Analysis of Historical BoQ data for Case-Based Reasoning in Preliminary Cost Estimating
DOI:
https://doi.org/10.26437/ajar.v11i3.1160Keywords:
BoQ. case-based reasoning. case knowledge. new buildings., preliminary costAbstract
Purpose: This study utilises historical cost data of priced bills of quantities to analyse the cost breakdown of new public building projects in Ghana as a case study in preliminary cost estimating. Leaning on relevant conceptual/theoretical frameworks, the study analyses the distribution of costs (i.e., direct costs of measured works and project allowances) in a typical work section bill of quantities.
Design/Methodology/Approach: The study adopted a quantitative research approach. A total of 367 SMM5- and SMM7-based work section bills of quantities obtained from public quantity surveying firms were used for the study. The data was organised in Microsoft Excel and analysed in Stata using descriptive statistics.
Research Limitation: The study was limited to 367 bills of quantities from SMM5 and SMM7-based work sections, which may not fully represent the entire spectrum of quantity surveying practices across different project types, scales, and geographic regions.
Findings: Mean and median values of the analysis show that, for both SMM5 and SMM7, substructure and finishes constitute the two cost-significant measured works in order of in magnitude. For project allowances, preliminaries, and contingency sums, averages 6% and 8% in both SMM5 and SMM7 projects, while provisional sums constitute about 11% in SMM5 projects but 2% in SMM7 projects. Consultancy fees is approximately 8%, but can reach a maximum of 16%. The observed significant gap in provisional sum in SMM5 and SMM7 bills of quantities sheds light on efficiency in cost estimate build-up resulting from the effectiveness of the Standard Method of Measurement (SMM7) as an instrument of measurement.
Practical implication: The study's results provide valuable insights for quantity surveyors during cost evaluation, budgeting, and planning of new building projects.
Social Implication: This improved cost predictability has significant social benefits, including better allocation of public resources, reduced burden on taxpayers, and enhanced delivery of essential infrastructure such as schools, hospitals, roads, and housing projects that serve community needs.
Original value: The study grounds a useful but otherwise intuitive professional practice in theory and supports this with empirical data.
References
Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological
variations, and system approaches. AI communications 7 (1), 39-59.
Abdelaty, A., Nesselhauf, K., & Munie, N. (2022). Using historical bid data for enhanced
conceptual estimating. In T. Leathem, W. Collins and A. Perrenoud (eds.), ASC2022. EPiC Series in Built Environment, 3, 192–199
Abourizk, S. M., Babey, G. M., & Karumanasseri, G. (2002). Estimating the cost of capital
projects: An empirical study of accuracy levels for municipal government projects. Can. J. Civ. Eng. 29, 653–661. DOI: https://doi.org/10.1139/l02-046
African Association of Quantity Surveyors (AAQS) (2016). Guide to Elemental Cost
Estimating and Analysis for Building Works 2016.
Ahmed Abdelaty, K., Nesselhauf, L., & Nathan, M. L. (2022). Using historical bid data for
enhanced conceptual estimating. EPiC Series in Built Environment (3), 192–199
Aladegbaiye O. B. (2002). A study of the variability of prime cost sums in the bill of
quantities for building contracts in south-western Nigeria, Unpublished M.Sc. thesis, Dept. of Quantity Surveying, O.A.U. Ile-Ife
American Association of Cost Engineers (AACE) (2020). AACE International Recommended
Practice No. 18R-97. Cost Estimate Classification System – As Applied in Engineering, Procurement, And Construction for the Process Industries TCM Framework: 7.3 – Cost Estimating and Budgeting. AACE International. https://library.aacei.org/pgd01/pgd01.shtml
An, S. H., Kim, G. H., & Kang, K.I. (2007). A case-based reasoning cost estimating
model using experience by analytic hierarchy process. Building and Environment 42 (7), 2573-2579.
Anthonie, E. (2009). Classes of construction cost estimates Buletinul Institutului Politehnic
Din Iaşi Publicat de Universitatea Tehnică, Gheorghe Asachi” din Iaşi Tomul LV (LIX), Secţia Construcţii. Ărhitectură.
