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May 29, 20260 citationsOpen Access

BEFORE THE CRACK: LOAD PATH TOPOLOGICAL REDUNDANCY AND THE ARCHITECTURE OF STRUCTURAL DAMAGE TOLERANCE The CMPF vs LPTR Named Binary in Structural Materials Science

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JMJosé Caetano de Mattos

Key Points

  • This research aims to unveil the structural factors that influence damage tolerance in materials, focusing on load path topology.
  • Introduced the Load Path Topological Redundancy framework to assess damage tolerance.
  • Derived pre-damage Load Path Topological Redundancy Index (LPTRI) from microstructural data.
  • Confirmed cross-domain structural invariance using various field datasets.
  • Resolved dissociation between K_IC values and damage tolerance across materials.
  • Established that LPTRI is critical in predicting structural recovery in self-healing materials.
  • Found testable implications for structural inspection and damage tolerance certification based on LPTRI.

Abstract

Structured Abstract Background Fracture mechanics has a complete account of what damage does in structural materials: crack propagation rate, stress intensity factor, fatigue life prediction via the Paris law, and fracture toughness (KIC) as the primary resistance parameter. Despite over a century of refinement, fracture mechanics models leave substantial variance in damage tolerance outcomes unexplained: materials with identical KIC values show dramatically divergent damage tolerance under complex loading; the Paris law fails to predict cyclic failure in large specimens from small-specimen data; and self-healing materials show highly variable structural recovery despite identical chemical composition restoration. Gap The tradition has a complete account of what cracks do to materials. It has no structural account of what property of the pre-damage microstructural architecture determines how the material redistributes load around the growing crack — a property encoded in the topology of the load-bearing pathway network before any damage event and operating as a transfer function between crack input and residual strength output. Approach We introduce the LPTR framework (Load Path Topological Redundancy), which identifies the pre-damage Load Path Topological Redundancy Index (LPTRI) — the ratio of structurally independent, non-equivalent load-bearing pathway alternatives in the pre-damage microstructural network — as an independently significant determinant of post-damage structural performance. We derive the Named Binary CMPF vs LPTR, identify three structural anomalies CMPF cannot resolve, specify a pre-registerable CCS using published SEM microstructure datasets and fatigue databases from the NIST Materials Data Repository and published literature, confirm cross-domain structural invariance across seven domains, and derive the fₘin formula for structural inspection scheduling. Results LPTR resolves three anomalies: the KIC-damage-tolerance dissociation, the size-effect paradox, and the self-healing structural recovery variability paradox. Cross-domain structural invariance is confirmed with stroke (SRCT), TBI (SRCT-TBI), ecology (PRAT), immunology (IRITR), sleep memory (SMCT), and network engineering. The CCS is testable using published microstructure datasets and fatigue life data at zero additional experimental cost. Weil Protocol: not required (L1 — no direct human clinical implications). Implications If confirmed, LPTR restructures damage tolerance certification from KIC-centred to LPTRI-complemented; provides a formally derived inspection frequency schedule calibrated to material-specific LPTRI depletion rates; and explains the persistent failure of thickness-corrected fracture mechanics to predict complex-loading outcomes in structural composites. The LPTR article completes the six-domain empirical foundation of the ARI Unification Paper. Keywords: CMPF, LPTR, LPTRI, damage tolerance, load path redundancy, fracture mechanics, Paris law, KIC, composite materials, microstructure topology, self-healing materials, fatigue life, SEM network analysis, ALGUILAS-AI, ARI principle, structural redundancy Method ALGUILAS-AI Dialectical Engine

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Cite This Study

José Caetano de Mattos (2026) studied this question.

synapsesocial.com/papers/6a192f1bfab5b468c4418697https://doi.org/10.5281/zenodo.20408146
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