Impact Analysis on the Variations of the Thermo-physical Property of Building Envelopes and Occupancy in Building Energy Performance Assessment

AbstractUnderstanding the impact of uncertainty in modeling the thermo-physical property of building envelopes and building occupancy on energy analysis has recently received attention. This paper evaluates the impact of the variations of the thermo-physical property of building envelopes and occupancy on building energy analysis. As the data format for accessing and updating building information for energy analysis, gbXML-based BIM is leveraged. We first studied the impact of reflecting the as-is thermo-physical properties of different building envelopes from thermographic sensing on building energy load calculation. Then, the response of energy simulation model with respect to the variations of building occupancy is explored. Finally, the impact of each variation on building energy use intensity is analyzed through the regression analysis. Several experiments were conducted on a building located in six different climatic zones in the U.S. The perceived benefits of continuous updating of energy profiles for model calibration for reliable energy analysis under uncertainty and the related open research challenges are discussed in detail

Vision-based building energy diagnostics and retrofit analysis using 3D thermography and Building Information Modeling

The emerging energy crisis in the building sector and the legislative measures on improving energy efficiency are steering the construction industry towards adopting new energy efficient design concepts and construction methods that decrease the overall energy loads. However, the problems of energy efficiency are not only limited to the design and construction of new buildings. Today, a significant amount of input energy in existing buildings is still being wasted during the operational phase. One primary source of the energy waste is attributed to unnecessary heat flows through building envelopes during hot and cold seasons. This inefficiency increases the operational frequency of heating and cooling systems to keep the desired thermal comfort of building occupants, and ultimately results in excessive energy use. Improving thermal performance of building envelopes can reduce the energy consumption required for space conditioning and in turn provide building occupants with an optimal thermal comfort at a lower energy cost. In this sense, energy diagnostics and retrofit analysis for existing building envelopes are key enablers for improving energy efficiency. Since proper retrofit decisions of existing buildings directly translate into energy cost saving in the future, building practitioners are increasingly interested in methods for reliable identification of potential performance problems so that they can take timely corrective actions. However, sensing what and where energy problems are emerging or are likely to emerge and then analyzing how the problems influence the energy consumption are not trivial tasks. The overarching goal of this dissertation focuses on understanding the gaps in knowledge in methods for building energy diagnostics and retrofit analysis, and filling these gaps by devising a new method for multi-modal visual sensing and analytics using thermography and Building Information Modeling (BIM). First, to address the challenges in scaling and localization issues of 2D thermal image-based inspection, a new computer vision-based method is presented for automated 3D spatio-thermal modeling of building environments from images and localizing the thermal images into the 3D reconstructed scenes, which helps better characterize the as-is condition of existing buildings in 3D. By using these models, auditors can conduct virtual walk-through in buildings and explore the as-is condition of building geometry and the associated thermal conditions in 3D. Second, to address the challenges in qualitative and subjective interpretation of visual data, a new model-based method is presented to convert the 3D thermal profiles of building environments into their associated energy performance metrics. More specifically, the Energy Performance Augmented Reality (EPAR) models are formed which integrate the actual 3D spatio-thermal models (‘as-is’) with energy performance benchmarks (‘as-designed’) in 3D. In the EPAR models, the presence and location of potential energy problems in building environments are inferred based on performance deviations. The as-is thermal resistances of the building assemblies are also calculated at the level of mesh vertex in 3D. Then, based on the historical weather data reflecting energy load for space conditioning, the amount of heat transfer that can be saved by improving the as-is thermal resistances of the defective areas to the recommended level is calculated, and the equivalent energy cost for this saving is estimated. The outcome provides building practitioners with unique information that can facilitate energy efficient retrofit decision-makings. This is a major departure from offhand calculations that are based on historical cost data of industry best practices. Finally, to improve the reliability of BIM-based energy performance modeling and analysis for existing buildings, a new model-based automated method is presented to map actual thermal resistance measurements at the level of 3D vertexes to the associated BIM elements and update their corresponding thermal properties in the gbXML schema. By reflecting the as-is building condition in the BIM-based energy modeling process, this method bridges over the gap between the architectural information in the as-designed BIM and the as-is building condition for accurate energy performance analysis. The performance of each method was validated on ten case studies from interiors and exteriors of existing residential and instructional buildings in IL and VA. The extensive experimental results show the promise of the proposed methods in addressing the fundamental challenges of (1) visual sensing: scaling 2D visual assessments to real-world building environments and localizing energy problems; (2) analytics: subjective and qualitative assessments; and (3) BIM-based building energy analysis: a lack of procedures for reflecting the as-is building condition in the energy modeling process. Beyond the technical contributions, the domain expert surveys conducted in this dissertation show that the proposed methods have potential to improve the quality of thermographic inspection processes and complement the current building energy analysis tools

A Digital Twin City Model for Age-Friendly Communities: Capturing Environmental Distress from Multimodal Sensory Data

As the worldwide population is aging, the demands of aging-in-place are also increasing and require smarter and more connected cities to keep mobility independence of older adults. However, today’s aging built environment often poses great environmental demands to older adults’ mobility and causes their distresses. To better understand and help mitigating older adults’ distress in their daily trips, this paper proposes constructing the digital twin city (DTC) model that integrates multimodal data (i.e., physiological sensing, visual sensing) on environmental demands in urban communities, so that such environmental demands can be considered in mobility planning of older adults. Specifically, this paper examines how data acquired from various modalities (i.e., electrodermal activity, gait patterns, visual sensing) can portray environmental demands associated with older adults’ mobility. In addition, it discusses the challenges and opportunities of multimodal data fusion in capturing environmental distresses in urban communities

