# Regression Models for Predicting Quantities and Estimates of Steel Reinforcements in Concrete Beams of Frame Buildings

## Main Article Content

## Abstract

The traditional method of quantifying reinforced concrete steel reinforcements via taking off can be tedious, time consuming and prone to errors which can affect project success due to cost and schedule overruns, disputes and in certain cases, outright abandonment. In Nigeria, some quantity surveyors have developed ‘rule of thumb’ techniques to quantify reinforcements in order to beat pre-contract datelines based on their past experience, but there are still not widely accepted and a unified formulae or empirical basis of generating these quantities is still lacking. This study thus, developed easy-to-apply, time saving regression models for predicting the quantities/weight and material cost estimates of 16mm, 12mm and 8mm diameter high yield reinforcement bars in beams of varying sizes, using the volume of beam concrete as the independent or predictor variable. Data on concrete volume, weight of Y16, Y12 and Y8 reinforcement was collected via taking off/measurement process from 30 structural drawings of frame buildings of varying nature obtained from registered structural engineers and analyzed using correlation and regression statistics. Results indicate high coefficients of determination (R^{2}) ranging from 0.82 to 0.92 which indicate that the predicted values from a forecast models fit with the real-life data. Thus, 3 predictive models were advanced as follows: W_{Y16}= -811.265+ 177.339 (Vc) ;W_{Y12}= -510.189 + 63.218(Vc); W_{Y8} = **-**43.273+ 22.533 (Vc), where: W = reinforcement weight and Vc = volume of concrete. The study concludes that concrete volume is a good predictor variable when establishing the weight of reinforcement in beams. The import of these predictive models for construction cost professionals cannot be overemphasized for ease and accuracy of feasibility estimating, preparation of bills of quantities, material ordering, auditing construction costs, vetting consultants’ estimates and contractors’ quotations.

## Article Details

*Journal of Scientific Research and Reports*,

*26*(7), 60-74. https://doi.org/10.9734/jsrr/2020/v26i730285

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