,100 > c, <50,100 > c, <50,100 > a nodal domain of the criterion Ba may be: U ce <50,100> R(B,) = c, <50,100 > c, <50,100 > a nodal domain of the criterion Bs may be: U Co <50,100> R(B,) = c, <50,100 > c, <50,100 > c, <50,100 > the evaluation matter element may be:
U Construction process and technology c, 59 LU500223 Site survey and measurement c, 90 Construction equipment and materials c, 62 Post — operation and maintenance c, 58 Construction organization design c, 73 Operator education and training c, 121 R= Topography c, 75 Climate and meteorology c, 91 Potential natural disasters c, 80 Surrounding rock grade €, 88 Rock formation occurrence c,, 86 Geological structure c, 85 Poor geological conditions c,, 61 Further, in step S106, the correlation function may be: v.,V. © POLY) =p(v,V) DS a) ET v, eV, and pv, V =p, V)
J where, /‘ ‘’ represents a correlation function value of a j-th evaluation grade V; of the i-th evaluation index Gi Vi represents a score value of the j-th evaluation index Gi, which is obtained according to the evaluation matter element R; V represents a total score interval of the 5 evaluation grades, that is, 50-100; V; represents a score interval of the j-th evaluation grade; | V| represents a length of the score interval of the /-{h evaluation grade; POV) represents a distance between a score ” and the score interval 4 , which is calculated as follows: a+b| b-a
CS where, a and b denote a lower limit and an upper limit of the score interval Y, , respectively, =1,2,..,13; =1,2,...,5; the correlation degree may be calculated as follows: K=YwK (v) i=l where, Yi is the weight of the evaluation index Cr K, is the correlation degree between the correlation function value of the j-th evaluation grade V; of the i-th evaluation index Ci and the weight Wi ; nis a total number of evaluation indexes in the criterion corresponding to the evaluation index “. Further, in step S107, the determining an evaluation grade for the disease of the existing tunnel lining structure according to the correlation degree may include: selecting a correlation degree 500223 with a largest value among all correlation degrees, and using an evaluation grade corresponding to the correlation degree as the evaluation grade for the disease of the existing tunnel lining structure.
5 The technical solution provided by the present disclosure has the following beneficial effects. The present disclosure adopts the AHP process to construct an evaluation index system and weight distribution for an actual problem, and then combines the Exteneics theory to construct a correlation function to calculate the correlation degree of the evaluation index and the evaluation grade, thereby intuitively evaluating the actual engineering problem. The method for disease evaluation of an existing tunnel lining structure based on an AHP-Extenics analysis makes the disease evaluation of the existing tunnel lining structure more comprehensive and objective.
BRIEF DESCRIPTION OF THE DRAWINGS The present disclosure is described in further detail with reference to the accompanying drawings and embodiments.
FIG. 1 is a flowchart of a method for disease evaluation of an existing tunnel lining structure based on an AHP-Extenics analysis according to an embodiment of the present disclosure. FIG. 2 is an overall block diagram of the method for disease evaluation of an existing tunnel lining structure based on the AHP -Extenics analysis according to an embodiment of the present disclosure.
FIG. 3 shows an evaluation index system according to an embodiment of the present disclosure.
DETAILED DESCRIPTION OF THE EMBODIMENTS In order to describe the technical features, objectives and effects of the present disclosure more clearly, the specific implementations of the present disclosure are described in detail below with reference to the accompanying drawings.
An embodiment of the present disclosure provides a method for disease evaluation of an existing tunnel lining structure based on an analytic hierarchy process (AHP)-Extenics analysis. Referring to FIGS. 1 and 2, FIG. 1 is a flowchart of a method for disease evaluation of an existing tunnel lining structure based on an analytic hierarchy process (AHP)-Extenics analysis according to an embodiment of the present disclosure, and FIG. 2 is an overall block diagram of the method for disease evaluation of an existing tunnel lining structure based on the AHP - Extenics analysis according to an embodiment of the present disclosure.
The method for disease evaluation of an existing tunnel lining structure based on an analytic hierarchy process-Extenics analysis specifically includes the following steps: S101: Construct an evaluation index system for the disease of the existing tunnel lining structure based on the AHP.
