Original scientific paper
https://doi.org/10.1515/otmcj-2016-0006
Preliminary estimate for reinforcement steel quantity in residential buildings
Ibrahim Mahamid
; Civil Engineering Department, Engineering Faculty, Hail University, Hail, Saudi Arabia
Abstract
The objective of this study was to develop prediction mathematical equations to compute reinforcement steel quantity in traditional residential buildings based on 158 sets of data collected in the West Bank in Palestine. The records related to the quantities were collected from consultancy firms that provide reinforced concrete design services. The data were collected for residential buildings up to four floors. Linear regression analysis was chosen to show the correlation between the included variables. The following variables were used in the regression models: quantity of reinforcement steel (dependent variable), structural element volume (independent variable) and floor area (independent variable). Fourteen models were developed; nine models were developed to compute the quantity of reinforcement steel in different structural elements: slabs, beams, columns and footings. The other five models were used to estimate the total steel quantity in a residential building. The coefficient of multiple determination (R2) of the developed models ranged from 0.70 to 0.82. This confirms a good correlation between the dependent and the independent variables. The accuracy of the developed models was tested using the mean absolute percentage error (MAPE) test. With MAPE values ranging from 21% to 36%, the results compare favourably with past research that indicated that accuracy between ±25% and ±50% at the early stages is acceptable. The results also show that the models built on structural element size have better accuracy than the models using floor area. Such types of equations are very useful, especially in their simplicity and ability to be handled by calculators or simple computer programmes.
Keywords
reinforcement steel; quantity surveying; floor area; regression models; preliminary estimate
Hrčak ID:
175425
URI
Publication date:
15.12.2016.
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