Experimental Measurement and Prediction Modeling of Concrete Thermal Conductivity скачать в хорошем качестве

Experimental Measurement and Prediction Modeling of Concrete Thermal Conductivity

Presented By: Yogiraj Sargam, CarbonCure Technologies The thermal conductivity, k, is a significant property of concrete that affects the heat trans-fer mechanism, design, and energy efficiency of concrete-based structures. This study aimed to determine the effects of contemporary concrete components, such as SCMs, lightweight and recycled aggregates, fibers, and more, on concrete's thermal conductivity through exper-imentation. The results demonstrated varying degrees of the influence of these parameters, with aggregate mineralogy having a considerable effect while polypropylene fibers having little impact. To address the challenge of measuring k for every concrete structure with a sophisticated test procedure, a machine learning (ML) based prediction model was devel-oped using literature data and experimentally measured data. After assessing a range of pa-rameters and models, a final multilayer perceptron model was adopted that performed well with mean absolute errors of 0.07, 0.14, and 0.10 W/m-K on the training, validation, and independent test sets, respectively. The developed model can provide valuable information for informed decision-making in the design and construction of critical structural elements.