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Key uncertainty events impacting on the completion time of highway construction projects |
Alireza MOGHAYEDI( ), Abimbola WINDAPO |
| Department of Construction Economics and Management, University of Cape Town, Cape Town 7701, South Africa |
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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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| Keywords
ANFIS, construction time
impact assessment, highway project
South Africa
uncertainty
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Corresponding Author(s):
Alireza MOGHAYEDI
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Online First Date: 26 April 2019
Issue Date: 17 May 2019
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| 1 |
AAdam, P E B Josephson, G Lindahl (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
https://doi.org/10.1108/ECAM-09-2015-0135
|
| 2 |
MAhmadi, K Behzadian, AArdeshir, ZKapelan (2017). Comprehensive risk management using fuzzy FMEA and MCDA techniques in highway construction projects. Journal of Civil Engineering and Management, 23(2): 300–310
https://doi.org/10.3846/13923730.2015.1068847
|
| 3 |
S DAnderson, K R Molenaar, C J Schexnayder (2007). Guidance for Cost Estimation and Management for Highway Projects During Planning, Programming, and Preconstruction. Transportation Research Board
|
| 4 |
RAntunes, V Gonzalez (2015). A production model for construction: A theoretical framework. Buildings, 5(1): 209–228
https://doi.org/10.3390/buildings5010209
|
| 5 |
M SAsgari, A Abbasi, MAlimohamadlou (2016). Comparison of ANFIS and FAHP-FGP methods for supplier selection. Kybernetes, 45(3): 474–489
https://doi.org/10.1108/K-09-2014-0195
|
| 6 |
S AAssaf, S Al-Hejji (2006). Causes of delay in large construction projects. International Journal of Project Management, 24(4): 349–357
https://doi.org/10.1016/j.ijproman.2005.11.010
|
| 7 |
R FAziz, A A Abdel-Hakam (2016). Exploring delay causes of road construction projects in Egypt. Alexandria Engineering Journal, 55(2): 1515–1539
https://doi.org/10.1016/j.aej.2016.03.006
|
| 8 |
DBaloi, A D Price (2003). Modelling global risk factors affecting construction cost performance. International Journal of Project Management, 21(4): 261–269
https://doi.org/10.1016/S0263-7863(02)00017-0
|
| 9 |
NBanaitiene, A Banaitis (2012). Risk management in construction projects. In: Banaitiene N, ed. Risk Management-Current Issues and Challenges. InTech
|
| 10 |
KBarker, Y Y Haimes (2009). Assessing uncertainty in extreme events: Applications to risk-based decision making in interdependent infrastructure sectors. Reliability Engineering & System Safety, 94(4): 819–829
https://doi.org/10.1016/j.ress.2008.09.008
|
| 11 |
N GBunni (2003). Risk and Insurance in Construction. London: Routledge
|
| 12 |
MBuragohain, C Mahanta (2008). A novel approach for ANFIS modelling based on full factorial design. Applied Soft Computing, 8(1): 609–625
https://doi.org/10.1016/j.asoc.2007.03.010
|
| 13 |
A PChan, D C Ho, C Tam (2001). Design and build project success factors: Multivariate analysis. Journal of Construction Engineering and Management, 127(2): 93–100
https://doi.org/10.1061/(ASCE)0733-9364(2001)127:2(93)
|
| 14 |
CChapman (2006). Key points of contention in framing assumptions for risk and uncertainty management. International Journal of Project Management, 24(4): 303–313
https://doi.org/10.1016/j.ijproman.2006.01.006
|
| 15 |
T TChen, C H Wang (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
https://doi.org/10.3846/13923730.2015.1068841
|
| 16 |
