Work

CNN-based model for automated anomaly recognition in facade execution to support Quality Management

Year: 2025

Type: article

Abstract: Unmanned Aerial Systems (UAS) are frequently used to inspect building envelopes. Computer vision technologies and Convolutional Neural Networks (CNN) have emerged as promising solutions for automating image-based inspections. However, some anomalies still occur during construction due to the lack of an efficient Quality Management System (QMS). To address this gap, this study proposes an automated CNN-based recognition model to detect and classify four types of anomalies in cast-in-place concrete facades during construction, aiming to support decision-making within the QMS. The research strategy adopted was a Case Study, in which eight CNN models were developed for training and testing using images of cast-in-place concrete wall facades collected by UASs during construction. The model achieved 51.80% precision, 68.50% recall, 65.00% mAP, and an F1-score of 58.99% during training, making it the most accurate among the eight models. Future studies will focus on fully integrating the proposed method’s workflow, enabling automated image analysis and the automatic generation of reports.

Source: Ambiente Construído

DOI: 10.1590/s1678-86212025000100867

Authors: Alisson Souza Silva, Roseneia Rodrigues Santos Melo, Reymard Savio Sampaio Melo, Dayana Bastos Costa

Institution: Universidade Federal da Bahia

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: diamond

Funders: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado da Bahia