CoChat
Sign Up
Cited by 47Construction Project Management and PerformanceCapital Investment and Risk AnalysisBIM and Construction Integration

The DOI matches its Crossref record.

Impact of aleatoric, stochastic and epistemic uncertainties on project cost contingency reserves

Open access
Curto, D., Acebes, F., González-Varona, J.M., Poza, D.·2022·International Journal of Production Economics
DOI 10.1016/j.ijpe.2022.108626
Open this paper in CoChat — free account

Abstract

In construction projects, contingency reserves have traditionally been estimated based on a percentage of the total project cost, which is arbitrary and, thus, unreliable in practical cases. Monte Carlo simulation provides a more reliable estimation. However, works on this topic have focused exclusively on the effects of aleatoric uncertainty, but ignored the impacts of other uncertainty types. In this paper, we present a method to quantitatively determine project cost contingency reserves based on Monte Carlo Simulation that considers the impact of not only aleatoric uncertainty, but also of the effects of other uncertainty kinds (stochastic, epistemic) on the total project cost. The proposed method has been validated with a real-case construction project in Spain. The obtained results demonstrate that the approach will be helpful for construction Project Managers because the obtained cost contingency reserves are consistent with the actual uncertainty type that affects the risks identified in their projects.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.ijpe.2022.108626

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Probabilistic representations of uncertainty have been successfully employed with uniform distributions to characterise uncertainty when knowledge is sparse or absent.

    “Probabilistic representations of uncertainty have been successfully employed with uniform distributions to characterise uncertainty when knowledge is sparse or absent (Curto et al. 2022;Vanhoucke, 2018;Helton et al. 2006).”

    From Beyond probability-impact matrices in project risk management: A quantitative methodology for risk prioritisation (2024) · cites this paper as Curto et al. 2022