LU500223 S102: Construct a judgment matrix for each type of evaluation indexes in the evaluation index system by using an expert scoring method.
S103: Calculate a weight vector of each type of evaluation indexes in the evaluation index system according to the judgment matrix, and check a consistency.
S104: Determine an evaluation matter element and an evaluation grade for the disease of the existing tunnel lining structure based on an Extenics theory.
S105: Construct classical domains, nodal domains and an evaluation matter element for the disease evaluation of the existing tunnel lining structure according to the evaluation grade.
S106: Construct a correlation function between each type of evaluation indexes in the evaluation index system and the evaluation grade, and calculate a correlation degree.
S107: Determine an evaluation grade for the disease of the existing tunnel lining structure according to the correlation degree.
Referring to FIG. 3, FIG. 3 shows an evaluation index system according to an embodiment of the present disclosure.
There are many factors influencing the disease of the existing tunnel lining structure, which can be divided into three types: engineering geology, natural environment and construction operation, including 13 major factors such as construction process and technology, site survey and measurement, construction equipment and materials.
Based on this, an evaluation index system shown in FIG. 2 is constructed.
The evaluation index system includes: an objective A of existing tunnel lining structure disease evaluation, where under the objective A, there are three types of indexes, namely three criteria: construction criterion Bs, natural environment criterion Ba and engineering geology criterion Bs.
The construction criterion B- includes 6 types of evaluation indexes: construction process and technology C4, site survey and measurement C,, construction equipment and materials Cs, post-operation and maintenance C4, construction organization design Cs and operator education and training Ce.
The natural environment criterion Bz includes 3 types of evaluation indexes: topography C7, climate and meteorology Cs and potential natural disasters Ce.
The engineering geology criterion Bs includes 4 types of evaluation indexes: surrounding rock grade C10, rock formation occurrence C1, geological structure C42 and poor geological conditions Crs.
In step S102, the constructing a judgment matrix for each type of evaluation indexes in the evaluation index system by using an expert scoring method specifically includes: Determine basic data through pairwise comparison of the evaluation indexes under the objective A, the criterion Bs, the criterion B2 and the criterion Bs by using a 1-9 scale method, quantify relative importance (refer to a value-importance relationship as shown in Table 1) between two evaluation indexes, obtain an importance of the three types of indexes (engineering geology, natural environment and construction operation) under the existing tunnel lining structure disease evaluation objective A through pairwise comparison by using an expert 500223 scoring method, and then derive a judgment matrix corresponding to the objective A, where determination results of the importance of the evaluation indexes under the objective A are shown in Table 2; the basic data are elements in each judgment matrix.
Table 1 Value-importance relationship Even Reciprocal Value 1 3 5 7 9 value value Slightly Fairly Very Importance Identical Important Median Reverse important important important Table 2 Determination results of importance of objective-level evaluation indexes Existing tunnel lining structure Construction operation Natural environment Engineering geology disease evaluation Engineering geology / Construction operation Natural environment / Construction ; ; ; construction / construction construction operation operation | | operation operation Identical 1 Importance 2 Importance 1/2 Natural Construction operation Natural environment/ Engineering geology / atura / natural environment natural environment natural environment environment Importance 1/2 Identical 1 Importance 1/2 | | Construction operation Natural environment/ Engineering geology / Engineering | / engineering geology engineering geology engineering geology geology Importance 2 Importance 2 Identical 1 Similarly, the importance of the 6 indexes under the criterion B (construction operation), the 3 indexes under the criterion Bz (natural environment) and the 4 indexes under the criterion Bs (engineering geology) in the existing tunnel lining structure disease evaluation is derived through pairwise comparison by expert scoring, and then the determination matrices of By to Bs are derived. In the embodiment of the present disclosure, the determination matrices of the objective A and the criteria B+, Ba and Bs are respectively expressed as follows:
B= 1/2 6 1 3 5 3 1 2 2 ! [1/3 5 1/3 1 3 2 1 1 2 A=|1/2 1 1/2 1/5 3 1/5 1/3 1 1 B,=| 1 1 2 1/2 2 1. 1/5 4 1/3 1/2 1 1 /. 1/2 1/2 1), 1 2 3 1 1/2 1 1 1 B,= 1/3 1 1 1/3 1 13 1 In step S103, the calculating a weight vector of each type of evaluation indexes in the evaluation index system according to the judgment matrix, and checking a consistency specifically includes: Calculate eigenvectors of the determination matrices corresponding to the objective A, the criterion B1, the criterion Ba and the criterion Bs respectively by using a square root method to obtain weight vectors of the 13 evaluation indexes in C+ to C43, and then subject the weight vectors of the 13 evaluation indexes to normalization and consistency check to obtain weight W = (w W W )" vectors \ 1 ?"> 17 where Yi denotes the weight of an i-th evaluation index Gi a larger Yi indicates a greater influence of the evaluation index Ci on the tunnel lining structure disease, i=1,2,...,13; if the consistency check fails, the determination matrices need to be reconstructed through expert scoring, and new weight vectors need to be calculated.
