IDENTIFICATION OF THE UNCERTAIN EVENTS IMPACTING ON CONSTRUCTION TIME OF SOUTH AFRICAN HIGHWAY PROJECTS Alireza MOGHAYEDI1, and Abimbola WINDAPO2 1 Department of Construction Economics and Management, University of Cape Town, South Africa, Email: MGHALI001@myuct.ac.za 2 Department of Construction Economics and Management, University of Cape Town, South Africa, Email: Abimbola.Windapo@uct.ac.za ABSTRACT This article examines the uncertain events encountered in the construction process of highway projects in South Africa, so as to evaluate their impact on the construction time of such projects. The rationale for this examination stems from the view held by scholars that highways are complex projects initiated in dynamic environments, which are often beset by different uncertainties and a lack of appropriate evaluation of the uncertain events that occur during the construction process. The research made use of a review of existing literature in the area of uncertainty management and modelling in infrastructure projects, to guide the direction of the study, brainstorming, and interviews conducted with highway experts to identify the factors of uncertainty that impact construction time on infrastructure projects. A simple uncertainty matrix for South African highway projects was developed using a quantitative model and descriptive statistics. It emerged from the study that the uncertain events that affect 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 contains several uncertain events, which impact on construction time differently, through a combination of the uncertain events of the individual construction activities. Based on the findings, it can be concluded that construction time on South African highway projects is significantly related to the social and technical factors of uncertainties. The matrix developed will be useful in modelling uncertainty of the cost and time of individual construction activities in highway projects. Keywords: construction time, highway projects, South Africa, uncertainty 1. INTRODUCTION Highway construction projects are subject to risks and uncertainties (Moret and Einstein, 2016). There are various risks and uncertainties existing in highway construction projects that affect construction performance differently. Risks have different probabilities of occurrence that impact project performance (Walker et al., 2003), causing schedule delays or cost overruns (Moghayedi, 2016; Chapman, 2006; Wang and Chou, 2003; Zayed and Halpin, 2004). The number and the importance of such events depend on the size and the complexity of the construction project (Zavadskas et al., 2010). Highway projects are one of the most dynamic, challenging, 2146 and complex construction projects, because they are exposed to different risks (Mills, 2001). According to Flyvbjerg (2007), there is more uncertainty in highway projects than there is in other construction projects, because of the unique features of such projects, including complexity in the long duration of the construction, the dynamic nature of the process, the repetitive linear nature of such projects, and the mobile nature of the construction sites. Uncertainty affecting construction projects has long been recognised by researchers as a major obstacle to achieving the objectives of the project, and as a cause of low levels of productivity (Antunes and Gonzalez, 2015; Bloom, 2014; Childerhouse and Towill, 2004; Moret and Einstein, 2016). Uncertainty means an unknown phenomenon (Walker et al., 2003). It is associated with the location, it is project-specific, and it has no root causes that can be generalised (Ramanathan et al., 2012). Therefore, there is an obvious need to effectively anticipate, identify and classify the uncertain events on different locations and projects to assess their influence on the objectives of construction projects. Uncertainty assessment involves identifying, evaluating and modelling various uncertain events in the construction process of highway projects, and developing a model for quantifying the impact of different events on the objectives of the project. The magnitude of the influence of uncertainty can be assessed by two parameters, namely probability of occurrence, and severity of the event (Gadd et al., 2003; ISO, 2009; Project Management Institute, 2013). Quantification of these factors with classical methods, such as probability analysis and influence diagrams, is very difficult (Zeng et al., 2005). Efficient applications and