Multi-Attribute Decision-Making Method in Preventive Maintenance of Asphalt Pavement Based on Optimized Triangular Fuzzy Number
Abstract
:1. Introduction
2. Optimized TFN-MADA Method
2.1. Triangular Fuzzy Numbers
2.2. Basic Model
2.3. Similarity
2.4. Reliability
2.5. Decision Sequencing
3. Evaluation Indicators
3.1. Benefit-Based Indicators
3.1.1. Material Strength
3.1.2. Pavement Performance
3.1.3. Pavement Life
3.1.4. Comfort
3.1.5. Pavement Skid Resistance
3.1.6. Pavement Aesthetics
3.2. Cost-Based Indicators
3.2.1. Engineering Costs
3.2.2. Traffic Disruption
3.2.3. Carbon Emissions
3.2.4. Noise Pollution
4. Road Maintenance Case
4.1. Overview of the Project
4.2. Case Calculations
- Step 1: Initial data
- Step 2: Normalization Processing
- Step 3: Numerical calculation
- Step 4: Weighted post-processing
- Step 5: Relationship Matrix
- Step 6: Output of the optimal solution
4.3. Case Validation
5. Conclusions
- (1)
- This paper applies the optimization TFN-MADA method, based on expert scoring data, to analyze the indicators and maintenance programs affecting the preventive maintenance of pavements, applies the principles of deviation maximization and entropy weighting to consider and analyze the indicators and the program perspectives, respectively, and scientifically selects the preferred maintenance program.
- (2)
- The validity of the model was verified by the case study, and the preferred order of maintenance programs for the target road section was obtained: fog sealing technology > joint sealing technology > micro-surfacing technology > slurry sealing technology > crushed stone sealing technology > hot in-place recycling technology > thin layer covering technology > composite seal overlay techniques.
- (3)
- Because the fog sealing layer technology has convenient construction, fast traffic, good economic benefits [62], more friendly to the environment [63] etc., it is more reasonable to obtain a higher score in this case; because the composite sealing layer and sealing cover technology adopt two technologies, their economic cost is significantly higher than other preventive maintenance technologies [64], so their score is lower in this case.
- (4)
- The decision-making method of small data pavement maintenance applied in this paper can quickly and scientifically select the preliminary maintenance scheme at the decision-making site, and can also select the scheme without the influence of data loss.
- (5)
- The small-data pavement maintenance decision-making methodology applied in this research allows for rapid, scientific, and reliable selection of preliminary maintenance programs at the decision-making site.
- (6)
- Because the decision-making method in this study is to deal with the expert score, and then sort the scheme, the score of the expert score has a great influence on the decision-making result. However, the number of expert score samples in this case is limited. Therefore, in the future, a database of scores can be constructed, and algorithms such as deep learning can be used to further optimize the initial triangular fuzzy number decision matrix, making the selection of preventive maintenance schemes more accurate.
- (7)
- Due to the limited data in this case, which is a road in Jiangsu, China, the extreme weather, slope, natural disasters, and other indicators are not considered. In the future, when using this method, it is necessary to improve the index set according to the local situation. This paper mainly discusses the road maintenance schemes commonly used in Jiangsu and does not study maintenance schemes such as ultra-thin wear layers. In the future, more practical cases need to be accumulated, and more maintenance schemes need to be considered.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Correction Statement
Appendix A
References
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Norm | Mileage/km | Score (of Student’s Work) | Make One’s Judgment | ||
---|---|---|---|---|---|
Excellent | Very Much | Total | |||
Pavement Damage Condition Index PCI | 90 | 6 | 96 | 97.89 | first class |
Road Travel Quality Index RQI | 96 | 0 | 96 | 94.34 | excellent |
Rutting Depth Index RDI | 88 | 8 | 96 | 92.42 | excellent |
Slip Resistance Index (SRI) | 32 | 64 | 96 | 89.19 | excellent |
Indicator | |
---|---|
Benefit-based indicators | Material strength Y1 |
Pavement Performance Y2 | |
Pavement life Y3 | |
Comfort Y4 | |
