台灣高速公路路網隨服役年限增加，鋪面劣化、養護經費限制及永續治理需求日益明顯。傳統鋪面養護維修（Maintenance and Rehabilitation, M&R）排程多以成本或鋪面績效作為單一決策目標，且劣化預測、碳排放、服務公平性及施工限制多分別處理，難以在多年期資源配置整合不同管理目標。為此，本研究以國道一號新營工務段為案例，建立整合本土化劣化預測、環境社會治理（Environmental, Social and Governance, ESG）、生命週期評估（Life Cycle Assessment, LCA）與多目標最佳化之多年期鋪面養護維修排程模式。
研究以車道百公尺為決策單元，採信賴域反射最小平方法（Trust Region Reflective Least Squares, TRRLS）校正車轍、裂縫及 IRI 模型，並透過決策樹判定維修工法；再建立成本、碳排放、績效曲線下面積及吉尼係數四項目標，納入工程施工限制，以非支配排序遺傳演算法第二代（Non-dominated Sorting Genetic Algorithm II, NSGA-II）搜尋 Pareto 非支配解，並以歐幾里得距離法與 AHP 選定折衷方案。
結果顯示，車轍模型校正後 RMSE 降低約 50%，Bias 由 −0.1416 in 降至 +0.0023 in；裂縫模型參數縮減後條件數降低約 11 個數量級，改善參數可辨識性；IRI 縮減模型五折交叉驗證 RMSE 僅較全資料校正增加約 0.3%。整體而言，本土化校正可降低系統性偏差，但裂縫與 IRI 對個別路段之解釋能力仍有限。NSGA-II 六組隨機種子所得 Pareto Front 具初步重現性，惟部分執行尚未完全平台化。折衷解顯示，AHP 方案反映績效優先之專家偏好，以較高成本與碳排放換取較佳路網績效與服務公平性；歐幾里得距離方案則在四項目標間呈現較均衡之折衷，且兩方案均符合年度預算限制。本研究可量化不同決策偏好下之經濟、環境、績效與公平性取捨，作為高速公路多年期養護資源配置與永續鋪面資產管理之決策支援。

Taiwan's freeway network faces growing pavement deterioration, tight maintenance budgets, and rising demands for sustainable, equitable infrastructure management. This study develops a five-year pavement maintenance and rehabilitation (M&R) scheduling framework integrating localized deterioration prediction, environmental, social and governance (ESG) considerations, life-cycle assessment, and multi-objective optimization. National Freeway No. 1 from km 251.1 to 320.0 (Xinying Maintenance Section) was divided into 4,134 lane-based 100 m units (20,670 unit-year positions). Rutting, cracking and International Roughness Index (IRI) models were calibrated with 300 field observations using the trust-region reflective least-squares method and linked to an engineering decision tree for treatment assignment. Four objectives — discounted cost, carbon emissions, area under the pavement performance curve (AUPC), and the Gini coefficient of network service inequality — were optimized under budget and constructability constraints using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), with compromise solutions selected by the Analytic Hierarchy Process (AHP) and the Euclidean distance method. Calibration reduced the rutting RMSE by about 50% and bias from −0.1416 to +0.0023 in; parameter reduction lowered the cracking-model condition number by roughly 11 orders of magnitude; the reduced IRI model increased cross-validation RMSE by only 0.3%. The Pareto fronts were preliminarily reproducible across six random seeds, although not all runs fully converged.

AASHTO, Aa. (2008). Mechanistic-empirical pavement design guide: A manual of practice. AAoSHaT Officials, Editor.
