Vivian Loftness

Vivian Loftness


Research Focus & Metrics

Academic Career Length

17Years (2010-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 June 2010 in Environment and Planning B: Planning and Design
Verified
Journal Article
Closed access
Ying HuaYing Hua
Ying Hua
VLVivian Loftness
Vivian Loftness
RKRobert Kraut
Robert Kraut
KPKevin M Powell
Kevin M Powell
The value of effective collaboration has become increasingly critical for organizational performance and agility. Along with technical and managerial strategies, the workspace spatial environment needs to be recognized and studied for its impact on collaboration and interactive behavior at work. Most spatial parameters studied in the workplace research literature are workstation-scale characteristics. However, these may not sufficiently describe the variety of shared spaces in which collaborative work and interactions take place. Based on a two-year multiple-site field study of workplace settings, this paper explores the space typology of a wide spectrum of formal and informal collaborative spaces, and it introduces a new set of layout-scale quantitative indices to describe the amount and distribution of collaborative spaces in a workplace. This research tested layout-scale spatial variables and compared them with workstation-scale variables in order to determine how well these variables predict the occupants' perception of the support from their workplace spatial environment for collaborative work and the distractions from others' interactive behavior in the work environment. The design implications of the findings are explored, and future research directions are identified.

21 August 2025 in Buildings
Verified
Journal Article
Open access
PDF file
ZLZhuorui NA Li
Zhuorui NA Li
JTJinzhao Tian
Jinzhao Tian
GJGuanzhou Ji
Guanzhou Ji
TCTiffany Cheng
Tiffany Cheng
VLVivian Loftness
Vivian Loftness
Xu HanXu Han
Xu Han
As people spend most of their time indoors, the quality of the indoor lighting environment plays a crucial role in occupant health, mood, and productivity. While modern glazed curtain walls improve daylighting potential, they also heighten the risks of glare and associated solar heat gains that may result in occupant discomfort and overheating. To continuously ensure visual comfort while providing shading, kinetic responsive facades controlled by sensors and actuators can change the angles of the elements. Conventional control methods for shading devices mainly involve the unified control of each element. However, as each element of the kinetic responsive facade can be controlled independently, the number of potential control actions increases exponentially with the number of facade elements and possible angles. Traditional rule-based methods are challenging for handling this multi-objective high-dimensional control problem. This paper introduces a novel self-learning, real-time reinforcement learning (RL) controller that can interact with the environment to find a globally optimal control solution for each element in kinetic responsive facades, thereby meeting visual quality and shading goals. The configuration and workflow of the proposed RL controller are introduced and tested vertically, diagonally, and radially folding responsive facades. The results demonstrate that the proposed RL controller effectively maintains horizontal and vertical illuminance, with 72.92% of test points in occupied spaces falling within the defined comfort range. Additionally, it keeps the daylight glare probability (DGP) below 0.35, a level generally considered imperceptible.