**3. Results and analysis**

### **3.1 Tests for factor analysis**

Kaiser-Mayer-Olkin (KMO) test and Bartlett's test of sphericity are carried out to check the suitability for factor analysis. The suitability of a sample in relations to the distribution of the data is checked by KMO test. Pallant [18] stated that KMO value should be more than 0.5. The researchers [18, 19] revealed that factor analysis is meaningless with an identity matrix. The tests showed that KMO value is 0.689 that is more than 0.5 and Bartlett's test of sphericity is large (chi-square value = 1626.4890 with small significance (p value < 0.001).

### **3.2 Analysis for internal consistency**

Cronbach's alpha (α) is used to check the internal consistency in the items involved in each factor [20] and the minimum recommended value is α = 0.7 [19]. Cronbach's α [19] also measures the reliability of all factors. Factor analysis shows that all 7 themes of the 37 factors had Cronbach's α ranging from 0.921 to 0.912, which means that all the variables are reliable [19]. For all 37 variables, the overall α is 0.917.

### **3.3 Relative importance index**

Chan and Kumaraswamy [21] reveal that the mean and standard deviation of individual factor are not appropriate to decide the total ranking as they do not indicate any association among the factors. As a substitute, we calculate the weighted average for every factor and formerly divide them with the highest scale of the dimension. The researchers [19, 21, 22] indicate that this results in a relative importance index. Respondents provide their responses on a Likert scale about the standing of the 37 risks affecting the cost and schedule aims of the project. Shash [22] provided the formula for relative importance index as:

$$\text{Relative importance index (RII)} = \Sigma(a\mathbf{X}) \times \mathbf{100}/\mathbf{5} \tag{1}$$

**139**

design risks.

**Table 1.**

*RII of risk categories.*

*Risk Analysis Related to Cost and Schedule for a Bridge Construction Project*

Financial risk 69.95 External risk 66.67 Design risk 66.28 Management risk 65.17 Construction risk 62.72 Contractual risk 59.42 Health and safety risk 53.82

**Risk category Relative importance index (RII)**

(RII = 66.28), management risks (RII = 65.17), construction risks (RII = 62.72), contractual risks (RII = 59.42), and health and safety risks (RII = 53.82). According to the results, financial risks are vital in affecting the cost and schedule aims of projects (see **Table 1**). The 2nd and 3rd most important risks are external risks and

Among the 37 factors, the highest 10 risk factors in order of importance are: unavailability of funds (RII = 85.80), financial failure of contractor (RII = 76.52), poor site management and supervision (RII = 74.20), inadequate site investigation (RII = 73.91), inadequate project planning (RII = 73.91), construction delays (RII = 73.62), unavailability of land and/or right of way for site access (RII = 72.17), defective work and or quality issue (RII = 71.88), financial delays (RII = 71.01), insufficient technology (RII = 69.86). These risk factors are important for clients, consultants and contractors. There is a need for an effective risk management system on construction projects. Health and safety risks are ranked at the bottom in the 37 factors. This indicates that clients and consultants do not demand from contractors to implement a proper health and safety management system. Management risks are rated with high importance. There is lack of construction management experts and only few institutions offer program in construction management. Small contractors generally do not hire qualified engineers unless it is mandatory by the client. There is a need for construction management and risk management education as well as research in the industry.

The Pearson product-moment correlation ('r' Rho) is a measure of the degree of linear relationship among the variables. The correlation coefficient ('r' Rho) is any value between plus and minus and the sign (±) explains the direction of the relationship, either positive or negative. A positive coefficient means that the value of the variable increases with the increase in value of the other variable; or if one goes down, the other also reduces. A negative coefficient indicates that as one variable increases, the other decreases, and vice-versa. The absolute value of the coefficient indicates the strength of the correlation. A coefficient of r = 0.50 shows a robust degree of linear relationship than that of r = 0.30. A coefficient of zero (r = 0.0) shows the lack of a linear relationship and coefficients of r = +1.0 and r = −1.0 show

