AU2021104851A4 - An integrated fuzzy approach for risk assessment in tunneling construction projects - Google Patents
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- 230000005641 tunneling Effects 0.000 title claims abstract description 18
- 238000010276 construction Methods 0.000 title claims abstract description 14
- 238000012502 risk assessment Methods 0.000 title claims abstract description 11
- 238000013459 approach Methods 0.000 title description 3
- 238000010586 diagram Methods 0.000 claims abstract description 7
- 238000012913 prioritisation Methods 0.000 claims abstract description 3
- 230000000694 effects Effects 0.000 claims description 5
- 239000011159 matrix material Substances 0.000 abstract description 22
- 238000000034 method Methods 0.000 abstract description 22
- 230000015556 catabolic process Effects 0.000 abstract description 4
- 238000013278 delphi method Methods 0.000 abstract description 4
- 238000011156 evaluation Methods 0.000 abstract description 4
- 238000012986 modification Methods 0.000 abstract description 2
- 230000004048 modification Effects 0.000 abstract description 2
- 238000007726 management method Methods 0.000 description 6
- 238000011161 development Methods 0.000 description 2
- 238000013439 planning Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 239000013598 vector Substances 0.000 description 2
- 206010011971 Decreased interest Diseases 0.000 description 1
- 230000001364 causal effect Effects 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- BTCSSZJGUNDROE-UHFFFAOYSA-N gamma-aminobutyric acid Chemical compound NCCCC(O)=O BTCSSZJGUNDROE-UHFFFAOYSA-N 0.000 description 1
- 230000003116 impacting effect Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
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Abstract
Tunneling construction projects encounter with various risk during execution. Risk events are mostly
interdependent and complicated nature of tunneling projects with uncertainty cause complexity in risk
assessment process. Herein, a hybrid FDEMATEL-ANP model for risk prioritization in tunneling
construction projects is proposed. Fuzzy Decision-Making Trial and Evaluation Laboratory
(FDEMATEL) in this model is used to define the interdependencies' relative intensity among the risk
events and fuzzy concept is used to consider uncertainty in experts'judgments. The Analytic Network
Process (ANP) method is used to assess the relative importance of the risk factors and to define risk
priorities in tunneling construction project.
1/1
DRAWINGS
Expert opinions
Delphi method
Risk identification and classification S
I i Step 1. Construct network
structure of ANP
Define Risk breakdown structure
Step 2. Construct pair
(B . . _ . _ . _ . wise comparison
_ .matrixes using ANP
Step 1. Select the expert panel Step3.Creatingthe
supermatrix
Step 2. Define the assessment
criteria and establish fuzzy Step3.1-Check
linguisticscalesonsistency of matrixes
Step 3. Establish the initial direct No
relation matrix CR 0.10
Yes Modifications
Step 4. Normalizing the fuzzy a
matrix of direct relation Step 4. Obtaining the
-. weighted supermatrix
CA~C4
ryC Step 5. Compute the total
relation fuzzy matrix Interation of Step 5. Limiting
FDEMATEL & ANP supermatrix
Step 6. Establishing the casual
diagram Obtain risk weights
Determine ters
priorities
(:Finish:
Fig.1. Schematically framework of the proposed hybrid FDEMATEL-ANP model for risk assessment in tunneling
construction projects
Description
1/1
Expert opinions
Delphi method
Risk identification and classification S I i Step 1. Construct network structure of ANP
Define Risk breakdown structure Step 2. Construct pair (B . . _ . _ . _ . wise comparison _ .matrixes using ANP
Step 1. Select the expert panel Step3.Creatingthe supermatrix
Step 2. Define the assessment criteria and establish fuzzy Step3.1-Check linguisticscalesonsistency of matrixes
Step 3. Establish the initial direct No relation matrix CR 0.10
Yes Modifications
Step 4. Normalizing the fuzzy a matrix of direct relation Step 4. Obtaining the -. weighted supermatrix CA~C4
ryC Step 5. Compute the total relation fuzzy matrix Interation of Step 5. Limiting FDEMATEL &ANP supermatrix
Step 6. Establishing the casual diagram Obtain risk weights
Determine ters priorities
(:Finish:
Fig.1. Schematically framework of the proposed hybrid FDEMATEL-ANP model for risk assessment in tunneling construction projects
[0001] The present invention generally relates to risk assessment in tunneling construction projects. Tunneling project is needed increasingly in infrastructure projects all around the world, such as mine development, underground transportation, hydropower projects and etc. Due to intrinsic uncertainty in tunneling construction projects, various risk events are associated with project environment. Project managers deal with different resource constraints and are not able to reply to all possible risk events simultaneously.
