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Key uncertainty events impacting on the completion time of highway construction projects

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Abstract

This paper examines the uncertainty events encountered in the process of constructing highways, and evaluates their impact on construction time, on highway projects in South Africa. The rationale for this examination stems from the view held by scholars that the construction of highways is a complex process, taking place in changing environments and often beset by uncertainties; and that there is a lack of appropriate evaluation of these uncertainty events occurring during the construction process. The research made use of a review of extant literature in the area of uncertainty management, and modeling in infrastructure projects, to guide the direction of the study. The inquiry process consisted of brainstorming by highway experts and interviewing them to identify the uncertainty factors that impact construction time.

An uncertainty matrix for South African highway projects was developed, using a quantitative model and descriptive statistics. It emerged from the study that the uncertainty events affecting the construction time of highway projects are distributed across economic, environmental, financial, legal, political, social and technical factors. Also, it was found that each factor might account for several uncertainty events which impact on construction time differently, through a combination of the uncertainty events of the individual construction activities.

Based on the obtained data, an Adaptive Neuro Fuzzy Inference System (ANFIS) has been developed, as a simple, reliable and accurate advanced machine learning technique to assess the impact of uncertainty events on the completion time of highway construction projects. To validate the ANFIS model, the Stepwise Regression (SR) models have been designed and their results are compared with the results of the ANFIS. Based on the predicted impact size of uncertainty events on the time of highway projects, it can be concluded that construction time on South African highway projects is significantly related to the social and technical uncertainties factors.

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References

  • Adam A, Josephson P E B, Lindahl G (2017). Aggregation of factors causing cost overruns and time delays in large public construction projects: Trends and implications. Engineering, Construction, and Architectural Management, 24(3): 393–406

    Article  Google Scholar 

  • Ahmadi M, Behzadian K, Ardeshir A, Kapelan Z (2017). Comprehensive risk management using fuzzy FMEA and MCDA techniques in highway construction projects. Journal of Civil Engineering and Management, 23(2): 300–310

    Article  Google Scholar 

  • Anderson S D, Molenaar K R, Schexnayder C J (2007). Guidance for Cost Estimation and Management for Highway Projects During Planning, Programming, and Preconstruction. Transportation Research Board

    Google Scholar 

  • Antunes R, Gonzalez V (2015). A production model for construction: A theoretical framework. Buildings, 5(1): 209–228

    Article  Google Scholar 

  • Asgari M S, Abbasi A, Alimohamadlou M (2016). Comparison of ANFIS and FAHP-FGP methods for supplier selection. Kybernetes, 45(3): 474–489

    Article  MathSciNet  Google Scholar 

  • Assaf S A, Al-Hejji S (2006). Causes of delay in large construction projects. International Journal of Project Management, 24(4): 349–357

    Google Scholar 

  • Aziz R F, Abdel-Hakam A A (2016). Exploring delay causes of road construction projects in Egypt. Alexandria Engineering Journal, 55(2): 1515–1539

    Article  Google Scholar 

  • Baloi D, Price A D (2003). Modelling global risk factors affecting construction cost performance. International Journal of Project Management, 21(4): 261–269

    Article  Google Scholar 

  • Banaitiene N, Banaitis A (2012). Risk management in construction projects. In: Banaitiene N, ed. Risk Management-Current Issues and Challenges. InTech

    Chapter  Google Scholar 

  • Barker K, Haimes Y Y (2009). Assessing uncertainty in extreme events: Applications to risk-based decision making in interdependent infrastructure sectors. Reliability Engineering & System Safety, 94(4): 819–829

    Article  Google Scholar 

  • Bunni N G (2003). Risk and Insurance in Construction. London: Routledge

    Book  Google Scholar 

  • Buragohain M, Mahanta C (2008). A novel approach for ANFIS modelling based on full factorial design. Applied Soft Computing, 8(1): 609–625

    Article  Google Scholar 

  • Chan A P, Ho D C, Tam C (2001). Design and build project success factors: Multivariate analysis. Journal of Construction Engineering and Management, 127(2): 93–100

    Article  Google Scholar 

  • Chapman C (2006). Key points of contention in framing assumptions for risk and uncertainty management. International Journal of Project Management, 24(4): 303–313