**Table 2** shows the Pearson's correlations for the risk factor categories. The maximum coefficient (0.756) is between the construction and management risks, which is significant at the p value = 0.01. This indicates that numerous construction and management risks are correlated to each other and they are to be jointly addressed

*DOI: http://dx.doi.org/10.5772/intechopen.83501*

**3.4 Pearson's product-moment correlation**

a perfect linear relationship [19].

where '*n'* is the frequency of the responses; and 'N' is the total number of responses that gives X = *n*/N. Where '*a'* is the constant that express weight specified to each response, ranging from 1 (insignificant) to 5 (extra ordinary).

The relative importance index categorized the seven risk factors in descending order as: financial risks (RII = 69.95), external risks (RII = 66.67), design risks

*Risk Analysis Related to Cost and Schedule for a Bridge Construction Project DOI: http://dx.doi.org/10.5772/intechopen.83501*


#### **Table 1.**

*Perspectives on Risk, Assessment and Management Paradigms*

are documented.

**3. Results and analysis**

**3.1 Tests for factor analysis**

**3.2 Analysis for internal consistency**

**3.3 Relative importance index**

In addition, a case study of a bridge project is documented to establish costs and time risk analysis. The researcher obtained assistance from the five-member expert panel (comprising on scheduling manager, project manager, resident engineer, construction manager, an academia) and Monte Carlo simulation to analyze risks on of the case study project. The panel members are having more than 20 years of experience in industry and academics. This panel identifies the risks relevant to the case study project and assigned probability to the risk factors. This panel assigned the probabilistic (optimistic, most likely, pessimistic) durations and costs in Pakistan Rupees (PKR). These probabilistic durations and costs permitted us to practice triangular distribution in Primavera Pertmaster. A 3 days' workshop is held with the attendance of all panel members. Their involvements for the risk analysis

Kaiser-Mayer-Olkin (KMO) test and Bartlett's test of sphericity are carried out to check the suitability for factor analysis. The suitability of a sample in relations to the distribution of the data is checked by KMO test. Pallant [18] stated that KMO value should be more than 0.5. The researchers [18, 19] revealed that factor analysis is meaningless with an identity matrix. The tests showed that KMO value is 0.689 that is more than 0.5 and Bartlett's test of sphericity is large (chi-square

Cronbach's alpha (α) is used to check the internal consistency in the items involved in each factor [20] and the minimum recommended value is α = 0.7 [19]. Cronbach's α [19] also measures the reliability of all factors. Factor analysis shows that all 7 themes of the 37 factors had Cronbach's α ranging from 0.921 to 0.912, which means that all the variables are reliable [19]. For all 37 variables, the overall

Chan and Kumaraswamy [21] reveal that the mean and standard deviation of individual factor are not appropriate to decide the total ranking as they do not indicate any association among the factors. As a substitute, we calculate the weighted average for every factor and formerly divide them with the highest scale of the dimension. The researchers [19, 21, 22] indicate that this results in a relative importance index. Respondents provide their responses on a Likert scale about the standing of the 37 risks affecting the cost and schedule aims of the project. Shash

where '*n'* is the frequency of the responses; and 'N' is the total number of responses that gives X = *n*/N. Where '*a'* is the constant that express weight specified

The relative importance index categorized the seven risk factors in descending order as: financial risks (RII = 69.95), external risks (RII = 66.67), design risks

to each response, ranging from 1 (insignificant) to 5 (extra ordinary).

Relative importance index (RII) = ∑(*a*X) × 100/5 (1)

value = 1626.4890 with small significance (p value < 0.001).

[22] provided the formula for relative importance index as:

**138**

α is 0.917.

*RII of risk categories.*

(RII = 66.28), management risks (RII = 65.17), construction risks (RII = 62.72), contractual risks (RII = 59.42), and health and safety risks (RII = 53.82). According to the results, financial risks are vital in affecting the cost and schedule aims of projects (see **Table 1**). The 2nd and 3rd most important risks are external risks and design risks.