quantification techniques are difficult and complex, and, furthermore, exact data are required (Winch, 2010). Unfortunately, such data either do not exist at all or are hard to obtain. Furthermore, most of the classical mathematical assessment methods, such as differential equations, are not able to examine the relationship between input variables and an output variable, and they are not well suited for uncertain problems (Youssef, 2004). Stepwise regression analysis (SRA), on the other hand, is used in modelling to examine the strength and the direction of the relationship between each dependent variable and an independent variable, and the results indicate whether this relationship is statistically valid. Also, SRA is able to estimate the value of dependent variable when the independent variables are known. Therefore, this current research examines the uncertain events in the construction of highway projects in South Africa, and whether there are key events that have a significant impact on the completion time of such projects, with the aim of developing SRA models to assess the impact of uncertainty on the completion time of highway construction projects. 2. LITERATURE REVIEW The effect of uncertain events on the objectives of infrastructure projects has been identified in several works of literature (Anderson et al., 2007; Antunes and Gonzalez, 2015; Barker and Haimes, 2009; Moret and Einstein, 2016; Renuka et al., 2014). Occurrence of uncertain events in highway construction projects is greater than in other construction projects, due to the unique features of such projects, including complexity, the long duration of the construction, the dynamic nature of the process, the repetitive linear nature of such projects, and the mobile nature of the construction sites (Flyvbjerg, 2007). Due to the peculiar nature of uncertainty, there is a need to identify and classify the uncertain events and their factors, using the breakdown structure and the risk and uncertainty management process to assess their impact. 2147 One of the most comprehensive studies in the field of uncertainty factors identification was conducted by Aziz and Abdel-Hakam (2016). They explored 293 disruptive events as delay causes of road construction projects in Egypt under 15 major groups. Another noteworthy study was conducted by Odediran and Windapo (2017). They identified 81 risks in African construction markets under five major factors, namely political, social, economic/financial, procurement, and design and construction. Similarly, Assaf and AlHejji (2006) evaluated 73 uncertain events that cause delays in different types of large construction projects in Saudi Arabia under the following factors: project, owner, contractor, design, materials, equipment, labour, and external. After an extensive review of literature in the field of risk and uncertainty in construction projects, the seven uncertainty-related factors most common to researchers in the field were identified. They are presented in Table 1. Table 1: Proposed uncertainty factors Factor Description Sources Economic Issues or concerns associated with the macroeconomic impact of the community and the region in which the construction project is to be located Banaitiene and Banaitis, 2012; Dey, 2001; Iyer and Jha, 2005; Kuo and Lu, 2013; Saqib et al., 2008; Tah and Carr, 2000; Wang and Yuan, 2011; Zavadskas et al., 2010 Environmental Issues associated with the environmental problems, concerns and activities confronting the project Banaitiene and Banaitis, 2012; Ehsan et al., 2010; Iyer and Jha, 2005; Saqib et al., 2008; Tah and Carr, 2000; Wang and Yuan, 2011 Financial Issues or concerns associated with the financing of the project. Several researchers emphasise financial uncertainties as one of the important factors affecting infrastructure project outcomes Banaitiene & Banaitis, 2012; Bunni, 2003; Dey, 2001; Ehsan, Mirza, Alam, & Ishaque, 2010; Fang, Marle, Zio, & Bocquet, 2012; Saqib, Farooqui, & Lodi, 2008; Shen, Wu, & Ng, 2001; Taghipour, Seraj, Hassani, & Kheirabadi, 2015; Tah & Carr, 2000; Zayed, Amer, & Pan, 2008 Legal Issues or concerns associated Bunni, 2003; Shen et al., 2001; with the significant legal Zou et al., 2007 consequences that flow from legal actions attributable to the project Political Issues or concerns associated with the local, regional and national political and regulatory situation confronting the project. Various researchers identify political uncertainty as a major factor affecting