| Popis: |
The research conducted in this thesis investigates the effects of phase change materials (PCMs) on the hydration kinetics and strength development of cement-based composites using extensive experimental and numerical analyses. Purposefully, the effect of microencapsulated PCMs (MPCMs) on the strength development of cement-based mortars and concretes was evaluated using powerful machine learning models trained with the largest available experimental data. Furthermore, a novel ternary machine learning approach was proposed to optimize the mixture design of mortars and concretes based on the thermos-physical properties of the MPCMs. The results obtained from machine learning simulations suggest the assessment of the effects of MPCMs on the maturity-strength relationship. Multitudinous laboratory experiments were therefore performed to collect data for the calculation of the apparent activation energy. The analysis of isothermal calorimetry and compressive strength measurements at various curing temperatures revealed the reduction of apparent activation energy after the addition of MPCMs, indicating less sensitivity of such composites to curing temperatures. Deep learning proved capable of predicting the hydration kinetics of MPCM-integrated cementitious systems, and thus calculating the apparent activation energy of diverse systems. Furthermore, eco-friendly shape-stabilized PCMs (SSPCMs) were fabricated using bio-based PCMs and recycled supporting agent to promote the sustainability of the built environment. Finally yet importantly, a low-carbon latent heat thermal energy storage (LHTES) system was developed based on bio-based MPCMs and limestone calcined clay cement (LC3) binder with lower clinker content. It was shown that utilizing such environmentally friendly construction materials could contribute to lowering the operational and embodied energy and emissions of major infrastructures. |
