Revisiting Core Housing Through Generative Design And Mass Customization An Alternative Housing Delivery Option For Addis Ababa

Core housing (CH) is one of the self-help incremental housing strategies in which the government provides rnan initial starter structure and then the resident takeover to achieve full housing incrementally. Around the rn1960s, following the critical challenges faced by third-world governments to deliver full modern housing, rna self-help incremental housing delivery option has been proposed and implemented in many countries, rnincluding Ethiopia. Regardless of some limitations, various studies and cases worldwide indicate that CHrnstrategies have advantages in alleviating housing shortages by combining governments effort with rnresidents’ self-help housing potential. However, the conventional CH strategy lacks density, rnstandardization, and scalability. The need for standardized speed of delivery on one hand and flexibility to rnaccommodate owners’ participation on the other poses is a unique challenge that requires an innovative rndesign and construction delivery strategy.rnGenerative Design (GD) uses the principles of nature’s evolutionary approach to design. It starts with rndesign goals and then explores the design solution space to generate optimized design solutions through rnselection, crossover, and mutation. On the other hand, Mass Customization (MC) combines two rncounteracting theories of mass production and customization to minimize cost and, at the same time, meet rnindividual needs. This study explores the potential use of GD and MC to upgrade CH strategy. rnTo achieve the study’s objective, GD methodology, which comprises three basic steps, is employed. The rnfirst step involves analyzing secondary data and contextual reviews to identify and define critical rncomputable parameters of CH. In the second step, parametric models are developed based on a multistory rnvertical expansion CH typology using Grasshopper in the Rhino 3D environment for the starter structure rnand unit expansion stages of CH. In the third step, an evolutionary algorithm engine ‘Wallacei’ is used to rngenerate, evaluate, and select design options. Furthermore, a case project is developed to demonstrate the rnresult of the experiment on a selected site in the inner city of Addis Ababa.rnThe GD experiments’ result indicates the potential use of MC and GD to create a CH strategy that is flexible rnto accommodate owners' participation and mass-producible for speed of delivery. However, certain rnlimitations are encountered in using an evolutionary solver in optimizing topological constraints for space rnlayout generation during the unit expansion stage. Thus, there is room for further development of this study rnand potential application strategies. Nonetheless, this study’s overall outcome indicated GD and MC’s rnpotential use in enhancing CH and its potential implementation as an alternative housing delivery strategyrnfor Addis Ababa

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