Content area

Abstract

As society confronts increasingly complex demands and the growing need for carbon-neutral architecture, Al-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.

Details

1009240
Business indexing term
Title
ArchiWeb: A web platform for AI-driven early-stage architectural design
Publication title
Volume
14
Issue
6
Pages
1551-1566
Number of pages
17
Publication year
2025
Publication date
Dec 2025
Publisher
KeAi Publishing Communications Ltd
Place of publication
Nanjing
Country of publication
China
Publication subject
ISSN
20952635
e-ISSN
20952643
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
ProQuest document ID
3276385698
Document URL
https://www.proquest.com/scholarly-journals/archiweb-web-platform-ai-driven-early-stage/docview/3276385698/se-2?accountid=208611
Copyright
© 2025. This work is published under http://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-11-29
Database
ProQuest One Academic