Ashworth, A., & Perera, S. (2015). Cost Studies of Buildings, 6th ed.; Routledge: Abingdon DOI: https://doi.org/10.4324/9781315708867
Oxon, UK.
Ashworth, A. (2008). Pre-contract studies: Development economics, tendering, and
estimating; Blackwell: Oxford, UK.
Baccarini, D. (2004). Accuracy in estimating project cost construction contingency – A
Statistical Analysis. In: Robert, E., and Malcolm, B. (eds), Proceedings of the Construction and Building Research Conference of RICS, 7-8 September 2004, Headingly Cricket Club, Leeds. RICS http://www.rics.org/site/download_feed.aspx?fileID=3460andfileExtension=PDF
Bannon Jr, W. (2015). Missing data within a quantitative research study: How to assess it, DOI: https://doi.org/10.1002/2327-6924.12208
Treat it, and why you should care. Journal of the American Association of Nurse Practitioners, 27(4), 230-232.
Blocher, E. J., Stout, D. E., & Cokins, G. (2010). Cost management: A strategic emphasis.
th Ed., New York: McGraw-Hill/Irwin, pp. 17-55
Cattel, D. W. (2012). An overview of component unit pricing theory. Construction DOI: https://doi.org/10.1080/01446193.2011.648948
Management and Economics, 30 (1), 81-92
Cheng, M. Y., Tsai, H. C. & Sudjono, E. (2010). Conceptual cost estimates using
evolutionary fuzzy hybrid neural network for projects in construction industry. Expert Systems Application, 37(6), 4224–4231. DOI: https://doi.org/10.1016/j.eswa.2009.11.080
Chicoca, D. & Utomo, C. (2019). Developers’ perception of cost drivers influencing project
cost performance on Surabaya high-rise residential, Indonesia IOP Conf. Ser.: Mater. Sci. Eng. 650 012009
Choi, S., Kim, D., Han, S. & Kwak, Y. (2014). Conceptual cost-prediction model for public
road planning via rough set theory and case-based reasoning, Journal of Construction Engineering and Management, 140 (1), 04013026.
Dippon, J., Fritz, P., & Kohler, M. (2002). BA statistical approach to case-based reasoning
with application to breast cancer data, Comput. Statist. Data Anal., 40 (3), 579–
Doğan, S. Arditi, D., & Günaydın, H. (2006). Determining attribute weights in a CBR
model for early cost prediction of structural systems, Journal of Construction Engineering and Management 132 (10), 1092-1098.
Earley, M. & Gould, H. F. (2004). Current Topic: Standard Method of Measurement.
Accessed at: www.rics.org.
Elmousalami, H. H., Elyamany, A. A., & Ibrahim, A. H. (2017). Evaluation of Cost Drivers
for Field Canals Improvement Projects Water Resource Management doi 10.1007/s11269-017-1747.
Goel, V., Navarrete, G., Noveck, I. A., & Prado, J. (2017). The reasoning brain: The
interplay 1023 between cognitive neuroscience and theories of reasoning. Frontiers in Human 1024 Neuroscience, 10, 673. https://doi.org/10.3389/fnhum.2016.00673 DOI: https://doi.org/10.3389/fnhum.2016.00673
Gupta, A., & Debnath, P. (2022). Artificial neural network analysis for cost estimation of
building projects in India. 5th International Conference of Contemporary Affairs in Architecture and Urbanism (ICCAUA-2022) 11-13 May 2022 ICCAUA2022 Conference full paper proceedings book, Alanya HEP University, Alanya, Turkey 193 DOI: 10.38027/ICCAUA2022EN0210 Ar. 2 DOI: https://doi.org/10.38027/ICCAUA2022EN0210
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2010).
Multivariate data analysis. Prentice Hall Upper Saddle River, NJ.
Hegazy, T. (2002). Computer-based construction project management. Upper Saddle River,
NJ: Prentice-Hall Inc.