the 2148 Baloi and Price, 2003; Banaitiene and Banaitis, 2012; Dey, 2001; Ehsan et al., 2010; Iyer and Jha, 2005; Saqib et al., 2008; Taghipour et al., 2015; performance of infrastructure Tah and Carr, 2000; Zavadskas projects. et al., 2010; Zayed et al., 2008 Social Issues or concerns associated Kuo and Lu, 2013; Saqib et al., with the social and cultural 2008; Wang and Yuan, 2011; impacts of the community and Zavadskas et al., 2010 the region in which the construction project is to be located Technical Issues or concerns associated with the technology used in the project by different stakeholders during construction Banaitiene and Banaitis, 2012; Bunni, 2003; Dey, 2001; Dikmen et al., 2007; Ehsan et al., 2010; Fang et al., 2012; Mahendra et al., 2013; NietoMorote and Ruz-Vila, 2011; Saqib et al., 2008; Shen et al., 2001; Tah and Carr, 2000; Wang and Yuan, 2011; Zavadskas et al., 2010; Zayed et al., 2008 Through a review of the literature, a list of uncertain events that impact on the completion time of construction projects was compiled. These events were analysed and ranked according to the number of times cited. The top 20 uncertain events cited in the literature which were adapted to the current study are listed in Table 2. Table 2: Top 20 uncertain events cited in the literature Dey (2001) Assaf and Al-Hejji (2006) Technical       Zou et al. (2007) Availability of skilled labour Saqib et al. (2008) Technical Zayed et al. (2008) Inaccurate management or supervision Ehsan et al. (2010) Technical       Nieto-Morote and Ruz-Vila (2011) Availability of materials  Kuo and Lu (2013) Environmental Fang et al. (2012) Weather Banaitiene and Banaitis (2012) Factor Mahendra et al. (2013) Marzouk and El-Rasas (2014) Taghipour et al. (2015) Aziz and Abdel-Hakam (2016) Santoso and Soeng (2016) Odediran and Windapo (2017) Adam et al. (2017) Event                      2149          Total 16     15       14       14 Health & safety Technical         Materials delivery Technical          Construction methods Technical         Availability of equipment Technical    Cash flow difficulties (contractor finance) Financial     Design, drawings, specifications, and samples Technical        Incompetent contractor/ subcontractor Technical     Low level of productivity Technical     Payment delays Financial   Planning and scheduling of project by contractor Technical  Difficulty of schedule Technical Lack of capital by owner Financial   Change order (change in the scope of the project) Technical  Legal/ industrial disputes between various parties in the construction project Legal Communication/ coordination between construction parties Technical   Fluctuation of prices of Economic                 13     12      12       12  12     11       10       10  10                   10          9  9                2150                8   9 8 8 materials and/or equipment It can be seen from Table 2 that the most cited uncertain events are technical-related. To verify the existence of these uncertain events in highway construction projects in South Africa, the research conducted further investigations. The methods used are presented in the following section. 3. RESEARCH METHODOLOGY The study made use of a sequential mixed-methods research approach in identifying the uncertain events and their main factors and assessing the impact of uncertainties on the completion time of highway construction projects in South Africa. Brainstorming sessions were held with six highway experts who have more than 25 years of experience in South African highway construction projects. The highway expert panel reviewed and modified the uncertain events identified in the literature to appropriately reflect the events occurring on South African highway construction projects. The expert panel also grouped these events into seven uncertainty factors, as seen in Table 1. A survey questionnaire was designed on a five-point linguistic Likert-scale form to assess the impact size of confirmed uncertain events in highway construction projects in South Africa. The questionnaire was administered to 32 highway project managers with a minimum of 20 years of experience in the South African construction industry, to rate the probability of occurrence and the severity of each uncertainty on the completion time of a highway project. 