Transforming an Adjacency Graph into Dimensioned Floorplan Layouts

Abstract

In recent times, researchers have proposed several approaches for building floorplans using parametric/generative design, shape grammars, machine learning, AI, etc. This paper aims to demonstrate a mathematical approach for the automated generation of floorplan layouts. Mathematical formulations warrant the fulfilment of all input user constraints, unlike the learning‐based methods present in the literature. Moreover, the algorithms illustrated in this paper are robust, scalable and highly efficient, generating thousands of floorplans in a few milliseconds. We present G2PLAN, a software based on graph‐theoretic and linear optimization techniques, that generates all topologically distinct floorplans with different boundary rooms in linear time for given adjacency and dimensional constraints. G2PLAN builds on the work of GPLAN and offers solutions to a wider range of adjacency relations (one‐connected, non‐triangulated graphs) and better dimensioning customizability. It also generates a catalogue of dimensionless as well as dimensioned floorplans satisfying user requirements.

CAADRIA 2023: HUMAN-CENTRIC - Volume 2

“…The first step involves the formation of multiple encoded matrices for a given graph. Using G2PLAN developed by (Bisht et al, 2022), numerous RFPs can be generated for the given graph. Then multiple encoded matrices are formed from the RFPs.…”

“…Upasani et al (Upasani et al, 2020) used graph-theoretic tools and linear optimisation to introduce dimensions to a rectangular floorplan. In 2022, Bisht et al (Bisht et al, 2022) presented a prototype G2PLAN, it uses graph-theoretical algorithms and optimisation approaches to generate dimensioned floorplan that satisfy given adjacency and size constraints. G2PLAN generates multiple floorplans satisfying the adjacency relations.…”

Congruence Mapping of the Activity Flows Allocated in Built Environments: A Pilot Application of Under-Development Software in an Emergency-Care Service

“…However, a realistic simulation requires a balance of probability distributions and an accurate and meaningful input dataset [28,31], which can be obtained through sensors [9]; georeferencing; a global positioning system (GPS), which is used to create hourly routine snapshots of all tagged individuals [12]; and so on. Different models have been applied in order to analyze the mapping of the workflow and the allocation of activities in built environments [12,27,32,33], as well as for other applications, such as generating floorplan layouts [32,34,35], lighting simulations [36,37], analyses of visual quality [38], behavior prediction [39], determining building envelope costs [40], etc. These simulation models mainly support the design phase when the design demands have been identified and the functional requirements have already been defined.…”

“…In order to reduce the complexity of the built environment, it is possible to discretize the geometry of the spaces of a given layout in a topological model, which is a graph with spaces as vertex attributes [27,34,46,48]. With the graph matrix of the spatial arrangement, it is possible to relate the dependencies with other matrices through a multi-domain matrix (MDM) [45,49].…”

A hypergraph model shows the carbon reduction potential of effective space use in housing

“…Most current implementations are linked to financial costing models, evaluating multiple ways to divide a building footprint into a desired number of apartment units [11], [12], [13]. Current approaches for within-unit room divisions are an active area of computer graphics research but are not currently used in the architecture industry due to a wide range of limitations: Being only able to represent rectangular [14] or orthogonal boundary conditions [15], or responding to only either topological or spatial or boundary constraints [16], [17], [18], [19], [20]. On a technical level, ML-based models create neural networks that relate the geometric graph structures from room walls to an adjacency graph (vector [21], [22] or pixel based [23]) or use reinforcement learning to subdivide a space [24].…”