ML Journal data
Nigeria, like many nations in the world, is embattled by a housing shortage. It has a housing deficit running to 20 million. There has been a proposition that Off-Site Construction (OSC), because of its speed of erection, can help in combating the housing shortage. however, for it to be adopted in society, some factors have to be considered. what then are these factors and which of them are most critical to successfully adopting OSC within the Nigerian context? This work is researched by utilizing prediction capabilities for OSC adoption in Nigeria. Data was collected through questionnaires from industry players within the Nigerian construction industry (Boothman et al., 2014.; Brannen & Moss, 2012). First, literature relating to OSC and its adoption was sourced through scholarly search engines like Google Scholar and Scopus (Almalki, 2016; O'Neill & Organ, 2016). The literature survey used key search words and phrases such as adoption, Design for Manufacture and Assembly, Offsite Construction and Prefab Constructions, and key authors' names. Specifically, research published on the subject between 2000 to 2021 were considered. A review of these papers informed the potential critical factors responsible for low DfMA adoption in Nigeria. The questionnaire was developed in Microsoft form. Using a purposeful sampling technique (Palinkas et al., 2015), the form link was administered to relevant key players in the Nigerian construction industry through e-mails and social media platforms for responses. The targeted responders were architects, civil/structural engineers, electrical engineers, mechanical engineers, building engineers, town/urban planners, quantity surveyors, contractors, academics, real estate investors, and developers. They were considered because they are thought to be actively involved in everyday construction processes in the country and involved in making and taking decisions bordering around the choice of building materials to be used on projects. The research outcome identifies seven (7) best performing algorithms: Decision Tree, Random Forest, K-Nearest Neighbour, Extra-Trees, AdaBoost, Support Vector Machine, and Artificial Neural Network. It also reported finance, awareness, use of Building Information Modeling (BIM), attitude and belief in OSC as the main influencing factors. Availability of expertise knowledge, favourable exchange rate and skilled personnel as other underlining influencing factors. It was concluded that with detailed attention paid to the identified factors, OSC usage could find its footing in Nigeria and, consequently, Africa. The models can also serve as a template for other regions where OSC adoption is being considered.
Steps to reproduce
Find out the problem Frame a topic Research on the topic via extant literature Deduce potential drivers and barriers Review with colleagues/supervisors Develop questionnaire Pilot the questionnaire Administer the questionnaire Collate response Analyze data Split, train and test data with algorithms Draw up inferences