Abu Dabous, S., Zeiada, W., Zayed, T., & Al-Ruzouq, R. (2020). Sustainability-informed multi-criteria decision support framework for ranking and prioritization of pavement sections. Journal of Cleaner Production, 244, 118755. https://doi.org/10.1016/j.jclepro.2019.118755
Alaloul, W., Altaf, M., Musarat, M., Javed, M., & Mosavi, A. (2021). Systematic Review of Life Cycle Assessment and Life Cycle Cost Analysis for Pavement and a Case Study. Sustainability, 13, 4377. https://doi.org/10.3390/su13084377
Amin, M. A., & Machemehl, R. B. (2025). Inequity-Averse Pavement Maintenance and Rehabilitation Budget Allocation Incorporating Gini Index and Social Vulnerability. Transportation Research Record: Journal of the Transportation Research Board, 2679(10), 186–205. https://doi.org/10.1177/03611981251341350
Anastasopoulos, P. Ch., Sarwar, M. T., & Shankar, V. N. (2016). Safety-oriented pavement performance thresholds: Accounting for unobserved heterogeneity in a multi-objective optimization and goal programming approach. Analytic Methods in Accident Research, 12, 35–47. https://doi.org/10.1016/j.amar.2016.10.001
Arezoumand, S., & Smadi, O. (2024). Equity in Transportation Asset Management: A Proposed Framework. Algorithms, 17(7), 305. https://doi.org/10.3390/a17070305
Bergmann, M., & Cordier, L. (2008). Optimal control of the cylinder wake in the laminar regime by trust-region methods and POD reduced-order models. Journal of Computational Physics, 227(16), 7813–7840. https://doi.org/10.1016/j.jcp.2008.04.034
Branch, M. A., Coleman, T. F., & Li, Y. (1999). A subspace, interior, and conjugate gradient method for large-scale bound-constrained minimization problems. SIAM Journal on Scientific Computing, 21(1), 1–23.
Bueno, P. C., Vassallo, J. M., & Cheung, K. (2015). Sustainability Assessment of Transport Infrastructure Projects: A Review of Existing Tools and Methods. Transport Reviews, 35(5), 622–649. https://doi.org/10.1080/01441647.2015.1041435
Celauro, C., Corriere, F., Guerrieri, M., Lo Casto, B., & Rizzo, A. (2017). Environmental analysis of different construction techniques and maintenance activities for a typical local road. Journal of Cleaner Production, 142, 3482–3489. https://doi.org/10.1016/j.jclepro.2016.10.119
Chen, C., & Flintsch, G. W. (2007). Fuzzy Logic Pavement Maintenance and Rehabilitation Triggering Approach for Probabilistic Life-Cycle Cost Analysis. Transportation Research Record: Journal of the Transportation Research Board, 1990(1), 80–91. https://doi.org/10.3141/1990-10
Chen, W., Zheng, M., Lu, C., Tian, N., Ding, X., & Li, N. (2022). Multi-objective decision support system for large-scale network pavement maintenance and rehabilitation management to enhance sustainability. Journal of Cleaner Production, 380, 135028.
Chong, D., Wang, Y., Dai, Z., Chen, X., Wang, D., & Oeser, M. (2018). Multiobjective optimization of asphalt pavement design and maintenance decisions based on sustainability principles and mechanistic-empirical pavement analysis. International Journal of Sustainable Transportation, 12(6), 461–472. https://doi.org/10.1080/15568318.2017.1392657
Coello, C. A. C., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 256–279.
Coffey, T. (2015). Bioassay case study applying the maximin D‐optimal design algorithm to the four‐parameter logistic model. Pharmaceutical Statistics, 14(5), 427–432. https://doi.org/10.1002/pst.1702
Coleman, T. F., & Li, Y. (1994). On the convergence of interior-reflective Newton methods for nonlinear minimization subject to bounds. Mathematical Programming, 67(1–3), 189–224. https://doi.org/10.1007/BF01582221
Coleman, T. F., & Li, Y. (1996). An Interior Trust Region Approach for Nonlinear Minimization Subject to Bounds. SIAM Journal on Optimization, 6(2), 418–445. https://doi.org/10.1137/0806023
Colglazier, W. (2015). Sustainable development agenda: 2030. Science, 349(6252), 1048–1050. https://doi.org/10.1126/science.aad2333
Collop, A. C., & Roque, R. (2004). Report on the Prediction of Surface-Initiated Longitudinal Wheel Path Cracking in Asphalt Pavements. Road Materials and Pavement Design, 5(4), 409–434. https://doi.org/10.1080/14680629.2004.9689979
Conn, A. R., Gould, N. I., & Toint, P. L. (2000). Trust region methods. SIAM.
Deb, K., & Jain, H. (2014). An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints. IEEE Transactions on Evolutionary Computation, 18(4), 577–601. https://doi.org/10.1109/TEVC.2013.2281535
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.
Eccles, R. G., Lee, L.-E., & Stroehle, J. C. (2020). The Social Origins of ESG.