Anti-skid road surface Y5 | |
Pavement aesthetics Y6 | |
Cost-based indicators | Project cost Y7 |
Traffic Disruption Y8 | |
Carbon emissions Y9 | |
Noise pollution Y10 |
D1 | Y1 | Y2 | Y3 | Y4 | Y5 | Y6 |
---|---|---|---|---|---|---|
X1 | [1.199, 1.276, 1.338] | [1.241, 1.330, 1.422] | [1.497, 1.567, 1.636] | [1.447, 1.548, 1.642] | [0.556, 0.529, 0.731] | [1.029, 1.109, 1.166] |
X2 | [1.243, 1.306, 1.400] | [1.064, 1.131, 1.216] | [1.241, 1.338, 1.400] | [1.184, 1.339, 1.496] | [1.349, 1.454, 1.644] | [1.260, 1.347, 1.445] |
X3 | [1.272, 1.350, 1.446] | [1.182, 1.269, 1.343] | [1.327, 1.426, 1.527] | [1.283, 1.409, 1.551] | [0.913, 1.101,1.324] | [1.214, 1.300, 1.379] |
X4 | [1.228, 1.306, 1.385] | [1.108, 1.193, 1.295] | [1.344, 1.444, 1.564] | [1.316, 1.426, 1.551] | [0.913, 1.101, 1.233] | [1.244, 1.331, 1.445] |
X5 | [1.127, 1.231, 1.308] | [1.167, 1.239, 1.311] | [0.629, 0.687, 0.782] | [0.625, 0.713, 0.858] | [1.230, 1.451, 1.644] | [1.275, 1.347, 1.429] |
X6 | [1.199, 1.276, 1.338] | [1.226, 1.315, 1.406] | [1.548, 1.620, 1.691] | [1.431, 1.600, 1.770] | [0.397, 0.529, 0.685] | [0.814, 0.887, 0.969] |
X7 | [1.040, 1.098, 1.185] | [1.152, 1.223, 1.311] | [0.578, 0.634, 0.709] | [0.543, 0.661, 0.785] | [2.421, 2.731, 2.922] | [1.321, 1.395, 1.494] |
X8 | [1.084, 1.157, 1.246] | [1.211, 1.300, 1.390] | [1.190, 1.285, 1.382] | [1.184, 1.304, 1.442] | [0.913, 1.101, 1.324] | [1.198, 1.284, 1.363] |
D2 | Y7 | Y8 | Y9 | Y10 |
---|---|---|---|---|
X1 | [1.575, 1.873, 2.269] | [1.285, 1.432, 1.585] | [1.027, 1.088, 1.180] | [1.675, 1.824, 1.994] |
X2 | [1.002, 1.150, 1.378] | [1.304, 1.411, 1.500] | [1.228, 1.297, 1.414] | [1.100, 1.164, 1.241] |
X3 | [1.002, 1.150, 1.285] | [1.087, 1.174, 1.255] | [1.135, 1.257, 1.372] | [1.129, 1.183, 1.241] |
X4 | [0.973, 1.150, 1.285] | [1.126, 1.195, 1.277] | [1.089, 1.201, 1.331] | [1.072, 1.144, 1.200] |
X5 | [0.705, 0.808, 0.919] | [1.126, 1.195, 1.309] | [1.290, 1.442, 1.548] | [1.139, 1.213, 1.292] |
X6 | [1.575, 1.873, 2.269] | [1.186, 1.256, 1.351] | [1.174, 1.241, 1.305] | [1.139, 1.203, 1.272] |
X7 | [0.516, 0.573, 0.652] | [1.067, 1.143, 1.213] | [1.228, 1.322, 1.439] | [1.053, 1.105, 1.180] |
X8 | [1.224, 1.424, 1.609] | [1.107, 1.195, 1.277] | [1.058, 1.152, 1.247] | [1.100, 1.164, 1.211] |
Rij | Y1 | Y2 | Y3 | Y4 | Y5 | Y6 | Y7 | Y8 | Y9 | Y10 |
---|---|---|---|---|---|---|---|---|---|---|
X1 | 0.573 | 0.746 | 0.573 | 0.801 | 0.721 | 0.561 | 2.853 | 1.232 | 0.628 | 1.312 |
X2 | 0.729 | 0.701 | 0.736 | 1.444 | 1.368 | 0.854 | 1.743 | 0.910 | 0.861 | 0.653 |
X3 | 0.798 | 0.736 | 0.915 | 1.232 | 1.886 | 0.757 | 1.301 | 0.771 | 1.087 | 0.516 |
X4 | 0.731 | 0.870 | 1.024 | 1.093 | 1.483 | 0.933 | 1.448 | 0.703 | 1.121 | 0.592 |
X5 | 0.869 | 0.690 | 0.733 | 1.114 | 1.983 | 0.740 | 1.027 | 0.876 | 1.236 | 0.733 |
X6 | 0.589 | 0.760 | 0.602 | 1.432 | 1.217 | 0.653 | 2.934 | 0.697 | 0.551 | 0.564 |
X7 | 0.738 | 0.807 | 0.662 | 1.225 | 2.542 | 0.875 | 0.693 | 0.738 | 1.073 | 0.647 |
X8 | 0.729 | 0.804 | 0.864 | 1.161 | 1.850 | 0.743 | 1.735 | 0.763 | 0.851 | 0.500 |
Xi | X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 |
---|---|---|---|---|---|---|---|---|
Unit cost (yuan) | 10 | 21.5 | 17.5 | 16.5 | 55 | 7.5 | 57.5 | 24 |
Service life (year) | 1.5 | 2.5 | 2.5 | 2 | 3.5 | 1.5 | 5 | 2 |
Xi | X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 |
---|---|---|---|---|---|---|---|---|
Equivalent annual cost method | 7.3366 | 9.8280 | 7.9995 | 9.2515 | 18.6299 | 5.5025 | 14.3964 | 13.4567 |
Overall comparative possibility degree | 0.9113 | 0.7322 | 0.5871 | 0.5236 | 0.0477 | 1.0002 | 0.1272 | 0.4630 |
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Xu, X.; Wang, S.; Kang, F.; Li, S.; Li, Q.; Wu, T. Multi-Attribute Decision-Making Method in Preventive Maintenance of Asphalt Pavement Based on Optimized Triangular Fuzzy Number. Sustainability 2024, 16, 2787. https://doi.org/10.3390/su16072787
Xu X, Wang S, Kang F, Li S, Li Q, Wu T. Multi-Attribute Decision-Making Method in Preventive Maintenance of Asphalt Pavement Based on Optimized Triangular Fuzzy Number. Sustainability. 2024; 16(7):2787. https://doi.org/10.3390/su16072787
Chicago/Turabian StyleXu, Xunqian, Siwen Wang, Fengyi Kang, Shue Li, Qi Li, and Tao Wu. 2024. "Multi-Attribute Decision-Making Method in Preventive Maintenance of Asphalt Pavement Based on Optimized Triangular Fuzzy Number" Sustainability 16, no. 7: 2787. https://doi.org/10.3390/su16072787