https://scholarworks.unist.ac.kr/handle/201301/24 2026-04-08T00:32:31Z https://scholarworks.unist.ac.kr/handle/201301/90938 Title: Acoustic Characteristics of Microparticle- Modified Cementitious Composites with Embedded Tesla Valve-Inspired Metamaterials Author(s): KEBEDE, ALEMAYEHU MOGES Abstract: Achieving efficient low-frequency sound absorption in concrete without compromising compressive strength remains a critical challenge in construction materials design. This dissertation addresses this challenge through the development of multifunctional cementitious composites that integrate engineered pore architectures and industrial byproducts to achieve a balance between acoustic performance, structural capacity, and environmental sustainability. To overcome the excessive material thickness typically required for low-frequency sound absorption, a hybrid acoustic strategy was developed using surface-perforated mortar embedded with Tesla valve–inspired acoustic metamaterials and Helmholtz-type resonant cavities. These geometries, fabricated via additive manufacturing, significantly modified internal acoustic pathways, resulting in a 64% reduction in sound reflection and a 68.75% increase in broadband sound absorption, particularly within the 250–2500 Hz frequency range. Sustainability was further enhanced through the incorporation of supplementary cementitious materials (SCMs), including hollow glass microspheres (HGMs), cenospheres (CS), and rubber powder (RP), as partial replacements for cement or sand. These materials reduced environmental impact while influencing hydration behavior and acoustic response. HGM3 accelerated early-age reactions, CS improved internal curing and thermal stability, and RP, despite inducing a 20–25% reduction in compressive strength, proved effective for non- structural sound-absorbing applications. Durability performance was assessed through thermal resistance, chloride penetration, carbonation, and electrical conductivity tests, demonstrating that the optimized composites satisfy relevant standards for lightweight structural materials (ACI, ASTM C330). To further refine and generalize acoustic performance, a data-driven optimization framework combining COMSOL-based thermoviscous simulations and machine learning models was implemented. This framework accurately predicted sound absorption behavior and identified optimal geometric configurations beyond the experimental design space. Overall, this dissertation presents a scalable and sustainable cementitious material platform that integrates acoustic metamaterial design, durability, and data-driven optimization, advancing the development of next-generation sound-absorbing concrete for noise-sensitive infrastructure. Major: Graduate School of UNIST (2013-2020) Department of Urban and Environmental Engineering 2026-01-31T15:00:00Z https://scholarworks.unist.ac.kr/handle/201301/90934 Title: Deep learning based harmful algal blooms modeling in inland water using multimodal monitoring Author(s): Kwon, Do Hyuck Abstract: Harmful algal blooms (HABs) represent a growing environmental and public health concern in inland water systems, which are driven by complex interactions among hydrological, climatic, and anthropogenic processes. Conventional in-situ monitoring and numerical modeling approaches have provided valuable insights into algal management. However, they have faced critical limitations in capturing the rapid, spatially heterogeneous, and event-driven phenomena of algal proliferation in dynamic freshwater environments. Therefore, an advanced monitoring framework is required that could deal with large-scale, high-frequency, and multi-source observations to effectively present the spatiotemporal dynamics of HABs. This dissertation presents a comprehensive deep learning framework for monitoring, modeling, and water management by integrating multi-source remote sensing, in-situ observation, and environmental datasets within an artificial intelligence (AI) paradigm. First, a deep learning-based super-resolution (SR) approach was developed to enhance the spatial resolution of satellite imagery in inland waters. Using convolutional neural networks and generative adversarial networks, satellite images were reconstructed at sub-pixel precision, providing fine-resolution chlorophyll-a (Chl-a) distribution maps from the Sentinel-2 dataset. The findings of the first research could provide advancement bridges with scale differences between coarse satellite imagery and narrow inland water bodies, thereby the deep learning-based SR approach contributed to improving the spatial representativeness of algal bloom monitoring. Second, a probabilistic machine learning frameworks were implemented to directly simulate phytoplankton abundance from the hyperspectral remote sensing data. Bayesian neural network (BNN) and natural gradient boosting (NGBoost) were utilized to estimate algal cell concentration and to quantify predictive uncertainty according to