The corresponding weight vector of a judgment matrix X is specifically calculated as follows; S201: Multiply each element of the judgment matrix X by rows to obtain U: u, = 116; j=1 where, * is an i-th element in the matrix U; by is an element in an j-th row and a j-th column of the judgment matrix X; n is an order of the judgment matrix X; /=1,2,...,n. S202: Take an n-th root of each element in the matrix U to obtain a matrix U7: S203: Normalize each element in U7 to obtain an eigenvector w: U, 0-40 Zu i=l where, * is an i-th element in the matrix U1, and ©: is an i-th weight in the eigenvector w. S204: Calculate a maximum characteristic root Amax Of the judgment matrix: (Xo), An 2 where, (Xe), represent elements in an j-th row of Xo
In order to verify the construction rationality of the judgment matrix, it is necessary to check the 500223 consistency of the evaluation indexes. A specific calculation process is as follows: S301: Calculate a consistency index C./.: fo = ex DA n-l 8302: Calculate a consistency index C.R.: CR.= Lr where, Amax is a maximum characteristic root of the judgment matrix; R./. is an average random consistency index, which can be determined by referring to Table 3. Table 3 Average random consistency index R./. Order 1 2 3 4 5 6 7 8 RL 0 O 052 089 112 126 136 141 When a random consistency ratio C.R. < 0.10, it is considered that the constructed judgment matrix meets the consistency requirements. Otherwise, it is necessary to adjust the value of the elements appropriately, that is, to derive each judgment matrix through expert scoring.
In step S104, the determining an evaluation matter element and an evaluation grade for the disease of the existing tunnel lining structure based on an Extenics theory specifically includes: Determine research of the existing tunnel lining structure disease evaluation as a matter element R, divide the 13 evaluation indexes under the matter element into 5 evaluation grades: us, Uz, ..., Us, and obtain an evaluation index set C={c1, C2, ..., C13} and an evaluation grade set U={u1, uz, ..., Us}.
Combine historical data to determine a score range for the disease of the existing tunnel lining structure as [50,100], and then determine the evaluation grade set U={u1,uz,us,us,us}={mild, light, moderate, heavy, severe}.
In step S105: construct classical domains, nodal domains and an evaluation matter element for the disease evaluation of the existing tunnel lining structure according to the evaluation grade: the classical domains include: mild (95-100), light (85-95), moderate (65-85), heavy (55-65) and severe (50-55); a nodal domain of the criterion Bs is: U ¢ <50,100> c, <50,100> = © Sao | c, <50,100 > c, <50,100> a nodal domain of the criterion B: is: [ c, <50,100 | R(B,)=| ¢ <50,100> c, <50,100 > a nodal domain of the criterion Bs is: LU500223 Co <50,100 : R(B,)= e <50,100 > c, <50,100 > c, <50,100 > the evaluation matter element is: U Construction process and technology c, 59 Site survey and measurement c, 90 Construction equipment and materials c, 62 Post — operation and maintenance c, 58 Construction organization design c, 73 Operator education and training c, 72 R= Topography c, 75 Climate and meteorology c, 91 Potential natural disasters c, 80 Surrounding rock grade €, 88 Rock formation occurrence c,, 86 Geological structure c, 85 Poor geological conditions c,, 61).
