Yichen Mo

selected papers

1. CDAC 2025

   基于混合模型的可持续空间布局优化设计

   Mo, Yichen, Hua, Hao, and Li, Biao

   In Proceedings of 2025 Computational Design Symposium and the Annual Conference of Computational Design Academic Committee of Architectural Society of China, 2025

   Abs Bib Website

   针对建筑学中结构形式与功能空间协同设计的本源问题，本文提出一种基于混合模型的生成设计方法，旨在探索可持续与适变性的建筑解决方案。该方法将设计目标转化为可计算的数学问题：第一阶段，运用以演化模型为基础的 NSGA-III 算法，系统性地探索满足结构性能与美学目标的结构形态；第二阶段，在生成的结构基准上，引入混合整数二次规划（MIQP），将具体的功能需求（如面积、邻接关系等）转化为不等式约束，以生成高度适配的功能布局。实验结果表明，该方法能够有效实现结构与功能的协同优化，并具备良好的灵活性与扩展性，能够支持不同结构限定和功能拓扑下的空间快速重构。本文的核心贡献在于，它以严格的数学语言为“形式与空间”这一经典议题建立了可计算的生成模型，展示了算法驱动下的理性设计决策流程，有力地推动了建筑设计方法从经验主导向模型驱动的转变。

   @inproceedings{moMixedModel2025,
     abbr = {CDAC 2025},
     selected = {true},
     bibtex_show = {true},
     title = {基于混合模型的可持续空间布局优化设计},
     author = {Mo, Yichen and Hua, Hao and Li, Biao},
     booktitle = {Proceedings of 2025 Computational Design Symposium and the Annual Conference of Computational Design Academic Committee of Architectural Society of China},
     year = {2025},
     website = {https://www.archialgo.com/publications/2025-11-13-mixed-model-form-function}
   }

2. Journal

   ArchiWeb: A web platform for AI-driven early-stage architectural design

   Mo, Yichen, and Li, Biao

   Frontiers of Architectural Research, 2025(14)

   Abs Bib Website

   As society confronts increasingly complex demands and the growing need for carbon-neutral architecture, AI-driven design methodologies are evolving rapidly. However, the lack of a unified integration platform in the design process continues to hinder AI’s integration into real-world workflows. To address this challenge, we introduce ArchiWeb, a web-based platform specifically built to support AI-driven processes in early-stage architectural design. ArchiWeb transforms architectural representation and problem formulation by utilizing lightweight data protocols and a modular algorithmic network within an interactive web environment. Through its cloud-native, open-architecture framework, ArchiWeb enables deeper integration of AI technologies while accelerating the accumulation, sharing, and reuse of design knowledge across projects and disciplines. Ultimately, ArchiWeb aims to drive architectural design toward greater intelligence, efficiency, and sustainability―supporting the transition to data-informed, computationally enabled, and environmentally responsible design practices.

   @article{moArchiWeb2025,
     abbr = {Journal},
     selected = {true},
     bibtex_show = {true},
     title = {ArchiWeb: A web platform for AI-driven early-stage architectural design},
     author = {Mo, Yichen and Li, Biao},
     journal = {Frontiers of Architectural Research},
     volume = {14},
     pages = {1551-1566},
     year = {2025},
     doi = {10.1016/j.foar.2025.06.002},
     website = {https://www.archialgo.com/publications/2025-07-11-archiweb}
   }

3. Journal

   ArchIndex: 基于多源数据的城市街区检索引擎

   Mo, Yichen, Ludger, HOVESTADT, Li, Biao, and Tang, Peng

   新建筑, 2024(1):15-21

   Abs Bib PDF

   城市街区检索引擎是一种城市认知智能，通过整合和呈现多源城市数据，建立城市空间检索模型，从而简化城市设计前期的认知和调研。文章通过动态网页提供城市街区检索引擎ArchIndex，在可定期更新的空间关系数据库的基础上，采集街区实例数据，建立形态、功能、活力三个方面的向量化指标，用于城市街区的检索与可视化。最终方案实现了分布式运行部署，可对任意城市建立索引，用云端计算与网页应用，为城市形态学研究提供了易用的工具和新的方法。