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 74

  1. 2011, Integrated cost and schedule risk analysis using Monte Carlo simulation of a CPM model, AACE International Recommended Practice No. 57R-09
  2. Acebes, 2014, A new approach for project control under uncertainty. Going back to the basics, Int. J. Proj. Manag., 32, 423, 10.1016/j.ijproman.2013.08.003 · DOI · Paper page
  3. Acebes, 2015, Stochastic earned value analysis using Monte Carlo simulation and statistical learning techniques, Int. J. Proj. Manag., 33, 1597, 10.1016/j.ijproman.2015.06.012 · DOI · Paper page
  4. Acebes, 2022, Stochastic earned duration analysis for project schedule management, Engineering, 9, 148, 10.1016/j.eng.2021.07.019 · DOI
  5. Acebes, 2021, On the project risk baseline : integrating aleatory uncertainty into project scheduling, Comput. Ind. Eng., 160
  6. Afzal, 2020, Cost-risk contingency framework for managing cost overrun in metropolitan projects: using fuzzy-AHP and simulation, Int. J. Manag. Proj. Bus., 13, 1121, 10.1108/IJMPB-07-2019-0175 · DOI
  7. Ahmad, 1992, Contingency allocation: a computer-aided approach, AACE Transactions, F5, 1
  8. Akintoye, 1997, Risk analysis and management in construction, Int. J. Proj. Manag., 15, 31, 10.1016/S0263-7863(96)00035-X · DOI
  9. Alleman, 2018, Increasing the probability of program succes with continuous risk management, Coll. Perf. Manag. Meas. News, 27
  10. Alleman, 2018, What is Risk?, Meas. News, 1, 25
  11. Baccarini, 1996, The concept of project complexity - a review, Int. J. Proj. Manag., 14, 201, 10.1016/0263-7863(95)00093-3 · DOI
  12. Baccarini, 2004, Estimating project cost contingency - a model and exploration of research questions, 20th Ann. ARCOM Conf., 1, 105
  13. Baccarini, 2014, Statistical characteristics of cost contingency in water infrastructure projects, J. Construct. Eng. Manag., 140, 10.1061/(ASCE)CO.1943-7862.0000820 · DOI
  14. Bae, 2004, Epistemic uncertainty quantification techniques including evidence theory for large-scale structures, Comput. Struct., 82, 1101, 10.1016/j.compstruc.2004.03.014 · DOI
  15. Ballesteros-Pérez, 2019, Performance comparison of activity sensitivity metrics in schedule risk analysis, Autom. ConStruct., 106
  16. Baloi, 2003, Modelling global risk factors affecting construction cost performance, Int. J. Proj. Manag., 21, 261, 10.1016/S0263-7863(02)00017-0 · DOI
  17. Barraza, 2007, Cost contingency management, J. Manag. Eng., 140
  18. Bu-Qammaz, 2009, Risk assessment of international construction projects using the analytic network process, Can. J. Civ. Eng., 36, 1170, 10.1139/L09-061 · DOI
  19. Cagliano, 2015, Choosing project risk management techniques. A theoretical framework, J. Risk Res., 18, 232, 10.1080/13669877.2014.896398 · DOI
  20. Chang, 2017, New approach to estimating the standard deviations of lognormal cost variables in the Monte Carlo analysis of construction risks, J. Construct. Eng. Manag., 143, 10.1061/(ASCE)CO.1943-7862.0001207 · DOI
  21. Chapman, 2004, Why risk efficiency is a key aspect of best practice projects, Int. J. Proj. Manag., 22, 619, 10.1016/j.ijproman.2004.05.001 · DOI
  22. Clark, 2001, Monte Carlo analysis: ten years of experience, Cost Eng., 43, 40
  23. Colin, 2016, Empirical perspective on activity durations for project-management simulation studies, J. Construct. Eng. Manag., 142, 10.1061/(ASCE)CO.1943-7862.0001022 · DOI
  24. Diab, 2017, Modeling the construction risk ratings to estimate the contingency in highway projects, J. Construct. Eng. Manag., 143, 10.1061/(ASCE)CO.1943-7862.0001334 · DOI
  25. Duijm, 2015, Recommendations on the use and design of risk matrices, Saf. Sci., 76, 21, 10.1016/j.ssci.2015.02.014 · DOI · Paper page
  26. El-Kholy, 2020, Prediction of simulated cost contingency for steel reinforcement in building projects: ANN versus regression-based models, Int. J. Construct. Manag., 1
  27. El-Matbaegy, 2017, Risk analysis of construction sector in Egypt (during the economic recession periods), Port-Said Eng. Res. J., 21, 37, 10.21608/pserj.2017.33231 · DOI
  28. El-Sayegh, 2008, Risk assessment and allocation in the UAE construction industry, Int. J. Proj. Manag., 26, 431, 10.1016/j.ijproman.2007.07.004 · DOI · Paper page
  29. Eldosouky, 2014, Management of construction cost contingency covering upside and downside risks, Alex. Eng. J., 53, 863, 10.1016/j.aej.2014.09.008 · DOI
  30. Elms, 2004, Structural safety: issues and progress, Prog. Struct. Eng. Mater., 6, 116, 10.1002/pse.176 · DOI
  31. Fan, 2008, Choosing a project risk-handling strategy: an analytical model, Int. J. Prod. Econ., 112, 700, 10.1016/j.ijpe.2007.06.006 · DOI
  32. Frank, 1999, Treatment of uncertainties in space nuclear risk assessment with examples from Cassini mission implications, Reliab. Eng. Syst. Saf., 66, 203, 10.1016/S0951-8320(99)00002-2 · DOI
  33. Gosling, 2013, Identifying and categorizing the sources of uncertainty in construction supply chains, J. Construct. Eng. Manag., 139, 102, 10.1061/(ASCE)CO.1943-7862.0000574 · DOI
  34. Hammad, 2015, A new method of cost contingency management, IEEE Int. Conf. Ind. Eng. Eng. Manag., 38
  35. Hammad, 2016, Allocation and management of cost contingency in projects, J. Manag. Eng., 32, 1
  36. Han, 2001, Approaches for making risk-based go/no-go decision for international projects, J. Construct. Eng. Manag., 127, 300, 10.1061/(ASCE)0733-9364(2001)127:4(300) · DOI
  37. Helton, 2006, Sensitivity analysis in conjunction with evidence theory representations of epistemic uncertainty, Reliab. Eng. Syst. Saf., 91, 1414, 10.1016/j.ress.2005.11.055 · DOI · Paper page
  38. Hillson, 2005, vol. 1
  39. Hillson, 2014, How to manage the risks you didn't know you were taking, PMI® Glob. Congr., 1