    Article  MathSciNet  Google Scholar 

  • Chen T T, Wang C H (2017). Fall risk assessment of bridge construction using Bayesian network transferring from fault tree analysis. Journal of Civil Engineering and Management, 23(2): 273–282

    Article  Google Scholar 

  • Chen L H, Lu H W (2001). An approximate approach for ranking fuzzy numbers based on left and right dominance. Computers & Mathematics with Applications, 41(12): 1589–1602

    Article  MathSciNet  MATH  Google Scholar 

  • Creedy G D, Skitmore M, Wong J K (2010). Evaluation of risk factors leading to cost overrun in delivery of highway construction projects. Journal of Construction Engineering and Management, 136(5): 528–537

    Article  Google Scholar 

  • Dey P K (2001). Decision support system for risk management: A case study. Management Decision, 39(8): 634–649

    Article  Google Scholar 

  • Diab M F, Varma A, Panthi K (2017). Modeling the construction risk ratings to estimate the contingency in highway projects. Journal of Construction Engineering and Management, 143(8): 04017041

    Article  Google Scholar 

  • Dikmen I, Birgonul M T, Han S (2007). Using fuzzy risk assessment to rate cost overrun risk in international construction projects. International Journal of Project Management, 25(5): 494–505

    Article  Google Scholar 

  • Ebrat M, Ghodsi R (2014). Construction project risk assessment by using adaptive-network-based fuzzy inference system: An empirical study. KSCE Journal of Civil Engineering, 18(5): 1213–1227

    Article  Google Scholar 

  • Ehsan N, Mirza E, Alam M, Ishaque A (2010). Notice of Retraction Risk management in construction industry. 2010 3rd IEEE International Conference on the Computer Science and Information Technology (ICCSIT)

    Book  Google Scholar 

  • Elhag T M, Wang Y M (2007). Risk assessment for bridge maintenance projects: Neural networks versus regression techniques. Journal of Computing in Civil Engineering, 21(6): 402–409

    Article  Google Scholar 

  • Fang C, Marle F, Zio E, Bocquet J C (2012). Network theory-based analysis of risk interactions in large engineering projects. Reliability Engineering & System Safety, 106: 1–10

    Article  Google Scholar 

  • Flyvbjerg B (2007). Policy and planning for large-infrastructure projects: Problems, causes, cures. Environment and Planning B: Planning & Design, 34(4): 578–597

    Article  Google Scholar 

  • Fragiadakis N, Tsoukalas V, Papazoglou V (2014). An adaptive neuro-fuzzy inference system (anfis) model for assessing occupational risk in the shipbuilding industry. Safety Science, 63: 226–235

    Article  Google Scholar 

  • Gadd S, Keeley D, Balmforth H (2003). Good practice and pitfalls in risk assessment. Health & Safety Laboratory

    Google Scholar 

  • Gosling J, Naim M, Towill D (2012). Identifying and categorizing the sources of uncertainty in construction supply chains. Journal of Construction Engineering and Management, 139(1): 102–110

    Article  Google Scholar 

  • Güneri A F, Ertay T, Yücel A, (2011). An approach based on ANFIS input selection and modelling for supplier selection problem. Expert Systems with Applications, 38(12): 14907–14917

    Article  Google Scholar 

  • Huang J, Negnevitsky M, Nguyen D T (2002). A neural-fuzzy classifier for recognition of power quality disturbances. IEEE Transactions on Power Delivery, 17(2): 609–616

    Article  Google Scholar 

  • Institute P M (2013). A Guide to the Project Management Body of Knowledge: PMBOK Guide. Project Management Institute

    Google Scholar 

  • Islam M S, Nepal M P, Skitmore M, Attarzadeh M (2017). Current research trends and application areas of fuzzy and hybrid methods to the risk assessment of construction projects. Advanced Engineering Informatics, 33: 112–131

    Article  Google Scholar 

  • ISO (2009). 31000: 2009 Risk Management-Principles and Guidelines. International Organization for Standardization, Geneva, Switzerland

    Google Scholar 

  • Iyer K, Jha K (2005). Factors affecting cost performance: Evidence from Indian construction projects. International Journal of Project Management, 23(4): 283–295

    Article  Google Scholar 

  • Jang J S (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3): 665–685

    Article  Google Scholar 

  • Jin X H (2010). Neurofuzzy decision support system for efficient risk allocation in public-private partnership infrastructure projects. Journal of Computing in Civil Engineering, 24(6): 525–538

