Igor Augusto Guimarães Campos
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Igor Augusto Guimarães Campos has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Daniel Hasan Dalip
Centro Federal de Educação Tecnológica de Minas Gerais (Cefet-MG)
1 shared publication - Elvys Dias Reis
Université Paris-Saclay, França
1 shared publication - Flávia Spitale Jacques Poggiali
Centro Federal de Educação Tecnológica de Minas Gerais
1 shared publication - Péter Ludvig
Centro Federal de Educação Tecnológica de Minas Gerais
1 shared publication - Rafael Aredes Couto
Federal Center for Technological Education of Minas Gerais, Department of Civil Engineering, Belo Horizonte 30510-000, Brazil
1 shared publication
Latest Publications
- Open Access
Machine Learning Models for Carbonation Depth Prediction in Reinforced Concrete Structures: A Comparative Study
The durability of reinforced concrete (RC) structures is strongly influenced by carbonation, a phenomenon governed by material and environmental interactions. This study applied machine learning (ML) techniques—Random Forest (RF), Support Vector Regression (SVR), and Artificial Neural Networks (ANNs)—to predict carbonation depth using a synthetic dataset of 20,000 instances generated from the validated Possan equation. Model performances were evaluated across multiple scenarios, with compressive strength and exposure time identified as the most influential features, while relative humidity and exposure conditions had intermediate effects. SVR consistently captured linear and nonlinear trends, the ANN achieved the highest R2 values but showed minor overestimations, and RF exhibited lower adaptability to feature variations. The results highlight the applicability of ML models for durability assessments, particularly under complex conditions where traditional approaches are limited. Moreover, this study reinforces the strategic value of synthetic datasets in developing predictive models when experimental data collection is time-consuming or impractical. The methodologies developed here can be extended beyond carbonation modeling to other deterioration processes, supporting data-driven strategies for maintenance planning and resilience design in RC structures.
Open AccessMachine Learning Models for Carbonation Depth Prediction in Reinforced Concrete Structures: A Comparative Study
The durability of reinforced concrete (RC) structures is strongly influenced by carbonation, a phenomenon governed by material and environmental interactions. This study applied machine learning (ML) techniques—Random Forest (RF), Support Vector Regression (SVR), and Artificial Neural Networks (ANNs)—to predict carbonation depth using a synthetic dataset of 20,000 instances generated from the validated Possan equation. Model performances were evaluated across multiple scenarios, with compressive strength and exposure time identified as the most influential features, while relative humidity and exposure conditions had intermediate effects. SVR consistently captured linear and nonlinear trends, the ANN achieved the highest R2 values but showed minor overestimations, and RF exhibited lower adaptability to feature variations. The results highlight the applicability of ML models for durability assessments, particularly under complex conditions where traditional approaches are limited. Moreover, this study reinforces the strategic value of synthetic datasets in developing predictive models when experimental data collection is time-consuming or impractical. The methodologies developed here can be extended beyond carbonation modeling to other deterioration processes, supporting data-driven strategies for maintenance planning and resilience design in RC structures.