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Modelling uncertainty of cost and time in infrastructure projects
The construction of infrastructure projects is characterised by cost overruns and time delays. Scholars view that the estimation approach and inappropriate tools and techniques used to forecast possible uncertainty in the construction processes are a primary cause of cost overruns and time delays on...
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| Format: | Thesis |
| Language: | English |
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Department of Construction Economics and Management
2020
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| _version_ | 1867613291285577728 |
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| access_status_str | Open Access |
| author | Moghayedi, Alireza |
| author2 | Windapo, Abimbola Olukemi |
| author_browse | Moghayedi, Alireza Windapo, Abimbola Olukemi |
| author_facet | Windapo, Abimbola Olukemi Moghayedi, Alireza |
| author_sort | Moghayedi, Alireza |
| collection | Thesis |
| description | The construction of infrastructure projects is characterised by cost overruns and time delays. Scholars view that the estimation approach and inappropriate tools and techniques used to forecast possible uncertainty in the construction processes are a primary cause of cost overruns and time delays on construction projects. Uncertainties encountered in the construction process are underestimated and these impact on the final cost and time of construction projects through a combination of individual construction activities. The study, therefore, examines the initial and final cost of construction activities, towards developing a hybrid tool that captures and models’ different sources of uncertainty in infrastructure projects and their effect on cost and time underestimation. The study adopted a sequential exploratory mixed method research approach that went beyond the basic mixed method approaches, employing a combination of sequential and concurrent aspects of mixed methods. Data was gathered through a series of expert panel estimation sessions, technical brainstorming of experienced professionals (with 30 years’ experience and more) in the construction of infrastructure projects, and a structured self-administered questionnaire survey distributed to project managers of South African highway projects. The developed hybrid tool models the main structures from the activity level to the entire highway project. Consequently, three identified uncertainties in the construction process of infrastructures, namely variability in the construction process, correlations between the costs, times and cost-time of construction activities and disruptive events, are modelled jointly at the construction activity level. Data obtained from both qualitative and quantitative approaches were analysed using various techniques. The probability distribution function of cost and time were modelled using the lognormal and triangular probability distributions; while Monte Carlo Simulation (MCS), Copula analysis technique, the Markov processes, and Adaptive Neuro-Fuzzy Inference System (ANFIS) analytical technique were used in modelling the variability of the cost and time activity, correlation between costs, time and cost-time activities, and to model the occurrence of disruptive events, so that the impact size of disruptive events on the cost and time of activities respectively, can be intelligently assessed. The developed uncertainty model was validated against the final cost and time of a project case study, as well as against historical data of construction cost overruns and time delays in infrastructure projects. The study found that the different uncertainties had a distinct influence on construction cost and time of different project structures. Furthermore, the comparison of the deterministic estimates with the uncertainty estimates shows that the accumulated impact of the three uncertainty sources significantly increases the construction cost and time of infrastructure projects. Based on these findings, the research concludes that the disruptive event is the main cause of cost overruns and time delays in infrastructure projects. In the scale of activity, the correlation between the costs of different activities in the same structure causes the largest increase in the cost of activity, while the correlation between the times of repeated activity in the same structure causes the largest increase in time of the activity. Furthermore, the study concludes that the improvement in the accuracy of cost and time estimation of infrastructure projects depends on a combination of probability analysis and intelligent machine learning. The contributions of the study to construction management knowledge include a clear definition of uncertainty and the sources of uncertainties in the construction of infrastructure projects; an in-depth understanding of the construction process of linear infrastructure projects; and an improvement in the quality of data used (combination of experts’ estimation and historical data) for research in the area of project performance. The developed uncertainty model based on three sources of uncertainty at the activity level provides infrastructure project planners with a hybrid dynamic tool to accurately model and predict the construction cost and time of infrastructure projects at any stage of the project. Also, the uncertainty model has three other purposes: it is the preparatory point for allocation of budget, it facilitates the update of the impact of uncertainties and evaluates the effectiveness of countermeasures to mitigate against the threat of uncertainties. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/31079 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:33:48.261Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Department of Construction Economics and Management |