  40. Hong, 2016, Uncertainty analysis for measuring greenhouse gas emissions in the building construction phase: a case study in China, J. Clean. Prod., 129, 183, 10.1016/j.jclepro.2016.04.085 · DOI
  41. Hoseini, 2020, Cost contingency and cost evolvement of construction projects in the preconstruction phase, J. Construct. Eng. Manag., 146, 10.1061/(ASCE)CO.1943-7862.0001842 · DOI
  42. Hosny, 2018, Risk management framework for Continuous Flight Auger piles construction in Egypt, Alex. Eng. J., 57, 2667, 10.1016/j.aej.2017.10.003 · DOI
  43. Idrus, 2011, Development of project cost contingency estimation model using risk analysis and fuzzy expert system, Expert Syst. Appl., 38, 1501, 10.1016/j.eswa.2010.07.061 · DOI
  44. Islam, 2021, Risk induced contingency cost modeling for power plant projects, Autom. ConStruct., 123
  45. Jung, 2015, The computer-based contingency estimation through analysis cost overrun risk of public construction project, KSCE J. Civ. Eng., 20, 1119, 10.1007/s12205-015-0184-8 · DOI
  46. Kwon, 2019, Improving project budget estimation accuracy and precision by analyzing reserves for both identified and unidentified risks, Proj. Manag. J., 50, 86, 10.1177/8756972818810963 · DOI · Paper page
  47. Lam, 2017, Risk management and contingency sum of construction projects, J. Finance Manag. Prop. Construct., 22, 237
  48. Lee, 2017, Management-reserve estimation for international construction projects based on risk-informed k-NN, J. Manag. Eng., 33
  49. Lhee, 2012, Prediction of financial contingency for asphalt resurfacing projects using artificial neural networks, J. Construct. Eng. Manag., 138, 22, 10.1061/(ASCE)CO.1943-7862.0000408 · DOI
  50. Liu, 2017, Improving risk assessment in financial feasibility of international engineering projects: a risk driver perspective, Int. J. Proj. Manag., 35, 204, 10.1016/j.ijproman.2016.11.004 · DOI
  51. Long, 2008, Fuzzy critical chain method for project scheduling under resource constraints and uncertainty, Int. J. Proj. Manag., 26, 688, 10.1016/j.ijproman.2007.09.012 · DOI
  52. Lorance, 2001, Basic techniques for analyzing and presentation of cost risk analysis, Cost Eng., 43, 25
  53. Mahendra, 2013, A study of risk management techniques for construction projects in developing countries, Int. J. Innovative Technol. Explor. Eng., 3, 139
  54. Mak, 2000, Using risk analisys to determine construction project contingencies, J. Construct. Eng. Manag., 126, 130, 10.1061/(ASCE)0733-9364(2000)126:2(130) · DOI
  55. Maronati, 2019, Estimating cost uncertainties in nuclear power plant construction through Monte Carlo sampled correlated random variables, Prog. Nucl. Energy, 111, 211, 10.1016/j.pnucene.2018.11.011 · DOI
  56. Mohamed, 2020, Simulation-based approach for risk assessment in onshore wind farm construction projects. 2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling, APARM, 2020
  57. Moreno-Cabezali, 2020, Application of a fuzzy-logic based model for risk assessment in additive manufacturing R&D projects, Comput. Ind. Eng., 145
  58. Moret, 2016, Construction cost and duration uncertainty model: application to high-speed rail line project, J. Construct. Eng. Manag., 142, 10.1061/(ASCE)CO.1943-7862.0001161 · DOI
  59. Moselhi, 1997, Risk assessment and contingency estimating, AACE Int. Trans., 90, 1
  60. Para-González, 2018, Obtaining the budget contingency reserve through the Monte Carlo method: study of a ferry construction project, Brodogradnja, 69, 79, 10.21278/brod69305 · DOI
  61. Salah, 2015, Contingency modelling for construction projects using fuzzy-set theory, Eng. Construct. Architect. Manag., 22, 214, 10.1108/ECAM-03-2014-0039 · DOI
  62. Seyedhoseini, 2009, An integrated methodology for assessment and selection of the project risk response actions, Risk Anal., 29, 752, 10.1111/j.1539-6924.2008.01187.x · DOI
  63. Shahtaheri, 2017, Applying very large scale integration reliability theory for understanding the impacts of type II risks on megaprojects, J. Manag. Eng., 33
  64. Sonmez, 2007, Quantitative methodology for determination of cost, J. Manag. Eng., 23, 35
  65. Thal, 2010, Estimation of cost contingency for air force construction projects, J. Construct. Eng. Manag., 136, 1181, 10.1061/(ASCE)CO.1943-7862.0000227 · DOI
  66. Touran, 2003, Probabilistic model for cost contingency, J. Construct. Eng. Manag., 129, 280, 10.1061/(ASCE)0733-9364(2003)129:3(280) · DOI
  67. Traynor, 2019, Time and cost contingency management using Monte Carlo simulation, Aust. J. Civ. Eng., 17, 11, 10.1080/14488353.2019.1606499 · DOI · Paper page
  68. Trietsch, 2012, Modeling activity times by the Parkinson distribution with a lognormal core: theory and validation, Eur. J. Oper. Res., 216, 386, 10.1016/j.ejor.2011.07.054 · DOI
  69. Uzzafer, 2013, A contingency estimation model for software projects, Int. J. Proj. Manag., 31, 981, 10.1016/j.ijproman.2012.12.002 · DOI
  70. Van, 2019, Predicting implementation cost contingencies for residential construction projects in flood-prone areas, Int. J. Manag. Proj. Bus., 12, 1097, 10.1108/IJMPB-04-2018-0071 · DOI
  71. Vasvári, 2015, Risk, risk perception, risk management - a review of the literature, Publ. Finance Quart., 60, 29
  72. Zhang, 2014, An optimization method for selecting project risk response strategies, Int. J. Proj. Manag., 32, 412, 10.1016/j.ijproman.2013.06.006 · DOI
  73. Zhi, 1995, Risk management for overseas construction projects, Int. J. Proj. Manag., 13, 231, 10.1016/0263-7863(95)00015-I · DOI
  74. Zou, 2010, Understanding and improving your risk management capability : assessment model for construction organizations, J. Construct. Eng. Manag., 136, 854, 10.1061/(ASCE)CO.1943-7862.0000175 · DOI

