MESSAGE FROM THE JOURNAL EDITOR
At first, almost everything in our industry looks like a technical problem. We need better ways to locate a utility, inspect a pipe, detect infiltration, understand deterioration, or predict failure. And the technology has advanced enormously. Today we have multi-sensor inspection, 3D mapping, increasingly connected GIS and asset management systems, and now artificial intelligence capable of processing information at a scale that was difficult to imagine even a few years ago.
After 20 more years working in underground infrastructure, I have come to believe that technology is not the hardest part of our problem.
The harder problem is turning what we see into what we know—and turning what we know into what we do.
A utility may have thousands of inspection records and still not understand its system. It may know that an asset is deteriorating and still not act. It may purchase sophisticated technology but lack the data structure, workflow, funding, or organizational responsibility necessary to turn that technology into better decisions.
This issue returns to that gap again and again.
The people behind the new CTAM curriculum have spent decades working inside it. Their experiences range from handwritten sewer inspection records in the 1970s to modern pressure-pipe assessment, GIS, financial planning, and risk-based asset management. What makes their experience valuable is not simply that they know current best practices. They also know where earlier approaches failed, what the profession learned, and why collecting information is only the beginning.
North Texas Municipal Water District’s condition assessment program shows why inspection must move beyond simply seeing defects toward quantified and repeatable evidence. The team in Xinyu shows the same movement compressed into a single workflow: a survey vehicle narrows a suspected gas leak to a stretch of ground, a trained dog walks it, an instrument confirms the location, and only then does a crew go out. The Western Virginia Water Authority’s digital transformation experience reminds us that sophisticated systems cannot compensate for poor data or poorly defined processes. Our articles on utility mapping, AI, material selection, and I/I all approach different technical problems, but underneath them is the same question:
Can we create a trustworthy chain from the physical asset, to the data, to professional understanding, to a decision, and finally to action?
I believe this question will become even more important as AI enters infrastructure engineering.
AI can help us inspect faster, organize records, recognize patterns, automate repetitive work, and make decades of information more accessible. But greater computational capability does not automatically produce greater understanding. If the underlying information is incomplete or unreliable, AI can process uncertainty faster without eliminating it. That is why I do not see the future of our industry as a choice between human experience and technology.
We need both.
We need the accumulated knowledge of people who have spent years learning how infrastructure actually behaves. We need young engineers willing to question inherited assumptions. We need operators who understand what happens in the field. We need researchers who can test new ideas. And we need new technologies that make this collective knowledge more visible, structured, traceable, and usable.
This is also how I increasingly think about BAMI-I. Its work is to connect people who hold different parts of the same problem—utilities, engineers, contractors, technology developers, researchers, educators, and students—and then to preserve and transfer what they learn. A conference, a training course, a journal: each is useful in proportion to how much knowledge it actually moves from the people who have it to the people who need it.
Our underground infrastructure will always be partly invisible.
Our knowledge of it does not have to be.
If we can improve how we observe, record, share, question, and act on what we know, we can move from reacting to infrastructure failure toward understanding infrastructure before it fails.
That, to me, is the direction of buried asset management—and the direction I hope this Journal continues to explore.
Thank you for your reading.
Wei Liao, PWAM
Lead Research Engineer, Purdue University