Embacher, R. A., & Snyder, M. B. (2001). Life-cycle cost comparison of asphalt and concrete pavements on low-volume roads; case study comparisons. Transportation Research Record, 1749(1), 28–37.
European Committee for Standardization. (2022). Sustainability of construction works—Sustainability assessment of civil engineering works—Calculation methods (European Standard EN 17472:2022).
Feighan, K. J., Shahin, M. Y., Sinha, K. C., & White, T. D. (1988). Application of dynamic programming and other mathematical techniques to pavement management systems. Transportation Research Record, (1200).
Fernandes, T. J., Muniz, J. A., Pereira, A. A., Muniz, F. R., & Muianga, C. A. (2015). <b>Parameterization effects in nonlinear models to describe growth curves. Acta Scientiarum. Technology, 37(4), 397. https://doi.org/10.4025/actascitechnol.v37i4.27855
France-Mensah, J., Kothari, C., O’Brien, W. J., & Jiao, J. (2019). Integrating social equity in highway maintenance and rehabilitation programming: A quantitative approach. Sustainable Cities and Society, 48, 101526. https://doi.org/10.1016/j.scs.2019.101526
France-Mensah, J., & O’Brien, W. J. (2018). Budget Allocation Models for Pavement Maintenance and Rehabilitation: Comparative Case Study. Journal of Management in Engineering, 34(2), 05018002. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000599
Fwa, T. F., & Farhan, J. (2012). Optimal Multiasset Maintenance Budget Allocation in Highway Asset Management. Journal of Transportation Engineering, 138(10), 1179–1187. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000414
Gao, L., & Zhang, Z. (2008). Robust Optimization for Managing Pavement Maintenance and Rehabilitation. Transportation Research Record: Journal of the Transportation Research Board, 2084(1), 55–61. https://doi.org/10.3141/2084-07
Gassman, S. L., & Rahman, M. M. (2016). Calibration of the AASHTO pavement design guide to South Carolina conditions-phase I.
Gini, C. (1912). Variabilità e mutabilità: Contributo allo studio delle distribuzioni e delle relazioni statistiche.[Fasc. I.]. Tipogr. di P. Cuppini.
Godeau, U., Bouget, C., Piffady, J., Pozzi, T., & Gosselin, F. (2020). Lack of definition of mathematical terms in ecology: The case of the sigmoid class of functions in macro‐ecology. Ecology and Evolution, 10(24), 14209–14220. https://doi.org/10.1002/ece3.7016
Godoy, P., Mourgues, C., & Chamorro, A. (2015). Incorporating socio-political criteria into the maintenance prioritization of Chilean urban pavement networks. Transportation Research Part A: Policy and Practice, 80, 151–162. https://doi.org/10.1016/j.tra.2015.07.014
Gong, H., Huang, B., Shu, X., & Udeh, S. (2017a). Local calibration of the fatigue cracking models in the Mechanistic-Empirical Pavement Design Guide for Tennessee. Road Materials and Pavement Design, 18(sup3), 130–138. https://doi.org/10.1080/14680629.2017.1329868
Gong, H., Huang, B., Shu, X., & Udeh, S. (2017b). Local calibration of the fatigue cracking models in the mechanistic-empirical pavement design guide for Tennessee. Road Materials and Pavement Design, 18(sup3), 130–138.
Guan, J., Yang, X., You, L., Ding, L., & Cheng, X. (2022). Multi-objective optimization for sustainable road network maintenance under traffic equilibrium: Incorporating costs and environmental impacts. Journal of Cleaner Production, 334, 130103.
Gulfam-E-Jannat, Yuan, X.-X., & Shehata, M. (2016). Development of regression equations for local calibration of rutting and IRI as predicted by the MEPDG models for flexible pavements using Ontario’s long-term PMS data. International Journal of Pavement Engineering, 17(2), 166–175.
Haider, S. W., Brink, W. C., Buch, N., & Chatti, K. (2015). Process and data needs for local calibration of performance models in the AASHTOWARE Pavement ME Software. Transportation Research Record, 2523(1), 80–93.
Hamed Naseri, Golroo, A., Shokoohi, M., & Gandomi, A. H. (2024). Sustainable pavement maintenance and rehabilitation planning using the marine predator optimisation algorithm.