Taking the criterion Bı (construction operation) in the research of the existing tunnel lining structure disease evaluation as an example, the classical domains of 5 disease grades and a nodal domain and an evaluation matter element covering the total range of the disease grades are constructed respectively, as shown in Table 4:
Table 4 Classical domains, Nodal domains and evaluation matter element LU500223 Nodal domains and evaluation matter Description Classical domains element U, ¢ <95,100 > U ¢ <50,100 > Us to Us c, <95100> c, <50,100> RE © <98.100> Nodal Rp © <50400> represent the 5 1 - 17 = c, <95,100 > domain c, <50,100 > disease Cs <95,100 > Cs <50,100 > c, <95,100 > c, <50,100> evaluation U, c <85,95> grades in the 85,95 © =H research of the Light R(B)- c, <85,95> 1g ! c, <85,95> existing tunnel Cs <85,95> lini t t c, <85,95> ining structure U Construction process and tech . disease U, ¢ <65,85> Site survey and measureme ce <6585> Construction equipment and md evaluation, Modera c, <65,85> pP | d mai R(B)= c <6585> ost — operation and mainten respectively; C1 te + 65.85 Evaluati Construction organization de ‘ € , oc i <65.85> on Operator education and trai 13 R= Topography c, represent the Us a PR matter Climate and meteorolog 13 evaluation G <0» element Potential natural disaster] . _ ce, <55,65> indexes in the Heavy | R(B)= e <5565> Surrounding rock grade . <55,65> Rock formation occurrenc research of the c, <55,65> Geological structure existing tunnel Poor geological condition] Us a <50,55> lining structure c, <50,55> . s RB) ce, <50,55> disease evere | R(B)= G <50,55> evaluation, c, <50,55> c, <50,55> respectively. In step S106, the correlation function is: Pv, V2) Ken py,,V)-ply.V,) (AV. )= PS POV) _ 27 el, and p(n, V)FA0V) lé K (v ; ; . ; .
where, / (m) represents a correlation function value of a j-th evaluation grade V; of the i-th evaluation index Gi Vi represents a score value of the j-th evaluation index Gi, which is obtained according to the evaluation matter element R; V represents a total score interval of the 5 evaluation grades, that is, 50-100; V; represents a score interval of the j-th evaluation grade; | V| represents a length of the score interval of the j-th evaluation grade; POV) represents a LU500223 distance between a score ” and the score interval 4 , which is calculated as follows: a+b| b-a vv, V) = - — | - — pP V,) | TS | 5 where, a and b denote a lower limit and an upper limit of the score interval V;, respectively, #1,2,...,13; /=1,2,...,5; the correlation degree is calculated as follows: K=YwK (v) i=l where, Yi is the weight of the evaluation index Cr K, is the correlation degree between the correlation function value of the j-th evaluation grade V; of the i-th evaluation index Ci and the weight Wi ; nis a total number of evaluation indexes in the criterion corresponding to the evaluation index “. In step S107, the determining an evaluation grade for the disease of the existing tunnel lining structure according to the correlation degree includes: select a correlation degree with a largest value among all correlation degrees, and use an evaluation grade corresponding to the correlation degree as the evaluation grade for the disease of the existing tunnel lining structure.
In the embodiment of the present disclosure, a 1-9 scale method is used to construct the determination matrices of the indexes at various levels, and a tunnel is taken as an example to calculate the weight of each evaluation index.
