A Six-Layer Framework for RCC Crack Analysis: Engineering Judgment Meets Machine Learning

Abstract

This study presents a comprehensive, field-based diagnostic model for analysing cracks in reinforced cement concrete (RCC) structures, based on empirical observations across forty buildings in Tamil Nadu and Karnataka. Despite adherence to structural design codes, these structures continue to exhibit cracking due to a convergence of causes such as material shrinkage, poor detailing, substandard construction practices, and environmental stressors. Conventional diagnostic approaches, which rely heavily on permissible crack width thresholds, often neglect crucial indicators like orientation, spatial distribution, and progression behaviour. To overcome these diagnostic limitations, this study introduces a six-level analytical framework that integrates descriptive statistics, frequency pattern analysis, chi-square and correlation testing, risk profiling, and machine learning-based decision tree classification. Results demonstrate that crack orientation and structural location are significantly correlated with risk levels (?² = 109.32 and 134.37, p < 0.001). The findings also reveal a weak inverse relationship between crack width and length (r = –0.13), which questions the assumption that wider or longer cracks necessarily indicate greater severity. Crack widths ranged from 0.4 mm to 20 mm (mean = 3.51 mm), and lengths ranged from 23 cm to 1000 cm (mean = 148.6 cm), covering a wide range of structural responses. High-risk cracks— defined as those exceeding 5 mm in width or 500 cm in length—were typically found in slabs and retaining walls, suggesting vulnerability to moisture ingress and flexural stresses. The predictive model achieved 75% accuracy and 100% precision in classifying high-risk cracks, highlighting the potential for integrating data-driven tools into structural diagnostics. This work presents more than just an improvement in inspection methodology; it represents a paradigm shift in understanding cracks as structural indicators rather than superficial defects. The study urges the revision of design codes like IS 456:2000 and ACI 224R-01 to accommodate multifactorial diagnostic criteria and recommends training for engineers in statistical and predictive diagnostic skills. The framework proposed here also lays the groundwork for further integration with sensor-based monitoring systems and geotechnical calibration models, particularly in the context of climate variability and rapid urban development.

Introduction

Cracks in RCC structures are often underestimated as cosmetic flaws, but they frequently signal underlying structural issues related to poor construction, material behavior, or environmental stress. Traditional reliance on visual inspection and personal judgment for crack assessment leads to inconsistency and misclassification. Existing design codes (like IS 456:2000 and ACI 224R-01) offer limited real-world applicability as they are based on ideal conditions.

This study develops a data-driven diagnostic model using field observations from 40 RCC buildings in Tamil Nadu and Karnataka. It emphasizes interpreting cracks as indicators of stress and structural health rather than merely surface defects.

Methodology:

• The study used a field-based, empirical approach.

• Data on crack width, length, location, orientation, and type were collected.

• Six analytical tools were used:

  1. Descriptive statistics to capture crack dimensions.

  2. Frequency analysis to determine common locations.

  3. Chi-square tests to find associations between crack location/orientation and type.

  4. Pearson correlation to study the relation between width and length.

  5. Risk profiling based on severity thresholds.

  6. Supervised machine learning (decision trees) to predict high-risk cracks.

Key Findings:

• Crack width ranged from 0.4 mm to 20 mm; lengths up to 10 meters.

• 35% of cracks were structural, mostly found in walls, lintels, and slabs—zones of stress concentration.

• Diagonal and horizontal cracks often indicated significant structural issues.

• Chi-square analysis confirmed a strong correlation between crack type and its location/orientation.

• Pearson correlation showed no strong link between crack width and length (r = –0.13), debunking the myth that larger cracks are always more severe.

• 15% of cracks were high-risk, especially in slabs and retaining walls, while 35% were moderate-risk and 50% low-risk.

• The decision tree model achieved 75% accuracy and 100% precision for high-risk crack prediction, validating its practical utility.

Conclusion

This study has demonstrated that cracks in RCC structures are not merely aesthetic blemishes but meaningful indicators of deeper structural or procedural deficiencies. The field data collected from forty RCC buildings across Tamil Nadu and Karnataka reveal that spatial location and orientation of cracks often provide more consistent indicators of structural vulnerability than crack width alone. The weak correlation between width and length further supports the necessity for multifactorial diagnostic frameworks. The six-layered analytical model, integrating descriptive statistics, spatial pattern analysis, association testing, empirical risk profiling, and supervised classification, has shown robust predictive capability. The success of the decision tree classifier—with 75% accuracy and 100% precision in identifying high-risk cracks—validates the integration of machine learning into civil diagnostic workflows. This study recommends that national codes such as IS 456:2000 and international standards like ACI 224R-01 be revised to account for orientation and location-based criteria and not rely solely on width thresholds. Furthermore, it advocates for the creation of standardized diagnostic protocols—including photographic documentation, orientation tagging, and digital geolocation—to ensure consistency in assessments across varied field conditions. Training modules for field engineers and supervisors should incorporate diagnostic reasoning based on empirical data, statistical tools, and predictive algorithms. The findings also suggest the integration of these diagnostic models with real-time monitoring systems, including sensor-based and geotechnical inputs, for long-term infrastructure health management. Ultimately, this research provides a replicable and scalable framework for early detection, classification, and risk-based prioritization of cracks in RCC structures. By bridging intuitive field judgment with analytical precision, it contributes to a more resilient, responsive, and contextually informed civil engineering practice.

References

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Copyright

Copyright © 2025 Parameswaran Easwaran, 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.