Dr. Youngjib Ham, Assistant Professor at FIU School of Construction has been implementing his research on construction and building performance monitoring and control. He mainly focuses on advanced sensing and analytics, Information & Communication Technology (ICT), Unmanned Aerial Vehicles (UAVs), and Building Information Modeling (BIM), especially collecting and analyzing Big Visual Data (e.g., images or videos) from construction and building environments. In particular, his research thrust consists of two parts as follows:
(1) Improving disaster preparedness of construction projects and neighboring communities through autonomous visual sensing and analytics
Unstructured construction sites including incomplete structures and unsecured resources (e.g., materials, equipment, and temporary facilities) are among the most vulnerable environments to windstorms such as hurricanes. Wind-induced cascading damages cause substantial losses, disruption, and considerable schedule delays in construction projects. For example, 2012 Hurricane Sandy caused over $185 million worth of damages to the World Trade Center construction project in New York City. Moreover, such wind-induced damages originated from jobsites would negatively affect neighboring interdependent infrastructures (e.g., adjacent buildings or transportation systems), which triggers serious injuries and casualty as well as economic losses in our community. Nonetheless, prior works on disaster management mainly focus on post-disaster assessment and reconstruction process of built environments, and thus predicting potential risks associated with expected disasters for proactive preparedness remain largely unknown. This research presents a new framework that can uncover potential risks of wind-induced cascading damages to construction projects and neighboring communities. First, vision-based modeling from both ground and aerial perspective (using camera-equipped UAVs) characterizes the extent to how the as-is construction sites are exposed to expected disasters. Then, the outcomes of the visual recognition are used for multi-body/multi-physics simulations, which can help rapidly describe a chain reaction of wind-induced cascading damages originated from unstructured construction environments while still maintaining reasonable physical realism. This research is expected to benefit our society as it will enhance current windstorm preparedness and mitigation plans, which ultimately promote public safety, property loss reduction, insurance cost reduction, and raise awareness of culture of preparedness for disasters. This research is supported by a NSF-sponsored research grant (CMMI Civil Infrastructure Systems (CIS) Award #1635378, $379,144 for 3 years).
(2) Big visual data-driven building energy performance modeling and analysis to improve the energy efficiency of existing buildings
The issues on building energy efficiency are not only limited how new buildings are designed and constructed energy-efficiently. Today, significant amounts of input energy in existing buildings is still being wasted during the operational phase. One primary source of the energy waste is influenced by unnecessary heat flows through envelopes during hot and cold seasons. This inefficiency increases the operational frequency of HVAC systems to keep the desired thermal comfort of occupants, and ultimately results in excessive energy use for space conditioning. According to a recent report from U.S. DOE, the building sector accounts for about 40% of the energy consumption in the U.S., and the space heating and cooling accounts for about 53% of the energy consumed in buildings. Thus, around 21% of the energy consumed in the U.S. is potentially affected by problems related to space conditioning. Improving thermal performance of building envelopes can reduce the energy consumption required for space conditioning and in turn provide occupants with an optimal thermal comfort at a lower energy cost. However, sensing what and where energy problems are emerging or are likely to emerge and analyzing how the problems influence the energy consumption are still challenging. To address such challenges, first, to improve sensing of the as-is energy performance, Dr. Ham has developed a new vision-based method to automatically reconstruct 3D spatio-thermal models of built environments using unordered collections of thermal and digital images. Second, to improve performance analytics, he also has created and validated new model-based methods for advanced building energy diagnostics, supported by a new Energy Performance Augmented Reality (EPAR) in which actual (‘as-is’) and expected (‘as-designed’) performances are fused in 3D. In the EPAR models, the presence and location of potential energy problems in building environments are inferred based on performance deviations. 3D thermal profiles are then converted into the as-is thermal resistances of building elements at point level in 3D, which help better characterize the as-is building condition non-uniformity such as partial degradations and calculate the amount of unnecessary heat transfer through defective areas and the associated energy cost. Finally, by mapping ‘the as-is building condition’ to BIM, my method reduces the gap between the architectural information in the as-designed BIM and the as-is building condition. The ‘semantically updated’ BIM reflecting the ‘as-is’ building conditions will improve the reliability of BIM-based energy performance modeling and analysis for existing buildings.