What caused the Glasgow School of Art fire? Here’s what we know

The fire on Friday night. Image: Getty.

For the second time in four years, the Glasgow School of Art has been devastated by fire. The art school’s historic Mackintosh building, which was well on the way to being restored after the fire of 2014, has been extensively damaged in the blaze, badly affecting every floor.

More than 120 firefighters and 20 fire engines were at the scene late on Friday 15 June, but were unable to stop the fire spreading to the Campus nightclub and O₂ ABC music venue on Sauchiehall Street. The ABC has also been badly damaged, with a major part of the roof collapsing.

Iain Bushell, the deputy chief fire officer on the scene, called the fire an “extremely challenging and complex incident”. Thankfully nobody was injured. Yet well into Saturday afternoon, firefighters were still working hard to put the blaze out.

While the city’s residents come to terms with yet another dreadful fire in the city centre – an area only a couple of blocks away is still cordoned off following a major nightclub fire in March – here are our early thoughts about the causes and implications:


1. The cause of the fire

At this stage there are more questions than answers. It could have been caused by a small fire that burned for a substantial length of time and then accelerated – or it could have grown much more rapidly. Either way, there was a fully developed fire when the Fire Service arrived soon after the alarm was raised in the late evening.

The undiscovered, slow burning fire seems less likely. The upper floors and roof appear to have been well ablaze from the first images reported, which suggests the fire started on the upper levels and burned down through the building.

When a building is under construction – or in this case reonstruction – it is much more vulnerable to fire. It can mean more timber is exposed, as well as there being other openings in the structure that can allow a fire to spread unchecked.

Having said that, a typical cause of ignition on construction sites is “hot work” involving flames. Yet our understanding is that there was no such work taking place, and no workpeople actually on site.

Another common cause of fires is old faulty wiring. In 2002, for instance, a fire in the Gilded Balloon building in Edinburgh’s Old Town started from a faulty fuse box. It took 52 hours to fully extinguish and engulfed 11 buildings. Yet in the case of the Mackintosh building, faulty wiring is unlikely to have been the cause, given the late stage of the refurbishment.

2. How it spread

While it is not certain from the video footage and photos, the collapse of the roof of the O₂ ABC appears to have been caused by fire inside the building as opposed to fire penetrating the roof from the McIntosh building. This might raise issues about the fire separation between the two buildings.

When such a close group of buildings is erected today, there are strict rules about separation in the building regulations. But these cannot apply to historic buildings that have been adapted over many decades. Fires in historic buildings are not uncommon – see here for all those that were damaged in the UK last year, for example.

The School of Art building in 2005. Image: Wikimedia Commons.

The restoration work to the Mackintosh building was well underway from the fire four years ago – apparently around 80 per cent completed. It was due to reopen next year with a final bill estimated at between £20m and £35m. Investigators will want to know about the specialist work that was being done, what materials were being used and which were on site.

Once you have high enough temperatures, of course, most things will start to burn. The Fire Service appear to have had a very challenging job just to limit the spread – let alone put the fire out altogether.

3. What happens next?

The damage at the Mackintosh building appears overwhelming, and much worse than in 2014, when recovered materials were painstakingly assessed and used in the refurbishment wherever possible. It seems questionable whether anything will be salvaged in the same way after this fire.

The ConversationIt remains to be seen if it will be possible to retain a facade from the current building. If not, damaged buildings have been taken down almost stone by stone in the past and rebuilt with a new, internal frame. This sort of project would cost a great deal more than the current refurbishment.

Iain Sanderson, Lecturer, Fire Risk Engineering, Glasgow Caledonian University; Billy Hare, Professor, Construction Management, Glasgow Caledonian University, and Tony Kilpatrick, Senior Lecturer, Fire Risk, Glasgow Caledonian University

This article was originally published on The Conversation. Read the original article.

 
 
 
 

Just like teenagers, self-driving cars need practice to really learn to drive

A self-driving car, of unknown level of education. Image: Grendelkhan/Flickr/creative commons.

What do self-driving cars and teenage drivers have in common?

Experience. Or, more accurately, a lack of experience.

