Qualitative Risk Analysis in Construction Projects

In project management, every task carries risks. Success often results not because of avoiding risks but because of identifying, assessing, and prioritizing them efficiently. This is where qualitative risk analysis becomes necessary. Unlike quantitative, qualitative analysis evaluates risks using non-numerical metrics, focusing on their probability and impact through expert judgment and relative ranking.

This article is part of a series where we discuss Risk Management in Construction Projects in detail. In our previous articles, we discussed identifying risks using Risk Registers and learned how to analyze risks using Quantitative Risk Analysis.

Ready to take your skills to the next level? 🚀 Check out our Special Online Courses! 🚀
Gain valuable insights, boost your career, and learn from industry experts.

Planning Courses

This guide focuses on qualitative risk analysis, exploring its methodologies, benefits, and real-world applications. By the end, you’ll be equipped to implement this process to enhance project outcomes and stakeholder confidence.

Difference Between Qualitative Risk Analysis and Quantitative Risk Analysis

Simply, qualitative risk analysis is more subjective, quicker, more frequently used in projects, doesn’t require specialized tools, and is used in almost all risk analyses. Whereas quantitative risk analysis is more objective, time-consuming, not used for all risks, and sometimes uses specialized tools.

Qualitative risk analysis is a process of prioritizing risks as per the probability and impact. Later on, highest risks probability and impact will be subjected to a quantitative risk process to translate the impact on the schedule and costs by numbers. So you can treat qualitative analysis as an introductory analysis to give you the main direction on where to focus the most.

For a quick example, if there is a high risk in getting particular information from a major stakeholder (as qualitative risk), then a forecast for a 2-day overrun and additional cost of 1000$ in regular time wages (quantitative risk).

Just knowing that getting this information is a high risk, that’s qualitative. When you start calculating its impact numerically, that’s quantitative.

Sometimes, when there are not any previous records or data to quantify the risk you may need to use a qualitative approach. For example, you can quantify the risk of machine failure if you keep a record of previous failure data. But, when it comes to a new machine/technology for your company, you probably have no recorded data, so you may choose to apply qualitative analysis.

Many organizations will use qualitative risk analysis to identify and prioritize risks, which will allow the use of quantitative analysis on the most important/impactful risks.

The main reason for that is quantitative analysis is time and money consuming, so usually no need for it unless we know exactly that we’re going to get benefit from conducting it.

While detailed quantitative analysis of risks is always preferred [if you have the tools/money/time for it], in many cases this is neither practical nor possible. Qualitative assessment of risks, however, can always be performed, and will usually take far less time and resources than quantitative analysis.

How to do Qualitative Risk Analysis?

The severity [danger] of any risk can be defined in terms of two quantities: impact, the effect that a risk will have on the project if it occurs; and likelihood, the extent to which the risk effects are likely to occur.

To these, we must add a third quantity defining the extent to which we trust our information: precision, the degree to which the risk is currently known and understood. In the following part of the article, we will discuss these terms and how to define and calculate each, and how they impact our assessment.

A- Impact

The impact of a risk (sometimes called its consequence) is defined in terms of a scale, such as 1=very low, 2=low, 3=medium, 4=high, and 5=very high.

There is no particular significance to the use of a five-level scale, and other scales can be used. However, most people feel that five levels are a reasonable compromise between too little, such as a simple three-level scale (low/medium/high), and too much, such as a 10-level scale.

To give this scale meaning, consider the four effects that any risk can have: cost, project costs can increase; schedule, project deliverables can be late; functionality, the level of performance or capability provided by the project deliverables can be reduced; and quality, the level of excellence of the deliverables can be reduced. Quality in this sense includes factors such as safety, reliability, and environmental impact.

Overall Impact Rating. The overall impact rating of a risk is the greatest of these four risk effects. The fact that we use the greatest rather than the average of them can be seen from a simple example. Consider a risk whose effect in a cost-sensitive project will be to increase costs by 100%, but otherwise have no other effects. The four ratings for cost, schedule, functionality, and quality will therefore be 5, 1, 1, 1, respectively. If we were to use the average of these, we would arrive at an overall impact level of 2, implying that a 100% cost increase is only a low risk. This is clearly not logical.

B- Likelihood

Likelihood is the point at which the risk effects are likely to occur. As with impact, we generally define likelihood on a five-level scale, such as 1=very unlikely, 2=low likelihood, 3=likely, 4=highly likely, and 5=near certain.

Before we can arrive at an acceptable method of measuring likelihood based on this scale, there are some difficulties we must overcome.

First, we must decide under what conditions to measure likelihood. The likelihood of an event often depends not only on blind statistical chance but also on human intervention.