the measurement noise and data scarcity. These results demonstrated that probabilistic models not only showed superior performance but also provided a credible measure of monitoring uncertainty, which could be essential for ecological interpretation and risk-aware bloom forecasting. Third, a multi-modal deep learning was developed for algal phyla classification by integrating heterogeneous modalities, including microscopic images and particle properties. The proposed multimodal learning combined visual and quantitative representations of the algal phyla from data fusion strategies. The framework achieved high classification performance across major algal phyla and was further interpreted using eXplainable AI (XAI) using Shapley Additive Explainations (SHAP), and Gradient- weighted Class Activation Mapping (Grad-CAM). Therefore, this study demonstrated that multimodal learning could capture and integrate the morphological and particle-based features that contributed to the differentiation for algal identification. Fourth, a tower-based hyperspectral monitoring system was integrated with a multimodal deep learning model to predict high-frequency monitoring of cyanobacterial blooms. This research incorporated continuous hyperspectral reflectance, environmental, and in-situ RGB imagery to simulate temporally bloom dynamics. The integration of high-frequency tower observations with multimodal deep learning allows continuous detection of short-term variations in bloom intensity and spatial heterogeneity, providing a practical framework for real-time HAB monitoring in inland waters. Hence, this dissertation advances HAB research by integrating in-situ observations, remote sensing, and data-driven modeling into a reliable and complementary framework. The findings of this dissertation deal with scalable approaches that combine spatial precision, temporal continuity, and model interpretability. The proposed AI-driven framework can enhance transparency and reliability in ecological prediction. This research could contribute to the development of a water quality monitoring system and sustainable water resource management in response to accelerating climatic and anthropogenic pressure within AI paradigm. Major: Department of Civil, Urban, Earth, and Environmental Engineering 2026-01-31T15:00:00Z https://scholarworks.unist.ac.kr/handle/201301/90933 Title: Transforming Anaerobic Granular Sludge Systems for Mainstream Municipal Wastewater Treatment through Magnetite-Embedded Granules and Electrochemical Stimulation Author(s): Park, Jihun Abstract: The global shift toward sustainable and energy-efficient wastewater treatment calls for alternatives to conventional aerobic activated sludge processes, which are highly energy-intensive due to aeration and dissipate the intrinsic chemical energy of wastewater. Anaerobic treatment offers a promising solution by converting organic matter into methane-rich biogas while substantially reducing energy input. However, its application to low-strength municipal and domestic wastewater under mainstream conditions remains limited by low substrate concentrations, biomass washout, and suppressed microbial activity at ambient and low temperatures. This doctoral research addresses these challenges by developing magnetite-embedded granular sludge (MEG) and integrating electrochemical stimulation to promote direct interspecies electron transfer (DIET), thereby improving methanogenic activity, granule stability, and process resilience under mainstream conditions. Study I examined the feasibility of applying MEG to the anaerobic treatment of low-strength municipal wastewater at 25℃ using expanded granular sludge bed (EGSB) reactors. Duplicate reactors, with and without submicron magnetite supplementation, were compared in terms of granule morphology, physicochemical characteristics, and microbial communities. Magnetite was successfully self-embedded within granules, improving density, settling, and structural stability. The formation of conductive MEG promoted DIET between electroactive bacteria and methanogens, improving COD removal and operational stability without major design modifications. These results established MEG as a simple and cost-effective approach for improving mainstream anaerobic treatment efficiency. Study II extended the concept to low-temperature conditions (25–10℃), which addressed one of the key barriers to anaerobic treatment in temperate and cold regions. Despite reduced enzymatic activity, the MEG maintained higher COD removal, methane productivity, and biomass retention than the control. Physicochemical and microbial analyses confirmed that magnetite embedding facilitated DIET and stabilized methanogenesis under kinetic limitations. Higher electron transport system (ETS) activity, greater granule conductivity, and higher levels of DIET-related genes (pilA and omcS) provided potential evidence of promoting extracellular electron transfer. These findings demonstrate that MEG