Safety Evaluation of Subway Tunnel Construction under Extreme Rainfall Weather Conditions Based on Combination Weighting–Set Pair Analysis Model
Abstract
:1. Introduction
2. Methods
2.1. The SPA Theory
2.2. Index Weights
2.2.1. The IAHP Approach
2.2.2. EWM Approach
2.2.3. Comprehensive Weight
2.3. Comprehensive Correlation Degree Model
2.4. Set Pair Potential Analysis
2.5. Procedure of Safety Evaluation Based on SPA and IAHP–EWM
3. Safety Evaluation Model
3.1. Safety Evaluation Index System
3.2. Grade Division Standard of Evaluation Indexes
3.3. Grades of Safety Assessment
4. Case Study
4.1. Data Acquisition
4.2. Safety Assessment
4.2.1. Correlation Calculation
4.2.2. Weight Calculation
- Subjective Weight Calculation
- 2.
- Objective Weight calculation
4.2.3. Combined Weight Calculation
4.2.4. Calculation of Comprehensive Connection Degree
4.3. Results and Discussion
4.3.1. Evaluation Result Analysis
4.3.2. Discussion
5. Conclusions
- (1)
- We studied the safety risk of subway tunnel construction under heavy rainfall conditions. The damaging effects of heavy rainfall on the subway tunnel structural system and the influence factors of internal construction status on subway tunnel construction safety were identified.
- (2)
- Evaluating subway tunnel safety during heavy rains is a complicated, methodical, multidisciplinary issue. In this study, the central safety evaluation index system and its assessment standards were developed based on a complete investigation of the safety risk elements for subway tunnel construction during extreme rainfall, including disaster-inducing variables and disaster-nurturing environments.
- (3)
- Based on the idea of linear weighting, we optimized the combination of subjective and objective weights calculated by IAHP and EWM. More importantly, the method considers the subjective values of decision makers and the objectivity contained in the data, which makes the calculation results of the study closer to reality. Then, to address the ambiguity and uncertainty of the evaluation indexes, we introduced the SPA theory and made a quantitative judgment on the safety level and development trend of subway tunnel construction under extreme weather conditions by establishing pooled pairs.
- (4)
- Identifying the influencing factors on the safety of subway tunnel construction under extreme rainfall weather conditions in this study is not comprehensive, and there are various uncertainties. In reality, identifying such factors and the solution methods are still to be improved. In addition, sensitivity analysis of these influencing factors is needed in future studies.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Value of | Definition |
---|---|
0 | Indicates that is more critical than |
1 | Indicates that is as crucial as |
2 | Indicates that is more critical than |
Situation | Gradation | Discriminant Condition | Coordination | Development Trend |
---|---|---|---|---|
Same potential | Quasi same potential | a > 0, b = 0 | Stronger identity | Stronger improvement trend |
Strong same potential | a > c, c > b | Strong identity | Strong improvement trend | |
Weak same potential | a > c, a > c > b | Weaker identity | Weaker improvement trend | |
Micro same potential | a = c, b > a | Weak identity | Weak improvement trend | |
Balance of power | Quasi balance of power | a = 0, b = 0 | Strong stability | Weak improvement trend |
