All the time, concrete structures surround you. We feel convenience and safe in the structures. We are using concrete very well, and will keep riding on it and its improved form. Our research focuses on innovating our technology for concrete structures, materials and construction. Research articles published by the group can be found in Scopus database or Prof. Kim's CV
Automated experimentation is a tool that generates material-property data that can be integrated with various machine learning tools. Artificial intelligence functions as a black box that interprets input to produce meaningful output, and this black box always requires 'high-quality' input data. Construction materials, in particular, exhibit significant variability compared to other engineering materials. Therefore, input data must be reliable and sufficient to ensure statistical validity.
Our laboratory has proposed and conducted automated experiments to evaluate the performance of cement dispersants. Through the abundant data obtained from these automated experiments, we can precisely capture the subtle responses influenced by the polymeric structure of cement dispersants.
The automated experimentation has the potential to transform the quality control paradigm of construction materials by integrating data science and machine learning. Before developing an automated experimental system for evaluating the performance of construction materials, our research team developed machine learning algorithms suitable for the characteristics of construction materials. Various algorithms have been developed, including observation-based learning and domain adaptation learning algorithms.
Kim JH, Kang IK, Shin TY, and Park CK (2025), Automated experimentation for evaluating cement dispersant performance, Cem Concr Res 194: 107895, https://authors.elsevier.com/a/1kwJ%7E21ISwigq
Kang IK, Shin TY, and Kim JH (2025), Unbiased rheological properties determined by adversarial training with Bingham equation, Cem Concr Comp 157: 105943, https://doi.org/10.1016/j.cemconcomp.2025.105943
Kang IK, Shin TY, and Kim JH (2023), Observation-informed modeling of artificial neural networks to predict flow and bleeding of cement-based materials, Constr Build Mater 409: 133811, https://doi.org/10.1016/j.conbuildmat.2023.133811
Kim JH, Shin TY, Yekaterina S, and Park CK (2023), Data science approach to find an outlier in the group of cement dispersants, Constr Build Mater 368: 130347, https://doi.org/10.1016/j.conbuildmat.2023.130347
Slump and slump flow of concrete are requisitely measured. A rheological model, proposed in our group, converts the test results into the Bingham parameters such as yield stress and plastic viscosity. That would be the first step to start rheological evaluation on the construction performance of concrete.
Advanced techniques have been developed to evaluate the rheological behavior of mortar. These new testing methods provide deeper insights into the rheological behavior of construction materials.
Aqueous CO2 curing technology does not need a huge chamber supplying gaseous CO2 for the early carbonation to enhance the performance of cement-based materials. CO2 is dissolved in an alkaline solution beforehand, and then blast-furnace slag mortar can be cured in the CO2 solvent. The resultant carbonation in early age improves the strength of the mortar samples together with a high CO2 uptake. In addition, the technology is easily augmented on the existing carbon capture process.
Jun Y, Han SH, Kim JH (2021), Early-age strength of CO2 cured alkali-activated blast furnace slag pastes, Constr Build Mater 288: 123075, https://doi.org/10.1016/j.conbuildmat.2021.123075
Han SH, Jun Y, and Kim JH (2022), The use of alkaline CO2 solvent for the CO2 curing of blast-furnace slag mortar, Constr Build Mater 342: 127977, https://doi.org/10.1016/j.conbuildmat.2022.127977
Han SH, Jun Y, Kim JH (2023), The use of monoethanolamine CO2 solvent for the CO2 curing of cement-based materials, Sustain Mater Techno 35: e00559, https://doi.org/10.1016/j.susmat.2022.e00559
Han SH, Kim SM, Jun Y, Han TH, Kim JH (2024), Carbon-captured sodium hydroxide solution for sustainable alkali-activated slag, Sustain Mater Techno 40: e00915, https://doi.org/10.1016/j.susmat.2024.e00915
Han S, Han TH, Hilario JHP, Kim JH (2024), Pre-treatment of waste hydrated cement used for replacement of fine aggregate using amine-based CO2 solvent, J Build Eng 97: 110711, https://doi.org/10.1016/j.jobe.2024.110711
Han S, Han TH, Kim JH (2024), Surface coating method for cement-based materials to improve chloride ion penetration resistance using amine-based CO2 solvent, J CO2 Util 83: 102823, https://doi.org/10.1016/j.jcou.2024.102823
Han S, Kim JH, Singh RK, and Shah SP (2025), CO2 utilization for cementitious materials - A review, KSCE J Civ Eng 29(4): 100227, https://doi.org/10.1016/j.kscej.2025.100227
Our interests in the lightweight aggregates originate from the recycling of industrial by-products. Artificial (coarse) lightweight aggregates is produced by sintering coal-combustion ash and dredged soil, and we also use bottom-ash lightweight sand as it is. Consuming the ashes for producing concrete contributes to the sustainability of construction industry. However, their porous properties make it hard to have a stable concrete mixture. An equilibrium on the capillary pressure and pore suction of surround pastes accounts for the water absoprtion of the porous lightweight aggregates in the fresh-state mix. A physical model is proposed to consistently evaluate that of bottom-ash lightweight sand or coarse lightweight aggregate.
Kim JH, Lee JH, Kim YH (2021), Equilibrium of capillary and pore water pressure in lightweight aggregates concrete, Mech Adv Mater Struct 29(26): 5156–5162, https://doi.org/10.1080/15376494.2021.1949510
Yoon JY, Kim JH (2019), Mechanical properties of preplaced lightweight aggregates concrete, Construct Build Mater 216: 440-449, https://doi.org/10.1016/j.conbuildmat.2019.05.010
Yoon JY, Lee JY, Kim JH (2019), Use of raw-state bottom ash for aggregates in construction materials, J Mater Cycles Waste Manag 21: 838–849, https://doi.org/10.1007/s10163-019-00841-5
