Is it too late to start with the AI in 2026? It wen't so far, does it still make sense to get interested in this technology?

Absolutely. Today we sit down with MZ Naser of Clemson University to map a clear, useful path for engineers who want results without the hype. We start with the basics - clean data, the right algorithm, and a realistic mindset - and climb toward explainability, causality, and even philosophy to show where AI informs decisions and where it can quietly mislead.

We dig into the limits of our experiments: when tests are expensive, we control only a few variables and then celebrate when explainable AI “finds” the same drivers. That’s not discovery; that’s confirmation. MZ explains how broader sampling, anomaly detection, and careful clustering can reveal patterns we miss, while acknowledging that physics is fixed but our datasets are narrow. We also talk scale: a model that predicts whole-building fire behavior from scratch is a fantasy without impossible data. The practical play is combining reasoning, physics, and simulation to guide where AI adds value - sometimes leading to a simpler equation that replaces the model altogether.

Then we get tactical. What is agentic AI, and how can it save engineers real time? Think delegated workflows: data gathering, parametric setup, code lookups, Excel design sheets, quality checks, and concise summaries. Train agents with explicit steps and tight guardrails, keep them away from money and safety-critical controls, and make human review mandatory. We also confront traceability and model retirement - why freezing working versions, documenting assumptions, and cross-verifying with independent methods matter for audits years down the line.

Throughout, we balance open local models versus cloud LLMs, the trade-offs between control and convenience, and the hard truth that black boxes don’t absolve us of understanding. The big takeaway: AI is a lever, not a miracle. Use it to widen your view, automate routine work, and challenge your priors - while keeping physics, data quality, and professional judgment at the center.

If this conversation helps you think clearer about where AI fits in your workflow, follow the show, share it with a colleague, and leave a quick review so more engineers can find it.

----
The Fire Science Show is produced by the Fire Science Media in collaboration with OFR Consultants. Thank you to the podcast sponsor for their continuous support towards our mission.

I’m not stressed by AI itself. I’m stressed by the insatiable greed of those who profit from it, even if it means sacrificing large parts of the population. I'm also stressed about how ruthlessly it can be abused to cause deliberate harm.

In this episode I'm not taking you into world of fire science, but rather into my own thoughts on how the AI revolution influences our lives. And I was influenced it just last week - through a phishing attack on the IAFSS, and through reading a very disturbing piece of fiction I found on the Internet...

In the episode I comment on the targeted phishing attack against our association that used well-researched details and a cloned voice pulled from public audio. From there, we step into a stark forecast of near-term AI disruption in white-collar work. Agent teams can already write, review, and ship production code in loops, compressing time and cost while jolting stock prices across entire sectors the moment capabilities drop.

Then we get specific about our field. Some tasks in fire safety are ripe for automation—code interpretation, routine calculations, device placement, and documentation—where speed and consistency help. But holistic fire strategy is contextual and slow to validate, with scarce, standardized case data and long feedback loops. Buildings are messy, multidisciplinary systems; that friction is a temporary moat against full automation. The larger risk may be macroeconomic: if AI compresses demand and margins across white-collar industries, construction cools, and safety work gets squeezed. Paradoxically, low digitalization in construction buys time, making it harder to train and deploy one-size-fits-all models.

I'm still to large extent positive Fire Safety Engineering won't be directly disrupted at the same scale as Software Engineers got, but as a part of a larger ecosystem we won't be untouched either... I hope the version of the future that plays out is more optimistic than the one I got worried about.

Read the Citrini piece here, if you have not yet: https://www.citriniresearch.com/p/2028gic

Update: A week later there has been a lot of works that have refuted the dark scenario in Citrini piece, like here: https://www.businessinsider.com/ai-job-losses-wall-street-strategist-citrini-research-citadel-securities-2026-2?IR=T

Some are showing Software Engineering job openings rising etc. Perhaps world is not in the dark scenario - I highly hope so!

----
The Fire Science Show is produced by the Fire Science Media in collaboration with OFR Consultants. Thank you to the podcast sponsor for their continuous support towards our mission.

A tunnel can ride out a fire without collapsing (or even critical visible structural damage), but a question whether it is safe for operations, and what is its long-term residual fire resistance remains. With repair bills being in high seven-eight figures, this is more than just a theoretical question... In this episode we dig into the hard middle ground of fire damage post mild/large fires, and cover where modeling and fire science can help reducing the uncertainty and guiding decisions. With Professor Negar Elhami-Khorasani from University at Buffalo, we map how ventilation settings, tunnel slope, and fuel push temperatures into either safe or punishing regimes, and why spalling can turn a survivable event into a structural headache.

We break spalling down to first principles—vapor pressure, thermal gradients, and restraint—then translate that into a practical method: update the section as concrete “disappears” so the thermal boundary moves and heat penetrates realistically. From there, we track damage you can act on: concrete volumes beyond 300°C, steel temperatures that risk incomplete recovery, and bond loss that forces major repairs. Just as important, we model through cooling, when heat keeps migrating and residual capacity sinks. The result isn’t a guess; it’s a bounded map of what to replace and why.

We also take on the tactical questions that matter: How long would an extreme fire need to threaten collapse, given different soils and depths? What’s the real value of polypropylene fibers in high-strength mixes? How should owners structure a fast, post-fire workflow—quick checks for reopening within days, followed by a deeper, simulation-informed durability plan? By pairing observed spalling and known operations with targeted heat transfer and mechanical analysis, you can reconstruct the event, communicate risk clearly, and spend repair budgets where they return the most resilience.

If you care about structural fire engineering, tunnel safety, spalling mitigation, and performance-based design that reduces downtime, this conversation delivers a roadmap you can use.

Further reading - recommended papers by Negar Elhami-Khorasani and her team:

Structural fire behavior of tunnel sections: assessing the effects of full burnout and spalling effects

Numerical modeling of the fire behavior of reinforced concrete tunnel slabs during heating and cooling

Fire Damage Assessment of Reinforced Concrete Tunnel Linings

----
The Fire Science Show is produced by the Fire Science Media in collaboration with OFR Consultants. Thank you to the podcast sponsor for their continuous support towards our mission.