For this reason, we divide likelihood into two components: probability of occurrence (the probability that the risk events will occur if we take no action), and intervention difficulty (the level of difficulty that we would experience in preventing the risk event from occurring). Note that intervention difficulty does not define response actions, but merely indicates the availability of such actions.

For example, standing on a railway track presents a risk of being hit by a train. The likelihood depends on the train’s frequency (probability) and how easily one can step off the track (intervention difficulty). If stepping off is straightforward, the likelihood remains low regardless of train frequency. In opposition, if exiting is difficult such as in a tunnel, the likelihood increases significantly.

Another example from the construction field: On a construction site where a tower crane operates near a busy urban road, the risk of the crane’s load swinging over the road poses a significant danger to vehicles and pedestrians. The likelihood of this risk depends not only on external factors like strong winds or operator errors but also on how easily the situation can be controlled. For example, a crane equipped with advanced anti-sway technology and wind sensors that automatically stop during bad weather can significantly reduce the likelihood. Additionally, experienced operators following strict safety protocols further minimize the chances of an incident.

On the other hand, if the crane lacks modern safety features or operators are less experienced, intervention becomes more difficult, increasing the likelihood of the load swinging over the road, even under moderate external pressures. This example highlights that likelihood is not solely based on external probabilities but is heavily influenced by the ease of intervention.

C- Precision

Precision defines the extent of one’s current knowledge and understanding of a risk. More specifically, it defines the level of confidence placed in the estimates of impact and likelihood.

While precision does not tell us anything in itself about the severity of the risk, it does tell us how much we can trust our assessment of that severity.

For example, consider the risk of an archeological discovery while excavating a construction site. If this were to happen to a company working on a site in a populated place like Cairo, you know that this would have a very high impact, since there would be a major delay while the discovery was examined.

It is possible to estimate the probability of this occurrence by examining the frequency of similar events in the same area. Intervention difficulty can be accurately assessed as very high since there is little that the construction company can do to speed matters. We can therefore characterize our assessment of this risk as being of high precision.

Now consider the same company working on a site in a less developed part of the world with which the company is not familiar.

In the event of an archeological discovery while excavating, do you know what the reaction of the responsible authorities would be? Who are the responsible authorities in such a case anyway? Has the site you are excavating been used for human habitation for the last 5,000 years, or has it only been in use for the last 50 years? In this case, your estimates of impact and likelihood, and hence of risk severity, will be much less precise.

A low precision rating serves as a warning that your analysis may underestimate or overestimate the risk’s true severity. It highlights areas where additional research or expert consultation is needed to refine estimates and improve decision-making accuracy.

The Risk Matrix

Impact and likelihood are combined within the risk matrix to provide a measurement of risk severity.

A risk matrix consists of a 5 x 5 array of elements, as shown in the below figure, each element representing a different set of impact and likelihood values. (Of course, if you decide to use six levels for impact and likelihood, you would use a 6 x 6 matrix, and so on.)

The risk matrix is normally divided into red, yellow, and green zones (or any other three color shades), representing major, moderate, and minor risks, respectively.

The red zone is centered on the corner of the risk matrix (high impact and high likelihood), while the green zone is centered on the diagonally opposite corner (low impact and low likelihood).

Conventionally, impact is considered more important than likelihood (a 10 percent chance of losing $1,000,000 is usually considered a more severe risk than a 90 percent chance of losing $1,000).

RECOMMENDED COURSE

Quantitative Schedule Risk Analysis with Primavera Online Course

In this course we will show you how to evaluate the risk in you schedule and will teach you how to use…

1,330 students
WhatsApp support group
Certificate

View course

The matrix in the following figure is based on the relationship of Severity = Likelihood + 2 × Impact, which expresses the belief that impact is twice as important as likelihood in determining severity.

This relationship assigns values to each element of the matrix, ranging from 3 in the lower left corner to 15 in the upper right corner. Risk zones are then defined as red=12–15, yellow=8–11, and green=less than 8.

5 (Near Certain)	7	9	11	13	15
4 (Highly Likely)	6	8	10	12	14
3 (Likely)	5	7	9	11	13
2 (Low)	4	6	8	10	12
1 (Very Unlikely)	3	5	7	9	11
Probability \ Impact	1 (Very Low)	2 (Low)	3 (Medium)	4 (High)	5 (Very High)

Other relationships between impact and likelihood, which can be expressed as Severity = Likelihood + N × Impact, where N is a numerical value, will result in different zone boundaries.

Others may decide to use a relation of Severity = Likelihood x Impact. In the end, you should tailor your risk matrix up to your organization’s standards and lessons learned from your previous projects.

Organizations are free to choose whatever zone boundaries they wish, as long as the same boundaries are used consistently throughout an organization or a department.

