Conceptual cost estimation framework for modular projects: a case study on petrochemical plant construction
Abstract
Modularization, which allows for pre-assembly away from a construction site, has been known to be more cost-effective than stick-built; however, contractors have difficulty ascertaining the benefits and adopting it. Calculating the benefits and costs of adopting modularization precedes decision making. However, modular cost estimation is challenging since relevant information in the early stages of a project and historical data about industrial modularization both have limited availability. To solve this problem, this study developed a conceptual cost estimation framework for industrial modular projects by converting stick-built project information. The framework is composed of eight steps based on two approaches. This study conducted a case study to demonstrate the applicability of the framework, which compared the project cost of modularization scenarios 1 and 2 with that of the stick-built version of the ongoing project. In addition, the estimated modular cost was compared with the engineers’ estimation to verify the accuracy of the framework. The contributions of this study are in identifying and quantifying the factors influencing the differences in cost between the modularization and stick-built versions, and developing the conceptual cost estimation framework for an industrial modular project. This framework is expected to support deciding on adopting modularization, budgeting, and project viability.
Keyword : modularization, industrial modular projects, conceptual cost estimation, quantity-based estimation, Monte-Carlo simulation
How to Cite
Choi, Y., Park, C. Y., Lee, C., Yun, S., & Han, S. H. (2022). Conceptual cost estimation framework for modular projects: a case study on petrochemical plant construction. Journal of Civil Engineering and Management, 28(2), 150–165. https://doi.org/10.3846/jcem.2022.16234
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Hashemi, S. T., Ebadati E, O. M., & Kaur, H. (2019). A hybrid conceptual cost estimating model using ANN and GA for power plant projects. Neural Computing and Applications, 31(7), 2143–2154. https://doi.org/10.1007/s00521-017-3175-5
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Hsu, P.-Y., Angeloudis, P., & Aurisicchio, M. (2018). Optimal logistics planning for modular construction using two-stage stochastic programming. Automation in Construction, 94, 47–61. https://doi.org/10.1016/j.autcon.2018.05.029
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Kim, G.-H., An, S.-H., & Kang, K.-I. (2004). Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning. Building and Environment, 39(10), 1235–1242. https://doi.org/10.1016/j.buildenv.2004.02.013
Kim, K. J., Kim, K., & Kang, C. S. (2009). Approximate cost estimating model for PSC Beam bridge based on quantity of standard work. KSCE Journal of Civil Engineering, 13(6), 377–388. https://doi.org/10.1007/s12205-009-0377-0
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Koo, C., Hong, T., & Hyun, C. (2011). The development of a construction cost prediction model with improved prediction capacity using the advanced CBR approach. Expert Systems with Applications, 38(7), 8597–8606. https://doi.org/10.1016/j.eswa.2011.01.063
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Shelley, S. (1990). Making inroads with modular construction. Chemical Engineering, 97(8), 30–35.
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Touran, A., & Wiser, E. P. (1992). Monte Carlo technique with correlated random variables. Journal of Construction Engineering and Management, 118(2), 258–272. https://doi.org/10.1061/(ASCE)0733-9364(1992)118:2(258)
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Back, W. E., Boles, W. W., & Fry, G. T. (2000). Defining triangular probability distributions from historical cost data. Journal of Construction Engineering and Management, 126(1), 29–37. https://doi.org/10.1061/(ASCE)0733-9364(2000)126:1(29)
Barbosa, F., Woetzel, J., Mischke, J., Ribeirinho, M. J., Sridhar, M., Parsons, M., Bertram, N., & Brown, S. (2017). Reinventing construction: A route to higher productivity. McKinsey Global Institute.
Bell, L. C., & Kaminsky, A. (1987). Data base for preliminary cost estimating. Journal of Transportation Engineering, 113(4), 341–347. https://doi.org/10.1061/(ASCE)0733-947X(1987)113:4(341)
Bertram, N., Fuchs, S., Mischke, J., Palter, R., Strube, G., & Woetzel, J. (2019). Modular construction: From projects to products. McKinsey & Company.
