When: 14.08.2023 16:00
Where: Venture Lab Built Environment (Technical Universtiy of Munich, Stammgelände, Room 2345)
The convergence of Industry 4.0 demands, and the rapid advancement of artificial intelligence technologies presents unprecedented opportunities for the architecture, engineering, and construction (AEC) industry. Natural Language Processing (NLP) offers numerous tailor-made solutions for smart design and construction in the AEC domain. Among them, one crucial aspect is the characterization of user needs, which typically involves transforming unstructured user texts into structured information, facilitating mass customization within the realm of smart configuration. However, current approaches to understanding user needs usually rely on heuristic or traditional machine learning methods, resulting in significant errors and limited accuracies. This lecture will introduce a novel perspective on smart configuration for mass customization by incorporating language models into user needs understanding. Specifically, we begin by framing the user needs understanding task as a spoken language understanding task, addressing practical challenges through concrete methodologies and experimental designs. In this process, we identify two key roles for language models in smart configuration: serving as knowledge bases and functioning as backbone models. Additionally, we discuss the opportunities and challenges associated with employing language models in this domain.
Speaker:
Dr. Xiao LI joined the Department of Civil Engineering, The University of Hong Kong, as an assistant professor in December 2022. Before joining HKU, he was a Research Assistant Professor at HKPolyU (2021-2022), an RGC Postdoctoral Fellowship awardee at HKU (2020-2021). He was also a visiting scholar at the University of Cambridge. He has led six research projects (e.g., NSFC, GRF, HK-Germany Joint Research Scheme) as PI and has authored 40+ papers in peer-reviewed academic journals . He is a fellow of the SYLFF Association, a member of CIB and the American Society of Civil Engineers (ASCE), and guest editors of several leading journals in construction engineering and management. He held several international academic awards, e.g., CIC Innovation Award 2022, SYLFF Research Grant Award, Research Abroad Award, CRIOCM Outstanding Paper Award, CIB Sebestyén Future Leaders Award, ASCE Best Paper Award, and CIOB (HK) Outstanding Paper Award.
His previous research mainly contributes to the decentralized adaptive work packaging methodology for collaborative planning and control in industrialized construction. Firstly, he investigated graph-based work package generation mechanisms for complex products of industrialized construction.. Finally, he developed a blockchain-enabled smart work packaging system for crowd intelligence in collaborative planning and control.