- Integrating electro-mechanical impedance data with machine learning for damage detection and classification of blended concrete systems. Dans: Construction and Building Materials, v. 445 (septembre 2024). (2024):
- Exploring chloride-induced corrosion in reinforced concrete structures through embedded piezo sensor technology: an experimental and numerical study. Dans: Smart Materials and Structures, v. 33, n. 3 (2 février 2024). (2024):
- A review on health monitoring of concrete structures using embedded piezoelectric sensor. Dans: Construction and Building Materials, v. 405 (novembre 2023). (2023):
- Comparative study of machine learning methods to predict compressive strength of high-performance concrete and model validation on experimental data. Dans: Asian Journal of Civil Engineering, v. 25, n. 2 (octobre 2023). (2023):
- Recommendation of RILEM TC 281-CCC: Test method to determine the effect of uniaxial compression load and uniaxial tension load on concrete carbonation depth. Dans: Materials and Structures, v. 56, n. 7 (10 août 2023). (2023):
- Report of RILEM TC 281-CCC: effect of loading on the carbonation performance of concrete with supplementary cementitious materials — an interlaboratory comparison of different test methods and related observations. Dans: Materials and Structures, v. 56, n. 6 (19 juin 2023). (2023):
- Durability aspects of blended concrete systems subjected to combined mechanical and environmental loading using piezo sensor. Dans: Construction and Building Materials, v. 348 (septembre 2022). (2022):
- Durability performance of binary and ternary blended cementitious systems with calcined clay: a RILEM TC 282 CCL review. Dans: Materials and Structures, v. 55, n. 5 (juin 2022). (2022):
- A Machine Learning Approach for Predicting the Electro-mechanical Impedance Data of Blended Rc Structures Subjected to Chloride Laden Environment. Dans: Smart Materials and Structures, v. 31, n. 1 (23 novembre 2021). (2021):