A Data-Driven Computational Framework for Intelligent Interior Space Optimization in Smart Environments
Year: 2026
Type: conference-paper
Abstract: The growing adoption of smart technologies in the built environment has opened up possibilities for data-informed design of interior spaces. Conventional interior design methods typically rely on subjective evaluations, which can be static and unresponsive to user preferences and environmental factors. This study presents a data-driven computational framework for intelligent interior space optimization within smart environments. The approach combines spatial information, environmental conditions, and user-related data to facilitate dynamic design and decision-making. A computational process is established, involving data input, system processing, scenario simulation, and visualisation. This system presents several spatial optimization scenarios that illustrate how environmental and user factors affect interior performance. The proposed framework illustrates the potential of intelligent systems to support spatial efficiency, user comfort, and transparent decision-making through conceptual scenario-based analysis. This research advances the convergence of interior architecture and intelligent systems by providing a framework that facilitates data-driven design, leading to more dynamic and adaptive interior spaces. The proposed approach is structured as a computational framework consisting of data input, processing, simulation, and decision-support modules.
Authors: Hanan AbuKarki, Islam A Alshafei
Institutions: Philadelphia University, Jerash University
Language: English
Subfield: Architecture
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7224850563