Intelligent Design of Pavement Concrete Based on RSM-NSGA-III-CRITIC-VIKOR
Climate-change-induced extreme environments exacerbate pavement degradation in arid regions, where traditional concrete incurs 23~40% higher life-cycle costs due to premature cracking. Particularly in the Gobi Desert, concrete pavements suffer from conflicting performance requirements—high flexural-...
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MDPI AG
2025-04-01
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| author | Yuren Huo Zhaoguang Li Yan Wang |
| author_facet | Yuren Huo Zhaoguang Li Yan Wang |
| author_sort | Yuren Huo |
| collection | DOAJ |
| description | Climate-change-induced extreme environments exacerbate pavement degradation in arid regions, where traditional concrete incurs 23~40% higher life-cycle costs due to premature cracking. Particularly in the Gobi Desert, concrete pavements suffer from conflicting performance requirements—high flexural-to-compressive strength ratio (R<sub>f</sub>/R<sub>c</sub>), low shrinkage, and controlled porosity—with traditional design methods failing to address multi-objective trade-offs. Existing optimization methods have proven insufficient for such complex environments, with conventional approaches addressing only individual parameters or employing subjective weighting techniques that fail to capture the interrelated nature of critical performance indicators. This study develops an integrated optimization framework combining Response Surface Methodology (RSM), Non-dominated Sorting Genetic Algorithm III (NSGA-III), Criteria Importance Through Intercriteria Correlation (CRITIC) weighting, and VIšekriterijumsko KOmpromisno Rangiranje (VIKOR) decision-making to optimize the mix proportions water–cement ratio (W/C), sand ratio, and an air-entraining agent (AEA) for sustainable pavement concrete. Response Surface Methodology (RSM) analysis via Box–Behnken design revealed distinct parameter dominance: AEA exhibited the strongest non-linear effects on R<sub>f</sub>/R<sub>c</sub> and porosity, while W/C primarily governed shrinkage. NSGA-III generated 73 Pareto-optimal solutions, with CRITIC selecting an optimal mix (W/C = 0.35), sand ratio = 36%, AEA = 0.200%) validated experimentally (R<sub>f</sub>/R<sub>c</sub> = 0.141), shrinkage = 0.0446%, porosity = 2.82%. Microstructural characterization using scanning electron microscopy and low-field nuclear magnetic resonance (SEM/LF-NMR) demonstrated refined pore distribution and enhanced compactness. This framework effectively resolves trade-offs between performance indicators, providing a scientifically robust method for designing durable pavement concrete that reduces shrinkage by 13.0% and porosity by 13.5% compared to conventional mixes, lowering maintenance costs in arid regions. |
| format | Article |
| id | doaj-art-4bb35c2b1f93497dad776524f112361e |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-4bb35c2b1f93497dad776524f112361e2025-08-20T01:49:20ZengMDPI AGApplied Sciences2076-34172025-04-01159503010.3390/app15095030Intelligent Design of Pavement Concrete Based on RSM-NSGA-III-CRITIC-VIKORYuren Huo0Zhaoguang Li1Yan Wang2College of Materials Science & Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaCollege of Materials Science & Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaCollege of Materials Science & Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaClimate-change-induced extreme environments exacerbate pavement degradation in arid regions, where traditional concrete incurs 23~40% higher life-cycle costs due to premature cracking. Particularly in the Gobi Desert, concrete pavements suffer from conflicting performance requirements—high flexural-to-compressive strength ratio (R<sub>f</sub>/R<sub>c</sub>), low shrinkage, and controlled porosity—with traditional design methods failing to address multi-objective trade-offs. Existing optimization methods have proven insufficient for such complex environments, with conventional approaches addressing only individual parameters or employing subjective weighting techniques that fail to capture the interrelated nature of critical performance indicators. This study develops an integrated optimization framework combining Response Surface Methodology (RSM), Non-dominated Sorting Genetic Algorithm III (NSGA-III), Criteria Importance Through Intercriteria Correlation (CRITIC) weighting, and VIšekriterijumsko KOmpromisno Rangiranje (VIKOR) decision-making to optimize the mix proportions water–cement ratio (W/C), sand ratio, and an air-entraining agent (AEA) for sustainable pavement concrete. Response Surface Methodology (RSM) analysis via Box–Behnken design revealed distinct parameter dominance: AEA exhibited the strongest non-linear effects on R<sub>f</sub>/R<sub>c</sub> and porosity, while W/C primarily governed shrinkage. NSGA-III generated 73 Pareto-optimal solutions, with CRITIC selecting an optimal mix (W/C = 0.35), sand ratio = 36%, AEA = 0.200%) validated experimentally (R<sub>f</sub>/R<sub>c</sub> = 0.141), shrinkage = 0.0446%, porosity = 2.82%. Microstructural characterization using scanning electron microscopy and low-field nuclear magnetic resonance (SEM/LF-NMR) demonstrated refined pore distribution and enhanced compactness. This framework effectively resolves trade-offs between performance indicators, providing a scientifically robust method for designing durable pavement concrete that reduces shrinkage by 13.0% and porosity by 13.5% compared to conventional mixes, lowering maintenance costs in arid regions.https://www.mdpi.com/2076-3417/15/9/5030pavement concretemulti-objective optimizationresponse surface methodologyNSGA-IIIVIKOR |
| spellingShingle | Yuren Huo Zhaoguang Li Yan Wang Intelligent Design of Pavement Concrete Based on RSM-NSGA-III-CRITIC-VIKOR Applied Sciences pavement concrete multi-objective optimization response surface methodology NSGA-III VIKOR |
| title | Intelligent Design of Pavement Concrete Based on RSM-NSGA-III-CRITIC-VIKOR |
| title_full | Intelligent Design of Pavement Concrete Based on RSM-NSGA-III-CRITIC-VIKOR |
| title_fullStr | Intelligent Design of Pavement Concrete Based on RSM-NSGA-III-CRITIC-VIKOR |
| title_full_unstemmed | Intelligent Design of Pavement Concrete Based on RSM-NSGA-III-CRITIC-VIKOR |
| title_short | Intelligent Design of Pavement Concrete Based on RSM-NSGA-III-CRITIC-VIKOR |
| title_sort | intelligent design of pavement concrete based on rsm nsga iii critic vikor |
| topic | pavement concrete multi-objective optimization response surface methodology NSGA-III VIKOR |
| url | https://www.mdpi.com/2076-3417/15/9/5030 |
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