@article{abdirad_mathur_2021, title={Artificial intelligence for BIM content management and delivery: Case study of association rule mining for construction detailing}, volume={50}, ISSN={["1873-5320"]}, DOI={10.1016/j.aei.2021.101414}, abstractNote={The proliferation of Building Information Modeling (BIM) applications, in tandem with the extensive variation of building products, pose new demands on design and engineering firms to efficiently manage and reuse BIM content (i.e., data-rich parametric model objects and assembly details). Tasks such as classifying BIM objects, indexing them with meta-data (e.g., category), and searching digital libraries to load objects into models still plague practice with inefficient manual workflows. This research aims to improve the productivity of BIM content management and retrieval by developing an AI-backed BIM content recommender system. Using data from a case-study firm, this research extracted content from over 30,000 technical BIM views (e.g., plans, sections, details) in historical projects to build an unsupervised machine-learning prototype with association rule mining. This prototype explicated the strength of relationships among co-occurring BIM objects. Using this prototype as the backbone AI-engine in live BIM sessions, this research developed a context-aware recommender system that dynamically provides BIM users with a set of objects associable with their modeling context (e.g., type of view, existing objects in the model) and human–computer interactions (e.g., objects selected by the user). By mining association data from hundreds of historical projects, this development marks a departure from the existing prototypes that rely on explicit coding, recurring user input, or subjective ratings to recommend BIM content to users. The simulation and experimental implementation of this recommender system yielded high efficacy in predicting content needs and achieved significant savings in the time spent on conventional BIM workflows.}, journal={ADVANCED ENGINEERING INFORMATICS}, author={Abdirad, Hamid and Mathur, Pegah}, year={2021}, month={Oct} }
 @article{salamati_mathur_kamyabjou_taghizade_2020, title={Daylight performance analysis of TiO2@W-VO2 thermochromic smart glazing in office buildings}, volume={186}, ISSN={["1873-684X"]}, DOI={10.1016/j.buildenv.2020.107351}, abstractNote={Thermochromic glazing (TC) is able to modulate the solar radiation transmittance through windows in response to temperature, passively. Apart from the significant role of TC windows in thermal energy conservation in buildings, they affect the indoor daylight performance due to their lower visible transmittance rate and tinted appearance. In this paper, we carried out a comprehensive study on the impact of [email protected] thermochromic glazing, fabricated by the authors, on indoor daylighting performance of a typical office room. The spectrophotometry test on the fabricated TC glass shows a significant modulation ability in near-infrared wavelengths. Based on the measured optical transmittance, daylight behaviors of the glazing were elaborated in terms of visual comfort, color quality of the transmitted light, non-visual daylight availability, and artificial lighting load, using computer simulation methods and numerical calculations. Finally, the paper offers an interactive approach between material development and fabrication methods on one hand, and holistic thermal and daylight analysis of the product on the other hand. This approach optimizes the physical properties of a TC glazing in tradeoff between solar modulation ability, visible transmittance, and color appearance.}, journal={BUILDING AND ENVIRONMENT}, author={Salamati, Mohammad and Mathur, Pegah and Kamyabjou, Ghazal and Taghizade, Katayoun}, year={2020}, month={Dec} }