Hegazy, T., & Ayed A. (1998). Neural network model for parametric cost estimation of DOI: https://doi.org/10.1061/(ASCE)0733-9364(1998)124:3(210)
highway projects. Journal of Construction Engineering and Management 124(3),
–8.
Hong, T., Hyun, C., & Moon, H. (2011). CBR-based cost prediction model-II of the design
phase for multi-family housing projects. Expert systems with applications, 38(3), 2797-2808.
Howell, D., Rogier, M., Yzerbyt, V., & Bestgen, Y. (1998). Statistical Methods in Human
Sciences: New York: Wadsworth.
Hullermeier, E. (2007). Case-Based Approximate Reasoning. New York: Springer-Verlag
Ji, C., Hong, T., & Hyun, C. (2010). CBR revision model for improving cost prediction
accuracy in multifamily housing projects. Journal of Management in Engineering 26 (4), 229-236.
Ji, S., Park, M., & Lee, H. (2012). Case adaptation method of case-based reasoning for
construction cost estimation in Korea, Journal of Construction Engineering and
Management 138 (1), 43-52
Ji, S.H., Park, M., & Lee, H.-S. (2011). Cost estimation model for building projects using DOI: https://doi.org/10.1139/l11-016
case-based reasoning, Canadian Journal of Civil Engineering 38 (5), 570-581.
Ji, S. H., Park, M. S., Lee, H. S., Seong, K. H., & Yoon, Y. S. (2008). Method of quantity
data analysis for building construction cost estimation: Focusing on finish work of public apartment project. Korean Journal of Construction Engineering and Management, 9(6), 235-243.
Jin, R., Han, S., Hyun, C., & Kim, J. (2014). Improving accuracy of earl stage cost
estimation by revising categorical variables in a case-based reasoning model, Journal of Construction Engineering and Management 140 (7), 04014025.
Kim, M. Lee, S., Woo, S., & Shin, D. (2012). Approximate cost estimating model for
river facility construction based on case-based reasoning with genetic algorithms, KSCE J Civ Eng 16 (3), 283-292. DOI: https://doi.org/10.1007/s12205-012-1482-z
Kim, B., & Hong, B. (2012). Revised case-based reasoning model development based on
multiple regression analysis for railroad bridge construction, Journal of Construction Engineering and Management 138 (1), 154-162.
Kim, S. (2013). Hybrid forecasting system based on case-based reasoning and analytic DOI: https://doi.org/10.3846/13923730.2012.737829
hierarchy process for cost estimation, Journal of Civil Engineering and Management 19 (1), 86- 96.
Kim, S. (2012). Interval Estimation of Construction Cost Using Case-Based Reasoning and DOI: https://doi.org/10.1061/9780784412343.0012
Genetic Algorithms. Journal of Asian Architecture and Building Engineering 11 (2), 327-334.
Kim, S., & Shim, J. H. (2014). Combining case-based reasoning with genetic algorithm
optimization for preliminary cost estimation in construction industry, Canadian Journal of Civil Engineering 41 (1), 65-73.
Kolodner, J. L. (1992). An Introduction to Case-Based Reasoning. Artificial Intelligence DOI: https://doi.org/10.1016/B978-1-55860-237-3.50017-0
Review 6, 3-34.
Leake, D. (1998). Cognition as case-based reasoning, in: W. Bechtel, G. Graham (Eds.), A
Companion to Cognitive Science, Blackwell, Oxford, pp.465–476.
Leake, D. B. (1996). CBR in context: The present and future, in: D. B. Leake (Ed.), Case-
Based Reasoning, Experiences, Lessons and Future Directions, AAAI Press/MIT Press, Menlo Park, 1-35.
Lee, S., Jin, Y., Woo, S., & Shin, D. (2013). Approximate cost estimating model of eco-
type trade for river facility construction using case-based reasoning and genetic algorithms, KSCE J Civ Eng 17(2), 292-300. DOI: https://doi.org/10.1007/s12205-013-1638-5
Leys, C., Ley, C., Klein, O., Bernard, P., & Licata, L. (2013). Detecting outliers: Do not use
standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology, 49(4), 764-766.
