# How road deterioration is modelled, and what a model cannot tell you

*Projecting a network ten years forward is arithmetic, not prophecy. The useful
question is not how clever the model is — it is which of its inputs your council
actually holds.*

Every ten-year capital plan contains a projection. A council states what the
network will look like under one spending level and under another, and the
difference between them is the argument for the budget. Those projections come
out of a deterioration model, and most of the time nobody in the room has seen
what is inside it.

That is a problem when the projection is challenged, because the answer *"the
software calculated it"* concedes the point.

## What a deterioration model is doing

At its simplest it answers one question per segment: given this surface, in this
condition, carrying this traffic, how much worse will it be in a year?

Austroads sets out what a proper treatment-design investigation draws on
(*Guide to Pavement Technology Part 5*, 2025): the original pavement design,
construction details, maintenance and rehabilitation records, climatic
conditions, and the effect of traffic on past performance. Those are the inputs
that explain why two visually identical roads behave differently.

Most small councils hold two of the five. That is not a failure — it is the
condition the method has to work in, and a model that quietly assumes the other
three is making things up on your behalf.

## Three honest limits

**1. A model projects; it does not predict.** It applies a published rate to a
measured condition and reports where that arithmetic lands. Everything it
produces is conditional on the rate being right for your roads, which is a
question about your roads and not about the software.

**2. Heavy vehicles matter more than vehicle counts.** Pavement damage scales
roughly with the fourth power of axle load, so a route with a modest traffic
count and a high heavy-vehicle share can deteriorate faster than a busier street.
A model that takes only AADT is taking the wrong number. One that takes a
heavy-vehicle share should say so visibly, because it is a large effect driven by
a figure many councils estimate rather than measure.

**3. The things a model leaves out do not stop existing.** Climate — rainfall,
temperature, frost — is a genuine driver of deterioration and is absent from most
local-government models, including ones that look sophisticated. So is subgrade.
A model that omits them is still useful; a model that omits them *silently* lets
a reader believe the projection accounts for things it has never seen.

## The test that actually matters

Not "how accurate is the model", which cannot be answered in advance, but:

> **Can somebody who disagrees with the projection see exactly what produced it?**

That means the rates are published rather than embedded, the thresholds are
visible, the inputs per segment are inspectable, and where a number is modelled
rather than measured it is labelled as modelled wherever it appears. A council
officer should be able to take one segment, do the arithmetic by hand, and get
the same answer.

A model that passes that test can be argued with, which is the point. A model
that cannot is asking to be trusted, and trust is not a budget argument.

## Testing the rates you are already using

There is a check available to any council with more than one year of roughness
data, and it does not need a model at all.

Austroads suggests using a roughness time series to assess whether the
intervention levels currently in force are working — and gives an example of a
network where "the currently applied intervention levels do not appear to be
holding the network in a stable condition", concluding that a change is probably
required (*Guide to Asset Management Part 5B*, 2007).

That is a question your own data can answer about your own network, and it
outranks any projection. If the network is drifting down under the rules you
have, the rules are the finding.

Victoria's auditor-general reached the same conclusion about the state network
from the other direction: around 48% of state roads were in poor or very poor
condition in 2024 against around 39% in 2021, with the department advising
government that funding was insufficient to maintain or improve condition
(*Maintaining State Roads*, VAGO, September 2026). Those are state roads, around
15% of Victoria's network — but the method of noticing is the same one.

## On machine learning

It works, and it is mostly not for councils yet.

The strongest published results come from state and national road authorities
with deflection testing, mix records and construction histories spanning decades
— data a small council does not have and will not have soon. More importantly, a
model whose reasoning cannot be stated in a sentence breaks the one property a
council most needs: the ability to explain a number to somebody who did not want
to hear it.

There is a narrower use that is clearly worthwhile: statistics to **flag** an
implausible survey reading for a person to check. That is a different job from
producing the projection.

## What to ask a vendor

1. What rate are you applying, and where can I read it?
2. Which inputs do you use, and what do you do when one is missing?
3. Is traffic loading in the model, and is it axle-based or count-based?
4. Which numbers in the output are measured and which are modelled — and does the
   output say which, where a councillor reads it?
5. What happens to a road that has never been surveyed?

The fifth is the one that separates systems quickly.

## How PaveKeep handles this

PaveKeep projects condition forward from the council's own data and a published
method — the rates, thresholds and rules are on a page in the product, read live
from the council's own configuration, including the ones it does **not** use.
Heavy-vehicle loading changes the expected rate and the change is visible rather
than buried. Where a number is modelled rather than measured, it is labelled as
modelled wherever it is shown, and the two are never blended into one figure.

Climate is not an input. The structure for it exists and nothing consumes it, and
the method page says so in those words.

PaveKeep is built for small and regional councils and is not yet in use at any
council.

## Sources

- Austroads (2025), *Guide to Pavement Technology Part 5: Pavement Evaluation and
  Treatment Design* — §3.2, the information a rehabilitation investigation draws
  on; §4, roughness measurement at network level.
- Austroads (2026), *Guide to Pavement Technology Part 2: Pavement Structural
  Design* — traffic loading expressed in equivalent standard axles.
- Austroads (2007), *Guide to Asset Management Part 5B: Roughness*, AGAM05B-07 —
  §6.5 on intervention levels and testing them against a roughness time series.
- Victorian Auditor-General's Office (9 September 2026), *Maintaining State
  Roads*, Report 2026–27.
- Federal Highway Administration / National Highway Institute (1999), *Pavement
  Preservation: The Preventive Maintenance Concept*, FHWA-HI-00-006 — on why
  pre-treatment condition has to be controlled before any before-and-after
  comparison means anything. **US evidence.**

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Source: https://pavekeep.com.au/knowledge/how-deterioration-is-modelled · PaveKeep · bound content