Modelling rework cost for TETFund building construction projects in Nigeria

M. A. Dandajeh, A. Ibrahim, Y. Ibrahim, K. J. Adogbo, H. Ahmadu
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Abstract

Accurate predictions of rework cost enable contractors to evaluate how successful their projects are likely to be, thus, improve their operations and  productivity. Studies have developed project scope-based and non-scopebased rework cost predictive models. However, researchers have criticized the  accuracy of these models because of their weaknesses in giving individualistic treatments to project scope-based and non- scope-based rework cost influencing factors. This study therefore aimed at developing rework cost predictive models that combine the synergistic effects of both project scope- based and non-scope-based rework cost factors. 440 sample size was drawn from 2,638 population of Tertiary Education Trust Fund (TETfund) building  projects constructed between 2009 to 2015. Data of 287 (65%) of 440 (sample size) of completed TETfund building projects on project scope factors as  well as the extent of influence of the non-scope factors on cost of rework in TETfund building projects was collected. Principal Component Analysis (PCA)  and Multiple Linear Regression (MLR) were used in developing an integrated rework cost predictive model. More accuracy was achieved in the integrated  model through the incorporation of impact of project scope and non-scope influencing factors into rework cost prediction model, this increased the  variability in cost of rework to R2 = 0.759 (75.9%), while errors in rework cost predictions reduced by as much as 9.75%.The model serve as useful tool  used to enhance accuracy of rework cost predictions for TETfund building contractors, thus reduce cost of rework for building construction projects.
尼日利亚TETFund建筑施工项目返工成本建模
对返工成本的准确预测使承包商能够评估他们的项目可能有多成功,从而提高他们的运营和生产力。研究开发了基于项目范围和非基于项目范围的返工成本预测模型。然而,研究人员对这些模型的准确性提出了批评,因为它们在对基于项目范围和非范围的返工成本影响因素进行个性化处理方面存在缺陷。因此,本研究旨在开发返工成本预测模型,该模型结合了基于项目范围和非基于项目范围的返工成本因素的协同效应。从2009年至2015年间建造的高等教育信托基金(TETfund)建筑项目的2638名人口中抽取440个样本。收集了已完成的440个(样本量)TETfund建筑项目中287个(65%)项目范围因素的数据,以及非范围因素对TETfund建筑项目返工成本的影响程度。采用主成分分析(PCA)和多元线性回归(MLR)建立了返工成本综合预测模型。通过将项目范围和非范围影响因素的影响纳入到返工成本预测模型中,在集成模型中获得了更高的准确性,这将返工成本的可变性提高到R2 = 0.759(75.9%),而返工成本预测的误差减少了高达9.75%。该模型可用于提高TETfund建筑承包商返工成本预测的准确性,从而降低建筑施工项目的返工成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
0.10
自引率
0.00%
发文量
126
审稿时长
11 weeks
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