4. DATA ANALYSIS To evaluate the effect of uncertainties on construction time of highway projects using collected data on the probability of occurrence and the severity of uncertainties from the six highway experts, the ISO (International Standards Organization) 31000 impact matrix was utilised (ISO, 2009). The ISO (2009) defined the impact size of an event as a function of the probability of occurrence and the severity of that event should it occur. Table 3 shows the probability of occurrence and the severity as two input variables, and relevant impact size as the output variable. 2151 Table 3: Impact size matrix Probability of occurrence Severity 4.1 Insignificant Minor Moderate Major Catastrophic (1) (3) (5) (7) (9) Rare (.1) Minimal Minimal Low Low Moderate Unlikely (.3) Minimal Low Moderate Moderate High Possible (.5) Low Moderate Moderate High High Likely (.7) Low Moderate High High Extreme Almost certain (.9) Moderate High Extreme Extreme High Developing a stepwise regression model The main objective of this study is to quantitatively analyse and assess the impact of uncertainties on completion time of highway construction projects, through numerical analysis of the uncertainty variables. Stepwise regression analysis (SRA) is an extension of multiple regression analysis. The SRA model is a mathematical model used in estimating the relationship between a dependent variable and independent variables, with a strong mathematical background. SRA models have been used extensively in different areas of construction management, particularly assessing risk and uncertainty, assessing the critical factors affecting cost performance of Ethiopian public construction projects (Sinesilassie et al., 2018), modelling the construction risk ratings and estimating contingencies in highway projects (Diab et al., 2017), identifying the success factors for public-private partnership projects in Korea (Yun et al., 2015), evaluating project risks in Iran (Ebrat and Ghodsi, 2014), evaluating the risk factors leading to cost overruns in highway construction projects in Australia (Creedy et al., 2010), analysing the risk perception of build-operate-transfer road project participants in India (Thomas et al., 2003), developing models to forecast the actual construction cost and time (Skitmore and Ng, 2003), and designing a multivariate analysis to build project success factors in Hong Kong (Chan et al., 2001). The impact size of uncertain events as the dependent variable is a function of two independent variables (probability of occurrence, and severity) of relative uncertainty (ISO, 2009), as shown in Equation 1. 𝑈𝑛𝑐𝑒𝑟𝑡𝑎𝑖𝑛𝑡𝑦 𝑖𝑚𝑝𝑎𝑐𝑡𝑖 = 𝑎𝑖 𝑝 + 𝑏𝑖 𝑠 + 𝑟𝑖 (Equation 1) Where ri is a constant value. ai and bi represent regression coefficients of the independent variables. Because each input variable can have a low correlation with the output variable, the SRA model was used in this study. Table 4 presents the values of the correlation coefficients. 2152 Table 4: Correlation coefficients among the input and output variables Probability Severity Impact Probability 1 Severity 0 1 Impact 0.685061 0.685061 1 Table 4 shows the low correlation between the independent variables and the dependent variable. The general SRA model for impact size, based on the impact size matrix in Table 3, has been developed to predict the impact size of each uncertainty on cost and time of highway construction projects. The SRA model test details are shown in Table 5. Table 5: Regression test details Regression statistics Multiple R 0.968822 R-squared 0.93861607 Adjusted Rsquared 0.93303571 Standard error 0.31622777 Coefficient Standard error t-statistic P-value Intercept 0.18 0.170294 1.056996 0.301982 Probability 2.9 0.223607 12.96919 8.82E-12 Severity 0.29 0.022361 12.96919 8.82E-12 Table 5 reveals that the correlation value (R-Squared) of the model is close to 1, and the P-value is very low (<0.05). The very low P-value indicates the statistically significant relationship of each independent variable to the dependent variable of the model, and the closeness of the R-value to 1 verifies the close fit of the estimated output model to real data. The developed stepwise regression analysis model for general uncertainty impact size is outlined in Equation 2. 