Cheung, F. K., Rihan, J., Tah, J., Duce, D., & Kurul, E. (2012). Early stage multi-level cost estimation for schematic BIM models. Automation in Construction, 27, 67–77. https://doi.org/10.1016/j.autcon.2012.05.008
Choi, J. O. (2014). Links between modularization critical success factors and project performance [Dissertation]. The University of Texas at Austin. https://doi.org/10.1061/9780784413517.167
Choi, J., & Song, H. (2014). Evaluation of the modular method for industrial plant construction projects. International Journal of Construction Management, 14(3), 171–180. https://doi.org/10.1080/15623599.2014.922728
Choi, S., Kim, D. Y., Han, S. H., & Kwak, Y. H. (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. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000743
Choi, J. O., O’Connor, J. T., Kwak, Y. H., & Shrestha, B. K. (2019a). Modularization business case analysis model for industrial projects. Journal of Management in Engineering, 35(3), 04019004. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000683
Choi, J. O., Chen, X. B., & Kim, T. W. (2019b). Opportunities and challenges of modular methods in dense urban environment. International Journal of Construction Management, 19(2), 93–105. https://doi.org/10.1080/15623599.2017.1382093
Chou, J.-S., Peng, M., Persad, K. R., & O’Connor, J. T. (2006). Quantity-based approach to preliminary cost estimates for highway projects. Transportation Research Record: Journal of the Transportation Research Board, 1946(1), 22–30. https://doi.org/10.1177/0361198106194600103
Chou, J.-S., Yang, I.-T., & Chong, W. K. (2009). Probabilistic simulation for developing likelihood distribution of engineering project cost. Automation in Construction, 18(5), 570–577. https://doi.org/10.1016/j.autcon.2008.12.001
Chowdhury, M. M. (2016). Simulation of value stream mapping and discrete optimization of energy consumption in modular construction [MSc thesis]. Illinois State University. http://doi.org/10.30707/ETD2016.Chowdhury.M
Christensen, P., Dysert, L. R., Bates, J., Burton, D., Creese, R., & Hollmann, J. (2005). Cost estimate classification system-as applied in engineering, procurement, and construction for the process industries. AACE International.
Construction Industry Institute. (2002). Prefabrication, preassembly, modularization and offsite fabrication in industrial construction – A framework for decision making.
Construction Industry Institute. (2003). IPRA: International project risk assessment.
Compass International Inc. (2016a). Global construction costs yearbook (16th Annual ed.).
Compass International Inc. (2016b). Pipelines, mining & offshore cost data yearbook (15th Annual ed.).
De La Torre, M. L. (1994). A review and analysis of modular construction practices [MSc thesis]. Lehigh University.
Dursun, O., & Stoy, C. (2016). Conceptual estimation of construction costs using the multistep ahead approach. Journal of Construction Engineering and Management, 142(9), 04016038. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001150
El Asmar, M., Hanna, A. S., & Chang, C.-K. (2009). Monte Carlo simulation approach to support alliance team selection. Journal of Construction Engineering and Management, 135(10), 1087–1095. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000074
Fazeli, A., Dashti, M. S., Jalaei, F., & Khanzadi, M. (2020). An integrated BIM-based approach for cost estimation in construction projects. Engineering, Construction and Architectural Management, 28(9), 2828–2854. https://doi.org/10.1108/ECAM-01-2020-0027
Gibb, A., & Isack, F. (2003). Re-engineering through pre-assembly: client expectations and drivers. Building Research & Information, 31(2), 146–160. https://doi.org/10.1080/09613210302000
Haas, C. T., O’Connor, J. T., Tucker, R., Eickmann, J., & Fagerlund, W. (2000). Prefabrication and preassembly trends and effects on the construction workforce (Issue 14 of Report). University of Texas at Austin, Center for Construction Industry Studies, USA.
Hashemi, S. T., Ebadati E, O. M., & Kaur, H. (2019). A hybrid conceptual cost estimating model using ANN and GA for power plant projects. Neural Computing and Applications, 31(7), 2143–2154. https://doi.org/10.1007/s00521-017-3175-5
Hesler, W. E. (1990). Modular design-where it fits. Chemical Engineering Progress, 86(10), 76–80.
Hsu, P.-Y., Angeloudis, P., & Aurisicchio, M. (2018). Optimal logistics planning for modular construction using two-stage stochastic programming. Automation in Construction, 94, 47–61. https://doi.org/10.1016/j.autcon.2018.05.029
Jameson, P. (2007). Is modularization right for your project? Hydrocarbon Processing, 86(12), 47–47.
Jung, W., & Han, S. H. (2017). Which risk management is most crucial for controlling project cost? Journal of Management in Engineering, 33(5), 04017029. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000547
Kang, H.-W., & Kim, Y.-S. (2018). A model for risk cost and bidding price prediction based on risk information in plant construction projects. KSCE Journal of Civil Engineering, 22(11), 4215–4229. https://doi.org/10.1007/s12205-018-0587-4
Kim, S.-B., & Cho, J.-H. (2013). Development of the approximate cost estimating model for PSC box girder bridge based on the breakdown of standard work. Journal of the Korean Society of Civil Engineers, 33(2), 791–800. https://doi.org/10.12652/Ksce.2013.33.2.791
Kim, G.-H., An, S.-H., & Kang, K.-I. (2004). Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning. Building and Environment, 39(10), 1235–1242. https://doi.org/10.1016/j.buildenv.2004.02.013
Kim, K. J., Kim, K., & Kang, C. S. (2009). Approximate cost estimating model for PSC Beam bridge based on quantity of standard work. KSCE Journal of Civil Engineering, 13(6), 377–388. https://doi.org/10.1007/s12205-009-0377-0
Kinney, C. L., & Soubiran, N. (2004). Interactive roadmap to conceptual cost estimating. Cost Engineering, 46(9), 31–40.