Among the 37 factors, the highest 10 risk factors in order of importance are: unavailability of funds (RII = 85.80), financial failure of contractor (RII = 76.52), poor site management and supervision (RII = 74.20), inadequate site investigation (RII = 73.91), inadequate project planning (RII = 73.91), construction delays (RII = 73.62), unavailability of land and/or right of way for site access (RII = 72.17), defective work and or quality issue (RII = 71.88), financial delays (RII = 71.01), insufficient technology (RII = 69.86). These risk factors are important for clients, consultants and contractors. There is a need for an effective risk management system on construction projects. Health and safety risks are ranked at the bottom in the 37 factors. This indicates that clients and consultants do not demand from contractors to implement a proper health and safety management system. Management risks are rated with high importance. There is lack of construction management experts and only few institutions offer program in construction management. Small contractors generally do not hire qualified engineers unless it is mandatory by the client. There is a need for construction management and risk management education as well as research in the industry.

#### **3.4 Pearson's product-moment correlation**

The Pearson product-moment correlation ('r' Rho) is a measure of the degree of linear relationship among the variables. The correlation coefficient ('r' Rho) is any value between plus and minus and the sign (±) explains the direction of the relationship, either positive or negative. A positive coefficient means that the value of the variable increases with the increase in value of the other variable; or if one goes down, the other also reduces. A negative coefficient indicates that as one variable increases, the other decreases, and vice-versa. The absolute value of the coefficient indicates the strength of the correlation. A coefficient of r = 0.50 shows a robust degree of linear relationship than that of r = 0.30. A coefficient of zero (r = 0.0) shows the lack of a linear relationship and coefficients of r = +1.0 and r = −1.0 show a perfect linear relationship [19].

**Table 2** shows the Pearson's correlations for the risk factor categories. The maximum coefficient (0.756) is between the construction and management risks, which is significant at the p value = 0.01. This indicates that numerous construction and management risks are correlated to each other and they are to be jointly addressed


#### **Table 2.**

*Pearson's product-moment correlation of risk factor categories.*

with good risk management practices. There is another essential coefficient of 0.605 at significance p value = 0.01 between construction and external risks. External risks impact on project costs and schedule more than the construction risks (see **Table 1**). They are in fact the second most important risk factor category. A positive correlation of health and safety risks with construction (0.459) at a significance p value = 0.01 confirms the importance of health and safety on bridge projects. Higher rate of risks in construction indicate an increase in physical vulnerabilities. The health and safety risks are correlated positively with contractual risks (0.428) at a significance p value = 0.01, indicating improvement in health and safety in construction may reduce contractual and health and safety risks.

#### **3.5 Bridge project: a case study**

The case study project is a bridge construction in Islamabad that links the Islamabad highway with a residential community. The project is located in the capital city of Pakistan. It has the following features: (a) bridge total length 166 m (544.8 ft), (b) constructed over a river with an annual peak discharge of 11,170 cusecs, (c) 56 piles of diameter 762 mm (2.5 ft) and abutment piles 15.24 m (50 feet) deep, (d) 4 spans, (e) pier piles 9.14 m (30 feet) deep, (f) 12 pile caps, (g) 4 abutment walls, (h) 2 abutments, (i) 12 piers, (j) 6 transoms or cross-beams, (k) 24 precast girders of 44.09 m (144.66 ft), (l) 14.32 m (47 feet) width of deck slab on one side, (m) 3.66 m (12 feet) length of approach slab on each side and, (n) asphalt 166.12 m (545 ft) long and the bridge is designed for 3 + 3 lanes of traffic.