The determination matrices and weight distribution of the objective level A-B and the criterion levels B1-C, Bz-C and Bs-C are listed in Tables 5 to 8:
Table 5 Determination matrices and weight distribution of A-B Consistency A Bs B2 Bs Wi check
Table 6 Determination matrices and weight distribution of B1-C LU500223 Consistency =F C1 C2 C3 C4 Cs Cs Ww; check \max=6.20 C.1.=0.04 cI pI I pp pM C.R.=0.03<0.1 Table 7 Determination matrices and weight distribution of B>-C Consistency B2 C7 Cs Co wi check Table 8 Determination matrices and weight distribution of B3-C Amax=4.12 C.1.=0.05 01343 C.R.=0.05<0.1 The calculation results from Table 5 to Table 8 show that the index weight wg of the objective level A-B in the analysis of the tunnel lining structure disease evaluation = (0.4938,0.1958,0.3108)", the index weight wc of the criterion level B-C = (0.3922,0.0319,0.2725,0.1470,0.0704,0.0860,0.4000,0.4000,0.2000,0.3640,0.1956,0.1343,0.30 61)", and the consistency check index C.R. of the judgment matrix at each level < 0.1, all meeting the consistency requirements.
If the consistency of the judgment matrix ispoor, the reliability of the ranking result is low, and the judgment matrix needs to be adjusted.
On this basis, comprehensive weights of each index are calculated, and then sorted from high to low.
The results of the overall ranking made according to the evaluation index levels of the tunnel lining structure disease evaluation are shown in Table 9.
Table 9 Level-based overall ranking LU500223 Overall first First-level Second- | Second Comprehensive Objective level weight level level weight Ranking index index weight Bi 0.4938 Cr [room | owas |e , According to the ranking results in Table 9, among the evaluation indexes of the tunnel lining structure disease, the construction process and technology C1 has the largest influence on the tunnel lining structure disease, followed by the construction equipment and materials Cs, the surrounding rock grade Cio and the poor geological conditions Cis.
According to the relevant national industry regulations, combined with the conclusions and standards of the exposure, sensitivity and adaptability of the various influencing factors in the disease evaluation of the existing tunnel lining structure, the grades of the disease of the existing tunnel lining structure are determined through expert scoring.
When the disease grades of the existing tunnel lining structure are determined, both the possibility of the disease's imminent occurrence and the degree of damage caused by the disease are considered.
According to historical data, the score range for the disease of the existing tunnel lining structure is determined as 50-100, and the disease of the existing tunnel lining structure is divided into five grades, which are described as follows: "Mild", an acceptable disease grade, with a score of 95-100, which means the lowest level of potential disease risk. "Light", a grade of disease that is allowed to occur, with a score of 85-95, which means that the level of potential disease risk is slightly higher than "mild". "Moderate", a grade of disease that is allowed to occur but requires control measures, with a score of 65-85, which means a moderate level of potential disease risk.
"Heavy", an unacceptable grade of disease, with a score of 55-65, which means a higher level 500223 of risk. "Severe", a completely unacceptable grade of disease, with a score of 50-55, which means a highest level of risk.
Therefore, the disease evaluation grade U={u1,u2,u3,u4,us}={mild, light, moderate, heavy, severe}. Combined with the classification of the disease grades of the tunnel lining structure, the classical domains R(Bs) of the engineering geology criterion B; are constructed as follows: Mild Ligh = Moderate Heavy Severe U; ¢; <95,100> <85,95> <6585> <55,65> <50,55> R(B,) = ce, <95,100> <85,95> <6585> <55,65> <50,55> ce, <95,100> <85,95> <65,85> <55,65> <50,55> | c, <95,100> <85,95> <65,85> <55,65> = The score range for the disease of the tunnel lining structure is 50-100, and the classical domain R(Bs) of the engineering geology criterion Bs is constructed as follows: U ce, <50,100 > ce, <50,100 > R(B;) = ce, <50,100 > | ce, <50,100 ; In accordance with relevant regulations and standards, the influencing factors for the disease evaluation of the existing tunnel lining structure are evaluated and scored.
A total of 13 indexes are involved.
According to the actual scores of each index, a classical domain R for the tunnel lining structure disease evaluation is constructed as follows: | Construction process and technology c, Site survey and measurement c, 90 Construction equipment and materials c, 62 Post — operation and maintenance c, 58 Construction organization design c, 73 Operator education and training c, 72 R= Topography c, 75 Climate and meteorology c, 91 Potential natural disasters c, 80 Surrounding rock grade €, 88 Rock formation occurrence c,, 86 Geological structure c, 85 Poor geological conditions c,, 61 According to the Extenics correlation function, the calculation results of the correlation degrees K; of the single-level evaluation indexes for the disease of the tunnel lining structure are incorporated into Table 10.