𝑈𝑛𝑐𝑒𝑟𝑡𝑎𝑖𝑛𝑡𝑦 𝑖𝑚𝑝𝑎𝑐𝑡 = 2.9 × 𝑝 + 0.29 × 𝑠 + 0.18 (Equation 2) Similar steps were repeated to develop the SRA models for each uncertainty impact on construction time of highway projects. To assess the optimum impact size of each uncertainty, the optimum values of two independent variables (probability of occurrence, and severity of event) are identified using sensitivity analysis, and are inserted to develop the SRA models. For instance, the maximum probability of occurrence (0.6625) and severity (5) value of event weather were inserted to develop the SRA model (Y = 0.2915 + 2.9728p + 0.27905s), and the estimated impact size of this uncertainty (3.66) on the completion time of highway construction projects. The 2153 impact sizes of all identified uncertainties on the completion time of highway construction projects were estimated and ranked. Table 6 presents the top 20 events with significant impact size on construction time of highway projects, from a ranking perspective. Table 6: Top 20 uncertain events with significant impact size on construction time of highway projects Code Event Probability of Severity Impact Rank occurrence of event size TG11 Latent ground conditions 0.84375 8.3125 4.94 1 TCS6 Inaccurate time and cost estimation 0.79375 8.3125 4.78 2 3 TG5 Inadequate planning and scheduling of project by contractor 0.8125 7.6875 SO4 Rehabilitation of affected people 0.78125 7.8125 4.66 4 PL3 Human-made disaster (war, protest, strike, etc.) 0.8125 7.4375 5 SO5 Disease (HIV, Ebola, etc.) 0.70625 6.8125 4.19 6 TG9 Change order by owner (scopes and specifications) 0.73125 6.4375 7 TG4 Difficulty of schedule 0.55625 7.75 4.00 8 0.64375 5.5625 3.84 9 10 TCR5 Rework due to contractor errors 4.72 4.66 4.16 EN2 Natural disasters (earthquake, floods, hurricane, etc.) 0.4125 7.9375 SO3 Social and cultural impacts 0.76875 4.375 3.81 11 TT1 Obsolete technology 0.58125 6.1875 3.78 12 LE9 Problem in dispute settlement due to law 0.625 5.6875 3.78 13 TCR4 Management or supervision of project by 0.66875 contractor 5.5 14 TL1 Low level of productivity 0.725 4.625 3.75 15 0.56875 6.1875 3.75 16 17 TM2 Materials delivery 3.81 3.78 TCS4 Design, drawings, specifications, and samples 0.675 5.1875 PL1 Political situation 0.64375 5.6875 3.72 18 TCS3 Frequent design changes 0.60625 5.8125 3.72 19 TM3 Bad quality of materials 0.61875 5.5 20 3.75 3.72 It can be seen from Table 6 that the top three uncertain events, based on estimated impact on completion time of highway construction projects, from a ranking perspective, are latent ground conditions (4.94), inaccurate time and cost estimation (4.78), and inadequate planning and scheduling (4.72). Likewise, latent ground 2154 conditions and inaccurate time and cost estimation are two uncertain events with catastrophic consequences. To evaluate the SRA models’ performance, the root-mean-square error (RMSE), the mean absolute percentage error (MAPE), and the R-squared of 76 developed assessment models were calculated. The results for the top 20 events in South Africa are presented in Table 5. Table 5: Performance evaluation of the SRA models for the top 20 events Code Model P-value R-squared RMSE MAPE TG11 Y = 2.9897 + 1.604p + 0.071255s 2.21e-10 0.784 0.244 0.0638 TCS6 Y = 1.3931 + 1.9967p + 0.21694s 1.01e-07 0.671 0.249 0.0481 TG5 Y = 0.89851 + 2.3235p + 0.25137s 3.56e-08 0.694 0.261 0.0533 SO4 Y = 0.5412 + 2.6448p + 0.26225s 7.5e-09 0.725 0.262 0.0537 PL3 Y = 0.80427 + 2.7748p + 0.21478s 3.7e-09 0.738 0.255 0.0518 SO5 Y = 0.064987 + 2.9907p + 0.29509s 2.85e-12 0.84 0.245 0.0521 TG9 Y = 0.88561 + 2.6217p + 0.21025s 2.1e-08 0.705 0.252 0.0556 TG4 Y = 0.88358 + 2.4275p + 0.22789s 2.21e-08 0.703 0.248 0.0557 TCR5 Y = 0.97812 + 2.1685p + 0.26421s 1.05e-06 0.613 0.237 0.0553 EN2 Y = 1.2336 + 2.0528p + 0.21823s 6.52e-07 0.626 0.251 0.0621 SO3 Y = 1.4539 + 1.8779p + 0.20912s 1.74e-07 0.658 0.24 0.0571 TT1 Y = 0.68305 + 2.7031p + 0.24679s 3.56e-09 0.739 0.259 0.0664 LE9 Y = 0.52521 + 2.481p + 0.29985s 1.55e-07 0.661 0.253 0.0628 TCR4 Y = 1.2381 + 2.0753p + 0.21005s 1.52e-07 0.661 0.253 0.0632 TL1 Y = 0.75 + 2.3438p + 0.28125s 2.25e-08 0.703 0.286 0.0729 TM2 Y = 0.98332 + 2.0923p + 0.25482s 0.68 0.257 0.0660 6.62e-08 2155 TCS4 Y = 0.92489 + 2.6345p + 0.20179s 3.96e-08 0.691 PL1 Y = 0.80167 + 2.2434p + 0.25897s 1.08e-08 0.718 TCS3 Y = 0.92834 + 2.1309p + 0.25781s 2.62e-08 0.7 TM3 Y = 0.24167 + 3.1541p + 0.27735s 1.54e-10 0.789 0.253 0.0635 0.251 0.0618 0.259 0.0669 0.248 0.0620 The small value of errors (RMSE and MAPE) and P-values (p<.01) proved the reliability and statistical significance of all the developed models. However, the fit of the estimated values to real data varies from 0.442 to 0.954. Fifty-two of the developed models have a strong fit (r>0.7), 22 models have a moderate fit (.5<r<.7), and two of the models have a low fit to the real data (Moore and Kirkland, 2007). Furthermore, the estimated impact size