A baseline work schedule is prepared for the project. The project has a base cost-estimate. Each activity in the schedule had its cost allocated. The allocation includes cost estimate for materials, equipment, labor, and overhead costs for each activity. The risks that are identified in the project are presented to the experts. The expert panel identifies specific risks to the case study project. These risks are loaded into the schedule to determine the impact on project schedule and cost. Primavera Pertmaster is used for risk analysis. The inputs to Pertmaster for the risk register are: (a) risk description, (b) risk ID number, (c) threat or opportunity, (d) effect of this risk on activity, (e) probability of occurrence, (f) type of risk e.g. schedule or cost, (g) distribution e.g. triangular, (h) correlation with other risk factors. The risk register (see **Figure 1**) is developed for the whole project.

Pertmaster uses Monte Carlo simulation for risk analysis. Monte Carlo simulation uses random independent variables to obtain solutions of problems. Lian

**141**

*Risk Analysis Related to Cost and Schedule for a Bridge Construction Project*

and Yen [23] reveal that Latin hypercube sampling and simple random number sampling are among the sampling techniques that are used with Monte Carlo simulations. This simple and elegant method delivered a means to solve equations with triangular probability distributions [24, 25]. Critical path is found and further calculations are documented with activities that are on the critical path. The time schedule loaded with costs and risks is analyzed. Real versus simulation outcomes

The cumulative distribution for project cost, finish date, and duration are calculated with Monte Carlo simulations. The project duration (maximum = 792 days, minimum = 628 days, mean = 701 days) is displayed in **Figure 2**. The cumulative distribution for project duration and cost is determined. The results showed that the probability of finishing the project within the allotted time (628 days) is 4% and within the budget (PKR 129 million) is less than 1%. Terms P100 and P80 indicate the probabilities of 100 and 80% respectively. For instance, P80 shows that the project could be completed in 730 days with an amount of PKR 161 Million. There shall be 100% sure that the project would be completes in 792 days or even less with

The observations are performed for 5 months for the case study project. We have

Finish dates are also compared for the case study project. Piles activity finishes between the forecasted dates of P80 and P100. Pile-cap activity finishes between the expected dates of P80 and P100. The 'pier-shaft' activity also accomplished 22 days before the P80 finish date. Transom activity finishes 19 days after the P100 completion. This indicates that the simulation results are precise as the activities are

actually completing either within the predicted dates or within ±20 days. For the case study, costs are compared that are important to the contract partners. The project cost is at all times important to the management team. Probabilistic cost calculation with the model is very precise as all the genuine costs fell within the P80 and P100. The project cost incurred up to the completion of transoms is PKR 72.8 Million, while that forecasted by simulation with 80% probability is PKR 69.8 Million. The evaluation is PKR 76.0 Million with 100% probability, indicating that the expected cost using Monte Carlo simulation is precise

compared Pertmaster results with the actual completed activities. The researchers spent full time on-site to ensure extreme communication with the project implementation team. Documents are cautiously reviewed and are assimilated in analysis. On-site real data are equated with the simulation outcomes. The evaluation associated with schedule start dates is noted. For piling activity, actual start dates matched with the base line as well as with P80 and P100. For pier-shaft, actual start

are compared. A total of 1000 iterations are conducted for risk analysis.

a cost amounting to PKR 166.5 Million or less.

dates are between P80 and P100.

*DOI: http://dx.doi.org/10.5772/intechopen.83501*

**Figure 1.**

*Risk register of the project.*

*Risk Analysis Related to Cost and Schedule for a Bridge Construction Project DOI: http://dx.doi.org/10.5772/intechopen.83501*

**Figure 1.**

*Perspectives on Risk, Assessment and Management Paradigms*

**Risk factor category**

**Table 2.**

Financial 1

Contractual .442\*\* 1

*\*Correlation is significant at 0.05 level. \*\*Correlation is significant at 0.01 level.*

