Work

A Knowledge-Augmented Two-Stage Workflow for Architectural Concept-to-Massing Generation and Evaluation

Year: 2026

Type: article

Abstract: Large language models (LLMs) and diffusion-based image generators can rapidly produce architectural ideas and imagery, yet translating conceptual narratives into massing composition is often implicit and difficult to reproduce. In this paper, we present a knowledge-augmented two-stage workflow for architectural concept-to-massing generation and evaluation. The outputs are represented as axonometric massing proxy images, which serve as 2D visual proxies for early-stage massing refinement rather than editable 3D models. The workflow integrates a prototype library and Knowledge Graph (KG) routing to map narrative cues into executable strategy and operation tokens and compile stage-specific prompts. Stage 1 produces structural concept sketches emphasizing legible composition, while Stage 2 generates axonometric massing proxy images conditioned on Stage 1 sketches to stabilize composition across candidates. Under a fixed sampling budget, candidates are ranked using a Rubric-based scoring protocol with Top-K selection, and evaluation signals can be written back to update prompt compilation iteratively. Across diverse project briefs, ablation studies demonstrate that knowledge augmentation improves constraint compliance and composition readability while maintaining controlled diversity for early exploration. We report expert ratings with inter-rater agreement and paired statistical tests to support reproducible comparisons.

Source: Buildings

DOI: 10.3390/buildings16061265

Authors: Shangci Sun, Yao Fu

Institutions: Architectural Association School of Architecture, Shenyang Jianzhu University

Language: English

Topic: Architecture and Computational Design

Subfield: Architecture

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: National Natural Science Foundation of China