Strong balance of power | a = c, a > b >0 | Stronger stability | Weaker improvement trend | |
Weak balance of power | a = c, b = a | Weaker stability | Stronger improvement trend | |
Micro balance of power | a = c, b > a | Weak stability | Strong improvement trend | |
Counter potential | Quasi counter potential | a < 0, b = 0 | Stronger opposition | Stronger improvement trend |
Strong counter potential | a < c, 0 < b < a | Strong opposition | Strong improvement trend | |
Weak counter potential | a < c, b > a, b < c | Weaker opposition | Weaker improvement trend | |
Micro counter potential | a < c, b > c | Weak opposition | Weak improvement trend |
Primary Indicators | Secondary Indicators | Number | Source |
---|---|---|---|
Rainfall B1 | Amount of Rainfall (mm/d) | C11 | [9,10] |
Surface water depth (mm) | C12 | [5] | |
Hydrogeology B2 | Groundwater level (m) | C21 | [11,52] |
Poisson’s ratio | C22 | [9,10] | |
Cohesion (KPa) | C23 | [9,10] | |
Angle of internal friction (°) | C24 | [10,13] | |
Water content (%) | C25 | [4,12] | |
Permeability coefficient (cm/s) | C26 | [5] | |
Construction design B3 | Tunnel depth (m) | C31 | [6,52] |
Tunnel diameter (m) | C32 | [4] | |
Lining thickness (mm) | C33 | [6,11] | |
Management B4 | Monitoring and testing efforts | C41 | —— |
Construction organization and design | C42 | —— | |
Safety organization and system | C43 | —— | |
Professional skills of construction personnel | C44 | —— |
Index | Grading Standard | ||||
---|---|---|---|---|---|
Extremely Safe | Safe | Basically Safe | Unsafe | Extremely Unsafe | |
C11 | < 25 | 25–50 | 50–100 | 100–250 | > 250 |
C12 | 0–400 | 400–800 | 800–1200 | 1200–1600 | 1600–2000 |
C21 | > 6 | 5–6 | 3–5 | 2–3 | < 2 |
C22 | 0–0.2 | 0.2–0.25 | 0.25–0.3 | 0.3–0.35 | 0.35–0.5 |
C23 | > 5.5 | 2.1–5.5 | 0.7–2.1 | 0.2–0.7 | 0–0.2 |
C24 | 60–90 | 50–60 | 39–50 | 27–39 | 0–27 |
C25 | 0–5 | 5–10 | 10–15 | 15–20 | 20–30 |
C26 | < 10−6 | 10−6–10−4 | 10−4–10−3 | 10−3–10−1 | > 10−1 |
C31 | 32–40 | 24–32 | 16–24 | 8–16 | > 8 |
C32 | 0–6 | 6–9 | 9–12 | 12–15 | > 15 |
C33 | 700–800 | 600–700 | 400–600 | 200–400 | 50–200 |
C41 | Very high (80–100) | High (60–80) | Normal (40–60) | Low (20–40) | Very low (0–20) |
C42 | Perfect (80–100) | Good (60–80) | Normal (40–60) | Poor (20–40) | Worst (0–20) |
C43 | Perfect (80–100) | Good (60–80) | Normal (40–60) | Poor (20–40) | Worst (0–20) |
C44 | Very high (80–100) | High (60–80) | Normal (40–60) | Low (20–40) | Very low (0–20) |
Judgment Interval | (0.6,1] | (0.2,0.6] | (−0.2,0.2] | (−0.6,−0.2] | [−1,−0.6] |
---|---|---|---|---|---|
Safety Level | I | II | III | IV | V |
Risk Level | very low | low | medium | high | very high |
Index | Object of Evaluation | ||
---|---|---|---|
L1 | L2 | L3 | |
C11 | 45 | 45 | 45 |
C12 | 13 | 20 | 16 |
C21 | 9.1 | 8.3 | 9.0 |
C22 | 0.25 | 0.28 | 0.23 |
C23 | 12 | 25 | 10 |
C24 | 37 | 35 | 38 |
C25 | 19.7 | 16.2 | 19.7 |
C26 | 0.0405 | 0.0035 | 0.0347 |
C31 | 15 | 15.5 | 15 |
C32 | 6.2 | 5.5 | 6.0 |
C33 | 460 | 450 | 460 |
C41 | High (75) | High (75) | High (75) |
C42 | Good (75.4) | Good (74.8) | Good (75.4) |
C43 | Good (75) | Good (75) | Good (75) |
C44 | High (75.6) | High (75.4) | High (75.4) |
Index | L1 | L2 | L3 | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a | b1 | b2 | b3 | c | a | b1 | b2 | b3 | c | a | b1 | b2 | b3 | c | |
