ESTIMATING DEFECT RECTIFICATION BUDGET USING TWO-DIMENSIONAL MATRIX AND MONTE CARLO SIMULATION
Keywords:
Defect rectification budget, two-dimensional matrix, Monte Carlo simulation, residential housing projectAbstract
The defect rectification budget at the post-handover stage remains an uncertainty in the developer’s estimations. At the primary stage, the traditional estimation method used a specific percentage of the total project cost based on the average defect rectification cost in the calculation. However, this practice still provided an unreliable result in the estimation of the budget requirement for handling the defect rectification work. To overcome the limitation, this paper aimed to propose a hybrid estimation method to study the behaviour of the defect rectification cost of each contractor and lead to determining the defect rectification budget more accurately. In this research study, the hybrid estimation method was integrated with a two-dimensional matrix method and Monte Carlo simulation. First, the two-dimensional matrix method was developed by considering 2 main factors, the number and the severity of the defects encountered at the post-handover stage. Both factors could influence the behaviour of the defect rectification cost of each contractor. Secondly, a Monte Carlo simulation was applied in the estimation and could reflect the real situation of the defect rectification cost occurrence in each defective house. The research methodology was defined as a qualitative research approach. The case study of a housing project was used to develop and verify this concept. As a result, the paper found that the hybrid estimation method could provide a better result by using an accuracy level compared with the traditional estimation method. Thus, this hybrid estimation method is useful for determining the defect rectification budget in residential housing projects.
References
Christensen, P. and Burton, D.J. (2005). Cost Estimate Classification System – as Applied in Engineering , Procurement , and Construction for the Process Industries. AACE International Recommended Practices. AACE International, Morgantown, WV, USA, p. 1-10.
Elkjaer, M. (2000). Stochastic budget simulation. Int. J. Proj. Manag., 18:139-147.
Forcada, N., Macarulla, M., and Love, P.E.D. (2013a). Assessment of residential defects at post-handover. J. Constr. Eng. Manag., 139:1-12.
Forcada, N., Macarulla, M., Gangolells, M., Casals, M., Fuertes, A., and Roca, X. (2013b). Posthandover housing defects: Sources and origins. J. Perform. Constr. Fac., 27:756-762.
Georgiou, J., Love, P.E.D., and Smith, J. (1999). A comparison of defects in houses constructed by owners and registered builders in the Australian State of Victoria. Struc. Surv., 12:160-169.
Home Builders Federation (2017). National New Homes Customer Satisfaction Survey. Home Builders Federation, London, UK, 4p.
Josephson, P.E. and Hammarlund, Y. (1999). Causes and costs of defects in construction a study of seven building projects. Automat. Constr., 8:681-687.
Karim, K., Marosszeky, M., and Davis, S. (2006). Managing subcontractor supply chain for quality in construction. Eng. Constr. Archit. Manag., 13:27-42.
Rotimi, F.E., Tookey, J., and Rotimi, J.O. (2015). Evaluating defect reporting in new residental buildings in New Zealand. Buildings, 5:39-55.
Taggart, M., Koskela, L., and Rooke, J. (2014). The role of the supply chain in the elimination and reduction of construction rework and defects: an action research approach. Constr. Manag. Econ., 32:1-14.
Wyatt, D.P. (1980). The housing stock of G.B. 1800 – 2050, [Ph.D thesis]. School of the Built Environment, Department of Computing, Science and Engineering, University of Salford, Manchester, UK