[0002] Risk management can be determined as a procedure to identify, analyze and respond to project risks in order to increase chances and reduce threats influencing the objectives of the project. Risk assessment is the main component of risk management process, which can help project managers to identify and prioritize risk events and planning for appropriate respond considering risk significance. Thus, risk identification and prioritization in tunneling projects is a vital issue for project successful fulfillment. Failure in tunneling projects led to a high time and cost forfeit.
[0003] Risk elimination in projects is not possible but it can be reduced, retained or transferred. Risk assessment is one of the main components of the risk management process, which can help project managers to identify and prioritize risk events and planning for appropriate respond considering risk significance.
[0004] Risk assessment in tunneling projects can be define as multi attribute decision making (MADM) problems. In this research, an integrated model based on FDEMATEL-ANP approach is proposed to prioritize risk factors in tunneling construction projects.
[0005] Risk identification is the initial step in risk management and a process to define risks that may effect on project objectives. PMBOK introduce risk classification as a framework that certify quality and efficiency of the identification stage into acceptable degree. Due to tunneling construction projects covering various risk events during the life cycle, risk identification and classification is a notable stage in risk management process for construction projects. Hence, identifying all risks in a project is time consuming and probably impossible, it is necessary to highlight the most crucial risks in projects. Risks in tunneling construction projects can be identified and classified with different methods. Several studies classify risks with respect to the origin and source of risk. Interviews, questionnaires, expert s' opinion, checklists, brainstorming, Delphi technique are qualitative risk identification methods. Project management institute (PMI) declare that suitable method for risk identification is the one that the project team is familiar.
[0006] The model constructed with three stages: risk identification and classification, obtaining interdependencies between risk factors with FDEMATEL calculations, determining the risks priorities with ANP calculations. Figure 1 indicates a schematically framework for proposed hybrid model. The proposed methodology consist of three stages and procedural stages are described below.
[0007] Stage 1: With respect to the expert opinions, in the first stage of model potential risk factors impacting on project will carefully identify. Risk breakdown structure is established to classify different risk factors. The Delphi method is apply for risk identification and attempts to purify and combine experts' opinions. It uses a repetitious feedback technique in several rounds and results of previous rounds are sharing with participants from second round onward. Experts' opinions are influenced by answers from other colleagues until consensus is obtained. Facilitator coordinates panel of experts to conduct Delphi survey.
[0008] Stage 2. Fuzzy DEMATEL calculations: Battelle Geneva Research Center applied DEMATEL method to solve complicated problems with various criteria that have influence on a system and generate casual relationships between criteria to illustrate structural relations between system components. DEMATEL can determine the composition of relations between various criteria. It can also provide cause and effect diagrams to depict the causal relationship of criteria. DEMATEL method is approved in different academic studies and used as an appropriate technique to solve complex problems. Although DEMATEL is an appropriate method, Zadeh declared that crisp values are insufficient to evaluate problems in the real world. Due to uncertain and subjective nature of human thinking judgments and preferences, fuzzy theory is used with DEMATEL method to consider vagueness and uncertainty of human judgments and preferences in decision making.
[0009] Steps of FDEMATEL method are as follow: Step 1: Select a group of experts with good experience and knowledge on research topic as decision makers for evaluating influence between criteria with pairwise comparison.
[0010] Step 2: Define the assessment criteria and establish fuzzy linguistic scales: linguistic variables provide values with linguistic terms which are qualitative words or phrases in natural language.
A = (l,m,u) on X is a triangular fuzzy number (TFN) if its membership function p (x): X -0,1]
conforms Eq. (1).