Design .306\* .374\*\* 1

Safety .098 .428\*\* .341\*\* 1

*Pearson's product-moment correlation of risk factor categories.*

Management .174 .445\*\* .374\*\* .366\*\* 1

Construction .113 .380\*\* .250\* .459\*\* .756\*\* 1

with good risk management practices. There is another essential coefficient of 0.605 at significance p value = 0.01 between construction and external risks. External risks impact on project costs and schedule more than the construction risks (see **Table 1**). They are in fact the second most important risk factor category. A positive correlation of health and safety risks with construction (0.459) at a significance p value = 0.01 confirms the importance of health and safety on bridge projects. Higher rate of risks in construction indicate an increase in physical vulnerabilities. The health and safety risks are correlated positively with contractual risks (0.428) at a significance p value = 0.01, indicating improvement in health and safety in

External .162 .290\* .399\*\* .373\*\* .430\*\* .605\*\* 1

**Financial Contractual Design Safety Management Construction External**

The case study project is a bridge construction in Islamabad that links the Islamabad highway with a residential community. The project is located in the capital city of Pakistan. It has the following features: (a) bridge total length 166 m (544.8 ft), (b) constructed over a river with an annual peak discharge of 11,170 cusecs, (c) 56 piles of diameter 762 mm (2.5 ft) and abutment piles 15.24 m (50 feet) deep, (d) 4 spans, (e) pier piles 9.14 m (30 feet) deep, (f) 12 pile caps, (g) 4 abutment walls, (h) 2 abutments, (i) 12 piers, (j) 6 transoms or cross-beams, (k) 24 precast girders of 44.09 m (144.66 ft), (l) 14.32 m (47 feet) width of deck slab on one side, (m) 3.66 m (12 feet) length of approach slab on each side and, (n) asphalt

166.12 m (545 ft) long and the bridge is designed for 3 + 3 lanes of traffic.

register (see **Figure 1**) is developed for the whole project.

A baseline work schedule is prepared for the project. The project has a base cost-estimate. Each activity in the schedule had its cost allocated. The allocation includes cost estimate for materials, equipment, labor, and overhead costs for each activity. The risks that are identified in the project are presented to the experts. The expert panel identifies specific risks to the case study project. These risks are loaded into the schedule to determine the impact on project schedule and cost. Primavera Pertmaster is used for risk analysis. The inputs to Pertmaster for the risk register are: (a) risk description, (b) risk ID number, (c) threat or opportunity, (d) effect of this risk on activity, (e) probability of occurrence, (f) type of risk e.g. schedule or cost, (g) distribution e.g. triangular, (h) correlation with other risk factors. The risk

Pertmaster uses Monte Carlo simulation for risk analysis. Monte Carlo simulation uses random independent variables to obtain solutions of problems. Lian

construction may reduce contractual and health and safety risks.

**3.5 Bridge project: a case study**

**140**

*Risk register of the project.*

and Yen [23] reveal that Latin hypercube sampling and simple random number sampling are among the sampling techniques that are used with Monte Carlo simulations. This simple and elegant method delivered a means to solve equations with triangular probability distributions [24, 25]. Critical path is found and further calculations are documented with activities that are on the critical path. The time schedule loaded with costs and risks is analyzed. Real versus simulation outcomes are compared. A total of 1000 iterations are conducted for risk analysis.

The cumulative distribution for project cost, finish date, and duration are calculated with Monte Carlo simulations. The project duration (maximum = 792 days, minimum = 628 days, mean = 701 days) is displayed in **Figure 2**. The cumulative distribution for project duration and cost is determined. The results showed that the probability of finishing the project within the allotted time (628 days) is 4% and within the budget (PKR 129 million) is less than 1%. Terms P100 and P80 indicate the probabilities of 100 and 80% respectively. For instance, P80 shows that the project could be completed in 730 days with an amount of PKR 161 Million. There shall be 100% sure that the project would be completes in 792 days or even less with a cost amounting to PKR 166.5 Million or less.