| publisherStr | Department of Construction Economics and Management |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/31079 Modelling uncertainty of cost and time in infrastructure projects Moghayedi, Alireza Windapo, Abimbola Olukemi Construction Economics and Management The construction of infrastructure projects is characterised by cost overruns and time delays. Scholars view that the estimation approach and inappropriate tools and techniques used to forecast possible uncertainty in the construction processes are a primary cause of cost overruns and time delays on construction projects. Uncertainties encountered in the construction process are underestimated and these impact on the final cost and time of construction projects through a combination of individual construction activities. The study, therefore, examines the initial and final cost of construction activities, towards developing a hybrid tool that captures and models’ different sources of uncertainty in infrastructure projects and their effect on cost and time underestimation. The study adopted a sequential exploratory mixed method research approach that went beyond the basic mixed method approaches, employing a combination of sequential and concurrent aspects of mixed methods. Data was gathered through a series of expert panel estimation sessions, technical brainstorming of experienced professionals (with 30 years’ experience and more) in the construction of infrastructure projects, and a structured self-administered questionnaire survey distributed to project managers of South African highway projects. The developed hybrid tool models the main structures from the activity level to the entire highway project. Consequently, three identified uncertainties in the construction process of infrastructures, namely variability in the construction process, correlations between the costs, times and cost-time of construction activities and disruptive events, are modelled jointly at the construction activity level. Data obtained from both qualitative and quantitative approaches were analysed using various techniques. The probability distribution function of cost and time were modelled using the lognormal and triangular probability distributions; while Monte Carlo Simulation (MCS), Copula analysis technique, the Markov processes, and Adaptive Neuro-Fuzzy Inference System (ANFIS) analytical technique were used in modelling the variability of the cost and time activity, correlation between costs, time and cost-time activities, and to model the occurrence of disruptive events, so that the impact size of disruptive events on the cost and time of activities respectively, can be intelligently assessed. The developed uncertainty model was validated against the final cost and time of a project case study, as well as against historical data of construction cost overruns and time delays in infrastructure projects. The study found that the different uncertainties had a distinct influence on construction cost and time of different project structures. Furthermore, the comparison of the deterministic estimates with the uncertainty estimates shows that the accumulated impact of the three uncertainty sources significantly increases the construction cost and time of infrastructure projects. Based on these findings, the research concludes that the disruptive event is the main cause of cost overruns and time delays in infrastructure projects. In the scale of activity, the correlation between the costs of different activities in the same structure causes the largest increase in the cost of activity, while the correlation between the times of repeated activity in the same structure causes the largest increase in time of the activity. Furthermore, the study concludes that the improvement in the accuracy of cost and time estimation of infrastructure projects depends on a combination of probability analysis and intelligent machine learning. The contributions of the study to construction management knowledge include a clear definition of uncertainty and the sources of uncertainties in the construction of infrastructure projects; an in-depth understanding of the construction process of linear infrastructure projects; and an improvement in the quality of data used (combination of experts’ estimation and historical data) for research in the area of project performance. The developed uncertainty model based on three sources of uncertainty at the activity level provides infrastructure project planners with a hybrid dynamic tool to accurately model and predict the construction cost and time of infrastructure projects at any stage of the project. Also, the uncertainty model has three other purposes: it is the preparatory point for allocation of budget, it facilitates the update of the impact of uncertainties and evaluates the effectiveness of countermeasures to mitigate against the threat of uncertainties. 2020-02-13T09:39:16Z 2020-02-13T09:39:16Z 2019 2020-02-12T10:54:03Z Doctoral Thesis Doctoral PhD http://hdl.handle.net/11427/31079 eng application/pdf Department of Construction Economics and Management Faculty of Engineering and the Built Environment |
| spellingShingle | Construction Economics and Management Moghayedi, Alireza Modelling uncertainty of cost and time in infrastructure projects |
| thesis_degree_str | Doctoral |
| title | Modelling uncertainty of cost and time in infrastructure projects |
| title_full | Modelling uncertainty of cost and time in infrastructure projects |
| title_fullStr | Modelling uncertainty of cost and time in infrastructure projects |
| title_full_unstemmed | Modelling uncertainty of cost and time in infrastructure projects |
| title_short | Modelling uncertainty of cost and time in infrastructure projects |
| title_sort | modelling uncertainty of cost and time in infrastructure projects |
| topic | Construction Economics and Management |
| url | http://hdl.handle.net/11427/31079 |
| work_keys_str_mv | AT moghayedialireza modellinguncertaintyofcostandtimeininfrastructureprojects |