Parametric time overrun estimation of building projects

Abstract

Purpose Time overruns are commonplace within the construction industry. These result in deception because project managers critically assess the economic and financial viability of a project before implementation. Forecasting the likelihood of time overruns will not only lead to a reconsideration on the decision to build but also help put in place the necessary control measures – exactly what this research achieved. Design/methodology/approach The paper argues that rather than depending on the critical failure factors that are unknown at the pre-contract stage to forecast the likelihood of occurrence, it will be more useful to rely on project attributes that are known before contract signing. A multiple linear regression analysis is used for the model development based on ten independent variables. Findings About 86.6 per cent of all the projects experienced time overruns. The mean time overrun is 106.5 per cent. Initial contract sum, initial duration, gross floor area, contractor class D2K2, competitive tendering, sole sourcing and single-storey buildings explained about 44.7 per cent of the variations within time overruns, with a mean absolute percentage error of 60.7 per cent. Research limitations/implications The predictive accuracy of the model can, in practice, be tested after the completion of a project by comparing the actual project schedule with the planned schedule. Any disparity in the expected outputs should result in a reassessment of the significant independent variables to improve the forecasting abilities of the model. Practical implications The model is expected to be very useful at the pre-contract stage when detailed designs are unavailable. As a decision support system, it will help the practitioners and decision-makers make informed decisions while minimizing the time and resources spent to arrive at these decisions. Originality/value This research presents a unique opportunity to forecast the likelihood of time overruns within the building sector based on project attributes that are known before the contract-signing phase.

Duration estimation model for bridge construction projects in Ghana

“…A MAPE of 26 per cent at the validation stage is also consistent with Wang et al (2000) and Bode (1998) with a deviation of 24.5 and 24 per cent, respectively. According to Asiedu et al (2016), an MAPE of 26 per cent can be regarded as good and is much better than the MAPE values of 138 and 60.7 per cent obtained by the studies conducted by Asiedu et al (2017) and Asiedu et al (2016), respectively. Model 2 is eventually selected with a good predictive ability at the validation stage (coefficient of determination, R 2 = 88.5 per cent).…”

Exploring the adoption of technology against delays in construction projects

“…The ANN method was adopted to predict productivity in construction projects. Several machine learning methods were then implemented afterward to predict various measurable units such as decision tree for binary delay prediction (Soibelman and Kim, 2002), multiple linear regression for time overrun prediction (Asiedu et al. , 2016) and Bayesian Belief networks for time performance prediction (Joko Wahyu Adi et al.…”

“…The ANN method was adopted to predict productivity in construction projects. Several machine learning methods were then implemented afterward to predict various measurable units such as decision tree for binary delay prediction (Soibelman and Kim, 2002), multiple linear regression for time overrun prediction (Asiedu et al, 2016) and Bayesian Belief networks for time performance prediction (Joko Wahyu Adi et al, 2016). Recently, the utilization of prediction tools has been expanded both in terms of the number of studies and machine learning tools such as Gradient Boosting Trees (Hassan et al, 2018), Naı €ve Bayes (Hassan et al, 2018), Support Vector Machine (Mahmoodzadeh et al, 2020), Random Forest (Yaseen et al, 2020) and K-Nearest Neighbor (Sanni-Anibire et al, 2020).…”

A systems thinking approach for incremental reduction of non-physical waste

“…Construction post-contract cost control should ensure that resources of construction projects are kept within the budget for the timely delivery of construction projects (Potts, 2008). The resources used during construction projects are typically identified within the categories of plant, labour, and materials but also include professional expertise from skilled workers, finance, and information technology (Emuze, Smallwood and Han, 2014;Ameyaw et al, 2015;Amoatey et al, 2015;Asiedu, Frempong and Nani, 2016;Broft, Badi and Pryke, 2016). These resources are dependent on a finite budget.…”