Florida Engineering Society: How is Technology Changing the Engineering Profession?

By David Cowan, Jr. Senior Engineer, CMA

David Cowan, Jr., P.E., ENV SP, is a senior engineer at CMA in the West Palm Beach office. David has over 10 years of experience and holds a bachelor’s and master’s degree in civil engineering with a water resource focus. David serves as a state director for FES, District Director for TBP, and on the Urban Drainage Standards Committee for EWRI.

Surveying Over Time And Adoption Of Technology

Surveying has been part of civil engineering for a long time. It has traditionally involved manual tasks like cutting brush, pulling chains, and leveling, which were labor-intensive and time-consuming.

The advent of total stations and software like AutoCAD revolutionized these processes, making data collection faster and more precise and significantly reducing human error. GPS-RTK (Global Positioning Systems with Real-Time Kinematic positioning) further enhanced the accuracy and efficiency of data collection, allowing for centimeter-level precision.

This technological evolution has continued with the integration of drones equipped with Light Detection and Ranging (LiDAR) and Artificial Intelligence (AI) processing, which create high-resolution point clouds for detailed topographic surveys. These advancements have dramatically reduced the time and cost compared to traditional methods. However, they also introduce challenges in quality control, as professionals must now manage vast data sets and rely increasingly on AI for data processing.

The ability to gather mega-datasets quickly necessitates a new level of scrutiny and validation to ensure data integrity and accuracy, prompting engineers to adapt their quality assurance processes continually.

Automotive Manufacturing And Advancements

The automotive industry has undergone significant transformation since the Model T, which was produced through individual methods and revolutionized personal transportation. Ford's introduction of the assembly line marked a considerable shift, increasing efficiency and production volume while reducing costs.

By the mid-20th century, almost all Ford models, such as the Mustang, were produced using assembly lines, which relied heavily on human labor. In recent decades, modern vehicles have benefited from extensive robotic automation in manufacturing. The assembly line has enhanced precision, reduced labor costs, and minimized workplace injuries, although it has also reduced the number of jobs required to produce a vehicle.

The automotive industry faces the dual challenges of transitioning to electric vehicles, such as the Ford Lightning, and developing autonomous vehicles. Electric vehicles represent a significant shift towards sustainability, driven by regulatory pressures and consumer demand. Autonomous vehicles, meanwhile, pose complex technological and ethical questions about the role of machines in making critical driving decisions. As sensors and computer systems become more sophisticated, the industry must address how comfortable society is with technology taking control in life-and-death situations on the road.

Data Management And Analysis

Engineers have historically relied on hand calculations and simple tools like slide rules for analysis, which were labor-intensive and prone to human error. The advent of computers brought punch cards and early programming, which began to automate these processes. The development of spreadsheets like Excel and more advanced math software has significantly streamlined data management, allowing for more complex analyses and simulations with greater accuracy.

Today, engineers use sophisticated modeling tools that can handle vast amounts of data and perform intricate calculations quickly. However, the principle of "garbage in, garbage out" remains critical, emphasizing the need for thorough data verification to ensure the accuracy of computational results.

Professional judgment is essential in reviewing computer-based outputs, as even the most advanced software can produce erroneous results if fed with incorrect or incomplete data. Engineers must balance the efficiency and power of these tools with a rigorous approach to data quality and validation.

High-Volume of Work and Quality Control

As industries handle increasingly large datasets and produce more deliverables, maintaining quality control becomes a significant challenge. High-volume production requires rigorous standards and regular inspections to ensure product quality.

AI-generated analysis offers powerful tools for real-time monitoring and defect identification, enabling quicker responses to quality issues. However, the reliance on technology for quality control also necessitates robust systems for validating findings and managing anomalies.

Human oversight remains essential to ensure consistent product quality, as professionals must interpret computer generated data and make final judgments on the acceptability of products. Integrating technology in quality control processes can enhance efficiency but requires continuous improvement and adaptation of quality assurance protocols.

Business Of Engineering And Soft Skills

Engineering is inherently technical, but it also demands strong business acumen and soft skills. Modern engineers need to be proficient not only in technical aspects but also in communication, networking, and empathy. These non-technical skills, often called emotional intelligence skills, are crucial for professional growth and effective collaboration.

The shift to remote work and virtual meetings, facilitated by platforms like MS Teams and Zoom, has become increasingly common, creating efficiencies and enabling flexible work arrangements. However, this shift also presents challenges in mentoring and leadership development, which are vital for nurturing the next generation of engineers.

Face-to-face interactions provide spontaneous learning and mentorship opportunities that are harder to replicate in virtual environments. As the workplace continues to evolve, it is crucial to find ways to develop these skills and ensure that young engineers receive the guidance they need to succeed.

Education

Deep learning and problem-solving skills are essential in engineering education. Collaborative projects and teamwork foster an environment where students learn to work effectively with others. Strong communication skills are also prioritized to ensure future engineers can convey complex ideas clearly in both written and verbal forms.

Schools and universities must foster an environment where students learn to work effectively with others. Some of the best learning occurs in collaborative settings, such as lab work or group projects, where students can engage with their peers and tackle problems together. This collaborative approach enhances learning outcomes and prepares students for the collaborative nature of professional engineering practice.

As we use technology to solve problems in engineering, developing intuition about the correct answer becomes more important, requiring meaningful experiences to draw from, for informed judgments.

If you are looking for an opportunity to be part of a community that is collectively working on fostering the engineering practice, consider joining the Florida Engineering Society and being part of one of our committees:

• Education Committee

• Interest Groups Committee

• Society Development Committee

• Commission on Legislative & Government Relations

Each committee has several subcommittees, such as the Education Committee with the K-12 Subcommittee, MATHCOUNTS Subcommittee, Scholarship Subcommittee, Student Professional Development Subcommittee.

For more information, check out: fleng.org/membership/committee-opportunities.