Discovering hidden structural failure regimes in pavement mechanics using topological data analysis of high-dimensional response spaces
International Journal of Pavement Engineering, cilt.27, sa.1, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 27 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.1080/10298436.2026.2741258
- Dergi Adı: International Journal of Pavement Engineering
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO), Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: mapper algorithm, parameter robustness, persistent homology, rigid pavement, sobol sampling, structural vulnerability, Topological data analysis
- Recep Tayyip Erdoğan Üniversitesi Adresli: Evet
Özet
The structural behaviour of pavement systems is governed by interacting traffic, material, and environmental parameters, producing high dimensional and nonlinear response spaces that are difficult to interpret using conventional methods. This study proposes a Topological Data Analysis framework for analysing mechanistically generated rigid pavement responses while preserving links to the underlying design variables. A database of 2,048 cases was generated using Sobol sampling of slab thickness, elastic modulus, subgrade reaction modulus, wheel load, and temperature gradient. Westergaard formulations were used to calculate tensile stress, slab deflection, stress ratio, and fatigue damage index. Persistent homology and Mapper were applied to identify connectivity, transition pathways, and structural regimes within the standardised response manifold. The analysis revealed stable, transitional, and failure prone regimes and introduced the Topological Structural Vulnerability Index as a continuous measure of proximity to critical response states. Slab thickness and wheel load showed the strongest associations with vulnerability, while temperature gradient had a secondary effect. Robustness analysis across 72 Mapper configurations produced a mean adjusted Rand index of 0.861. The framework provides a physically traceable screening approach for interpreting high dimensional simulated pavement responses and structural transitions.