Now you may be wondering, when we will consider precision?

Effect of Precision

When we say, for example, that a risk is located at impact 2, and likelihood 4 on the risk matrix, we seem to imply that we know exactly the impact and likelihood values. However, this is only true for risks with precision levels rated as high. At lower precision levels, our knowledge of impact and likelihood is less exact. You should keep that in mind while comparing different risks using their matrix severity level.

This leads to an alternative method of defining precision, based on the probable errors in our estimates of impact and likelihood, as shown in the following figure.

A basic relation or equation could be conducted also to implement the precision into the game. For example, you can give each precision level a weight and multiply it by the severity of a risk.

That can’t be unified for all projects or organizations, you can know what works for you and represents your risks well by trial and error or by consulting risk management experts. They can help you tailor a relation that fits for your work nature after a deep study of your project numbers and previous similar projects’ data.

Risk Ranking and Management Priority Order

If a project has identified a large number of risks, it can often be difficult to decide which should be dealt with first. In such cases, a risk ranking system based on position in the matrix can be used to determine the order of priority.

In effect, this introduces a finer risk classification system than the simple three-level red/ yellow/green method.

For risks with the same color area, you can rank the risks by the highest impact first.

A risk matrix with severity rankings, not just colors, is an effective tool for deciding which risks to deal with first. For example, you may decide to deal with risks only with a severity rank of 10 or above and then study them more to rank them on better and more detailed parameters.

Risk assessment based on the concepts of impact, likelihood, and precision provides a consistent, comprehensive, and surprisingly easy method of risk assessment. Impact and likelihood between them define risk severity, while precision defines the level of confidence in the severity estimate. Assessing a risk is simply a matter of setting scales and comparing what you know about the risk with those scales. Precision is estimated by considering the level of confidence that you feel in the resultant estimates.

Risk Series and Ranking Using Qualitative Risk Analysis [Real World Example]

Project Overview

A construction company is building a multi-story office building. The project faces various risks related to schedule, cost, and quality. Below are five identified risks, their qualitative assessments, and the resulting rankings.

Risk 1: Delayed Delivery of Structural Steel

Impact: High (4/5)
Delayed steel delivery can pause structural works, directly affecting the critical path and causing cost escalation due to idle resources.
Likelihood: Likely (3/5)
Market conditions indicate a 30% chance of supplier delays based on recent disruptions.
Precision: High
Historical data and supplier performance records provide confidence in this assessment.
Risk Matrix Placement: Moderate to High Risk (Yellow Zone) Severity=Likelihood+2×Impact=3+2(4)=11

Risk 2: Unexpected Ground Conditions

Impact: Very High (5/5)
Encountering unforeseen ground conditions could lead to significant excavation delays and require specialized equipment.
Likelihood: Unlikely (2/5)
Initial geological surveys indicate a low probability of such conditions in the area.
Precision: Medium
Geological surveys were conducted but lacked detailed sampling.
Risk Matrix Placement: Moderate Risk (Yellow Zone)
Severity=Likelihood+2×Impact=2+2(5)=12

Risk 3: Stakeholder Approval Delays

Impact: Very High (5/5)
Approval delays could pause all construction activities, resulting in severe schedule overruns.
Likelihood: Highly Likely (4/5)
Similar projects in the same area often faced delays because of slow government processes.
Precision: High
Based on well-documented data from previous projects.
Risk Matrix Placement: Major Risk (Red Zone)
Severity=Likelihood+2×Impact=4+2(5)=14

Risk 4: Extreme Weather Events

Impact: High (4/5)
Rainy season can disrupt site works, increase material damage, and delay outdoor activities.
Likelihood: Highly Likely (4/5)
Forecasts predict a strong rainy winter during the project timeline.
Precision: High
Meteorological data and historical patterns are reliable.
Risk Matrix Placement: Major Risk (Red Zone)
Severity=Likelihood+2×Impact=4+2(4)=12

Risk 5: Equipment Breakdown

Impact: Medium (3/5)
Equipment breakdowns could delay specific activities but can often be resolved quickly.
Likelihood: Likely (3/5)
Aging machinery such as concrete mixers/pumps increases the probability of breakdowns during peak construction.
Precision: Medium
Maintenance records are available but inconsistent.
Risk Matrix Placement: Moderate Risk (Yellow Zone)
Severity=Likelihood+2×Impact=3+2(3)=9

Risk Ranking

We will base the risk ranking on the following Risk Matrix, which is based on the following severity equation: Severity=Likelihood+2×Impact.

You should implement a customized risk matrix in your projects which is tailored to your project needs as discussed before

Based on the risk severity calculated using the formula Severity=Likelihood+2×Impact the risks are ranked as follows:

Download the resources of this article