The observations are performed for 5 months for the case study project. We have compared Pertmaster results with the actual completed activities. The researchers spent full time on-site to ensure extreme communication with the project implementation team. Documents are cautiously reviewed and are assimilated in analysis. On-site real data are equated with the simulation outcomes. The evaluation associated with schedule start dates is noted. For piling activity, actual start dates matched with the base line as well as with P80 and P100. For pier-shaft, actual start dates are between P80 and P100.

Finish dates are also compared for the case study project. Piles activity finishes between the forecasted dates of P80 and P100. Pile-cap activity finishes between the expected dates of P80 and P100. The 'pier-shaft' activity also accomplished 22 days before the P80 finish date. Transom activity finishes 19 days after the P100 completion. This indicates that the simulation results are precise as the activities are actually completing either within the predicted dates or within ±20 days.

For the case study, costs are compared that are important to the contract partners. The project cost is at all times important to the management team. Probabilistic cost calculation with the model is very precise as all the genuine costs fell within the P80 and P100. The project cost incurred up to the completion of transoms is PKR 72.8 Million, while that forecasted by simulation with 80% probability is PKR 69.8 Million. The evaluation is PKR 76.0 Million with 100% probability, indicating that the expected cost using Monte Carlo simulation is precise

**Figure 2.** *Monte Carlo simulation results.*

(P80 costs = 69.8 Million, Actual = 72.8 Million, P100 costs = 76.0 Million). The baseline cost of the project is only PKR 37.2 Million up to 'Transoms' construction that shows a cost overrun of 96%, portraying the absenteeism of monitoring and control of cost practices. This shows a clear requirement of risk management on bridge construction projects.

For the case study project, risk analysis shows that project management can obtain a fair idea of schedule and cost changes and variations. For the case study project, risks (see **Figure 1**) that affected the schedule and cost objectives are: (a) delay in approval from the regulatory authority i.e. delay in sanctioning relocation of the railway track, (b) unexpected weather i.e. excess rainfall during monsoon, (c) design variations i.e. design changes due to insufficient site investigation, (d) insufficient work space i.e. land not available for pre-casting of girders, (e) lack of technology i.e. breakdown in asphalt paving equipment, (f) unavailability of funds i.e. delay in payment to subcontractor, (g) unavailability of material i.e. quality issues and material failure to meet specifications.

#### **3.6 Guidelines for risk analysis**

The study advocates the succeeding guidelines for an effective risk analysis of any bridge project:


**143**

guidelines.

*Risk Analysis Related to Cost and Schedule for a Bridge Construction Project*

in the design of questionnaire. Help from expert panel should be sought in identifying risks. Choudhry and Iqbal [1] have documented the risks identification techniques and they may be adopted. Especially, risk related to time and cost is to be evaluated as it plays a major role in affecting the project

3.*Quantifying risks*: The risk quantification is the most important process that requires skills, extensive experience and good judgment. In this process, one has to assess the probability of each risk [24, 25]. Next is to evaluate the impact of time or cost, or both. Generally, expert panels play a major role in calculating the probability of risks and their impact. The correlation of risks either positive or negative is addressed in this study. Lastly, the risk quantification

decides, whether they have an effect on cost or duration, or both.

4.*Prepare cost-loaded schedule for the project*: Mubarak [27] revealed that the project baseline schedule needed to be prepared at the initial stage the project to measure the project's progress against it. Probabilistic or deterministic durations of time and costs of activities are to be estimated. The critical path needs to be determined based on the probabilistic durations. Project cost is determined based on the probabilistic costs of the activities' information. The comparison of actual duration and actual cost of activities is carried out with

5.*Schedule loading with risks*: When cost-loaded schedule is complete, the next step is to allocate risks as they are quantified with each of the project activities. These risks are generally documented in a risk register. The risk register contains all particulars of each risk for the project. From the risk register, relevant

6.*Running of Monte Carlo simulations*: The schedule loaded with risk is run by Monte Carlo simulations to calculate the impact. One needs to perform the Monte Carlo simulations by using software, for example, @Risk. Pertmaster is