of uncertain events was classified into five groups, namely extreme, high, moderate, low, and minimal (see Table 7). Table 7: Impact size groups Group Impact Events size Extreme I ≥4 Latent ground conditions; inaccurate time and cost estimation; inadequate planning and scheduling of project by contractor; rehabilitation of affected people; human-made disaster; disease; change order by owner; difficulty of schedule High 3≤I<4 Rework due to contractor errors; natural disasters; social and cultural impacts; obsolete technology; problem in dispute settlement due to law; management or supervision of project by contractor; low level of productivity; materials delivery; design, drawings, specifications, and samples; political situation; frequent design changes; bad quality of materials; security; new technology adoption; corruption; remote location cost; availability of skilled workers; availability of materials; change order; delays in decision-making; weather; right of way acquisition; payment delays; fluctuation of prices of materials and/or equipment; health & safety; financing by contractor, cultural heritage issues; inaccurate investigation of construction site; monopoly of material and/or equipment suppliers; low efficiency of equipment; unreliable supplier of material; lack of technical staff; mistakes in design and/or specifications; encroachment problems; lack of capital by owner; poor quality of workmanship; planning and scheduling of project by contractor; frequent change of subcontractors; contract failure – new contract establishment cost; terrain or topographical; construction methods; lack of technical staff; ineffective delay penalties; poor communication/coordination between construction parties 2156 Moderate 2≤I<3 Absenteeism of labour; incompetent contractor/subcontractor; legal/industrial disputes between various parties in the construction project; changes in government regulations and laws; inadequate monitoring and supervision; poor financial control; type of contract; availability of equipment; deficient documentation; contractual claim; late delivery of equipment; personal conflicts among labour; lack of experience in the line of work; lack of experience in design and supervision; fluctuation in foreign exchange rate; high cost of materials and/or equipment; high tender price; changing of bankers’ policy for loans; high cost of labour; saturated market; difficulties in importing equipment and materials Low 1≤I<2 Slow mobilisation of equipment; tax and/or legal fees; size of contract Minimal 1<I NONE Key: I = impact size Table 7 groups the uncertain events identified in South African highway projects, based on their estimated impact on the completion time of projects. 5. DISCUSSION OF FINDINGS The current study modelled the existing uncertain events in South African highway construction projects using stepwise regression analysis (SRA) to assess their impact on the completion time of such projects. Impact assessment of uncertain events is essential to prevent delays on the project before signs of delay begin to appear. Comparison of the top 20 uncertain events from the literature review (see Table 2) with the 20 events with the highest estimated impact in South Africa (see Table 3) revealed that only seven events from this list are common with the top 20 cited events (inadequate planning and scheduling of project by contractor (3rd), change order by owner (7th), difficulty of schedule (8th), management or supervision of project by contractor (14th), low level of productivity (15th), materials delivery (16th), and design, drawings, specifications, and samples (17th)). The other 13 most-cited events are found to impact the construction time of highway projects differently (availability of skilled workers (25th), availability of materials (26th), weather (29th), payment delays (31st), fluctuation of prices of materials and/or equipment (32nd), health & safety (33rd), financing by contractor (34th), lack of capital by owner (43rd), construction methods (49th), poor communication/coordination between construction parties (52nd), incompetent contractor/subcontractor (54th), legal/industrial disputes between various parties in the construction project (55th), and availability of equipment (60th)). The study found that more than 70% of identified events impact on the completion time of highway construction projects. The results presented in Table 7 revealed that eight uncertain events (10.5%) have an extreme impact on