By combining BIM with 4D scheduling and machine learning, Aquila is looking to improve construction site equipment utilisation in real time, as Andrew Johnson, Project Manager at BIM Academy, explains
In 2019 BIM Academy, in collaboration with Buildstream, Costain and Northumbria University, conducted a feasibility study – code named SiteView – to research how combining BIM and 4D scheduling could improve the sequencing, timing and operational management of plant equipment on site. The results were phenomenal, and subsequently BIM Academy was awarded Innovate UK funding in 2020 and the making of “Aquila” began.
To find the solution, first we needed to address the problem. And the problem was poor utilisation of plant equipment meant unnecessary spending (it is estimated poor utilisation of plant equipment is wasting up to £100bn globally per year) and perhaps more importantly, this machinery spits out exceptionally high levels of emissions, having a negative impact on the environment (the construction sector is responsible for around a fifth of all global emissions). Improved and smarter analysis of plant equipment will produce efficiency savings in both of these areas, and more.
Plant equipment, particularly heavy earthmoving equipment such as excavators, bulldozers and dump trucks represent a major cost element in construction projects ranging from 10% in a commercial project, and up to 50% in major infrastructure projects such as highways, railways and energy projects.
The feasibility study investigated the opportunity of improving productivity on site by 15% or more, by increasing plant equipment utilisation throughout the construction phase – our research has proven this is achievable.
In addition, with the announcement earlier this year that the UK has brought forward laws to end its contribution to global warming by 2050, with the new target now set at achieving a 78% reduction of carbon emissions by 2035 – the pressure is on to bring all greenhouse gas emissions to net zero as quickly as possible.
This means good practice of energy management on site, in addition to budget savings, is needed now more than ever.
Aquila has been developed to optimise plant equipment operations using realtime data. It will eliminate earthwork estimations and automatically determine the plant and equipment’s productivity output through machine learning algorithms in the platform. This means no more performance predictions, we can accurately set project schedules for plant equipment, monitor and measure in-use, down-time and emissions output. Plus take lessons learned from one project to the next to maximise these efficiencies.
The consequences of equipment downtime can be severe, ranging from delays in a project caused by an unexpected breakdown to the inconvenience of idle equipment taking up valuable space — all contributing to an overspend in project budget.
The need to deliver cost savings is a prominent feature in the development of Aquila, with the desire to remove additional unnecessary project costs. Which is why it was essential to develop a commercially, as well as technically, viable solution.
Plug and play
Aquila utilises simple, yet smart, plug and play mobile technologies, rather than intrusive Internet of Things (IoT) installation to sense equipment operation.
IoT refers to a system of interrelated, internet-connected objects that can collect and transfer data over a wireless network without human intervention. IoT hardware is typically expensive and installation is disruptive on operational plant activity.
Real time feedback
Most 4D tools currently available aim to be planning simulation tools, competing with more established Gannt tool/chart derived systems which provide a visual view of tasks displayed against time.
However, Aquila provides a real-time, decision-enhancing platform where project teams can review what is happening on site through a 4D model, predicting and controlling the impact of future schedules and generating new benchmark datasets for future projects.
A 4D model allows designers and project teams to visualise the project sequencing, identify errors in the plan and optimise the best path of construction. It is also a better way of communicating the plan to the entire team.
Aquila’s further advancement in technology allows for each piece of equipment to be directly linked to the model, mapping its location. Data extracted from a vehicle tracker is then visualised within the model viewer, bringing all important information in one place.
Each vehicle will have a personalised Aquila tracker device that is given a unique reference. This will capture location, speed, idling time, harsh acceleration or braking, fuel consumption, vehicle faults, emissions output and more. Aquila can also link to third party telematic data.
At its core, this comprehensive telematic system will include a vehicle tracking device installed in each vehicle that allows the sending, receiving and storing of vehicle data. It connects via a mobile device installed in the vehicle, enabling communication through a wireless network.
The Aquila device collects GPS data as well as an array of other vehicle-specific data and transmits it via a satellite communication system to a centralised server in the Aquila platform. Aquila interprets the data and enables it to be displayed for project teams – all of which can be viewed on smartphones and tablets onsite.
When analysed, the mobile and third party telematics data can provide in-depth insights across an entire project fleet. This has resulted in the development of an intelligent, reactive web-based programme.
Using Vue.Js alongside the Autodesk Forge viewer, has given us the ability to have a reactive environment where a change on one part of the application shares its information with all other parts of the application, in real time.
If new data comes in via the trackers, the Aquila application can update itself, and re-process anything that needs to be changed visually as soon as that information is available.
One of the key aspects of any construction project is the programme of works, which decides where a given resource should be at any time and how long a task should take. Something that hasn’t been done before is linking a resource to a task and providing live and accurate data, which can be compared against the expected metrics of the programme of works.
The programme may expect that an excavator, for example, is working in a specific zone for, say five days, but the ultimate goal with Aquila is that you can see if that excavator is actually in that zone, for how long and is it doing the correct task.
We very quickly realised that the data requirements for the project were going to be highly complicated to manage, the potential for the amount of data to track and store could be huge. Therefore we needed to create a sophisticated, powerful backend system, to deal with all project data needs.
It was important for us to use mature standards for maximum reusability and flexibility. We use OpenAPI for our data endpoints, and GeoJSON and PostGIS for storing and managing locational data. These standards take real locations and tie the information to the project model, giving us the ability to accurately place the vehicle into the model.
It is clear Aquila is revolutionising the way we not only view, track and monitor plant equipment, but it offers a new digital process for saving time and cost on a project as well as contributing to sustainability goals.
Plant equipment is an essential part in any infrastructure or construction project and this new platform will change how we use these vehicles, optimising their performance for a smarter, greener future.