F(x-l)/(m-l) p(x) =. (u - x) /(u - m) ,lsxsmEqain , m:! x: u Equation 1 0 , otherwise
[0011] Step 3: Establish the initial direct relation matrix: Decision makers are asked to compare
evaluation factors for creating initial fuzzy direct relation matrix k, where k is the number of experts.
Thus, the direct relation matrix is created asI[a,] , where A is a nx n and non- negative
matrix. The direct influence of factor i on factor j is represented with aj and when i=j the diametrical elements a.=0 . The initial direct relation matrix for decision maker k is as Eq. (2) and i =1, 2, n are n evaluation factors.
0 d1(k) ... aa 2()) (k) 05(k) j(k) __ 21 0 ''' 2n - - , k= 1, 2 ,...p Equation 2
dnI(k) an 2 (k)
whra.(k) --=(IU(k)m(k)u(k)) where
[0012] Step 4. Normalizing matrix of direct relation: d(k) and $(k) values are the triangular fuzzy
numbers and calculated from Eq. (3) and (4). n n n n
U - ( , , 1) Equation 3 j= 1 j=1 j=1 j=1
$3(k):--m (Zu()) 1 i n Equation4 j=1
The normalized direct relation fuzzy matrix of expert k is denoted as (k) It is obtained with linear
scale transformation, following Eq. (5).
1(k) 12(k) .n(k) kk - (k) (k) ... kn(k) X(k)_ 222 -- 2n
k = 1, 2,...,p Equation 5
(k) kn(k)(k
whereXf..(k)= ii /kfik y k l() M() ()),( k (k)) w( * . It is suppose that there is one n that (1 U (k) < $(k). j=1
The average matrix of I is calculated with Eqs. (6) and (7).
Equation 6 P
X11 X ... X1, |
X21 X22 ... X2n Equation 7
X X ... Xnn
P ~k)
Where k
[0013] Step 5. Calculate the fuzzy total relation matrix: it is necessary to obtain the convergence of lim X' = 0. The fuzzy matrix i can be computed after direct relation matrix normalization. The
fuzzy matrix is shown as Eqs. (8) - (10).
IT= lim(I+ I2 I'W) +...+ Equation 8
11 12 ' ' tn
_ t21 t22 ... t2 n Equation 9
tnl tnl ... tn
where- (lJm;-,u -).
[m 1]= Xx(I -X,)-E
[m ]= X. X (I-- X.)-I Equation 10
[ul ]= Xx (I -XY)-
Eventually, it is to transform the fuzzy linguistic values into crisp values with defuzzification. Herein,
the CFCS defuzzification method is used as following Eq. (11). Let k -- (1, , u); k = 1,2,..., n,
be triangular fuzzy numbers and crisp value is defined with N . Calculating
L = min(lk); R = max(u); k =1,2,...,n and A = R -L then:
~u-_n) 2 (R-l)+(u-L)2(A +m-) 2 ~ kdef (2-L)(A L±Ax (A+m-l)(A+u -m) 2 (R -l)+(u -L)(A +m-l)2 (A+u-r) Equation 11
[0014] Step 6: Establishing the casual diagram: The sum of columns and the sum of rows in fuzzy
total relation matrix are defined as vectors NF and 7) is i respectively. The horizontal axis (N1 +E
named "Prominence" and calculated by adding E tofi, which indicates the degree of significance
for criteria. It means that the criteria with bigger values show more significance in relations with others
and vice versa. Also the vertical axis (1Ej -NF ) is named "Relation" and calculated by subtracting BE
from which represents the intensity influence and categorized factors into cause and effect group.
If ( E -Fi ) is negative, the criterion belongs to the effect group and for the positive values belongs to
the cause group. Thus, the casual diagram is drawn by mapping the dataset of the(E+ E i-Fi). Casual diagram facilitates interpretation of complex relationships between criteria and visualize them.
Vectors E and Fi are defined with Eqs. (12) - (14):
i = i, j= 1, 2,...n Equation 12
E= [Equation 13
n ,
F=Y i Equation 14
[0015] Stage 3. ANP Calculations: In the third stage, ANP calculations is applied to obtain risks weights and determine the risk priorities considering expert opinions. ANP is basically the development of the Analytic Hierarchy Process (AHP) to overcome hierarchical structure limitations.
