Hybrid Diagnostic Analysis of RCC Cracks: Integrating Field Experience with Statistical Validation

Abstract

Cracking in Reinforced Cement Concrete (RCC) structures is a multifaceted phenomenon that reflects both superficial flaws and deeper structural vulnerabilities. In rapidly urbanizing regions such as India, the limitations of conventional diagnostic methods—either highly codified visual inspections or resource-intensive laboratory tests—underscore the need for a more adaptable, field-grounded approach. This study presents a hybrid analysis framework that integrates qualitative insights from seasoned engineers with quantitative validation through statistical tools to evaluate crack patterns across forty RCC structures in Tamil Nadu and Karnataka. The methodology incorporates a two-tiered approach: field interviews with engineers offering contextual interpretation of crack orientation, width, recurrence, and severity, and empirical checklists subjected to chi-square and Cramér’s V testing. The results establish statistically significant associations between crack severity and structural risk levels, with high-severity cracks predominantly observed in buildings with inadequate drainage and plinth protection. A Severity–Integrity Matrix is developed to operationalize this relationship and support early-stage, site-sensitive diagnosis. This hybrid analytical model enhances both predictive reliability and field applicability. It demonstrates that when structured observational tools are interpreted through expert knowledge and statistically tested, they offer a scalable, low-tech, and context-sensitive diagnostic aid for practitioners. The study contributes a practically deployable model that strengthens structural assessment protocols while accommodating the complex realities of RCC behaviour on-site.

Introduction

Cracks in reinforced concrete (RCC) structures are important indicators of structural health, but conventional visual inspections and lab-based assessments often fail to capture their nuanced, context-dependent significance. In India’s diverse construction environment, crack patterns are influenced by soil conditions, climate, and construction practices, yet traditional severity classifications overlook interactions with foundational defects and environmental loading.

This study proposes a hybrid diagnostic framework that integrates field engineers’ experiential knowledge with statistically validated crack profiling. Empirical data from 40 RCC buildings in Tamil Nadu and Karnataka were combined with interviews of senior engineers to assess crack orientation, width, severity, and recurrence. Statistical analysis (chi-square and Cramér’s V) confirmed that crack severity and recurrence are strong predictors of structural risk, while orientation patterns (horizontal and diagonal) reveal underlying foundational issues.

Results show that 90% of observed cracks were structural, with 27.5% classified as high-severity and 20% recurring, often linked to poor drainage or clayey soils. The Severity–Integrity Matrix developed from this study enables early-stage, low-tech risk assessment, helping prioritize interventions based on both immediate damage and long-term recurrence trends. Overall, the hybrid approach bridges experiential knowledge and empirical evidence, allowing proactive, predictive structural health monitoring rather than reactive maintenance.

Conclusion

This study affirms that hybrid diagnostics—grounded in both empirical testing and expert field reasoning—represent a robust, scalable solution for early detection of structural vulnerabilities in RCC buildings. The Severity–Integrity Matrix developed here exemplifies how visually observed crack parameters, when interpreted in context and statistically validated, can reliably indicate deeper structural threats. Unlike conventional codal inspections or high-tech diagnostic systems that often ignore situational nuances, this model is designed to function effectively across diverse geotechnical and climatic zones, particularly in low-resource environments. Each of the study’s conclusions is substantiated either through strong statistical associations—such as the significant correlation between crack severity and structural risk (Wang, Han, Zhang, & Wang, 2023)—or through insights drawn from engineering practitioners with decades of diagnostic experience. Crack recurrence, for example, emerged not merely as a visible pattern but as a predictive indicator of latent vulnerability when tracked over time and mapped against environmental and foundational conditions (Wu, Li, Gu, & Su, 2007). By reconciling structured quantitative assessments with interpretive depth, the proposed hybrid framework expands the epistemic foundation of RCC diagnostics. It elevates field judgment to an analytical plane, enabling practitioners to navigate diagnostic complexity without relying on prohibitively sophisticated equipment. As a result, this approach empowers local engineers with a grounded and validated toolset to prioritize structural audits, initiate timely interventions, and enhance the safety and longevity of RCC infrastructure. The study thus contributes not only a new methodological pathway but also a conceptual realignment of what constitutes reliable structural diagnosis—anchored in the fusion of empirical data, experiential knowledge, and contextual intelligence.

References

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Copyright

Copyright © 2026 Er. Parameswaran Easwaran, Dr. Rajkiran Singh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.