Cited by

Sorted by citations

Metadata

Authors
Curto, D., Acebes, F., González-Varona, J. M., & Poza, D.
Affiliations
Universidad de Valladolid (ES)
Corresponding author
Acebes, F.
ORCID
Venue
International Journal of Production Economics
Publisher
Elsevier BV
Published
2022-11
Volume
253
Pages
108626
ISSN
0925-5273 (ISSN-L)
DOI
10.1016/j.ijpe.2022.108626
OpenAlex
W4293331463
Cited by
47
References
0
Citations by year
2026: 20 · 2025: 13 · 2024: 8 · 2023: 6
FWCI
3.57
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Production Economics (publishedVersion, cc-by-nc-nd)
  • Hispana (publishedVersion, cc-by-nc-nd)
  • RePEc: Research Papers in Economics (submittedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
  • Repositorio Institucional de la Universidad de Málaga (University of Málaga) (submittedVersion, cc-by-nc-nd)
  • UVaDOC UVaDOC University of Valladolid Documentary Repository (University of Valladolid) (submittedVersion, cc-by-nc-nd)
  • arXiv (Cornell University)
Funders
Junta de Castilla y León (VA180P20)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Construction Project Management and Performance
  • Social Sciences › Economics, Econometrics and Finance › Finance › Capital Investment and Risk Analysis
  • Physical Sciences › Engineering › Building and Construction › BIM and Construction Integration
Keywords
aleatoric uncertainty, epistemic uncertainty, construction risk, cost estimation, uncertainty quantification, construction project management

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review