AHP assumes that criteria are independent from each other and hierarchical relationships between criteria are one-way. ANP is capable to consider relationships among or within the groups of criteria by combining interdependencies among criteria in a decision model. ANP can predict more accurate with better priority calculations in cases of networks with interdependent criteria. It can solve decision models that cannot be modeled as a hierarchy. ANP method calculations steps are as follow:
[0016] Step 1. Establish the network structure of ANP, based on the risk breakdown structure of the model.
[0017] Step 2. Establish pair-wise comparison matrix: The nine point preference evaluation scale by Saaty (65) is used for the pair-wise comparisons. This scale from 1 to 9 indicates pairs of equal importance (1), up to excessive inequality in importance (9). A decision maker (expert) can express the relative influence between each pair of elements orally as: extremely more important, very strongly more important, strongly more important, moderately more important and equally important. These judgments can be converted into numerical values of 9, 7, 5, 3, and 1 respectively. Values of 8, 6, 4 and 2 can be recognized as intermediate values for comparisons between two consecutive points. According to the FDEMATEL calculations in stage 2, pairwise comparisons are carried out between risk factors that have interdependencies.
[0018] Step 3. Creating the supermatrix: the results from step (2) are used to create an unweighted
supermatrix. Equation (15) represents a general form of supermatrix: C, C 2 C. - eldng21 e2 n2 e,, 1. 2,mnm el ' ~1 .1m 1 W l . 12 ... WIn
C2 e2 Equation 15 21 22 ...2m e2.2
%,2 WMin ... WMM em,
In which Cm indicates the mth cluster, emn represents the mth element of the mth cluster. wi; is the
eigenvector of the influence of the elements, which are compared between i th andj th clusters.
[0019] Step 3.1. Check consistency of matrixes: Due to distraction or loss of interest, inconsistency in
pairwise comparisons may occur. In this case, decision makers are asked to make comparisons again.
Formulae (16) and (17) are used to calculate the consistency of the pair-wise comparisons. (C.I) stands
for consistency index and (C.R) stands for consistency ratio.
A-n C.I 'max Equation 16 n-1
C.I C.R - Equation 17 R.I
The pair-wise comparisons with value of (C.R) less than 0.1 are acceptable, otherwise they are not
acceptable. (R.I) stands for the average value of (C.I) for random matrices. (R.I) is 0.00; 0.58; 0.90;
1.12; 1.24; 1.32; 1.41; 1.45; 1.51, When the number of levels in the hierarchy is n = 2,...,10,
respectively.
[0020] Step 4. Obtaining the weighted supermatrix: the generated matrix in step (3) should be
normalized by each column of the matrix sums to unity. Then, the unweighted supermatrix is
multiplied with the corresponding cluster to reach the weighted supermatrix.
[0021] Step 5. Limiting supermatrix: the weighted supermatrix is enhanced enough power k with
equation (18) until it is stable sufficiently to derive risk factors weights. Based on obtained weights
for risk factors, priorities are generated.
Equation 18 im W2k+1 k->ao
Claims (4)
1. Define the priorities of risk factors in tunneling construction projects regarding the degree of importance and experts' judgments.
2. Define influences and interdependencies between different risk factors during risk assessment.
3. Depict the casual relationships and visualize them with cause and effect diagram.
4. It is facilitating risk assessment due to considering interdependencies between risk factors that will affect the risk priorities and eliminating unessential relationships among them with no consequence on prioritization results.
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| CN117273430A (en) * | 2023-05-31 | 2023-12-22 | 三峡生态环境投资有限公司 | Risk assessment method in highway construction process |
| CN116629712A (en) * | 2023-07-21 | 2023-08-22 | 武汉理工大学三亚科教创新园 | Submarine shield tunnel construction quality risk assessment method based on PSO-BP neural network |
| CN116629712B (en) * | 2023-07-21 | 2024-02-13 | 武汉理工大学三亚科教创新园 | Submarine shield tunnel construction quality risk assessment method based on PSO-BP neural network |
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