Byeong-Hun Woo

The construction field needs to reduce carbon emissions; therefore, many methods are being attempted applying to materials research. Concrete and asphalt are the representative materials for the pavement system. However, a lack of aggregates and certain limits in binder replacement are the main obstacles to achieving a reduction in carbon emissions. This study aims to address the lack of sustainable alternatives in pavement materials by investigating recycled plastic composites with Mg(OH)₂, thereby filling a research gap in low-carbon, mechanically viable solutions for smart mobility infrastructure. Three types of recycled plastics—polypropylene (PP), polyethylene terephthalate (PET), and OTHER (OTH) resins—were combined with Mg(OH)₂ to produce nine specimen configurations. The mechanical behavior and flexural performance were evaluated through displacement–flexural stress curve tests, while missing experimental data were reconstructed using a Bayesian long short-term memory (BLSTM) machine learning approach. The BLSTM model achieved an average R² of 0.8018 in testing and 0.7618 in validation, confirming reliable prediction capability even with a small dataset. All composites demonstrated a minimum flexural strength of 30 MPa, with PP-based specimens reaching approximately 40 MPa, confirming their suitability for pavement applications. These results highlight the flexural performance of each composite type, with PP emerging as the most promising candidate.

Cement-based materials are reliable materials that guarantee high efficiency and high performance and are one of the important materials for the civil engineering industries. It is an era in which carbon neutrality and energy efficiency are emphasized, and electric energy production is currently being challenged even in cement paste. At this time, the most difficult part is the process of efficiently manufacturing a positive type of P-type cement paste. In this study, a P-type cement paste impregnated with an acidic solution to remove the OH⁻ ions was prepared, and the Seebeck coefficient was obtained to confirm its electrical production capacity. In the case of cement, specimens impregnated with acetic acid and impregnated with undiluted solution for 48 h showed the highest value at 1270 µV/K, and those impregnated with pure lemon juice showed the highest value at 1220 µV/K. It is estimated that the compressive strength of the cement paste impregnated with pure lemon juice is about 10–40% greater than that of the cement paste impregnated with acetic acid, so the condition for producing the optimal P-type cement paste is to impregnate the pure lemon juice in a solution diluted to 20% for 48 h. This study provides the possibility of manufacturing P-type cement paste with optimal performance and manufacturing electric production cement using the Peltier effect in the future.

This study investigates the size effect on the Seebeck coefficient (SC) in cement composites incorporating silicon carbide (SiC). Two specimen shapes, cubic (50 × 50 × 50 mm³) and beam (40 × 40 × 160 mm³), were analyzed with varying SiC substitution ratios (0%, 50%, and 100%) for fine aggregates. Thermal and electrical conductivities were measured to assess their influence on the SC. The results showed that a higher SiC content increased porosity, which reduced mechanical strength but significantly improved thermal and electrical conductivities. Thermal conductivity increased from 1.88 W/mK (0% substitution) to 11.89 W/mK (100% substitution), while electrical conductivity showed an improvement from 0.0056 S/m to 0.065 S/m. Cubic specimens exhibited higher SC values compared to beam specimens, with a maximum SC of 1374 μV/K at 100% SiC substitution, attributed to shorter thermal diffusion distances. The findings suggest that optimizing member size and SiC content can significantly improve the thermoelectric performance of cement composites, potentially enhancing energy efficiency in construction applications.

Efficient energy use is crucial for achieving carbon neutrality and reduction. As part of these efforts, research is being carried out to apply a phase change material (PCM) to a concrete structure together with an aggregate. In this study, an energy consumption simulation was performed using data from concrete mock-up structures. To perform the simulation, the threshold investigation was performed through the Bayesian approach. Furthermore, the spiking part of the spiking neural network was modularized and integrated into a recurrent neural network (RNN) to find accurate energy consumption. From the training-test results of the trained neural network, it was possible to predict data with an R² value of 0.95 or higher through data prediction with high accuracy for the RNN. In addition, the spiked parts were obtained; it was found that PCM-containing concrete could consume 32% less energy than normal concrete. This result suggests that the use of PCM can be a key to reducing the energy consumption of concrete structures. Furthermore, the approach of this study is considered to be easily applicable in energy-related institutions and the like for predicting energy consumption during the summer.