A Framework of Human-Motion Based Structural Dynamics Simulation Using Mobile Devices

Due to the nature of real-world problems in civil engineering, students have had limited hands-on experiences in structural dynamics classes. To address this challenge, this paper aims to bring real-world problems in structural dynamics into classrooms through a new interactive learning tool that promotes physical interaction among students and enhances their engagement in classrooms. The main contribution is to develop and test a new interactive computing system that simulates structural dynamics by integrating a dynamic model of a structure with multimodal sensory data obtained from mobile devices. This framework involves integrating multiple physical components, estimating students’ motions, applying these motions as inputs to a structural model for structural dynamics, and providing students with an interactive response to observe how a given structure behaves. The mobile devices will capture dynamic movements of the students in real-time and take them as inputs to the dynamic model of the structure, which will virtually simulate structural dynamics affected by moving players. Each component of synchronizing the dynamic analysis with motion sensing is tested through case studies. The experimental results promise the potential to enable complex theoretical knowledge in structural dynamics to be more approachable, leading to more in-depth learning and memorable educational experiences in classrooms

Construction Disputes and Associated Contractual Knowledge Discovery Using Unstructured Text-Heavy Data: Legal Cases in the United Kingdom

Construction disputes are one of the main challenges to successful construction projects. Most construction parties experience claims—and even worse, disputes—which are costly and time-consuming to resolve. Lessons learned from past failure cases can help reduce potential future risk factors that likely lead to disputes. In particular, case law, which has been accumulated from the past, is valuable information, providing useful insights to prepare for future disputes. However, few efforts have been made to discover legal knowledge using a large scale of case laws in the construction field. The aim of this paper is to enhance understanding of the multifaceted legal issues surrounding construction adjudication using large amounts of accumulated construction legal cases. This goal is achieved by exploring dispute-related contract terms and conditions that affect judicial decisions based on their verdicts. This study builds on text mining methods to examine what type of contract conditions are frequently referenced in the final decision of each dispute. Various text mining techniques are leveraged for knowledge discovery (i.e., analyzing frequent terms, discovering pairwise correlations, and identifying potential topics) in text-heavy data. The findings show that (1) similar patterns of disputes have occurred repeatedly in construction-related legal cases and (2) the discovered dispute topics indicate that mutually agreed upon contract terms and conditions are import in dispute resolution

Construction policymaking: With an example of singaporean government`s policy to diffuse prefabrication to private sector

For construction policies to be effective and efficient, an integrated and systematic approach, with which the effectiveness of alternative policies can be examined in advance by anticipating the private sector`s response and subsequent changes in the industry environment is needed. As an effort to address this issue, a model-based approach is presented for construction policymaking and an example of a prefabrication policy in Singapore is given. In this study, the policy models developed using system dynamics simulate the performance of the Singapore construction industry and examine the effectiveness of alternative prefabrication policies in a qualitative and quantitative manner. Having obtained policy implications from the model structures and simulation results, policy initiatives are suggested along with discussions on the associated potential risks. Based on the research findings, we could conclude that a model-based approach is helpful in developing an effective construction policies by providing policymakers in a government or corporate with an integrated view on the policy application process and a tool to examine alternative policies in a systematic manner.Lyneis JM, 2001, SYST DYNAM REV, V17, P237Moxnes E, 2000, SYST DYNAM REV, V16, P325HO S, 2000, THESIS NATL U SINGAPSTERMAN J, 2000, BUSINESS DYNAMICS SY, P191TAN S, 2000, THESIS NATL U SINGAP, P157*VTT, 2000, WELLB CONSTR FINLRitchie-Dunham JL, 1999, SYST DYNAM REV, V15, P119*CMC, 1999, CONSTR 21Slaughter ES, 1998, J CONSTR ENG M ASCE, V124, P226Royston G, 1998, EUR J OPER RES, V105, P267GROAK S, 1996, INNOVATIONS JAPANESEKWAK S, 1995, THESIS MIT CAMBRIDGE, P34*BCA, 1994, CONSTR EC REPLIM K, 1993, THESIS NATL U SINGAP, P59EISENHARDT KM, 1992, STRATEGIC MANAGE J, V13, P17TAN W, 1982, THESIS NATL U SINGAP, P121CYERT RM, 1963, BEHAV THEORY FIRMFORRESTER J, 1961, IND DYNAMICS

Capturing Environmental Distress of Pedestrians Using Multimodal Data: The Interplay of Biosignals and Image-Based Data

Urban built environments often include many negative stimuli (e.g., unleashed dogs, dead animals, litter, graffiti, abandoned vehicles) that are linked with stress symptomatology among urban populations. Biosignals (e.g., electrodermal activity, gait patterns, and blood volume pulse) can help assess pedestrian distress levels induced by negative environmental stimuli by overcoming the measurement limitations of traditional self-reporting methods and field observations. Despite their potential, biosignals from naturalistic outdoor environments are often contaminated by uncontrollable extraneous factors (e.g., movement artifacts, physiological reactivity due to unintended stimuli, and individual variability). Thus, more quantitative evidence and novel methodological approaches are required to accurately capture pedestrian environmental distress resulting from negative environmental stimuli. In this context, we investigate the interplay between pedestrians' biosignal data and image-based data (built environment feature information and perceptual distress levels identified from images) in a machine learning model. Results from the statistical model estimated with the biosignal data demonstrated significant physiological responses to the negative environmental stimuli. The use of the features from image-based data increased the prediction accuracy of the computational model. This method can be applied to geospatial intelligence, further advancing built environmental assessments and evidencebased approaches to promote walking and walkable communities. (C) 2021 American Society of Civil Engineers.N