문주혁 교수

ABSTRACT This study investigates the feasibility of using ground coal bottom ash (CBA) as a partial replacement for Portland cement (PC) in ultra‐high‐performance concrete (UHPC). PC was replaced with CBA at levels of 10, 20, 30, and 50 wt.%, and the resulting effects on hydration behavior and mechanical properties were systematically evaluated. A 10 wt.% replacement of PC with CBA resulted in improved compressive strength, whereas higher replacement levels led to strength reduction mostly due t

Ultra-high-performance concrete (UHPC) has gained increasing attention for its superior mechanical and durability properties. Recent efforts have focused on advancing next-generation UHPC technologies that align with CO2 reduction and performance enhancement goals. This study presents a focused summary of four emerging research directions: (1) clinker-free UHPC through enhanced pozzolanic reactions, (2) in-situ CO2 premixing and curing for carbon fixation, (3) lightweight UHPC incorporating holl

A portion of chloride salt is usually introduced to improve the early strength of slag cement, but it increases the possibility of chloride corrosion. This study investigates the effect of diethanol-isopropanolamine (DEIPA) on the chloride distribution and mechanical strength in a high-volume slag cement (HVSC) system containing 60 wt% slag and 1.11 wt% NaCl. In the absence of NaCl, DEIPA enhanced the aluminate reaction of HVSC, producing more hemicarbonate. As a result, DEIPA addition increased

ABSTRACT In this study, the grinding impact of monoethylene glycol (MEG) in varying amounts (0%, 0.02%, and 0.1%) on ground‐granulated blast furnace slag (GGBFS) was assessed. The mechanical and hydration properties of ordinary Portland cement (OPC) blended with 50 wt.% of GGBFS were also investigated. The inclusion of MEG enhanced the grinding performance of the GGBFS, with a more pronounced effect at higher MEG concentrations. Moreover, MEG somehow altered the hydration reactions of OPC and GG

Carbon nanotube (CNT)/cement composite is a promising material for structural health monitoring, where CNT dispersion and content critically affect the conductive network for self-sensing. Still, a simple method to assess low CNT levels is lacking, limiting their large-scale production. This study applied machine learning (ML) and convolutional neural networks (CNN) to predict these factors from optical microscopic images. A custom 2D-CNN directly learned spatial features from cropped images, re