Li, W., Paraschiv, F. & Sermpinis, G. (2022). A data-driven explainable case-based
reasoning approach for financial risk detection, Quantitative Finance, p.1-18. DOI: 10.1080/14697688.2022.2118071 DOI: https://doi.org/10.1080/14697688.2022.2118071
Masterton, W. L. & Hurley, C. N. (2011). Chemistry: Principles and Reactions. Cengage
Learning.
Matela, E., Vahdatikhakia, F. & Hosseinyalamdaryc, S., Eversb, T. and Voordijka, H.
(2022). An artificial neural network approach for cost estimation of engineering services. International Journal of Construction Management, 22(7), pp.1274–1287 https://doi.org/10.1080/15623599.2019.1692400 DOI: https://doi.org/10.1080/15623599.2019.1692400
Nani, G., & Adjei-Kumi, T. (2007). Challenges of quantifying construction work for control
processes in Ghana. International Council for Research and Innovation in Building and Construction CIB World Building Congress 2007. Cape Town, South Africa, 14 – 17 May.
Okudan, O., Budayan, C., & Dikmen, I. (2021). A knowledge-based risk management tool
for construction projects using case-based reasoning. Expert Systems with Applications, 173. doi: https://doi.org/10.1016/j.eswa.2021.114776 DOI: https://doi.org/10.1016/j.eswa.2021.114776
Ozorhon, B., Dikmen, I., & Birgonul, M. T. (2006). Case-based reasoning model for
international market selection. Journal of Construction Engineering and Management, 132(9), 940–948. https://doi.org/10.1061/(ASCE)0733-9364(2006)132:9(940) DOI: https://doi.org/10.1061/(ASCE)0733-9364(2006)132:9(940)
Pallant, J. (2013). SPSS survival manual: McGraw-Hill Education (UK).
Project Management Institute [PMI]. (2016). Construction Extension to the PMBOK®Guide,
nd ed.; Project Management Institute: Newtown Square, PA, USA.
Royal Institution of Chartered Surveyors (RICS). (2012). New Rules of Measurement. In
NRM 1, Order of Cost Estimating and Cost Planning for Capital Building Works; RICS: London, UK
Smith, A. E., & Mason, A. K. (1997). Cost estimation modeling: regression versus neural DOI: https://doi.org/10.1080/00137919708903174
network. Engineering Economist, 42(2), 137–61.
Uysal, F., & Sonmez, R. (2023). Bootstrap Aggregated Case-Based Reasoning Method for
Conceptual Cost Estimation. Buildings, 13, 651. https://doi.org/10.3390/buildings13030651 DOI: https://doi.org/10.3390/buildings13030651
Wang, H., Lin, Q., & Zhang, Y. (2022). Risk cost measurement of value for money
evaluation based on case-based reasoning and ontology: a case study of the urban rail transit public-private partnership projects in China. Sustainability 14, 5547. https://doi.org/10.3390 DOI: https://doi.org/10.3390/su14095547
Watson, I. & Marir, F. (1994). Case-based reasoning: A review. The Knowledge DOI: https://doi.org/10.1017/S0269888900007098
Engineering Review 9 (04), 327-354.
Xiao, X. (2020). Improving the long-term use of case-based reasoning model in early
construction cost estimation. Unpublished PhD Thesis. The Hong Kong Polytechnic University and Queensland University of Technology
Yang, S. W., Moon, S. W., Jang, H., Choo, S., & Kim, S. A. (2022). Parametric method and
building information modeling-based cost estimation model for construction cost prediction in architectural planning. Applied sciences, 12(19), 9553. DOI: https://doi.org/10.3390/app12199553
Downloads
Published
Issue
Section
Categories
License
Copyright (c) 2025 AFRICAN JOURNAL OF APPLIED RESEARCH
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
By submitting and publishing your articles in the African Journal of Applied Research, you agree to transfer the copyright of the Article from the authors to the Journal ( African Journal of Applied Research).
