Data in Civil Engineering

How does data work for civil engineers?

Civil engineering is one of the oldest engineering disciplines. It encompasses a diversity of sub-disciplines, the major ones of which are structural, water resources, environmental, construction, transportation, and geotechnical. It deals with planning, analysis, design, construction and maintenance of the physical and naturally built environment.

Data is absolutely taking over every sector, even the ones that you may not immediately relate to data. For instance, construction management and civil engineering.

In this article, I will talk briefly about how data can help civil engineers achieve their objective goals. In addition, I will give two examples of data specialist roles in engineering companies.

How can data help civil engineers?

Information comes from everywhere; computers, people, sensors and any device that generates data. In the construction field, every structure that’s been erected is backed by plans that contain massive amounts of information.

Today, however, that data is complemented by an extraordinary amount of information generated by sources such as building engineering logs, construction workers, cranes, earth movers and materials logistics.

Now, engineers can evaluate metrics from disparate sources to determine exactly how and where to erect a structure. Engineers can also evaluate historical construction data to assess risks and avoid potential project setbacks.

The following entries highlight three ways in which data can help civil engineers successfully achieve their objectives.

1. Bidding smarter

Large structures can produce considerable costs. Such massive infrastructures require the expertise of civil engineers for efficient design, construction and ongoing management. For ventures of large magnitude, stakeholders will often recruit engineers with two or more decades of experience. Today, closely tracking things like job costs, change orders, material and equipment usage and worker productivity from your projects can help better forecast future work and lead to smarter bidding.

2. Improving jobsite processes and productivity

By streamlining and analyzing data collection from job sites, contractors can improve workflows, find ways to automate tasks, find efficiencies, cut costs and much more.

3. Improving Design Productivity

Compared to construction productivity, design productivity is much more difficult to measure because design is an iterative and innovative process. Today, with rapid extension of building information modeling (BIM) applications, tremendous volumes of design logs have been generated by design software systems, such as Autodesk Revit, that can be mined to monitor and measure the productivity of the design process.

Can a Civil Engineer become a data specialist?

As a recovering Civil Engineer (p.s. I miss it sometimes), the answer is YES.
With the right upskilling, a civil engineer can be a data specialist in many industries (i.e. Banking, Consulting, FMCGs, Supply Chain, Advertising, Tech,…etc.). However, below I will give examples of data roles at engineering companies, where a civil engineer might have an advantage given his knowledge in the field.

Please note that the requirements and responsibilities in the examples below differ from a company to another and are NOT a checklist.

Example1: Data Analyst

Responsibilities:

1. Build and maintain dashboards summarizing business, financial, or operational data for review by executives, managers, clients, and other stakeholders.
2. Identify data trends and make recommendations for quality improvement.
3. Ensure timely and accurate communication of business intelligence to appropriate audiences.
4. Troubleshoot data issues, validate result sets, recommend and implement process improvements.

Qualifications:

1. Experience with Construction life-cycle.
2. Qualified applicants should have a BS in Computer Science/Construction Management/Civil Engineering or related field.
3. Major or Minor in Data Analytics will be a plus.
4. Recognize the importance of key concepts relevant to construction and understand how this is used to manage projects.
5. Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy.
6. Good understanding of data models, databases, data mining techniques.
7. Good understanding of using reporting and dashboarding platforms.

Example2: Junior Data Scientist

Responsibilities:

1. Extract data from tests, inspections, submittals, and other performance metrics to uncover intelligence around Heavy Civil construction quality and efficiency.
2. Advance our capabilities for how statistics and machine learning should be used in analyzing digital trends, client data interface and quantitative research.
3. Transform data and insights into compelling and creative visualizations.
4. Creatively identify opportunities in our process for scalable applications, and collaborating with others to construct these tools.
5. Come up with ideas, or ask thoughtful questions that encourage ideas, to drive our continued growth.

Qualifications:

1. Have an engineering or technical degree.
2. Have solved a difficult or time-intensive problems by collaborating with a team.
3. Have conducted statistical analyses outside of an academic setting.
4. Be computer literate in excel macros and SQL databases.
5. Have deployed supervised and unsupervised data analytic tools.
6. Be an autodidact, who has a keen interest in finding solutions to problems by diving deep into the details.
7. Have the ability to juggle multiple priorities and pay attention to details when things are moving quickly.
8. Have knowledge of qualitative and quantitative research and/or digital analytics.
9. Have strong communication and writing skills.
10. Have experience in using applied statistics, and be comfortable working with imperfect data.

What’s next?

After reading this article, I hope that civil engineers, whether seniors willing to leverage data in their decision making process or juniors aspiring for a data role (or a career change), are more motivated to go and seek more knowledge about the tip of the iceberg that I scratched in this article.

Finally, I would be happy to answer your questions and read your feedback in the comments section below for further improvement :)