Table 10 Single-level correlation degree LU500223 ° 2 ; According to the correlation degree and weight distribution results of the level C evaluation indexes in Table 10, comprehensive correlation degrees of the level B and level A evaluation indexes in the research of tunnel lining structure disease evaluation are calculated in turn, as shown in Table 11. Table 11 Comprehensive correlation degree
PP FE M AE CEE Cc Sci fa] According to the calculation results of the comprehensive correlation degree in Table 11, the evaluation grade for the disease of the existing tunnel lining structure is obtained. According to the maximum comprehensive correlation degree Kmax = K2 = 0.0922 at the objective level A in this embodiment, it is determined that the evaluation grade for the disease of the tunnel lining structure is "light", which is an allowable disease grade. The present disclosure has the following beneficial effects. The present disclosure adopts the AHP process to construct an evaluation index system and weight distribution for an actual problem, and then combines the Exteneics theory to construct a correlation function to calculate oq the correlation degree of the evaluation index and the evaluation grade, thereby intuitively evaluating the actual engineering problem.
The method for disease evaluation of an existing tunnel lining structure based on an AHP-Extenics analysis makes the disease evaluation of the existing tunnel lining structure more comprehensive and objective.
The above described are merely preferred embodiments of the present disclosure, which are not intended to limit the present disclosure.
Any modifications, equivalent replacements and improvements made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (8)
1. A method for disease evaluation of an existing tunnel lining structure based on an analytic hierarchy process (AHP)-Extenics analysis, comprising the following steps: S101: constructing a disease evaluation index system for the existing tunnel lining structure based on the AHP; S102: constructing a judgment matrix for each type of evaluation indexes in the evaluation index system by using an expert scoring method; S103: calculating a weight vector of each type of evaluation indexes in the evaluation index system according to the judgment matrix, and checking a consistency; S104: determining an evaluation matter element and an evaluation grade for the disease of the existing tunnel lining structure based on an Extenics theory; S105: constructing classical domains, nodal domains and an evaluation matter element for disease evaluation of the existing tunnel lining structure according to the evaluation grade; S106: constructing a correlation function between each type of evaluation indexes in the evaluation index system and the evaluation grade, and calculating a correlation degree; and S107: determining an evaluation grade for the disease of the existing tunnel lining structure according to the correlation degree.
2. The method according to claim 1, wherein in step S101, the evaluation index system comprises: an objective A of existing tunnel lining structure disease evaluation, wherein under the objective A, there are three types of indexes, namely three criteria: construction criterion Bs, natural environment criterion Ba and engineering geology criterion Bs; the construction criterion Bs comprises 6 types of evaluation indexes: construction process and technology C1, site survey and measurement C,, construction equipment and materials Cs, post-operation and maintenance C4, construction organization design Cs and operator education and training Ce; the natural environment criterion B2 comprises 3 types of evaluation indexes: topography Cy, climate and meteorology Cs and potential natural disasters Co; and the engineering geology criterion B3 comprises 4 types of evaluation indexes: surrounding rock grade Cio, rock formation occurrence C1, geological structure C42 and poor geological conditions Crs.
3. The method according to claim 2, wherein in step S102, the constructing a judgment matrix for each type of evaluation indexes in the evaluation index system by using an expert scoring method specifically comprises:
determining basic data through pairwise comparison of the evaluation indexes under the LU500223 objective A, the criterion By, the criterion Bz and the criterion Bs by using a 1-9 scale method, quantifying relative importance between two evaluation indexes, obtaining importance of the three types of indexes under the objective A of existing tunnel lining structure disease evaluation through pairwise comparison by using an expert scoring method, and then deriving a judgment matrix corresponding to the objective A, wherein the basic data are elements in each judgment matrix; and similarly deriving determination matrices of the criteria B+ to Ba.
