Mass Customization
Mass Customizable Housing (MCH) is a program designed to democratically and interactively engage the ordinary end users in the process of making one’s ideal house. Each house is customized to fit individual family’s life style; each housing community is collectively planned and tailored to the need of co-op residents. It is a challenge to the currently accepted social code – only those with enough resources can afford to have architect designed (customized) dwellings, while the majorities have their individual life styles defied by homogeneous collective housing. It also aims to bring a revolution to industries related to housing constructions.
Mass Customizable House = Motherboard + Hot pluggable modules + Peripherals
Motherboard: Double-Walled Service Shell which houses structural bracing, HVAC, electrical and data wiring, mechanical plenum, plumbing pipes and so on in between of the double skin.
Hot-pluggable modules: Partitions; Kitchen unit; Bathroom fixtures; Storage units; Circulation elements (stair/ladder); Roof gardens…
Peripherals: available in wide and increasing varieties, such as Multimedia walls, Fitness equipment, Gardening kit, Children Playing gears …
Program database stores almost unlimited configurations for the motherboard, which are generated by computer and then carefully sifted through by predefined criteria – genetic algorithms.
Individual starts composing one’s ideal house from choosing a preferred motherboard based on his/her spatial need and available resources. Different life style will call for different quantity and arrangement of pluggable modules, as well as peripherals tailored to personal preferences.
Mass Customizable Housing = Individual Houses + Public spaces + Communal Services+ Circulation
MCH is easily adaptable to different topographical, environmental & social context. For each given site, designer could precisely control FAR, porosity, green space ratio & other program requirement through computation. With user input perimeters, the program will calculate and optimize the possible configurations – genetic algorithms.