Koo, C., Hong, T., & Hyun, C. (2011). The development of a construction cost prediction model with improved prediction capacity using the advanced CBR approach. Expert Systems with Applications, 38(7), 8597–8606. https://doi.org/10.1016/j.eswa.2011.01.063
Kwak, Y. H., & Watson, R. J. (2005). Conceptual estimating tool for technology-driven projects: exploring parametric estimating technique. Tech-novation, 25(12), 1430–1436. https://doi.org/10.1016/j.technovation.2004.10.007
Mao, C., Xie, F., Hou, L., Wu, P., Wang, J., & Wang, X. (2016). Cost analysis for sustainable off-site construction based on a multiple-case study in China. Habitat International, 57, 215–222. https://doi.org/10.1016/j.habitatint.2016.08.002
Mohaddes, K., & Pesaran, M. H. (2017). Oil prices and the global economy: Is it different this time around? Energy Economics, 65, 315–325. https://doi.org/10.1016/j.eneco.2017.05.011
Naqvi, D., Wey, E., Patel, J., & Siegert, G. (2014). Modularization in the petrochemical industry. In Structures Congress 2014, Boston, Massachusetts, United States. https://doi.org/10.1061/9780784413357.153
O’Connor, J., O’Brien, W., & Choi, J. (2013). Industrial modularization: How to optimize; How to maximize. University of Texas, Construction Industry Institute, Austin, TX.
O’Connor, J. T., O’Brien, W. J., & Choi, J. O. (2014). Critical success factors and enablers for optimum and maximum industrial modularization. Journal of Construction Engineering and Management, 140(6), 04014012. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000842
O’Connor, J. T., O’Brien, W. J., & Choi, J. O. (2015). Standardization strategy for modular industrial plants. Journal of Construction Engineering and Management, 141(9), 04015026. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001001
Oberlender, G. D., & Trost, S. M. (2001). Predicting accuracy of early cost estimates based on estimate quality. Journal of Construction Engineering and Management, 127(3), 173–182. https://doi.org/10.1061/(ASCE)0733-9364(2001)127:3(173)
Pan, W., Dainty, A. R., & Gibb, A. G. (2012). Establishing and weighting decision criteria for building system selection in housing construction. Journal of Construction Engineering and Management, 138(11), 1239–1250. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000543
Petroutsatou, K., Georgopoulos, E., Lambropoulos, S., & Pantouvakis, J. P. (2012). Early cost estimating of road tunnel construction using neural networks. Journal of Construction Engineering and Management, 138(6), 679–687. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000479
Renn, O. (1998). The role of risk perception for risk management. Reliability Engineering & System Safety, 59(1), 49–62. https://doi.org/10.1016/S0951-8320(97)00119-1
Sharafi, P., Rashidi, M., Samali, B., Ronagh, H., & Mortazavi, M. (2018). Identification of factors and decision analysis of the level of modularization in building construction. Journal of Architecture Engineering, 24(2), 04018010. https://doi.org/10.1061/(ASCE)AE.1943-5568.0000313
Shelley, S. (1990). Making inroads with modular construction. Chemical Engineering, 97(8), 30–35.
Smith, S., Braham, A., Hernandez, S., & Kent, J. (2018). Development of a cost estimation framework for potential transload facilities. Transportation Research Record: Journal of the Transportation Research Board, 2672(9), 24–34. https://doi.org/10.1177/0361198118774690
Sonmez, R., Ergin, A., & Birgonul, M. T. (2007). Quantitative methodology for determination of cost contingency in international projects. Journal of Management in Engineering, 23(1), 35–39. https://doi.org/10.1061/(ASCE)0742-597X(2007)23:1(35)
Soutos, M., & Lowe, D. J. (2005). ProCost – Towards a powerful early stage cost estimating tool. In International Conference on Computing in Civil Engineering 2005, Cancun, Mexico. https://doi.org/10.1061/40794(179)142
Staub-French, S., Fischer, M., Kunz, J., & Paulson, B. (2003). A generic feature-driven activity-based cost estimation process. Advanced Engineering Informatics, 17(1), 23–39. https://doi.org/10.1016/S1474-0346(03)00017-X
Stubbs, D. L., & Emes, P. D. (1990). Modularization: Prefabricating a process plant. Mechanical Engineering – CIME, 112(11), 63–66.
Taghaddos, H., Hermann, U., AbouRizk, S., & Mohamed, Y. (2010). Simulation-based scheduling of modular construction using multi-agent resource allocation. In Proceedings of the 2010 Second International Conference on Advances in System Simulation. IEEE Computer Society. https://doi.org/10.1109/SIMUL.2010.36
Touran, A. (1993). Probabilistic cost estimating with subjective correlations. Journal of Construction Engineering and Management, 119(1), 58–71. https://doi.org/10.1061/(ASCE)0733-9364(1993)119:1(58)
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