Interactive Design by Integrating a Large Pre-Trained Language Model and Building Information Modeling

• URL: http://arxiv.org/abs/2306.14165v1
• Date: Sun, 25 Jun 2023 08:18:03 GMT
• Title: Interactive Design by Integrating a Large Pre-Trained Language Model and Building Information Modeling
• Authors: Suhyung Jang and Ghang Lee
• Abstract summary: This study explores the potential of generative artificial intelligence (AI) models, specifically OpenAI's generative pre-trained transformer (GPT) series. Our findings demonstrate the effectiveness of state-of-the-art language models in facilitating dynamic collaboration between architects and AI systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This study explores the potential of generative artificial intelligence (AI) models, specifically OpenAI's generative pre-trained transformer (GPT) series, when integrated with building information modeling (BIM) tools as an interactive design assistant for architectural design. The research involves the development and implementation of three key components: 1) BIM2XML, a component that translates BIM data into extensible markup language (XML) format; 2) Generative AI-enabled Interactive Architectural design (GAIA), a component that refines the input design in XML by identifying designer intent, relevant objects, and their attributes, using pre-trained language models; and 3) XML2BIM, a component that converts AI-generated XML data back into a BIM tool. This study validated the proposed approach through a case study involving design detailing, using the GPT series and Revit. Our findings demonstrate the effectiveness of state-of-the-art language models in facilitating dynamic collaboration between architects and AI systems, highlighting the potential for further advancements.

Related papers

• Generative AI Agent for Next-Generation MIMO Design: Fundamentals, Challenges, and Vision [76.4345564864002]
  Next-generation multiple input multiple output (MIMO) is expected to be intelligent and scalable. We propose the concept of the generative AI agent, which is capable of generating tailored and specialized contents. We present two compelling case studies that demonstrate the effectiveness of leveraging the generative AI agent for performance analysis.
  arXiv  Detail & Related papers  (2024-04-13T02:39:36Z)
• Intelligent Canvas: Enabling Design-Like Exploratory Visual Data Analysis with Generative AI through Rapid Prototyping, Iteration and Curation [4.7576896880194495]
  We introduce a "design-like" intelligent canvas environment integrating generative AI into data analysis. Our dual contributions include the integration of generative AI components into a canvas interface, and empirical findings from a user study.
  arXiv  Detail & Related papers  (2024-02-13T21:33:12Z)
• Learning to Extract Structured Entities Using Language Models [52.281701191329]
  Recent advances in machine learning have significantly impacted the field of information extraction. We reformulate the task to be entity-centric, enabling the use of diverse metrics that can provide more insights. We introduce a new model that harnesses the power of Language Models (LMs) for enhanced effectiveness and efficiency.
  arXiv  Detail & Related papers  (2024-02-06T22:15:09Z)
• Generative Structural Design Integrating BIM and Diffusion Model [4.619347136761891]
  This study introduces building information modeling ( BIM) into intelligent structural design and establishes a structural design pipeline integrating BIM and generative AI. In terms of generation framework, inspired by the process of human drawing, a novel 2-stage generation framework is proposed to reduce the generation difficulty for AI models. In terms of generative AI tools adopted, diffusion models (DMs) are introduced to replace widely used generative adversarial network (GAN)-based models, and a novel physics-based conditional diffusion model (PCDM) is proposed to consider different design prerequisites.
  arXiv  Detail & Related papers  (2023-11-07T15:05:19Z)
• Experiments on Generative AI-Powered Parametric Modeling and BIM for Architectural Design [4.710049212041078]
  The study experiments with the potential of ChatGPT and generative AI in 3D architectural design. The framework provides architects with an intuitive and powerful method to convey design intent.
  arXiv  Detail & Related papers  (2023-08-01T01:51:59Z)
• A Systematic Survey in Geometric Deep Learning for Structure-based Drug Design [63.30166298698985]
  Structure-based drug design (SBDD) utilizes the three-dimensional geometry of proteins to identify potential drug candidates. Recent developments in geometric deep learning, focusing on the integration and processing of 3D geometric data, have greatly advanced the field of structure-based drug design.
  arXiv  Detail & Related papers  (2023-06-20T14:21:58Z)
• Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions [0.0]
  We review advances in AI-driven materials-by-design and their applications to energetic materials. We evaluate methods in the literature in terms of their capacity to learn from a small/limited number of data. We suggest a few promising future research directions for EM materials-by-design, such as meta-learning, active learning, Bayesian learning, and semi-/weakly-supervised learning.
  arXiv  Detail & Related papers  (2022-11-15T14:41:11Z)
• SIM-Trans: Structure Information Modeling Transformer for Fine-grained Visual Categorization [59.732036564862796]
  We propose the Structure Information Modeling Transformer (SIM-Trans) to incorporate object structure information into transformer for enhancing discriminative representation learning. The proposed two modules are light-weighted and can be plugged into any transformer network and trained end-to-end easily. Experiments and analyses demonstrate that the proposed SIM-Trans achieves state-of-the-art performance on fine-grained visual categorization benchmarks.
  arXiv  Detail & Related papers  (2022-08-31T03:00:07Z)
• Driving Digital Engineering Integration and Interoperability Through Semantic Integration of Models with Ontologies [0.0]
  This paper introduces the Digital Engineering Framework for Integration and. DEFII, for incorporating SWT into engineering design and analysis tasks. The framework includes three notional interfaces for interacting with ontology-aligned data. Use of the framework results in a tool-agnostic authoritative source of truth spanning the entire project, system, or mission.
  arXiv  Detail & Related papers  (2022-06-08T14:58:09Z)
• Revealing the Invisible with Model and Data Shrinking for Composite-database Micro-expression Recognition [49.463864096615254]
  We analyze the influence of learning complexity, including the input complexity and model complexity. We propose a recurrent convolutional network (RCN) to explore the shallower-architecture and lower-resolution input data. We develop three parameter-free modules to integrate with RCN without increasing any learnable parameters.
  arXiv  Detail & Related papers  (2020-06-17T06:19:24Z)
• A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment Analysis [73.74885246830611]
  We propose a novel dependency syntactic knowledge augmented interactive architecture with multi-task learning for end-to-end ABSA. This model is capable of fully exploiting the syntactic knowledge (dependency relations and types) by leveraging a well-designed Dependency Relation Embedded Graph Convolutional Network (DreGcn) Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-04-04T14:59:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.