Holland, J. H. (1992). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. The MIT Press. https://doi.org/10.7551/mitpress/1090.001.0001
Hossain, N., Singh, D., & Zaman, M. (2016). Enhancing Rutting Prediction of the Mechanistic–Empirical Pavement Design Guide by Using Data from a Field Test Section in Oklahoma. Transportation Research Record, 2590(1), 28–36.
Inti, S. (2016). A DECISION MAKING APPROACH FOR SELECTION OF SUSTAINABLE PAVEMENTS IN TEXAS BY INTEGRATING LIFE CYCLE COST ANALYSIS (LCCA), LIFE CYCLE ASSESSMENT (LCA) OF ENVIRONMENTAL AND SOCIAL IMPACTS.
Ishibuchi, H., Tsukamoto, N., & Nojima, Y. (2008). Evolutionary many-objective optimization: A short review. 2419–2426.
Ji, A., Xue, X., Wang, Y., Luo, X., & Zhang, M. (2020). An integrated multi-objectives optimization approach on modelling pavement maintenance strategies for pavement sustainability. Journal of Civil Engineering and Management, 26(8), 717–732.
Justo-Silva, R., Simões, F., & Ferreira, A. (2022). Mechanical-Empirical Pavement Design Guide Applied to Portuguese Pavement Structures. Applied Sciences, 12(11), 5656. https://doi.org/10.3390/app12115656
Khavandi Khiavi, A., & Mohammadi, H. (2018a). Multiobjective Optimization in Pavement Management System Using NSGA-II Method. Journal of Transportation Engineering, Part B: Pavements, 144(2), 04018016. https://doi.org/10.1061/JPEODX.0000041
Khavandi Khiavi, A., & Mohammadi, H. (2018b). Multiobjective optimization in pavement management system using NSGA-II method. Journal of Transportation Engineering, Part B: Pavements, 144(2), 04018016.
Kim, Y. R., Jadoun, F. M., Hou, T., & Muthadi, S. (2011). Local calibration of the MEPDG for flexible pavement design: Final report (FHWA/NC/2007-07). North Carolina State University. Department of Civil, Construction, and Environmental Engineering. https://digital.ncdcr.gov/Documents/Detail/local-calibration-of-the-mepdg-for-flexible-pavement-design-final-report/2560114
Kozel, M., Remek, Ľ., Ilovská, K., Mazurek, G., & Buczyński, P. (2024). Integrating Tensometer Measurements, Elastic Half-Space Modeling, and Long-Term Pavement Performance Data into a Mechanistic–Empirical Pavement Performance Model. Applied Sciences, 14(9), 3880. https://doi.org/10.3390/app14093880
La Torre, F., Domenichini, L., & Meocci, M. (2014). Calibration of fatigue and rutting distress models for non conventional asphalt concrete materials. In M. Losa & T. Papagiannakis (Eds.), Sustainability, Eco-efficiency, and Conservation in Transportation Infrastructure Asset Management (pp. 413–422). CRC Press. https://doi.org/10.1201/b16730-61
Lee, E.-B., Kim, C., & Harvey, J. T. (2011). Selection of pavement for highway rehabilitation based on life-cycle cost analysis: Validation of California interstate 710 project, phase 1. Transportation Research Record, 2227(1), 23–32.
Levenberg, K. (1944). A method for the solution of certain non-linear problems in least squares. Quarterly of Applied Mathematics, 2(2), 164–168.
Li, J., Pitt, M., Ma, L., Jia, J., & Jiang, F. (2022). A multi-objective differential evolutionary algorithm for optimal sustainable pavement maintenance plan at the network level. Journal of Cleaner Production, 381, 135212. https://doi.org/10.1016/j.jclepro.2022.135212
Li, N., Haas, R., & Huot, M. (1998). Integer programming of maintenance and rehabilitation treatments for pavement networks. Transportation Research Record, 1629(1), 242–248.
Li, Q., Xiao, D. X., Wang, K. C. P., Hall, K. D., & Qiu, Y. (2011). Mechanistic-empirical pavement design guide (MEPDG): A bird’s-eye view. Journal of Modern Transportation, 19(2), 114–133. https://doi.org/10.1007/BF03325749
Litman, T. (2011). Guidance for incorporating distributional impacts in transportation planning.
