There is a recognized need for accurate estimation of construction duration of buildings.rnUncertainties in duration estimation hindered a problem to the construction sector. Withrnthis regard, approximate estimation of the various phases of building constructionrnindicated to minimize the uncertainty in duration estimation. The structural frameworkrnconstruction phase covers nearly half of the building project duration. Further, also thisrnphase almost always present in the critical path of project schedule. With this regard,rnaccurate and early prediction of structural frameworks duration can help mobilize andrnmanage resources effectively. Thus, this study provides parametric models that ease thisrnneed. rnCase study samples of 41 apartment residential buildings constructed and completedrnbetween a period from 2010 and 2019 were collected using structured questionnaire. 33 ofrnthe sample used to develop duration prediction models using linear regression analysis,rnwhile the remaining 8 samples used to validate developed models. rnIn order to develop mathematical models first, the interrelationship that exist betweenrnstructural works duration influencing factors; project scope factors and quantity of workrnitems investigated. Project scope factors; total gross floor area, and typical floor arearnshowed a statistically significant moderate strength positive linear relation. Similar linearrnrelations were also observed with quantity of work items; quantity of excavation,rnformwork, reinforcing bar, and quantity of concrete. rnSimple and multiple linear regression analysis carried out resulted in models with lowrncoefficient of determination. But, best-fit models were developed, by splitting the modelrnbuilding data set into small-size and large-size projects based on total gross floor area andrnremoving outliers. More than 85 percent of significant variance in structural frame workrnduration by selected project scope and quantity of work item predictor variables wasrnaccounted by best-fit models. rnThe present study put efforts towards enhancing our understanding of relationshipsrnexisting between selected predictor variables and structural works construction duration.