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Open Access

ARTICLE

AI-Driven Object Detection Framework for Live Load Monitoring and Structural Optimization

Luis Sánchez Calderón*, David Valverde Burneo, Walter Hurtares Orrala
Faculty of Engineering in Earth Sciences, ESPOL Polytechnic University, ESPOL, Campus Gustavo Galindo, Km. 30.5 Vía Perimetral, Guayaquil, Ecuador
* Corresponding Author: Luis Sánchez Calderón. Email: email
(This article belongs to the Special Issue: AI-Driven and Computer-Vision-Based Sensing Technology for Real-Time, Non-Destructive Applications)

Structural Durability & Health Monitoring https://doi.org/10.32604/sdhm.2026.077137

Received 03 December 2025; Accepted 09 February 2026; Published online 13 April 2026

Abstract

Accurate characterization of live load histories remains critical for structural safety and efficient design; however, traditional codes often overestimate in-service loads. This study introduced an AI-driven framework integrating YOLOv8 object detection and DeepFace gender classification with continuous video surveillance to monitor live loads in academic buildings. Gender classification used local anthropometric data (77 kg males, 61 kg females) for precise load estimation, with privacy ensured via local processing and anonymized metadata only. Observed peaks were substantially below Eurocode and IBC provisions, confirming code conservatism. Uncertainty propagation from detector errors (recall 0.57, ±0.02 Kn/m2) minimally impacted projections. These findings demonstrate the potential of computer vision for data-driven structural optimization and sustainable design.

Keywords

Live loads; computer vision; Convolutional Neural Networks (CNN); video surveillance; structural engineering; YOLOv8; DeepFace; occupancy monitoring

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