Liu, J., Guo, Z., Shan, D., & Liu, X. (2024). Effective lane width for cars considering lateral oscillation characteristics based on high-precision field data. International Journal of Transportation Science and Technology, 16, 18–33. https://doi.org/10.1016/j.ijtst.2023.10.007
Lorenz, M. O. (1905). Methods of measuring the concentration of wealth. Publications of the American Statistical Association, 9(70), 209–219.
Lu, M., Guler, S. I., & Gayah, V. V. (2024). Multi-Objective Optimization of Maintenance, Rehabilitation, and Reconstruction Decision Making Considering Safety. Transportation Research Record: Journal of the Transportation Research Board, 2678(1), 507–520. https://doi.org/10.1177/03611981231171152
Marquardt, D. W. (1963). An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics, 11(2), 431–441.
Marumo, N., Okuno, T., & Takeda, A. (2023). Majorization-minimization-based Levenberg–Marquardt method for constrained nonlinear least squares: N. Marumo et al. Computational Optimization and Applications, 84(3), 833–874.
McKay, M. D., Beckman, R. J., & Conover, W. J. (2000). A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 42(1), 55–61.
Mollenhauer, K., & Wistuba, M. (2012). Evaluation of hot-mix asphalt susceptibility to temperature-induced top-down fatigue cracking by means of Uniaxial Cyclic Tensile Stress Test. Road Materials and Pavement Design, 13(1), 171–190. https://doi.org/10.1080/14680629.2012.664524
Moré, J. J. (1977). The Levenberg-Marquardt algorithm: Implementation and theory. 105–116.
Naseri, H., Aliakbari, A., Javadian, M. A., Aliakbari, A., & Waygood, E. O. D. (2024). A novel technique for multi-objective sustainable decisions for pavement maintenance and rehabilitation. Case Studies in Construction Materials, 20, e03037. https://doi.org/10.1016/j.cscm.2024.e03037
Naseri, H., Ehsani, M., Golroo, A., & Moghadas Nejad, F. (2022). Sustainable pavement maintenance and rehabilitation planning using differential evolutionary programming and coyote optimisation algorithm. International Journal of Pavement Engineering, 23(8), 2870–2887.
Ouyang, Y., & Madanat, S. (2004). Optimal scheduling of rehabilitation activities for multiple pavement facilities: Exact and approximate solutions. Transportation Research Part A: Policy and Practice, 38(5), 347–365. https://doi.org/10.1016/j.tra.2003.10.007
Presti, D. L., Carrion, A. J. D. B., Parry, T., Neves, L., Keijzer, E., Kalman, B., Buttitta, G., & Mantalovas, K. (2023). A Framework for Life Cycle Management of Road Pavements in Europe. Transportation Research Procedia, 72, 1552–1559. https://doi.org/10.1016/j.trpro.2023.11.623
Prins, N. (2019). Too much model, too little data: How a maximum-likelihood fit of a psychometric function may fail, and how to detect and avoid this. Attention, Perception, & Psychophysics, 81(5), 1725–1739. https://doi.org/10.3758/s13414-019-01706-7
Ross, G. J. S., Prajneshu, & Sarada, C. (2010). Reparameterization of nonlinear statistical models: A case study. Journal of Applied Statistics, 37(12), 2015–2026. https://doi.org/10.1080/02664760903207332
Saaty, T. (1980). The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation.
Salem, O., Deshpande, A., Genaidy, A., & Geara, T. (2013). User costs in pavement construction and rehabilitation alternative evaluation. Structure and Infrastructure Engineering, 9(3), 285–294.
Santos, J., & Ferreira, A. (2012). Pavement design optimization considering costs and M&R interventions. Procedia-Social and Behavioral Sciences, 53, 1182–1191.
Santos, J., Ferreira, A., & Flintsch, G. (2017). A multi-objective optimization-based pavement management decision-support system for enhancing pavement sustainability. Journal of Cleaner Production, 164, 1380–1393. https://doi.org/10.1016/j.jclepro.2017.07.027
Schott, J. R. (1995). Fault tolerant design using single and multicriteria genetic algorithm optimization.
Simpson, M. J., Browning, A. P., Warne, D. J., Maclaren, O. J., & Baker, R. E. (2021). Parameter identifiability and model selection for sigmoid population growth models.