    Article  Google Scholar 

  • Kangari R (1988). Construction risk management. Civil Engineering Systems, 5(3): 114–120

    Article  Google Scholar 

  • Kuo Y C, Lu S T (2013). Using fuzzy multiple criteria decision making approach to enhance risk assessment for metropolitan construction projects. International Journal of Project Management, 31(4): 602–614

    Article  Google Scholar 

  • Li K, Su H, Chu J (2011). Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: A comparative study. Energy and Building, 43(10): 2893–2899

    Article  Google Scholar 

  • Mahendra P A, Pitroda J R, Bhavsar J (2013). A study of risk management techniques for construction projects in developing countries. International Journal of Innovative Technology and Exploring Engineering, 3(5): 139–142

    Google Scholar 

  • Marzouk M M, El-Rasas T I (2014). Analyzing delay causes in Egyptian construction projects. Journal of Advanced Research, 5(1): 49–55

    Article  Google Scholar 

  • Mills A (2001). A systematic approach to risk management for construction. Structural Survey, 19(5): 245–252

    Article  MathSciNet  Google Scholar 

  • Moghayedi A (2016). Improving Critical Path Method (CPM) by applying safety factor to manage delays. Scientia Iranica, 23(3): 815

    Article  Google Scholar 

  • Moghayedi A, Windapo A (2017). Developing a data gathering tool for modelling uncertainty in highway projects. The Proceedings of the 9th International Conference on Construction in the 21st Century, 1: 773–781

    Google Scholar 

  • Moghayedi A, Windapo A (2018). Identification of the uncertain events impacting on construction time of South African highway projects. Journal of Construction Project Management Innovation, 8(S1): 2146–2163

    Google Scholar 

  • Moore D S, Kirkland S (2007). The Basic Practice of Statistics. New York: WH Freeman New York

    Google Scholar 

  • Moret Y, Einstein H H (2016). Construction cost and duration uncertainty model: Application to high-speed rail line project. Journal of Construction Engineering and Management, 142(10): 05016010

    Article  Google Scholar 

  • Negnevitsky M (2005). Artificial Intelligence: A Guide to Intelligent Systems. London: Pearson Education

    Google Scholar 

  • Nieto-Morote A, Ruz-Vila F (2011). A fuzzy approach to construction project risk assessment. International Journal of Project Management, 29(2): 220–231

    Article  MATH  Google Scholar 

  • Odediran S J, Windapo A O (2018). Risk-based entry decision into African construction markets: A proposed integrated model. Built Environment Project and Asset Management, 8(1): 91–111

    Article  Google Scholar 

  • Renuka S, Umarani C, Kamal S (2014). A review on critical risk factors in the life cycle of construction projects. Journal of Civil Engineering Research, 4(2A): 31–36

    Google Scholar 

  • Santoso D S, Soeng S (2016). Analyzing delays of road construction projects in Cambodia: Causes and effects. Journal of Management Engineering, 32(6): 05016020

    Google Scholar 

  • Saqib M, Farooqui R U, Lodi S H (2008). Assessment ofcritical success factors for construction projects in Pakistan. The First International Conference on Construction in Developing Countries (ICCIDC-1), Advancing and Integrating Construction Education, Research and Practice, Karachi, Pakistan

    Google Scholar 

  • Shahhosseini V, Sebt M (2011). Competency-based selection and assignment of human resources to construction projects. Scientia Iranica, 18(2): 163–180

    Article  Google Scholar 

  • Shen L, Wu G W, Ng C S (2001). Risk assessment for construction joint ventures in China. Journal of Construction Engineering and Management, 127(1): 76–81

    Article  Google Scholar 

  • Sinesilassie E G, Tabish S Z S, Jha K N (2017). Critical factors affecting schedule performance: A case of Ethiopian public construction projects-engineers’ perspective. Engineering, Construction, and Architectural Management, 24(5): 757–773

    Article  Google Scholar 

  • Skitmore R M, Ng S T (2003). Forecast models for actual construction time and cost. Building and Environment, 38(8): 1075–1083

    Article  Google Scholar 

  • Taghipour M, Seraj F, Hassani M A, Kheirabadi S F (2015). Risk analysis in the management of urban construction projects from the perspective of the employer and the contractor. International Journal of Organizational Leadership, 4(4): 356–373

