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<p>Surface resistivity testing has emerged as a widely accepted non-destructive technique within the realm of concrete assessment, facilitating the evaluation of critical properties such as mechanical strength, durability against corrosion, and the presence of defects. This research aims to investigate the intricate factors that potentially influence electrical resistivity measurements in hardened concrete. Through experimental investigation, this study analyzes the impact of various parameters, including concrete grade, the presence of mineral admixtures like fly ash, moisture conditions, specimen geometry, and the presence of reinforcement, on the electrical resistivity of concrete. The Wenner Probe technique, a commonly employed method for electrical resistivity measurement, was used on samples of both finite (150&#xa0;mm diameter, 300&#xa0;mm height cylinders) and semi-finite dimensions (150 × 150 × 150&#xa0;mm cubes and 500 × 100 × 100&#xa0;mm beams). The experimental data obtained was defuzzied using the Adaptive Neuro-Fuzzy Inference System (ANFIS) approach in MATLAB, followed by comprehensive statistical analysis conducted through Minitab. Results indicate that resistivity measurements are significantly affected by all investigated parameters. Descriptive statistical analysis reveals normal distribution for most datasets, except for rebar diameter variation, which exhibits negative skewness and negative kurtosis. A Kruskal–Wallis test confirms good dataset quality (<i>p</i> &lt; 0.05), while Spearman correlation coefficients show significant relationships: 0.377 for concrete grade, 0.367 for mineral admixture presence, 0.524 for moisture content, − 0.839 for specimen geometry, and − 0.463 for rebar presence. The Ryan-Joiner normality test further supports normal distribution. Adjustment factors for accurate resistivity evaluation were proposed: 0.702 for cylindrical geometry, 0.654 for moisture content, and 0.814 for mineral admixture presence. By introducing these adjustment factors, the study aims to minimize inaccuracies in resistivity readings, ensuring more reliable evaluations of concrete condition. This enhanced precision is essential for assessing durability, guiding maintenance decisions, and improving the management and longevity of concrete infrastructure.</p>

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Application of adaptive neuro fuzzy inference system for contemplating the factors affecting electrical resistivity of concrete

  • 期刊论文

  • 发表日期：

    2025年3月13日

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  • Jeena Mathew,
  • Subha Vishnudas

摘要

Surface resistivity testing has emerged as a widely accepted non-destructive technique within the realm of concrete assessment, facilitating the evaluation of critical properties such as mechanical strength, durability against corrosion, and the presence of defects. This research aims to investigate the intricate factors that potentially influence electrical resistivity measurements in hardened concrete. Through experimental investigation, this study analyzes the impact of various parameters, including concrete grade, the presence of mineral admixtures like fly ash, moisture conditions, specimen geometry, and the presence of reinforcement, on the electrical resistivity of concrete. The Wenner Probe technique, a commonly employed method for electrical resistivity measurement, was used on samples of both finite (150 mm diameter, 300 mm height cylinders) and semi-finite dimensions (150 × 150 × 150 mm cubes and 500 × 100 × 100 mm beams). The experimental data obtained was defuzzied using the Adaptive Neuro-Fuzzy Inference System (ANFIS) approach in MATLAB, followed by comprehensive statistical analysis conducted through Minitab. Results indicate that resistivity measurements are significantly affected by all investigated parameters. Descriptive statistical analysis reveals normal distribution for most datasets, except for rebar diameter variation, which exhibits negative skewness and negative kurtosis. A Kruskal–Wallis test confirms good dataset quality (p < 0.05), while Spearman correlation coefficients show significant relationships: 0.377 for concrete grade, 0.367 for mineral admixture presence, 0.524 for moisture content, − 0.839 for specimen geometry, and − 0.463 for rebar presence. The Ryan-Joiner normality test further supports normal distribution. Adjustment factors for accurate resistivity evaluation were proposed: 0.702 for cylindrical geometry, 0.654 for moisture content, and 0.814 for mineral admixture presence. By introducing these adjustment factors, the study aims to minimize inaccuracies in resistivity readings, ensuring more reliable evaluations of concrete condition. This enhanced precision is essential for assessing durability, guiding maintenance decisions, and improving the management and longevity of concrete infrastructure.