L HChen, H W Lu (2001). An approximate approach for ranking fuzzy numbers based on left and right dominance. Computers & Mathematics with Applications, 41(12): 1589–1602
https://doi.org/10.1016/S0898-1221(01)00124-9
|
| 17 |
G DCreedy, M Skitmore, J KWong (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
https://doi.org/10.1061/(ASCE)CO.1943-7862.0000160
|
| 18 |
P KDey (2001). Decision support system for risk management: A case study. Management Decision, 39(8): 634–649
https://doi.org/10.1108/00251740110399558
|
| 19 |
M FDiab, A Varma, KPanthi (2017). Modeling the construction risk ratings to estimate the contingency in highway projects. Journal of Construction Engineering and Management, 143(8): 04017041
https://doi.org/10.1061/(ASCE)CO.1943-7862.0001334
|
| 20 |
IDikmen, M T Birgonul, S Han (2007). Using fuzzy risk assessment to rate cost overrun risk in international construction projects. International Journal of Project Management, 25(5): 494–505
https://doi.org/10.1016/j.ijproman.2006.12.002
|
| 21 |
MEbrat, R Ghodsi (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
https://doi.org/10.1007/s12205-014-0139-5
|
| 22 |
NEhsan, E Mirza, MAlam, AIshaque (2010). Notice of Retraction Risk management in construction industry. 2010 3rd IEEE International Conference on the Computer Science and Information Technology (ICCSIT)
|
| 23 |
T MElhag, Y M Wang (2007). Risk assessment for bridge maintenance projects: Neural networks versus regression techniques. Journal of Computing in Civil Engineering, 21(6): 402–409
https://doi.org/10.1061/(ASCE)0887-3801(2007)21:6(402)
|
| 24 |
CFang, F Marle, EZio, J CBocquet (2012). Network theory-based analysis of risk interactions in large engineering projects. Reliability Engineering & System Safety, 106: 1–10
https://doi.org/10.1016/j.ress.2012.04.005
|
| 25 |
BFlyvbjerg (2007). Policy and planning for large-infrastructure projects: Problems, causes, cures. Environment and Planning B: Planning & Design, 34(4): 578–597
https://doi.org/10.1068/b32111
|
| 26 |
NFragiadakis, V Tsoukalas, VPapazoglou (2014). An adaptive neuro-fuzzy inference system (anfis) model for assessing occupational risk in the shipbuilding industry. Safety Science, 63: 226–235
https://doi.org/10.1016/j.ssci.2013.11.013
|
| 27 |
SGadd, D Keeley, HBalmforth (2003). Good practice and pitfalls in risk assessment. Health & Safety Laboratory
|
| 28 |
JGosling, M Naim, DTowill (2012). Identifying and categorizing the sources of uncertainty in construction supply chains. Journal of Construction Engineering and Management, 139(1): 102–110
https://doi.org/10.1061/(ASCE)CO.1943-7862.0000574
|
| 29 |
A FGüneri, TErtay, AYücel, (2011). An approach based on ANFIS input selection and modelling for supplier selection problem. Expert Systems with Applications, 38(12): 14907–14917
https://doi.org/10.1016/j.eswa.2011.05.056
|
| 30 |
JHuang, M Negnevitsky, D TNguyen (2002). A neural-fuzzy classifier for recognition of power quality disturbances. IEEE Transactions on Power Delivery, 17(2): 609–616
https://doi.org/10.1109/61.997947
|
| 31 |
P MInstitute (2013). A Guide to the Project Management Body of Knowledge: PMBOK Guide. Project Management Institute
|
| 32 |
M SIslam, M P Nepal, M Skitmore, MAttarzadeh (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
https://doi.org/10.1016/j.aei.2017.06.001
|
| 33 |
ISO (2009). 31000: 2009 Risk Management—Principles and Guidelines. International Organization for Standardization, Geneva, Switzerland
|
| 34 |
KIyer, K Jha (2005). Factors affecting cost performance: Evidence from Indian construction projects. International Journal of Project Management, 23(4): 283–295