7.*Understand the output*: The results that are produced by Monte Carlo simulations are easy to comprehend. Outputs reflects the probability of meeting the time and costs. The P80 and P100 values represent 80 and 100% probability. They specify the values of time and cost with 80 and 100% confidence level. The results shows how much an activity is behind from its initial time and how

This work reveals a systematic process to identify and quantify major risks related to construction and predominantly to bridge construction affecting cost and schedule of the project. All projects have their own special conditions; nonetheless, experts can acquire valuable evidence from the results as all projects have risks that need to be managed. Risks related to schedule create cost risk. The case study demonstrates with the help of Monte Carlo simulation that how schedule and cost risk can be analyzed and managed. The case study shows that understanding the probabilistic cost is vital to forecast long-term budgets. The risk management guidelines are documented from surveys, interviews and analysis. One can determine the probabilistic cost of project by adopting these

*DOI: http://dx.doi.org/10.5772/intechopen.83501*

computed results and with the baseline.

employed in this this research.

much cost can overrun (see **Figure 2**).

risks are assigned with the cost-loaded schedule.

performance.

#### *Risk Analysis Related to Cost and Schedule for a Bridge Construction Project DOI: http://dx.doi.org/10.5772/intechopen.83501*

*Perspectives on Risk, Assessment and Management Paradigms*

bridge construction projects.

*Monte Carlo simulation results.*

**Figure 2.**

**3.6 Guidelines for risk analysis**

are required to be recognized.

any bridge project:

issues and material failure to meet specifications.

(P80 costs = 69.8 Million, Actual = 72.8 Million, P100 costs = 76.0 Million). The baseline cost of the project is only PKR 37.2 Million up to 'Transoms' construction that shows a cost overrun of 96%, portraying the absenteeism of monitoring and control of cost practices. This shows a clear requirement of risk management on

For the case study project, risk analysis shows that project management can obtain a fair idea of schedule and cost changes and variations. For the case study project, risks (see **Figure 1**) that affected the schedule and cost objectives are: (a) delay in approval from the regulatory authority i.e. delay in sanctioning relocation of the railway track, (b) unexpected weather i.e. excess rainfall during monsoon, (c) design variations i.e. design changes due to insufficient site investigation, (d) insufficient work space i.e. land not available for pre-casting of girders, (e) lack of technology i.e. breakdown in asphalt paving equipment, (f) unavailability of funds i.e. delay in payment to subcontractor, (g) unavailability of material i.e. quality

The study advocates the succeeding guidelines for an effective risk analysis of

1.*Context development*: Developing the context for risk analysis is exceptionally vital as indicated in the 25 interviews documented. The expert panel emphasized the requirement for precise definition of the scope of the construction project; develop the project method statement, and conduct stakeholder analysis systematically. These points set the boundary for risk analysis as stressed by researchers [26]. Factors and variables contributing to project risks

2.*Identification of risks*: Tools and techniques such as checklists, historical data, brainstorming, and idea stimulating techniques may be employed [25, 26]. Nonetheless, risks are required to be identified and defined as is carried out

**142**

in the design of questionnaire. Help from expert panel should be sought in identifying risks. Choudhry and Iqbal [1] have documented the risks identification techniques and they may be adopted. Especially, risk related to time and cost is to be evaluated as it plays a major role in affecting the project performance.


This work reveals a systematic process to identify and quantify major risks related to construction and predominantly to bridge construction affecting cost and schedule of the project. All projects have their own special conditions; nonetheless, experts can acquire valuable evidence from the results as all projects have risks that need to be managed. Risks related to schedule create cost risk. The case study demonstrates with the help of Monte Carlo simulation that how schedule and cost risk can be analyzed and managed. The case study shows that understanding the probabilistic cost is vital to forecast long-term budgets. The risk management guidelines are documented from surveys, interviews and analysis. One can determine the probabilistic cost of project by adopting these guidelines.