4. The method according to claim 3, wherein in step S103, the calculating a weight vector of each type of evaluation indexes in the evaluation index system according to the judgment matrix, and checking a consistency specifically comprises: calculating eigenvectors of the determination matrices corresponding to the objective A, the criterion B1, the criterion Ba and the criterion Bs respectively by using a square root method to obtain weight vectors of the 13 evaluation indexes in C4 to C13, and then subjecting the weight vectors of the 13 evaluation indexes to normalization and consistency check to obtain weight vectors 7 (5005) , wherein Yi denotes the weight of an i-th evaluation index Gi a larger Yi indicates a greater influence of the evaluation index Ci on the tunnel lining structure disease, i=1, 2, ..., 13; if the consistency check fails, the determination matrices need to be reconstructed through expert scoring, and new weight vectors need to be calculated.
5. The method according to claim 4, wherein in step S104, the determining an evaluation matter element and an evaluation grade for the disease of the existing tunnel lining structure based on an Extenics theory specifically comprises: determining research of the existing tunnel lining structure disease evaluation as a matter element R, dividing the 13 evaluation indexes under the matter element into 5 evaluation grades: ui, Uz, ..., Us, and obtaining an evaluation index set C={c1, Ca, ..., C13} and an evaluation grade set U={u1, uo, ..., Us}, combining historical data to determine a score range for the disease of the existing tunnel lining structure as [50,100], and then determining the evaluation grade set {u1, uz, ..., Us}={mild, light, moderate, heavy, severe}.
6. The method according to claim 5, wherein in step S105: constructing classical domains, nodal domains and an evaluation matter element for the disease evaluation of the existing tunnel lining structure according to the evaluation grade: the classical domains comprise: mild (95-100), light (85-95), moderate (65-85), heavy (55- 65) and severe (50-55);
a nodal domain of the criterion B; is: LU500223 U ¢ <50,100> c, <350,100 > c, <50,100 > R(B) = c, <50,100 > c, <50,100 > c, <50,100 > a nodal domain of the criterion B: is: U ¢, <50,100> R(B,)= c, <50,100 > c, <50,100 > a nodal domain of the criterion Bs is: U ¢, <50,100> R(B,)= e <50,100 > c, <50,100 > c, <50,100 > the evaluation matter element is: U Construction process and technology c, 59 Site survey and measurement c, 90 Construction equipment and materials c, 62 Post — operation and maintenance c, 58 Construction organization design c, 73 Operator education and training c, 121 R= Topography c, 75 Climate and meteorology c, 91 Potential natural disasters c, 80 Surrounding rock grade €, 88 Rock formation occurrence c,, 86 Geological structure c, 85 Poor geological conditions c,, 61
7. The method according to claim 6, wherein in step S106, the correlation function is: v.,V. © PLY) =p.) OS wy) ET v, eV, and pv, V =p, V)
J wherein, /‘ ‘” represents a correlation function value of a j-th evaluation grade V; of the i- th evaluation index Gi Vi represents a score value of the j-th evaluation index Gi, which is obtained according to the evaluation matter element R; V represents a total score interval of the 5 evaluation grades, that is, 50-100; V; represents a score interval of the j-th evaluation grade;
|V| represents a length of the score interval of the j-th evaluation grade; P CF) represents aLU500223 distance between a score ” and the score interval 4 , which is calculated as follows: a+b| b-a v.V)y=Wy, - —- — pP V,) | TS | 5 wherein, a and b denote a lower limit and an upper limit of the score interval Vj, respectively, i=1,2,...,13; /=1,2,...,5; the correlation degree is calculated as follows: K=YwK (v) i=l wherein, Yi is the weight of the evaluation index Cr K, is the correlation degree between the correlation function value of the j-th evaluation grade V; of the j-th evaluation index Si and the weight Wi ; nis a total number of evaluation indexes in the criterion corresponding to the evaluation index “.
