Recent advancements in AI research, particularly in spatial layout generation, highlight its capacity to enhance human creativity by swiftly providing architects with numerous alternatives during the pre-design phase. The complexity of architectural design data, characterized by multifaceted elements and varying representations, presents significant challenges in creating uniform and robust datasets. This study addresses this challenge by developing a robust training dataset specifically tailored for AI-driven spatial layout generation in architecture. An algorithm capable of extracting spatial relationship diagrams from raster-based floor plan images and converting them into vector-based data was introduced. Through extensive web crawling, a dataset comprising 10,000 data rows, categorized into 21 classes and three spatial relationship categories, was collected. When tested with the You-Only-Look-Once (YOLO) model, the detection rate was 99%, the mean average precision was 85%, and the MIoU was 74.2%. The development of this robust training dataset holds significant potential to advance knowledge-based artificial intelligence design automation studies, paving the way for further innovation in architectural design.

The rapid evolution of the metaverse is driving the development of new digital design tools that integrate Computer-Aided Design (CAD) and Building Information Modeling (BIM) technologies. Core technologies such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) are increasingly combined with BIM to enhance collaboration and innovation in design and construction workflows. However, current BIM–VR integration often remains limited to isolated tasks, lacking persistent, multi-user environments that support continuous project collaboration. This study proposes a BIM-based Virtual World (VW) framework that addresses these limitations by creating an immersive, real-time collaborative platform for the Architecture, Engineering, and Construction (AEC) industry. The system enables multi-user access to BIM data through avatars, supports direct interaction with 3D models and associated metadata, and maintains a persistent virtual environment that evolves alongside project development. Key functionalities include interactive design controls, real-time decision-making support, and integrated training capabilities. A prototype was developed using Unreal Engine and supporting technologies to validate the approach. The results demonstrate improved interdisciplinary collaboration, reduced information loss during design iteration, and enhanced stakeholder engagement. This research highlights the potential of BIM-based Virtual Worlds to transform AEC collaboration by fostering an open, scalable ecosystem that bridges immersive environments with data-driven design and construction processes.

In this study, we address the challenge of efficiently handling the maintenance and remodeling of buildings constructed post-1960s, lacking architectural drawings. The conventional approach involves manual measurements and data recording, followed by digital drawing creation. However, we leverage Fourth Industrial Revolution technologies to develop a deep learning-based automatic object classification system using point cloud data. We employ the FCAF3D network with multiscale cells, optimizing its configuration for classifying building components such as walls, floors, roofs, and other objects. While classifying walls, floors, and roofs using bounding boxes led to some boundary-related errors, the model performed well for objects with distinct shapes. Our approach emphasizes efficiency in the remodeling process rather than precise numerical calculations, reducing labor and improving architectural planning quality. While our dataset labeling strategy involved bounding boxes with limitations in numerical precision, future research could explore polygon-based labeling, minimizing loss of space and potentially yielding more meaningful results in classification. In summary, our technology aligns with the initial research objectives, and further investigations could enhance the methodology for even more accurate building object classification.

The Building Supervision of South Korea has been developed under the government′s desire to prevent poor construction and improve the quality of buildings to promote a safer life for the people. Although the building environment of today has been achieved through several improvements, the introduction of such technology is insufficient. Especially since building supervision is primarily carried out almost by manpower, in an era where numerous convergence technologies from the 4th industry are being utilized. Therefore, this study aimed to solve this inefficiency in the building supervision system by using the object detection technology of deep learning in the BIM environment. As a basic study to develop a smart supervision checking system that checks whether the information on the construction site matches the design information, the column ties were selected as a supervision item, and research was conducted. For this, we constructed the tie detection network and suggested an algorithm for information checking between the construction site and BIM environment. Through this, it was possible to confirm the possibility of practical supervision work and improve the efficiency of the work, and furthermore, to see the possibility of using convergence technology.

This study addresses the systematic optimization of Low-Rank Adaptation (LoRA) parameters for architectural knowledge integration in diffusion models, where existing AI research has provided limited guidance for establishing plausible parameter ranges in architectural massing applications. While diffusion models show increasing utilization in architectural design, general models lack domain-specific architectural knowledge, and previous studies have offered insufficient hyperparameter optimization frameworks for architectural massing studies—fundamental components for expressing architectural knowledge. This research establishes a comprehensive LoRA training framework specifically for architectural mass generation, systematically evaluating caption detail levels, optimizers, learning rates, schedulers, batch sizes, and training steps. Through analysis of 220 architectural mass images representing spatial transformation operations, the study recommends the following parameter settings: detailed captions, Adafactor optimizer, learning rate 0.0003, constant scheduler, and batch size 4, achieving significant improvements in prompt-to-output fidelity compared to baseline approaches. The contribution of this study is not in introducing a new algorithm, but in providing a systematic application of LoRA in the architectural domain, serving as a bridging milestone for both emerging architectural-AI researchers and advanced scholars. The findings provide practical guidelines for integrating AI technologies into architectural design workflows, while demonstrating how systematic parameter optimization can enhance the learning of architectural knowledge and support architects in early-stage massing and design decision-making.