Abstract
<title>Abstract</title> <p>This paper introduces a comprehensive automated methodology for evaluating the tilt of urban utility poles, an essential parameter for structural health monitoring in electricity distribution systems. Conventional inspection processes often rely on subjective visual evaluations, which are inherently prone to human error and lack systematic digital records. This research presents a multi-algorithmic pipeline that employs high-fidelity Terrestrial Laser Scanning (TLS) data to accurately assess structural tilt. Three unique computational methodologies were devised and assessed: (1) a resilient estimation method combining Random Sample Consensus (RANSAC) with Least Squares (LS) circle fitting; (2) orientation evaluation through Principal Component Analysis (PCA); and (3) a hybrid PCA-Geometric Fitting approach. The empirical findings indicate that the hybrid approach attains enhanced accuracy in near-vertical conditions, whereas the LS & RANSAC methodology displays the greatest robustness and stability in managing high-variance datasets marked by considerable occlusions and environmental noise. Comparative validation with ground-truth hand measurements verifies that the proposed 3D point cloud analysis accurately identifies intricate geometric distortions and structural torsion that traditional techniques frequently overlook. The results indicate that using a multi-algorithmic automated approach improves technical accuracy and creates a scalable digital framework for proactive maintenance and forecasting structural fragility. This study establishes a foundational framework for integrating low-cost sensors and automated feature classification into future sustainable asset management techniques.</p>