Uncertainty quantification of the concrete Modulus of Elasticity through different experiment tests and sensors

The mechanical properties of construction materials, particularly the Modulus of Elasticity, are fundamental for estimating the structural response and, therefore, a key component in the design and analysis of buildings.Traditional test methods for estimating the Modulus of Elasticity of Concrete involve using ASTM-based specifications based on assumptions and simple compression tests.However, even in single test specimens, methodologies may vary significantly in the number of samples, affecting the likelihood of estimating the Modulus.This research aims to quantify the uncertainty associated with the Modulus of Elasticity of Concrete upon different test setups through Bayesian uncertainty quantification.The study utilizes experimental data obtained from multiple sensors, including Digital Image Correlation (DIC), strain gauges, and Linear Variable Differential Transducers (LVDTs), to provide robust and comprehensive analysis.This research highlights the challenges and biases in characterizing the mechanical behavior of materials commonly used in the construction industry.The findings emphasize the critical role of uncertainty quantification in improving structural safety and reliability, particularly in a context where big data associated with material performance are significantly available.Furthermore, the study discusses the application of ASTM methodologies in a stochastic fashion.