Abstract

Optimizing building space within technical, geographical, and technological constraints is essential for delivering affordable urban houses, especially in areas where land cost is the primary determinant of project feasibility. Historically, efforts have focused on reducing construction costs through efficient project management practices, but often, the emphasis has been on studying individual tools' capabilities rather than leveraging the combined benefits of multiple tools. In this study, precise project information management is achieved by synchronizing the competencies of building information modelling (BIM) and geographic information system (GIS). Multi-dimensional building data generated on a BIM platform are easily accessed, modified, integrated, and shared by all project stakeholders. This data is then digitally assimilated on an open-source GIS platform, namely quantum GIS (QGIS), where multiple buildings and other topographical layers are consolidated on a geographical canvas, to which various meaningful attributes are attached. Consequently, meaningful information can be derived through spatial and non-spatial query runs in QGIS. Building space optimization is achieved through a Python code utilizing genetic algorithm (GA) for an affordable housing project in a semi-congested urban area with 74 buildings of varied configurations tailored for economically weaker section (EWS) and low-income group (LIG) populations. This involved developing the code to distribute EWS and LIG buildings optimally across zones, enhancing spatial layout. Balancing optimal building heights and construction costs per square foot while considering constraints like maximum ground coverage and floor space index (FSI) was crucial. Utilizing synchronized GIS, BIM, and GA applications, adjustments were made to building heights without altering the horizontal layout, projecting significant cost savings of 41.5% and time savings of 16% while complying with regulatory requirements. The integration of BIM, GIS, and GA offers a powerful toolset for project optimization, allowing for tailored adjustments to meet specific project requirements and achieve both spatial and temporal efficiency.

Keyword

Affordable housing, Building information modelling, GIS, Genetic algorithm, Floor space index, Spatio-temporal, Cost reduction, Trade-off, Building height, Per square feet construction cost.

Cite this article

Parkar F, Magar R.Optimization of low-cost housing projects using BIM, GIS, and genetic algorithm. International Journal of Advanced Technology and Engineering Exploration. 2024;11(118):1217-1237. DOI:10.19101/IJATEE.2023.10102356

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