Cite this article
Rahimifard, R. (2025) ‘Innovative method for designing architectural plans and constructing prefabricated structural components using digital design solutions’, Architecture Papers of the Faculty of Architecture and Design STU, 30(3), pp. 15-22. https://www.doi.org/10.2478/alfa-2025-0014
SUMMARY
This study introduces a novel and comprehensive computational methodology for designing architectural floor plans and constructing prefabricated structural components through digital design solutions. The research is specifically focused on the application of this methodology to modular high-rise residential buildings, which represent a critical area of need in contemporary urban development. Amidst the growing complexity of urban housing and increasing construction demands, the proposed approach addresses inefficiencies in traditional workflows by integrating algorithmic design, adjacency validation, and evolutionary optimisation into a unified system.
The methodology begins with a thorough critical review of modular construction practices to identify systemic limitations and opportunities for innovation. It then employs parametric modelling to define modular spatial units and uses algorithmic tools to generate and filter diverse layout alternatives. The core of the system is a multi-objective optimisation algorithm (SPEA-2), implemented through the Octopus plugin in the Grasshopper environment, which evaluates spatial efficiency, adjacency relationships, and compactness to determine optimal configurations.
Unlike catalogue-based approaches, the system allows for dynamic, rule-based layout generation, enabling designers to customise spaces based on user preferences, functional requirements, site conditions, and environmental constraints. The system’s compatibility with BIM platforms such as Autodesk Revit (via Dynamo) further enhances its adaptability and potential integration into real-world architectural workflows.
Additionally, the methodology facilitates material savings and construction efficiency through reduced plan footprint and early identification of viable configurations. Case studies—including Nakagin Capsule Tower, Penda Tower, and Howe’s modular prototype—inform the algorithm’s development by demonstrating both precedents and limitations of prior modular approaches. The framework was further validated by applying it to a prototype modular tower project, where it proved capable of producing optimised floor plan layouts within seconds per iteration.
Beyond performance gains, the proposed system promotes design flexibility, scalability across building types, and responsiveness to evolving urban conditions. By embedding intelligence and adaptability into the early design phase, this research contributes to a new paradigm in computational architecture—one that bridges the gap between conceptual digital modelling and practical, prefabricated construction.
This study further explores the integration of advanced computational strategies to enhance architectural design and prefabrication processes, emphasising the need for innovative solutions in the rapidly evolving construction industry. It highlights the increasing demand for efficient, sustainable, and adaptable design frameworks capable of addressing complex spatial, functional, and environmental challenges in urban settings. Recognising the limitations of conventional modular construction methods—often constrained by rigid catalogues and lack of customisation—this research introduces a flexible, data-driven system that leverages parametric and algorithmic design tools to optimise building layouts dynamically.
The proposed methodology employs a systematic approach, starting with a comprehensive analysis of existing modular construction techniques, identifying bottlenecks related to spatial inefficiency, limited adaptability, and lack of integration between design and fabrication stages. This diagnostic phase informs the development of an algorithmic model that uses spatial adjacency matrices, performance criteria, and multi-objective evolutionary algorithms to generate optimal floor plans. The use of the Strength Pareto Evolutionary Algorithm 2 (SPEA-2) allows for balancing competing design goals such as maximising usable floor area, ensuring functional adjacency, and minimising construction complexity.
A distinctive feature of this approach is its capacity to produce design alternatives that are not only technically optimised but also highly customisable according to contextual parameters such as site constraints, user requirements, and environmental conditions. This adaptability is achieved through parametric inputs that can be adjusted in real-time, fostering a participatory design process where stakeholders can interact with and influence the outcomes. Integration with BIM software platforms further streamlines the transition from digital design to physical production, facilitating automated data exchange and reducing the potential for errors during fabrication.
The research also addresses potential challenges related to implementation, including computational complexity, data management, and user interface design, acknowledging these issues as critical factors influencing the system’s applicability in professional workflows and long-term adoption. By embedding this computational framework into the architectural design process, the study envisions a future in which architectural creativity is shaped and enhanced through algorithmic precision and automation, contributing to the development of higher-quality, cost-effective, and responsive built environments.
In summary, this study presents a transformative computational design methodology that bridges conceptual design and prefabricated construction. It combines critical analysis, parametric modelling, evolutionary optimisation, and BIM integration to produce modular building solutions that are efficient, flexible, and sustainable. This contribution paves the way for more intelligent, adaptive architectural systems that meet the demands of modern urban living while promoting innovation in construction technology and practice.