Singh, R. R., & Haider, S. W. (2025). Local calibration of flexible performance models using maximum likelihood estimation approach. Canadian Journal of Civil Engineering, 52(1), 29–44. https://doi.org/10.1139/cjce-2023-0568
Srinivas, N., & Deb, K. (1994). Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 2(3), 221–248.
Sund, E. K., & Lunaas, T. A. (2023). Innovative pavement contracts to reduce CO2-emissions. In M. Crispino & E. Toraldo, Roads and Airports Pavement Surface Characteristics (1st ed., pp. 678–685). CRC Press. https://doi.org/10.1201/9781003429258-66
Suwarto, F., Parry, T., & Airey, G. (2024). Review of methodology for life cycle assessment and life cycle cost analysis of asphalt pavements. Road Materials and Pavement Design, 25(8), 1631–1657. https://doi.org/10.1080/14680629.2023.2278149
Torres-Machí, C., Pellicer, E., Yepes, V., & Chamorro, A. (2013). Heuristic optimization model for infrastructure asset management. 300–309.
Umer, A., Hewage, K., Haider, H., & Sadiq, R. (2017). Sustainability evaluation framework for pavement technologies: An integrated life cycle economic and environmental trade-off analysis. Transportation Research Part D: Transport and Environment, 53, 88–101. https://doi.org/10.1016/j.trd.2017.04.011
UNEP. (2021, December 6). Methodological Sheets for Subcategories in Social Life Cycle Assessment (S-LCA) 2021—Life Cycle Initiative. https://www.lifecycleinitiative.org/library/methodological-sheets-for-subcategories-in-social-life-cycle-assessment-s-lca-2021/
Walls, J. (1998). Life-cycle cost analysis in pavement design: In search of better investment decisions. US Department of Transportation, Federal Highway Administration.
Yang, F., Jiang, Y., & Li, H. (2026). Sprinkler irrigation networks calibration using the improved finite element model coupled to a trust-region reflective algorithm. Flow Measurement and Instrumentation, 111, 103380. https://doi.org/10.1016/j.flowmeasinst.2026.103380
Yao, L., Leng, Z., Jiang, J., & Ni, F. (2024). A multi‐agent reinforcement learning model for maintenance optimization of interdependent highway pavement networks. Computer-Aided Civil and Infrastructure Engineering, 39(19), 2951–2970. https://doi.org/10.1111/mice.13234
Yixiao, Z., Jun, X., Lingyuan, L., Yu, S., Qing, X., & Guishu, L. (2025). Failure Modeling of Lithium-ion Batteries Based on TRRLS Parameter Identification and Energy-Guided Thermal Propagation Multiscale Characteristics Analysis under Thermal Runaway Scenarios.
Yu, B., Gu, X., Ni, F., & Guo, R. (2015). Multi-objective optimization for asphalt pavement maintenance plans at project level: Integrating performance, cost and environment. Transportation Research Part D: Transport and Environment, 41, 64–74. https://doi.org/10.1016/j.trd.2015.09.016
Yu, H., Zhang, X., Chen, Q., Lu, G., & Tan, Z. (2025). Carbon emission assessment in asphalt pavement maintenance: A comparative study of five methods. Case Studies in Construction Materials, 22, e04668. https://doi.org/10.1016/j.cscm.2025.e04668
Yuan, X.-X., & Nemtsov, I. (2018). Local calibration of the MEPDG distress and performance models for Ontario’s flexible roads: Overview, impacts, and reflection. Transportation Research Record, 2672(40), 207–216.
Zhang, Q., & Li, H. (2007). MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 11(6), 712–731.
Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm. TIK Report, 103.
金伯陽. (2022). 利用AASHTO MEPDG建立台灣國家高速公路鋪面管理系統之鋪面績效模型架構. https://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/ccd=8y2Mu0/record?r1=1&h1=6
柯博仁/Ko P.-J. (2024). 台灣高速公路鋪面養護工法決策樹建立之研究/Development of Decision Tree for Freeway Pavement Maintenance and Rehabilitation in Taiwan.
飯島 尚 & 今井 博. (1982). 舗装の維持修繕に関する報告書. 建設省土木研究所.
黃文正. (2009). 力學經驗鋪面設計法應用於台灣瀝青混凝土鋪面. https://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/ccd=8y2Mu0/search?s=id=%22097NCKU5015034%22.&openfull=1&setcurrent=1#XXX