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A New Interoperability Framework for Data-Driven Building Performance Simulation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A New Interoperability Framework for Data-Driven Building Performance Simulation./
作者:	Han, Jung Min
面頁冊數:	1 online resource (164 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-03, Section: B.
Contained By:	Dissertations Abstracts International84-03B.
標題:	Architectural engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29210611click for full text (PQDT)
ISBN:	9798841745600

A New Interoperability Framework for Data-Driven Building Performance Simulation.
Han, Jung Min

A New Interoperability Framework for Data-Driven Building Performance Simulation. - 1 online resource (164 pages)

Source: Dissertations Abstracts International, Volume: 84-03, Section: B.

Thesis (D.Des.)--Harvard University, 2022.

Includes bibliographical references

Machine learning (ML) and deep learning (DL) have become more prominent in the building, architecture, and construction industries. One area ideally suited to exploit this powerful new technology is building performance simulation (BPS) for sustainable building design. Physics-based models have traditionally been used to estimate the energy flow, air movement, and heat balance of buildings. The algorithms behind physics-based models, however, involve solving complex differential equations that require many assumptions, significant computational power, and a considerable amount of time to output predictions. With the advent of DL, which can handle large amounts of computation in a short period of time, data-driven models for predicting the physical properties of buildings are becoming increasingly popular due to their simplicity and efficiency. As such, artificial neural networks (ANNs) with measured or simulated data for environmental analysis are likely to be a more feasible option for designers during the early design phase. To train ANN models, 3D data is an asset to computer vision because they provide rich information about the geometry and the related environment. Depending on the 3D data representation considered, different challenges may emerge when using trained ANN models. Hence, an interoperability framework is required for converting building geometries and environment-related information into relevant 3D matrices for model training and utilization. However, to date there has been no research on this topic in the BPS field; thus, this research proposes a new data interoperability framework for ANN models with 3D buildings serving as inputs. The framework has been subjected to a trial investigation using several ANN modeling studies on radiation and airflow simulation. The result is a comprehensive process map that includes the BPS requirement for ANN modeling, related subprocesses (i.e., building geometry and environmental levels), specific rules and methods for modeling, and processing of input and output data. To accomplish this, data exchangers for the ANN models, geometry representation tool (GRT), and BIM specification tool (BST) were introduced and developed as computational tools. The comprehensive framework has been validated using the developed case studies, demonstrating its applicability for different Computer-aided design tools (i.e., Rhinoceros and Revit) and ANN models (i.e., radiation and airflow) and illustrating the future capacity of integrated ANNs to serve as a tool for use in BPS and early-stage modeling.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798841745600Subjects--Topical Terms:

3174102
Architectural engineering.
Subjects--Index Terms:

3D convolutional neural networksIndex Terms--Genre/Form:

542853
Electronic books.

A New Interoperability Framework for Data-Driven Building Performance Simulation.
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