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A platform for research: civil engineering, architecture and urbanism
Engineering Property Evaluation and Multiobjective Parameter Optimization of Argillaceous Gangue–Filled Subgrade Based on Grey Relational Analysis
Promoting the utilization of solid wastes such as mineral waste residue in highway engineering is a meaningful way to accelerate the development of green highway construction and to reduce environmental pollution caused by waste residue stacking. To comprehensively evaluate the engineering properties of waste argillaceous gangue–filled subgrade and recommend reasonable construction parameters, the effects of parameters such as moisture content, compaction work, and coarse grain content (CGC) on the engineering properties of waste argillaceous gangue were compared in this study. A multiobjective comprehensive evaluation model based on grey relational degree (GRD) and principal component analysis (PCA) was constructed, and the reliability of the model was verified. The results showed that the mechanical properties of argillaceous gangue are sensitive to various influencing factors. With increased coarse particle content, unconfined compressive strength (UCS) first increased and then decreased, while resilient modulus and California bearing ratio (CBR) gradually increased. The impact of compaction work on different mechanical properties was noticeable. When calculating the comprehensive GRD, the weights of UCS, CBR of unsoaked samples, CBR of soaked samples, and resilient modulus were 0.203, 0.318, 0.278, and 0.201, respectively. The comprehensive performance prediction model can be effectively applied to evaluate the correlation between technical parameters and engineering properties of argillaceous gangue, and the fitting degree reached 98.21%. The model provides a scientific basis for engineering design and construction parameter optimization of subgrade filled with waste argillaceous gangue.
Engineering Property Evaluation and Multiobjective Parameter Optimization of Argillaceous Gangue–Filled Subgrade Based on Grey Relational Analysis
Promoting the utilization of solid wastes such as mineral waste residue in highway engineering is a meaningful way to accelerate the development of green highway construction and to reduce environmental pollution caused by waste residue stacking. To comprehensively evaluate the engineering properties of waste argillaceous gangue–filled subgrade and recommend reasonable construction parameters, the effects of parameters such as moisture content, compaction work, and coarse grain content (CGC) on the engineering properties of waste argillaceous gangue were compared in this study. A multiobjective comprehensive evaluation model based on grey relational degree (GRD) and principal component analysis (PCA) was constructed, and the reliability of the model was verified. The results showed that the mechanical properties of argillaceous gangue are sensitive to various influencing factors. With increased coarse particle content, unconfined compressive strength (UCS) first increased and then decreased, while resilient modulus and California bearing ratio (CBR) gradually increased. The impact of compaction work on different mechanical properties was noticeable. When calculating the comprehensive GRD, the weights of UCS, CBR of unsoaked samples, CBR of soaked samples, and resilient modulus were 0.203, 0.318, 0.278, and 0.201, respectively. The comprehensive performance prediction model can be effectively applied to evaluate the correlation between technical parameters and engineering properties of argillaceous gangue, and the fitting degree reached 98.21%. The model provides a scientific basis for engineering design and construction parameter optimization of subgrade filled with waste argillaceous gangue.
Engineering Property Evaluation and Multiobjective Parameter Optimization of Argillaceous Gangue–Filled Subgrade Based on Grey Relational Analysis
J. Mater. Civ. Eng.
Wen, Penghui (author) / Wang, Chaohui (author) / Chen, Ming (author) / Chai, Zhi (author)
2023-04-01
Article (Journal)
Electronic Resource
English
| Taylor & Francis Verlag | 2025
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