Article | August 2, 2021
With major demographic challenges on the horizon – including the growth and ageing of the population, coupled with the need to adapt to a changing climate – it is essential that we take a long-term approach to infrastructure planning.The National Infrastructure Commission’s (NIC) National Infrastructure Assessment (NIA) aims to do exactly that.
Producing the NIA every five years is one of the NIC’s core responsibilities set out in its charter and its first such report, published in 2018, heavily influenced the government’s recent National Infrastructure Strategy.
Article | April 23, 2020
There are a handful of pretty cool accessories for your smartphone that can be very useful to those of us in the construction industry, like thermal imaging cameras, in-wall imagers, and inspection cameras. None of those, however, help with BIM coordination, unlike a system that I just found from construction software giant, Trimble. Many different systems have been created in recent years to harness the power of augmented reality on the jobsite, with BIM the core focus of many of those solutions, but this system from Trimble is a bit different than all of them. Using an AR enabled smartphone, Trimble SiteVision combines hardware and software to virtually project BIM models onto the jobsite you’re standing in.
Article | August 4, 2021
In amongst the chatter about how we can 'do' infrastructure better, there's now a growing consensus that we need to improve the way we design our interventions - 'design' in the broader sense of the word, rather than the narrow sense we tend to use as engineers.
My front-end principles for better infrastructure
Over the course of my career, the following front-end principles have served well to ensure we think through, before we rush in where angels fear to tread.
Be clear about the purpose and the expected outcomes, and engage communities in decision-making through an effective communication strategy.
Prioritise the user, aiming to offer services that are modern, effective and affordable.
Seek to improve people's quality of life and support the transition to a more sustainable future, while also facilitating the functioning of the economy, enhancing productivity and accommodating growth (to the extent possible, given other competing objectives).
Extract greatest value from existing infrastructure through timely maintenance, repurposing, renewal and upgrading. Seek to remove constraints and bottlenecks.
Aim to make best use of data, automation, innovation and technology (including for future asset management), recognising the complexity and risks this may introduce.
Recognise, analyse, mitigate and manage technical, environmental and climate risks, and complete any surveys necessary to support this.
Improve governance, with robust, timely and transparent decision-making, supported by strong evidence-based planning, clear prioritisation, and best practice technical design and delivery.
Seek an appropriate funding balance between 'user pays' and general taxation which incentivises behaviours in the best long-term social, economic and environmental interests.
Complete well-evidenced business cases and risk assessments of proposed initiatives before embarking on projects, including financing proposals. Aim to allocate the risks identified to those best able to carry them.
Facilitate collaboration between the government and business to promote delivery of the broader social, economic and environmental benefits.
Clearly, there are many other issues to consider as a project develops, and the above principles may seem obvious to some, and a counsel of perfection to others, but it's surprising how many are overlooked in the rush to build.
Article | July 19, 2021
While experts debate the finer points of what constitutes a true general artificial intelligence (AI) and the various steps that can get us there, it isn’t in any doubt that robots are getting smarter –and learning how to make better decisions-- thanks to various AI-related systems.
From the autonomous cars on our roads and the autonomous mobile robots in the world’s warehouses through to AI-powered machine vision systems, edge computing in manufacturing environments, and agricultural drones that can process huge amounts of data on the fly, AI greatly enhances robot performance by providing bots with intelligent decision-making capabilities based on an analysis of billions of data points coupled with neural network and deep learning strategies. Crucially, AI enables robots to be more flexible, while at the same time enhancing traceability across all types of automation processes.