C11 | 0.000 | 0.200 | 0.800 | 0.000 | 0.000 | 0.000 | 0.200 | 0.800 | 0.000 | 0.000 | 0.000 | 0.200 | 0.800 | 0.000 | 0.000 |
C12 | 0.968 | 0.032 | 0.000 | 0.000 | 0.000 | 0.950 | 0.050 | 0.000 | 0.000 | 0.000 | 0.960 | 0.040 | 0.000 | 0.000 | 0.000 |
C21 | 1.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 | 0.000 | 0.000 | 0.000 | 0.000 |
C22 | 0.000 | 1.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.400 | 0.600 | 0.000 | 0.000 | 0.400 | 0.600 | 0.000 | 0.000 | 0.000 |
C23 | 1.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 | 0.000 | 0.000 | 0.000 | 0.000 |
C24 | 0.000 | 0.000 | 0.000 | 0.833 | 0.167 | 0.000 | 0.000 | 0.000 | 0.670 | 0.330 | 0.000 | 0.000 | 0.000 | 0.920 | 0.080 |
C25 | 0.000 | 0.000 | 0.000 | 0.060 | 0.940 | 0.000 | 0.000 | 0.000 | 0.760 | 0.240 | 0.000 | 0.000 | 0.000 | 0.060 | 0.940 |
C26 | 0.000 | 0.000 | 0.000 | 0.600 | 0.400 | 0.000 | 0.000 | 0.000 | 0.970 | 0.030 | 0.000 | 0.000 | 0.000 | 0.660 | 0.340 |
C31 | 0.000 | 0.000 | 0.000 | 0.875 | 0.125 | 0.000 | 0.000 | 0.000 | 0.940 | 0.060 | 0.000 | 0.000 | 0.000 | 0.880 | 0.120 |
C32 | 0.000 | 0.930 | 0.070 | 0.000 | 0.000 | 0.080 | 0.920 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 | 0.000 | 0.000 | 0.000 |
C33 | 0.000 | 0.000 | 0.300 | 0.700 | 0.000 | 0.000 | 0.000 | 0.250 | 0.750 | 0.000 | 0.000 | 0.000 | 0.300 | 0.700 | 0.000 |
C41 | 0.000 | 0.750 | 0.250 | 0.000 | 0.000 | 0.000 | 0.750 | 0.250 | 0.000 | 0.000 | 0.000 | 0.000 | 0.750 | 0.250 | 0.000 |
C42 | 0.000 | 0.770 | 0.230 | 0.000 | 0.000 | 0.000 | 0.740 | 0.260 | 0.000 | 0.000 | 0.000 | 0.000 | 0.770 | 0.230 | 0.000 |
C43 | 0.000 | 0.750 | 0.250 | 0.000 | 0.000 | 0.000 | 0.750 | 0.250 | 0.000 | 0.000 | 0.000 | 0.000 | 0.750 | 0.250 | 0.000 |
C44 | 0.000 | 0.780 | 0.220 | 0.000 | 0.000 | 0.000 | 0.770 | 0.230 | 0.000 | 0.000 | 0.000 | 0.000 | 0.770 | 0.230 | 0.000 |
Index | Subjective Weight | Objective Weight | Comprehensive Weight |
---|---|---|---|
C11 | 0.302 | 0.077 | 0.153 |
C12 | 0.002 | 0.063 | 0.042 |
C21 | 0.178 | 0.066 | 0.104 |
C22 | 0.011 | 0.063 | 0.045 |
C23 | 0.028 | 0.059 | 0.048 |
C24 | 0.017 | 0.064 | 0.048 |
C25 | 0.137 | 0.058 | 0.085 |
C26 | 0.137 | 0.058 | 0.085 |
C31 | 0.007 | 0.058 | 0.041 |
C32 | 0.004 | 0.060 | 0.041 |
C33 | 0.017 | 0.067 | 0.050 |
C41 | 0.067 | 0.077 | 0.073 |
C42 | 0.004 | 0.077 | 0.052 |
C43 | 0.004 | 0.077 | 0.052 |
C44 | 0.085 | 0.077 | 0.079 |
Object of Evaluation | Comprehensive Connection Degree | Safety Level | Risk Level |
---|---|---|---|
L1 | 0.139 | III | medium level |
L2 | 0.168 | III | medium level |
L3 | 0.025 | III | medium level |
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Liu, P.; Wang, Y.; Han, T.; Xu, J.; Li, Q. Safety Evaluation of Subway Tunnel Construction under Extreme Rainfall Weather Conditions Based on Combination Weighting–Set Pair Analysis Model. Sustainability 2022, 14, 9886. https://doi.org/10.3390/su14169886
Liu P, Wang Y, Han T, Xu J, Li Q. Safety Evaluation of Subway Tunnel Construction under Extreme Rainfall Weather Conditions Based on Combination Weighting–Set Pair Analysis Model. Sustainability. 2022; 14(16):9886. https://doi.org/10.3390/su14169886
Chicago/Turabian StyleLiu, Ping, Yu Wang, Tongze Han, Jiaming Xu, and Qiangnian Li. 2022. "Safety Evaluation of Subway Tunnel Construction under Extreme Rainfall Weather Conditions Based on Combination Weighting–Set Pair Analysis Model" Sustainability 14, no. 16: 9886. https://doi.org/10.3390/su14169886