https://doi.org/10.1016/j.ijproman.2004.10.003
|
| 35 |
J SJang (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3): 665–685
https://doi.org/10.1109/21.256541
|
| 36 |
X HJin (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
https://doi.org/10.1061/(ASCE)CP.1943-5487.0000058
|
| 37 |
RKangari (1988). Construction risk management. Civil Engineering Systems, 5(3): 114–120
https://doi.org/10.1080/02630258808970516
|
| 38 |
Y CKuo, S T Lu (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
https://doi.org/10.1016/j.ijproman.2012.10.003
|
| 39 |
KLi, H Su, JChu (2011). Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: A comparative study. Energy and Building, 43(10): 2893–2899
https://doi.org/10.1016/j.enbuild.2011.07.010
|
| 40 |
P AMahendra, J R Pitroda, J Bhavsar (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
|
| 41 |
M MMarzouk, T I El-Rasas (2014). Analyzing delay causes in Egyptian construction projects. Journal of Advanced Research, 5(1): 49–55
https://doi.org/10.1016/j.jare.2012.11.005
|
| 42 |
AMills (2001). A systematic approach to risk management for construction. Structural Survey, 19(5): 245–252
https://doi.org/10.1108/02630800110412615
|
| 43 |
AMoghayedi (2016). Improving Critical Path Method (CPM) by applying safety factor to manage delays. Scientia Iranica, 23(3): 815
|
| 44 |
AMoghayedi, A Windapo (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
|
| 45 |
AMoghayedi, A Windapo (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
|
| 46 |
D SMoore, S Kirkland (2007). The Basic Practice of Statistics. New York: WH Freeman New York
|
| 47 |
YMoret, H H Einstein (2016). Construction cost and duration uncertainty model: Application to high-speed rail line project. Journal of Construction Engineering and Management, 142(10): 05016010
https://doi.org/10.1061/(ASCE)CO.1943-7862.0001161
|
| 48 |
MNegnevitsky (2005). Artificial Intelligence: A Guide to Intelligent Systems. London: Pearson Education
|
| 49 |
ANieto-Morote, F Ruz-Vila (2011). A fuzzy approach to construction project risk assessment. International Journal of Project Management, 29(2): 220–231
https://doi.org/10.1016/j.ijproman.2010.02.002
|
| 50 |
S JOdediran, A O Windapo (2018). Risk-based entry decision into African construction markets: A proposed integrated model. Built Environment Project and Asset Management, 8(1): 91–111
https://doi.org/10.1108/BEPAM-05-2016-0021
|
| 51 |
SRenuka, C Umarani, SKamal (2014). A review on critical risk factors in the life cycle of construction projects. Journal of Civil Engineering Research, 4(2A): 31–36
|
| 52 |
D SSantoso, S Soeng (2016). Analyzing delays of road construction projects in Cambodia: Causes and effects. Journal of Management Engineering, 32(6): 05016020
https://doi.org/10.1061/(ASCE)ME.1943-5479.0000467
|
| 53 |
MSaqib, R U Farooqui, S H Lodi (2008). Assessment of critical 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
|
| 54 |
VShahhosseini, M Sebt (2011). Competency-based selection and assignment of human resources to construction projects. Scientia Iranica, 18(2): 163–180
https://doi.org/10.1016/j.scient.2011.03.026
|
| 55 |
LShen, G W Wu, C S Ng (2001). Risk assessment for construction joint ventures in China. Journal of Construction Engineering and Management, 127(1): 76–81
https://doi.org/10.1061/(ASCE)0733-9364(2001)127:1(76)
|
| 56 |
E GSinesilassie, S Z STabish, K NJha (2017). Critical factors affecting schedule performance: A case of Ethiopian public construction projects–engineers’ perspective. Engineering, Construction, and Architectural Management, 24(5): 757–773
https://doi.org/10.1108/ECAM-03-2016-0062
