Field Judgment and Structural Repair in RCC Crack Diagnosis: Integrating Expertise with Remedial Action

Abstract

Crack diagnosis and repair decisions in RCC structures are often shaped by experienced engineers’ field judgment rather than formal classifications alone. This study draws on diagnostic insights from 40 cracked buildings in Tamil Nadu and Karnataka, using a hybrid dataset that integrates empirical testing with interpretive interviews. The analysis explores how crack type, recurrence risk, and observed severity influence remedial strategies such as epoxy grouting, slab repair, and structural strengthening. Chi-square tests validate that crack type alone does not predict the need for structural reinforcement, while recurrence risk shows significant association with long-term repair proposals. The study affirms the diagnostic value of experiential logic in engineering decision-making and proposes a Decision Integration Matrix to guide responsive, evidence-based repair planning.

Introduction

Diagnosing RCC cracking is a complex task that blends technical evaluation with interpretive expertise. While structural codes differentiate between structural and non-structural cracks, field engineers often prioritize contextual factors such as severity, recurrence, soil behavior, and environmental exposure when deciding on repairs. This study explores how experienced engineers move beyond visual typology to make practical, risk-informed decisions in the field.

Methodology

• Data Source: 40 real-world RCC crack cases from Tamil Nadu and Karnataka.

• Approach: Hybrid method combining empirical tests (visual inspection, non-destructive testing) with expert interviews.

• Engineers: 5 senior professionals with an average of 28 years of experience.

• Analysis Tools: Chi-square tests and a mixed-methods interpretive framework.

• Key Data Tables: Focused on engineer profiles, testing status, repair actions, and diagnostic-risk relationships.

Key Findings

1. Engineer Expertise Matters

• 60% of engineers were private consultants; 40% were from corporate/institutional backgrounds.

• All had deep field experience, which informed nuanced diagnostic decisions beyond formal codes.

2. Crack Type vs. Structural Strengthening

• Despite 90% of cracks being labeled structural, only 22.5% of buildings were recommended for strengthening.

• Statistical analysis (p = 0.987) showed no meaningful correlation between crack type and the decision to strengthen.

• Engineers instead relied on factors like recurrence, soil movement, and severity progression.

3. Recurrence Risk as a Key Factor

• High-risk cases were significantly more likely to receive long-term interventions (e.g., slab repair, partial demolition).

• This relationship was statistically significant (p = 0.041; Cramér’s V = 0.40), highlighting the importance of recurrence over visual appearance.

4. Repair Strategies

• Epoxy/resin injection was used in 50% of cases, mostly for minor or non-recurrent cracks.

• Structural repairs (slab/beam strengthening) and demolition/reconstruction were each recommended in 12.5% of cases.

• Decisions were based on layered diagnosis including material behavior, drainage, and previous failures.

Conclusion

Repair strategies in RCC cracking emerge not from linear rules but from interpretive synthesis—blending recurrence forecasting, severity appraisal, and contextual awareness. Engineers in the field exercise diagnostic maturity, weighing risk, cost, material fatigue, and environmental feedback. The study confirms that a Decision Integration Matrix may help engineers and agencies transition from reactive repairs to proactive, logic-driven care models, enhancing structural resilience in varied soil and design contexts.

References

[1] Garavaglia, E., Basso, N., and Sgambi, L., 2018, \"Probabilistic life-cycle assessment and rehabilitation strategies for deteriorating structures: A case study,\" International Journal of Architectural Heritage, Vol. 12, No. 6, pp. 1052–1071. [2] Mishra, M., Lourenço, P. B., and Ramana, G. V., 2022, \"Structural health monitoring of civil engineering structures by using the internet of things: A review,\" Journal of Building Engineering, Vol. 45, Article 103638. [3] Wu, Z., Li, J., Gu, C., and Su, H., 2007, \"Review on hidden trouble detection and health diagnosis of hydraulic concrete structures,\" Science China-Technological Sciences, Vol. 50, No. 3, pp. 401–409.

Copyright

Copyright © 2025 Er. Nivetha Ramachandran, Er. Parameswaran Easwaran, Dr. Akash Prakash. 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.