Improving infrastructure cost estimate accuracy

Too much data?

Machine learning approach

Our machine learning (ML) cost model approach uses algorithms to detect relationships between many different characteristics, or variables, of a project and their cost, as opposed to just one or two as with the more traditional methods. This leads to greater certainty of costs because it allows more sophisticated relationships between the variables to be discovered, removes subjectivity and uses more of the available project scope detail. Organisations with a lot of good quality historical data therefore benefit most from using the ML approach.

We’ve trialled the approach to derive and train an algorithm that can estimate costs using all available asset characteristics, using AECOM historical data for several hundred pipe laying projects with actual costs. We’ve analysed the project cost characteristics such as the length, diameter and ground condition of pipes, using the traditional manual modelling approach, followed by our ML approach. While the traditional approach estimate was eight per cent out from the total actual cost, our ML estimate was less than four per cent out, an increase in accuracy of 50 per cent.

Leveraging ML in this manner allows thousands of cost datapoints to be extracted and analysed in a few days by just one suitably experienced data scientist, achieving results that outperform those that would previously be produced by an entire team of analysts using traditional methods.

Not enough data?

Assemblies tools

Being able to track cost changes throughout a project gives a better understanding of the reasons why budgets may be exceeded and therefore where to target efficiencies. It also improves overall learning about specific costs for future estimating and allows greater management over change events and the contractual disputes that can arise from them. However, more traditional cost estimating methods provide very little visibility of why a cost changes from initial estimates to actual costs. In an effort to move away from obscure budget figures that are not based on any asset breakdown or scope build up, we have developed asset assemblies tools, which use high-level design parameters or characteristics to assemble a full bottom up bill-of-quantities estimate at unit or resource level.

Our assemblies tools:

are helpful for organisations with little of their own information to base estimates on
use Excel as a platform
can also be embedded into existing cost estimating systems
provide generic asset models based on agreed defined specifications taken from assets previously built by an organisation as well as on the organisation’s own standard design specifications
provides a fully detailed estimated asset cost using the bare minimum of information, such as basic asset dimensions, using design assumptions to ‘fill in’ scope detail that was initially absent
allows teams to explore procurement strategies with little effort
can be used to quantify carbon and construction duration.

As a project progresses and actual design and cost information becomes available, asset details and actual costs can be added to the model, updating the bill of quantities produced and providing fully-detailed cost breakdowns that can be compared throughout all project gateways, showing reasons for budget changes and providing an early warning that budgets may be exceeded, giving greater time for challenge or mitigation.

We’ve built a number of assemblies for High Speed 2 which form part of a suite of estimating tools being used to validate that the project is achieving value for money. Separately, we’re producing assemblies for the Environment Agency as part of its ongoing internal estimating system development and have been commissioned by Gatwick Airport to produce asset assemblies to benchmark tender returns.

Through development of new techniques such as machine learning and asset assemblies, AECOM is re-defining best practice in estimating and benchmarking, helping organisations improve their investment decision-making and ultimately secure value for money for their customers and shareholders.

Find out more about our cost estimating capabilities here

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