|
| 57 |
R MSkitmore, S T Ng (2003). Forecast models for actual construction time and cost. Building and Environment, 38(8): 1075–1083
https://doi.org/10.1016/S0360-1323(03)00067-2
|
| 58 |
MTaghipour, F Seraj, M AHassani, S FKheirabadi (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
https://doi.org/10.33844/ijol.2015.60284
|
| 59 |
JTah, V Carr (2000). A proposal for construction project risk assessment using fuzzy logic. Construction Management and Economics, 18(4): 491–500
https://doi.org/10.1080/01446190050024905
|
| 60 |
TTakagi, M Sugeno (1983). Derivation of fuzzy control rules from human operator’s control actions. IFAC Proceedings Volumes, 16(13): 55–60
|
| 61 |
W HTang, A Ang (2007). Probability Concepts in Engineering: Emphasis on Applications to Civil & Environmental Engineering. New Jersey: Wiley Hoboken
|
| 62 |
AThomas, S N Kalidindi, K Ananthanarayanan (2003). Risk perception analysis of BOT road project participants in India. Construction Management and Economics, 21(4): 393–407
https://doi.org/10.1080/0144619032000064127
|
| 63 |
LUgur (2017). A Neuro-Adaptive Learning (NAL) approach about costs of residential buildings. Acta Physica Polonica A, 132(3): 585–587
https://doi.org/10.12693/APhysPolA.132.585
|
| 64 |
XWang, J Zhu, FMa, CLi, Y Cai, Z.Yang (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
https://doi.org/10.1007/s00477-015-1113-6
|
| 65 |
JWang, H Yuan (2011). Factors affecting contractors’ risk attitudes in construction projects: Case study from China. International Journal of Project Management, 29(2): 209–219
|
| 66 |
M TWang, H Y Chou (2003). Risk allocation and risk handling of highway projects in Taiwan. Journal of Management Engineering, 19(2): 60–68
https://doi.org/10.1061/(ASCE)0742-597X(2003)19:2(60)
|
| 67 |
TWang, S Wang, LZhang, ZHuang, YLi (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
https://doi.org/10.1016/j.ijproman.2015.12.006
|
| 68 |
Y MWang, T M Elhag (2008). An adaptive neuro-fuzzy inference system for bridge risk assessment. Expert Systems with Applications, 34(4): 3099–3106
https://doi.org/10.1016/j.eswa.2007.06.026
|
| 69 |
G MWinch (2010). Managing Construction Projects. New Jersey: John Wiley & Sons
|
| 70 |
O AYoussef (2004). Combined fuzzy-logic wavelet-based fault classification technique for power system relaying. IEEE Transactions on Power Delivery, 19(2): 582–589
https://doi.org/10.1109/TPWRD.2004.826386
|
| 71 |
SYun, W Jung, S HHan, HPark (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
https://doi.org/10.3846/13923730.2013.802715
|
| 72 |
L AZadeh (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
|
| 73 |
E KZavadskas, ZTurskis, JTamošaitiene (2010). Risk assessment of construction projects. Journal of Civil Engineering and Management, 16(1): 33–46
https://doi.org/10.3846/jcem.2010.03
|
| 74 |
TZayed, M Amer, JPan (2008). Assessing risk and uncertainty inherent in Chinese highway projects using AHP. International Journal of Project Management, 26(4): 408–419
https://doi.org/10.1016/j.ijproman.2007.05.012
|
| 75 |
T MZayed, D W Halpin (2004). Quantitative assessment for piles productivity factors. Journal of Construction Engineering and Management, 130(3): 405–414
https://doi.org/10.1061/(ASCE)0733-9364(2004)130:3(405)
|
| 76 |
JZeng, M An, A H CChan (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
|
| 77 |
P XZou, G Zhang, JWang (2007). Understanding the key risks in construction projects in China. International Journal of Project Management, 25(6): 601–614
https://doi.org/10.1016/j.ijproman.2007.03.001
|
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