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[85] Running, Running, Stop Applying graph theory to pathfinding analysis to improve circulation efficiency in vertical high schools

Seed Paper Seed Paper (General / Unclassified) 2022

Paper Description extracted from PDF · intro excerpt · 135 words

Yu1 1University of New South Wales / 2TERROIR 1{s.rogers; b.doherty; n.gardner; m.haeusler; d.yu}@unsw.edu.au 2rubenach@terrior.com.au Understanding how people circulate in a space is crucial to effective spatial organisation. However, while many studies focus on wayfinding within environments such as train stations, there are few discussing multi-storey circulation and pathfinding within educational institutions. Vertical high schools prefer multiple stairways over elevators, and seek to devote the maximum floor area possible to learning spaces. These considerations impact a student’s ability to quickly traverse multiple floors. In this research, a computational tool is designed to simulate and assess circulation efficiency within a vertical high school with the goal of lightening stairwell congestion and decreasing classroom transit times. Using action research methodology, the research problem was formulated in cooperation with the industry partner, TERROIR, and solved in an iterative manner.

AuthorsScarlet Rogers and Tom Rubennach and Ben Doherty and Nicole Gardner and M. Hank Haeusler and K. Daniel Yu
Year2022
VenueProceedings of the International Conference on Education and Research in Computer Aided Architectural Design in Europe
DOI10.52842/CONF.ECAADE.2022.2.019
Source DatabaseSeed (CumInCAD)
Bridge-to-GNNCategory Seed
GNN ArchitectureNot yet extracted from PDF
Graph EncodingNot yet extracted from PDF
AEC TaskNot yet extracted from PDF
CohortPeak Adoption (2022)

Extracted Methodology heuristic — verify before citing

Implementation FrameworkGrasshopper / Rhino, Revit / Dynamo (heuristic — verify)
Primary MetricAccuracy (heuristic — verify)
Quality Assessment1/6 · rigor: Low ·
Baseline: ✓ (heuristic — verify)

Detected Technical Stack auto-detected from PDF · regex catalogue match

GNN architectures detectedKnowledge Graph Embedding (TransE/RotatE/DistMult)
Frameworks / librariesGrasshopper / Rhino · Revit / Dynamo
Quality (heuristic, 6-flag)0/6 · rigor: Low
Code/Data: —Benchmark: —Baseline: —CV/Split: —Ablation: —Reproducible: —

Lineage Families

None assigned

Enabled By / Precursors

None tracked

Extended By

None in corpus

Infrastructure Dependencies

BIM (auto-suggested) Grasshopper / Rhino (detected from PDF) Revit / Dynamo (detected from PDF)

Research Frontier Tags

graph-ml-aec

Part of the GML/GNN in AEC Systematic Review (PRISMA 2020) — 112 papers, 2020–2026