completion time of highway projects, 44 events (57.9%) have a high impact, 21 events (27.6%) have a moderate impact, and three events (4%) have a low impact on construction time of highway projects. This is evidence of the fact that the completion time of highway construction projects in South Africa is very sensitive to uncertain events. These results are consistent with those of previous studies. For instance, Adam et al. (2017), Assaf and Al-Hejji (2006), Aziz and Abdel-Hakam (2016), Baloi and Price (2003), Fang et al. (2012), Taghipour et al. (2015), Zayed et al. (2008), and Zou et al. (2007) also found 2157 that latent ground conditions was a key uncertain event impacting the construction time of projects. Also, the SRA model results indicate that inaccurate time and cost estimation, inadequate planning and scheduling, changes to specifications, and difficulty of schedule are the other four technical uncertain events that extremely affect completion time of construction projects. These technical events were also identified in previous studies (Assaf and Al-Hejji, 2006; Aziz and Abdel-Hakam, 2016; Bunni, 2003; Ehsan et al., 2010; Gosling et al., 2012; Huang et al., 2002; Mahendra et al., 2013; Marzouk and El-Rasas, 2014; Saqib et al., 2008; Zou et al., 2007). Human-made disaster and disease emerged from the study as two political and social uncertain events with extreme impact on construction projects in South Africa, which is consistent with the findings of studies conducted by Aziz and Abdel-Hakam (2016), Marzouk and El-Rasas (2014), and Odediran and Windapo (2017) in South Africa and Egypt. 6. CONCLUSION The current study adds to existing knowledge of construction management by including an extensive literature review in the field of uncertainty in construction projects, it established the uncertain events and uncertainty factors through brainstorming by a highway expert panel, and it verified the probability of occurrence and severity of 20 events through gathering data from highway construction experts. A significant number of the uncertainties were related to social and political factors. Therefore, it is recommended that the companies pursuing highway construction in African countries should seriously consider the social and political risks, along with the technical events, when involved in this market. The study also developed stepwise regression analysis models to assess the impact of each event on the completion time of highway construction projects, and it classified these events into five groups, based on their estimated impact. This study is relevant to both practitioners and researchers. It provides practitioners with a simple and straightforward tool to assess and prioritise the impact of uncertain events on highway construction projects, and it provides researchers with a qualitative and quantitative methodology and a mathematical model for use in evaluating the effect of uncertain events on highway construction projects in South Africa. The SRA was used in assessing the impact of uncertain events on highway construction completion time, due to the fact that the model has a strong mathematical background and has been employed in assessing risk and uncertainty in the field of construction management. The study found the SRA model to be a reliable and statistically significant method for assessing uncertainty on construction projects. However, the accuracy of the estimated impact of some of the models is low. Therefore, to accurately estimate the impact of these events, the study recommends using a systematic fuzzy inference system, such as an adaptive neuro-fuzzy inference system (ANFIS). The detailed analysis and estimated outputs from this research should be used as a platform and a benchmark for future studies in highway construction in South Africa. This platform should be utilised for estimating the duration of highway construction projects accurately, by assessing the uncertain events that impact on the completion time of each construction activity. 2158 7. ACKNOWLEDGEMENTS Funding from the National Research Foundation (NRF) towards this research is hereby acknowledged. Opinions expressed, and conclusions arrived at, are those of the authors, and are not necessarily to be attributed to the NRF. This article was language-edited by a freelance language editor, Anthony Sparg. He has edited several academic journal articles in the field of construction management. 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