Youngjib Ham
Seoul National University · Engineering
About the Lab
Professor Youngjib Ham's research lab specializes in digital twin technologies and data-driven infrastructure management, focusing on integrating real-time sensor data, UAV-based visual monitoring, and advanced analytics for smart cities and buildings. The lab develops innovative methods for energy performance diagnostics, thermal comfort modeling, and structural health monitoring using wearable sensors, thermography, and 3D modeling. A key focus is enabling objective, cost-effective, and personalized decision-making in building retrofitting and urban resilience through machine learning and spatiotemporal data fusion. The lab bridges civil infrastructure, building science, and digital innovation to support sustainable and risk-informed urban development.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15Abstract Over the past few years, the application of camera-equipped Unmanned Aerial Vehicles (UAVs) for visually monitoring construction and operation of buildings, bridges, and other types of civil infrastructure systems has exponentially grown. These platforms can frequently survey construction sites, monitor work-in-progress, create documents for safety, and inspect existing structures, particularly for hard-to-reach areas. The purpose of this paper is to provide a concise review of the most
The benefits of a digital twin city have been assessed based on real-time data collected from preinstalled Internet of Things (IoT) sensors (e.g., traffic, energy use, air pollution, water quality) for managing the complex systems of cities, but the sensor-based reality information is likely insufficient to provide dynamic spatiotemporal information about physical vulnerabilities. Understanding cities’ current states of physical vulnerability can support city decision makers in analyzing associa
As one of the representative parameters for human energy metabolism, the metabolic rate has been considered as the significant factor for occupants’ thermal comfort analyses. Despite the importance of metabolic rate as a predictor of thermal comfort modelling, prior works rely on uncertain metabolic rate estimation without considering actual activity variations while occupying a building. This study aims at identifying the effect of metabolic rate on the thermal comfort models by proposing a rob
Abstract Background Building deteriorations instigated by material degradations or moisture intrusions are the primary causes for energy inefficiency in many existing buildings. For choosing appropriate retrofits, it is important to carefully diagnose and analyze building areas in need of improvements. In addition to reliable sensing and analysis of as-is energy performance, an intuitive recording and visualization of energy diagnostic outcomes are also critical to effectively illustrate the as-
Quick and reliable identification of energy performance problems in buildings is a critical step in improving their efficiency. The current practice of building diagnostics typically involves nonintrusive data collection using thermal cameras. This requires large amounts of unordered and nongeo-tagged two-dimensional (2D) imagery to be manually analyzed at a later stage, which makes the analysis time-consuming and labor-intensive. Because of the absence of a benchmark for energy performance, ide
Recent research efforts to improve energy modeling and diagnostics for existing buildings have focused on devising methods based on digital photogrammetry or three-dimensional (3D) laser scanning and thermal imagery. Prior research has shown that fusing actual and expected 3D spatiothermal models provide valuable information for analyzing performance gaps. Until now, these methods have primarily focused on detecting and localizing potential performance problems without analyzing their associated
Inspecting and assessing existing utility poles has become increasingly important for reducing the vulnerability of power distribution infrastructure systems in disaster situations, which can enhance community resilience. Although vision-based systems have been applied to detect faults in power distribution infrastructures, little research currently exists on assessing component- and network-level failures of utility poles based on their geometric and environmental information. This paper aims t
Research Areas
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