Optimisation of Construction Process Inspection Rates using a Learning Approach

Swapan Saha
Colin Greville
Trevor Mullins


In the construction industry, the determination of the number of inspections/tests to be performed for a repetitive task is an important issue. The quality of a completed task depends on a number of factors including the cost of inspection, the cost of failure, the average proportion defective (error rate) and the inspection rate. To achieve a higher quality level, 100% inspection can be performed, however, this is unlikely to be cost effective. To determine the optimum inspection rate, this paper suggests a probabilistic approach incorporating the acceptance-sampling plan and minimum cost method. The limitation of various sampling plans including the attribute proportional sampling plan and the double sampling plan are outlined. An example is presented in this paper to demonstrate the calculation of the optimum number of inspections/tests per lot.

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A. A. Auff. Preliminary acceptance criteria for some road dimensions. Proceedings of Applications of Statistics & Probability in Soil and Structural Engineering, Third International Conference, Sydney, Australia, pp. 352-363, 1975.

A.W. Manton-Hall and R. Sym. Operating characteristics of overlapping and non-overlapping means of n" compliance criteria - A detail comparison. Proceedings of Applications of Statistics & Probability in Soil and Structural Engineering, Second International Conference, Aachen, Germany, pp. 278-291, 1975."

B. Dale. Counting up the Cost, Journal of Total Quality Management, Oct 1991, pp 22-34, 1991.

B.S. Dhillon. Quality control, reliability and engineering design. Marcel and Decker Inc., New York, 1985.

C.D. Ittner. The economics and measurement of quality costs: An empirical investigation, PhD thesis, Graduate School of Business Administration, Harvard University, USA, 1992.

CIDA, Measuring Up or Muddling Through: Best Practice in the Australian Non-Residential Construction Industry, Construction Industry Development Agency, Australia, Master Builders Association, Sydney Australia, pp 59-63. 1995.

DIST. Building for growth: A draft strategy for the building and construction industry, Department of Industry, Science and Tourism, Commonwealth of Australia Publication, February, Canberra, Australia, 1998.

N. Doganaksoy and G.J. Hahn. Moving from every lot inspection to audit sampling. Journal of Quality Technology, Vol. 26, No.4, pp261-273.

E. L. Grant and R. S. Leavenworth. Statistical Quality Control, McGraw-Hill, New York, 1988.

H. Gitlow, A. Oppenheim and R. Oppenheim. Tools and methods for the improvement of quality. Irwin, USA, 1989.

H.Y. Fang. Sampling plan and construction control. Proceedings of Applications of Statistics & Probability in Soil and Structural Engineering, Second International Conference, Aachen, Germany, pp. 323-337, 1975.

I. Blyth, QA sharing the experience, The Building Economist, March 1995, pp9-12.

L. Chang and M. Hsie. Developing acceptance sampling methods for quality construction. Journal of Construction Engineering and management, Vol. 121, No. 2, pp. 246-253, 1995. http://dx.doi.org/10.1061/(ASCE)0733-9364(1995)121:2(246)

P.E.D. Love, and H. Li, Quantifying the causes and costs of rework in construction, Journal of Construction Management and Economics, UK, .vol. 18, pp 479-490 2000, http://dx.doi.org/10.1080/01446190050024897

15 R.D. Leitch. Basic reliability engineering analysis. Butterworths & Co. Ltd., London, 1988."

S.K. Saha, Mathematical Modelling of Construction Error and Optimal Inspection Policy, The Sixth East Pacific Conference on Structural Engineering and Construction, January 14-16, 1998, The National University of Taiwan, Taipei, pp. 1155-1160, 1998.

M.G. Stewart, The Occurrence and Detection of Errors in Reinforced Concrete Beam Construction. The Department of Civil Engineering and Surveying, research report no. 063.05.1992. The University of Newcastle, NSW, 1992.

S. K Saha., C. Greville, T. Mullins, Simulation Experiment: The effects of experience and interruption in predicting error rate for a construction inspection task. International Congress on Modelling and Simulation Proceedings, The University of Waikato, New Zealand, December 6-9, pp. 1003â1008, 1999 null

19 W.W. Hines and D. C. Montgomery. Probability and statistics in engineering and management science. John Wiley & Sons, Singapore, 1990."

DOI: http://dx.doi.org/10.5130/AJCEB.v1i2.2871