Performance Evaluation and Comparison of Clog-Resisting Permeable Pavement and Conventional Permeable Pavement: Multicriteria Decision-Making Approach

This study investigates the performance of clog-resisting permeable pavement (CRP) and conventional permeable pavement (CPP) mixes by evaluating their mechanical properties, permeability, and clogging resistance. Concrete samples with porosity ranging from 3% to 28% were tested for compressive strength, flexural strength, permeability, and clogging resistance under cyclic exposure to sand and clay. The results show that CRP mixes consistently outperform CPP mixes across all evaluated parameters. At comparable porosity levels (14%–18%), CRP exhibited approximately two times higher compressive strength, while at higher porosity levels, CRP achieved nearly three times the strength of CPP. The overall strength reduction with increasing porosity was about 39% for CRP, compared to 60% for CPP, indicating superior strength retention. CRP mixes demonstrated significantly higher permeability, ranging from 1.2 to 10.5 cm / s , compared to 0.23 to 1.48 cm / s for CPP. Under cyclic clogging conditions, CRP maintained functional permeability over 8–9 cycles, whereas CPP experienced rapid blockage within 3–5 cycles, with permeability loss limited to 5%–25% for CRP and 40%–80% for CPP. To support rational pavement mix selection, multicriteria decision-making (MCDM) methods were applied under four design scenarios representing different performance priorities. CRP mixes consistently ranked higher across all scenarios, demonstrating the usefulness of MCDM in identifying optimal mixes based on functional requirements rather than individual performance indicators alone. Statistical validation using two-factor analysis of variance further confirmed that the pavement design method is the dominant factor governing performance variation. The findings demonstrate that combining laboratory evaluation with MCDM provides an effective decision-support framework for permeable pavement design, while highlighting the need for future field-scale validation and long-term performance assessment.