8. The method according to claim 7, wherein in step S107, the determining an evaluation grade for the disease of the existing tunnel lining structure according to the correlation degree comprises: selecting a correlation degree with a largest value among all correlation degrees, and using an evaluation grade corresponding to the correlation degree as the evaluation grade for the disease of the existing tunnel lining structure.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| LU500223A LU500223B1 (en) | 2021-05-29 | 2021-05-29 | Method for disease evaluation of existing tunnel lining structure based on analytic hierarchy process-extenics analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| LU500223A LU500223B1 (en) | 2021-05-29 | 2021-05-29 | Method for disease evaluation of existing tunnel lining structure based on analytic hierarchy process-extenics analysis |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| LU500223B1 true LU500223B1 (en) | 2021-11-29 |
Family
ID=78780128
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| LU500223A LU500223B1 (en) | 2021-05-29 | 2021-05-29 | Method for disease evaluation of existing tunnel lining structure based on analytic hierarchy process-extenics analysis |
Country Status (1)
| Country | Link |
|---|---|
| LU (1) | LU500223B1 (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115563813A (en) * | 2022-11-10 | 2023-01-03 | 服务型制造研究院(杭州)有限公司 | Product design method and system based on extension correlation function and FMEA |
-
2021
- 2021-05-29 LU LU500223A patent/LU500223B1/en active IP Right Grant
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115563813A (en) * | 2022-11-10 | 2023-01-03 | 服务型制造研究院(杭州)有限公司 | Product design method and system based on extension correlation function and FMEA |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108710984B (en) | Comprehensive evaluation method and system for mine geological environment | |
| CN111582718B (en) | Cable channel fire risk assessment method and device based on network analytic hierarchy process | |
| CN106022596A (en) | Urban gas pipeline system danger forecast and evaluation method | |
| CN111242499A (en) | Existing tunnel lining structure disease evaluation method based on hierarchy-extension analysis | |
| CN110610285A (en) | Underground metal mine goaf risk grading evaluation method | |
| US20220164908A1 (en) | Method And System For Earthquake Disaster Level Assessment | |
| CN108280553A (en) | Regional Torrent Risk Zonation based on GIS- Artificial neural network ensembles and prediction technique | |
| CN111445156B (en) | Bias tunnel construction safety evaluation method based on variable weight fuzzy comprehensive evaluation | |
| CN111861133A (en) | Evaluation method for prevention and treatment capacity of mountain torrent disasters | |
| CN110378574A (en) | Submerged tunnel Pressure Shield Tunnel face stability evaluation method, system and equipment | |
| He et al. | Classification technique for danger classes of coal and gas outburst in deep coal mines | |
| CN112541666B (en) | Shield tunnel risk assessment method considering uncertainty of earthquake vulnerability model | |
| Li et al. | Real‐Time Warning and Risk Assessment of Tailings Dam Disaster Status Based on Dynamic Hierarchy‐Grey Relation Analysis | |
| CN113505978A (en) | Disaster prevention function evaluation method and device for different forms of urban communities | |
| LU500223B1 (en) | Method for disease evaluation of existing tunnel lining structure based on analytic hierarchy process-extenics analysis | |
| CN110889440A (en) | Rockburst grade prediction method and system based on principal component analysis and BP neural network | |
| CN115829326A (en) | Mountain road construction risk evaluation method based on optimized combination weighting model | |
| CN112016857A (en) | Polyethylene pipeline earthquake vulnerability assessment method based on cloud theory | |
| CN115994398A (en) | Method for evaluating collapse risk of deep-buried granite tunnel | |
| CN111523796A (en) | Method for evaluating harmful gas harm of non-coal tunnel | |
| CN115879654A (en) | Method for predicting stability and evaluating reliability of underground roadway | |
| Safaeian Hamzehkolaei et al. | Performance evaluation of machine learning algorithms for seismic retrofit cost estimation using structural parameters | |
| Li et al. | Comprehensive evaluation model of coal mine safety under the combination of game theory and TOPSIS | |
| CN115829209A (en) | Environment-friendly intelligent warehouse environment-friendly quality analysis method and device based on carbon path | |
| CN110119522A (en) | A kind of stability ranking method excavated rock side slope and destroy risk analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| FG | Patent granted |
Effective date: 20211129 |