   @article{MoArchIndex2024,
     abbr = {Journal},
     selected = {true},
     bibtex_show = {true},
     title = {ArchIndex: 基于多源数据的城市街区检索引擎},
     author = {Mo, Yichen and Ludger, HOVESTADT and Li, Biao and Tang, Peng},
     year = {2024},
     journal = {新建筑},
     volume = {1},
     number = {15-21},
     pdf = {archindex-na.pdf}
   }

4. Journal

   Case-Based Reasoning Strategy for Urban Morphological Generative Design: A Case Study of Block Scale

   Zhang, Baizhou,  Mo, Yichen, and Li, Biao

   South Architecture, 2023(01):09-18

   Bib PDF

   @article{zhangMorphological2023,
     abbr = {Journal},
     selected = {true},
     bibtex_show = {true},
     author = {Zhang, Baizhou and Mo, Yichen and Li, Biao},
     journal = {South Architecture},
     number = {09-18},
     title = {Case-Based Reasoning Strategy for Urban Morphological Generative Design: A Case Study of Block Scale},
     volume = {01},
     year = {2023},
     pdf = {morphological.pdf}
   }

5. CDAC 2022

   Research on Light Weight JSON based Exchange Format for Architectural Design

   Mo, Yichen, and Li, Biao

   In Proceedings of 2022 Computational Design Symposium and the Annual Conference of Computational Design Academic Committee of Architectural Society of China, 2022

   Abs Website

   ArchiJSON is a light weight web data exchange format for architectural design. It mainly work with ArchiWeb, which is an open source web-based 3D editor for exhibiting computational design.

6. Journal

   Automatic Generation of High-rise Residential Facades from the Perspective of Building Industrialisation

   Wu, Jiaqian, Li, Biao, and Mo, Yichen

   South Architecture, 2022(04):25-31

   Bib PDF

   @article{wuResidentialFacades2022,
     abbr = {Journal},
     selected = {true},
     bibtex_show = {true},
     author = {Wu, Jiaqian and Li, Biao and Mo, Yichen},
     journal = {South Architecture},
     number = {25-31},
     title = {Automatic Generation of High-rise Residential Facades from the Perspective of Building Industrialisation},
     volume = {04},
     year = {2022},
     pdf = {residential-facades.pdf}
   }

7. CUPUM 2021

   ArchIndex : A Web-based and Data-driven Retrieval System for City Blocks

   Mo, Yichen, Zhang, Baizhou, and Li, Biao

   In Proceedings of the 17th International Conference on Computational Urban Planning and Urban Management (CUPUM),, 2021

   PDF Website
8. CAADRIA 2021

   Archibase: A City-Scale Spatial Database for Architectural Research

   Mo, Yichen, Li, Biao, Wu, Jiaqian, and Tang, Peng

   In PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, Hong Kong, 29 March - 1 April 2021, pages 519–528, 2021(2)

   Abs Bib PDF Video

   The explosion of geolocation data and data-based algorithms has the potential to analyze sophisticated urban areas and foster a more robust urban model. To better collect and organize the city data, this paper introduces a city-scale spatial database called ArchiBase, built upon Java and web APIs of open source databases. With hierarchical, layered, and regularly-updated spatial data defined by relation table, ArchiBase allows indexing and geometric searching of the entire city and supports applications and extensions for different cities. This research is from a graduate urban design course aiming to renew Prato, an industrial city in Italy. ArchiBase first creates the base version of Prato from multiple data sources, then illustrates the usability and expandability through three simple applications. The use of ArchiBase can better interpret future cities and demonstrate the unparalleled opportunities of collaboration and remote work for urban researchers and designers.

   @inproceedings{moArchiBase2021,
     abbr = {CAADRIA 2021},
     selected = {true},
     bibtex_show = {true},
     title = {Archibase: {{A City-Scale Spatial Database}} for {{Architectural Research}}},
     author = {Mo, Yichen and Li, Biao and Wu, Jiaqian and Tang, Peng},
     booktitle = {PROJECTIONS - Proceedings of the 26th <strong>CAADRIA</strong> Conference - Volume 2, Hong Kong, 29 March - 1 April 2021, pages 519–528},
     volume = {2},
     pages = {519-528},
     year = {2021},
     pdf = {archibase.pdf},
     video = {https://caadria2021.org/session-2C#135}
   }