    Article  Google Scholar 

  • Tah J, Carr V (2000). A proposal for construction project risk assessment using fuzzy logic. Construction Management and Economics, 18(4): 491–500

    Article  Google Scholar 

  • Takagi T, Sugeno M (1983). Derivation of fuzzy control rules from human operator’s control actions. IFAC Proceedings Volumes, 16(13): 55–60

    Article  Google Scholar 

  • Tang W H, Ang A (2007). Probability Concepts in Engineering: Emphasis on Applications to Civil & Environmental Engineering. New Jersey: Wiley Hoboken

    Google Scholar 

  • Thomas A, Kalidindi S N, Ananthanarayanan K (2003). Risk perception analysis of BOT road project participants in India. Construction Management and Economics, 21(4): 393–407

    Article  Google Scholar 

  • Ugur L (2017). A Neuro-Adaptive Learning (NAL) approach about costs of residential buildings. Acta Physica Polonica A, 132(3): 585–587

    Article  Google Scholar 

  • Wang X, Zhu J, Ma F, Li C, Cai Y, Yang Z. (2016a). Bayesian network-based risk assessment for hazmat transportation on the Middle Route of the South-to-North Water Transfer Project in China. Stochastic Environmental Research Risk Assessment, 30(3): 841–857

    Article  Google Scholar 

  • Wang J, Yuan H (2011). Factors affecting contractors’ risk attitudes in construction projects: Case study from China. International Journal of Project Management, 29(2): 209–219

    Article  MathSciNet  Google Scholar 

  • Wang M T, Chou H Y (2003). Risk allocation and risk handling of highway projects in Taiwan. Journal of Management Engineering, 19(2): 60–68

    Google Scholar 

  • Wang T, Wang S, Zhang L, Huang Z, Li Y (2016b). A major infrastructure risk-assessment framework: Application to a cross-sea route project in China. International Journal of Project Management, 34(7): 1403–1415

    Article  Google Scholar 

  • Wang Y M, Elhag T M (2008). An adaptive neuro-fuzzy inference system for bridge risk assessment. Expert Systems with Applications, 34(4): 3099–3106

    Article  Google Scholar 

  • Winch G M (2010). Managing Construction Projects. New Jersey: John Wiley & Sons

    Google Scholar 

  • Youssef O A (2004). Combined fuzzy-logic wavelet-based fault classification technique for power system relaying. IEEE Transactions on Power Delivery, 19(2): 582–589

    Article  Google Scholar 

  • Yun S, Jung W, Han S H, Park H (2015). Critical organizational success factors for public private partnership projects-a comparison of solicited and unsolicited proposals. Journal of Civil Engineering and Management, 21(2): 131–143

    Article  Google Scholar 

  • Zadeh L A (1996). Soft computing and fuzzy logic. In: Klir G J, Yuan B, eds. Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lotfi a Zadeh. Singapore: World Scientific, 796–804

    Chapter  Google Scholar 

  • Zavadskas E K, Turskis Z, Tamošaitiene J (2010). Risk assessment of construction projects. Journal of Civil Engineering and Management, 16(1): 33–46

    Article  Google Scholar 

  • Zayed T, Amer M, Pan J (2008). Assessing risk and uncertainty inherent in Chinese highway projects using AHP. International Journal of Project Management, 26(4): 408–419

    Article  Google Scholar 

  • Zayed T M, Halpin D W (2004). Quantitative assessment for piles productivity factors. Journal of Construction Engineering and Management, 130(3): 405–414

    Article  Google Scholar 

  • Zeng J, An M, Chan A H C (2005). A fuzzy reasoning decision making approach based multi-expert judgement for construction project risk analysis. The Proceedings of the Twenty-first Annual Conference, Association of Researchers in Construction Management (ARCOM), London, UK

    Google Scholar 

  • Zou P X, Zhang G, Wang J (2007). Understanding the key risks in construction projects in China. International Journal of Project Management, 25(6): 601–614

    Article  Google Scholar 

Download references

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Correspondence to Alireza Moghayedi.

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This work is based on the research support in part by the National Research Foundation of South Africa (Grant No. 105301).

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Moghayedi, A., Windapo, A. Key uncertainty events impacting on the completion time of highway construction projects. Front. Eng. Manag. 6, 275–298 (2019). https://doi.org/10.1007/s42524-019-0022-7

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