can effectively mitigate temperature-induced performance deterioration, thus extending the applicability of anaerobic technologies to colder climates. Study III advanced the concept by integrating electrochemical stimulation with MEG and developing an electro-assisted EGSB system for low-strength wastewater treatment at low temperatures (20–5℃). Optimal voltage application (0.6 V) increased methane production and organic removal, while excessive voltage (0.9 V) inhibited performance due to electrochemical stress. The combined effects of conductive magnetite and external voltage promoted electroactive biofilm development, increased ETS activity, and improved process stability at low temperatures. Microbial community analysis revealed enrichment of electrotrophic Methanothrix and hydrogenotrophic Methanobacterium, indicating stimulation of DIET-mediated and syntrophic acetate oxidation pathways. Energy balance analysis showed a net positive energy gain at 15℃, which confirmed the potential of the electro-assisted MEG-EGSB system for energy-efficient operation in mainstream conditions. Collectively, this thesis demonstrates that magnetite-embedded granule formation and electrochemical stimulation are effective and complementary strategies for overcoming kinetic and stability constraints in mainstream anaerobic treatment. Study I established the feasibility of MEG for improving granule structure and methanogenesis at ambient temperature. Study II confirmed the robustness and electroactivity of MEG at low temperatures. Study III introduced electro-assisted MEG- EGSB technology, which achieved higher methane yields and energy recovery at subambient temperatures. Together, these studies provide mechanistic insights and engineering guidance for developing next-generation, energy-positive anaerobic wastewater treatment processes that harmonize with global sustainability goals. Major: Department of Civil, Urban, Earth, and Environmental Engineering 2026-01-31T15:00:00Z https://scholarworks.unist.ac.kr/handle/201301/90932 Title: Sustainable valorization of industrial solid waste: Application of indirect carbonation and byproducts-derived activators Author(s): Ju, Suhawn Abstract: As global CO₂ emissions and waste generation continue to rise, the demand for sustainable construction materials is becoming essential. In response, growing research efforts are focused on reducing dependence on conventional construction materials and promoting the practical application of alternative resources. Therefore, this thesis proposes innovative methods for carbon emission reduction through the upcycling of industrial by-products. In particular, this approach aims to enhance mechanical performance and address environmental challenges, with potential implications for seismic resilience, through the utilization of coal combustion residues and electronic waste. First, this study evaluated the potential of indirect carbonation using fluidized bed combustion (FBC) fly ash (HFA) and bottom ash (HBA) for CO₂ sequestration and high-purity CaCO₃ production. The calcium extraction was conducted using distilled water and NH₄Cl. A NH₄Cl achieved high calcium extraction efficiency, yielding 8.4 wt.% and 10.2 wt.% of CaCO₃ from HBA and HFA, respectively, corresponding to CO₂ capture capacities of 36.8 kg and 45.0 kg per ton of raw ash. XRD, SEM, and TGA analyses confirmed the formation of high-purity CaCO₃, and the formation of vaterite was observed under low pH conditions. Therefore, these findings indicate the potential to contribute to sustainable waste management and carbon capture, utilization, and storage (CCUS). Second, a novel method of synthesizing alkaline activators from coal bottom ash (CBA) was proposed. Instead of using CBA as a binder or filler, this study dissolves amorphous silica from CBA into a sodium hydroxide solution to produce an alkaline activator. The alkaline activator synthesized from CBA was successfully applied to the production of metakaolin- based geopolymer composites. The CBA-based activator exhibited improved mechanical performance compared to the use of the same amount of CBA as a binder. This suggests a novel utilization approach for CBA and contributes to expanding the methods for industrial by-product recycling. Third, this study proposed a sustainable method to utilize waste liquid crystal display (LCD) powder by synthesizing alternative alkaline activators. The LCD-derived activator provided approximately 35% of the silica content compared to silica fume and resulted in phase transformation of residual LCD into sodalite. Compared to direct binder replacement, the use of LCD-derived activators improved compressive strength and reduced porosity. These findings demonstrate that waste LCD powder can be effectively valorized through byproduct-derived activator synthesis, offering both environmental and mechanical benefits. Major: Department of Civil, Urban, Earth, and Environmental Engineering 2026-01-31T15:00:00Z