Teenage drivers – novice drivers of any age, actually – begin with little knowledge of how to actually operate a car’s controls, and how to handle various quirks of the rules of the road. In North America, their first step in learning typically consists of fundamental instruction conveyed by a teacher. With classroom education, novice drivers are, in effect, programmed with knowledge of traffic laws and other basics. They then learn to operate a motor vehicle by applying that programming and progressively encountering a vast range of possibilities on actual roadways. Along the way, feedback they receive – from others in the vehicle as well as the actual experience of driving – helps them determine how best to react and function safely.

The same is true for autonomous vehicles. They are first programmed with basic knowledge. Red means stop; green means go, and so on. Then, through a form of artificial intelligence known as machine learning, self-driving autos draw from both accumulated experiences and continual feedback to detect patterns, adapt to circumstances, make decisions and improve performance.

For both humans and machines, more driving will ideally lead to better driving. And in each case, establishing mastery takes a long time. Especially as each learns to address the unique situations that are hard to anticipate without experience – a falling tree, a flash flood, a ball bouncing into the street, or some other sudden event. Testing, in both controlled and actual environments, is critical to building know-how. The more miles that driverless cars travel, the more quickly their safety improves. And improved safety performance will influence public acceptance of self-driving car deployment – an area in which I specialise.

Starting with basic skills

Experience, of course, must be built upon a foundation of rudimentary abilities – starting with vision. Meeting that essential requirement is straightforward for most humans, even those who may require the aid of glasses or contact lenses. For driverless cars, however, the ability to see is an immensely complex process involving multiple sensors and other technological elements:

  • radar, which uses radio waves to measure distances between the car and obstacles around it;
  • LIDAR, which uses laser sensors to build a 360-degree image of the car’s surroundings;
  • cameras, to detect people, lights, signs and other objects;
  • satellites, to enable GPS, global positioning systems that can pinpoint locations;
  • digital maps, which help to determine and modify routes the car will take;
  • a computer, which processes all the information, recognising objects, analysing the driving situation and determining actions based on what the car sees.

How a driverless car ‘sees’ the road.

All of these elements work together to help the car know where it is at all times, and where everything else is in relation to it. Despite the precision of these systems, however, they’re not perfect. The computer can know which pictures and sensory inputs deserve its attention, and how to correctly respond, but experience only comes from traveling a lot of miles.

The learning that is occurring by autonomous cars currently being tested on public roads feeds back into central systems that make all of a company’s cars better drivers. But even adding up all the on-road miles currently being driven by all autonomous vehicles in the U.S. doesn’t get close to the number of miles driven by humans every single day.

Dangerous after dark

Seeing at night is more challenging than during the daytime – for self-driving cars as well as for human drivers. Contrast is reduced in dark conditions, and objects – whether animate or inanimate – are more difficult to distinguish from their surroundings. In that regard, a human’s eyes and a driverless car’s cameras suffer the same impairment – unlike radar and LIDAR, which don’t need sunlight, streetlights or other lighting.

This was a factor in March in Arizona, when a pedestrian pushing her bicycle across the street at night was struck and killed by a self-driving Uber vehicle. Emergency braking, disabled at the time of the crash, was one issue. The car’s sensors were another issue, having identified the pedestrian as a vehicle first, and then as a bicycle. That’s an important distinction, because a self-driving car’s judgments and actions rely upon accurate identifications. For instance, it would expect another vehicle to move more quickly out of its path than a person walking.


Try and try again

To become better drivers, self-driving cars need not only more and better technological tools, but also something far more fundamental: practice. Just like human drivers, robot drivers won’t get better at dealing with darkness, fog and slippery road conditions without experience.

Testing on controlled roads is a first step to broad deployment of driverless vehicles on public streets. The Texas Automated Vehicle Proving Grounds Partnership, involving the Texas A&M Transportation Institute, University of Texas at Austin, and Southwest Research Institute in San Antonio, Texas, operates a group of closed-course test sites.

Self-driving cars also need to experience real-world conditions, so the Partnership includes seven urban regions in Texas where equipment can be tested on public roads. And, in a separate venture in July, self-driving startup Drive.ai began testing its own vehicles on limited routes in Frisco, north of Dallas.

These testing efforts are essential to ensuring that self-driving technologies are as foolproof as possible before their widespread introduction on public roadways. In other words, the technology needs time to learn. Think of it as driver education for driverless cars.

People learn by doing, and they learn best by doing repeatedly. Whether the pursuit involves a musical instrument, an athletic activity or operating a motor vehicle, individuals build proficiency through practice.

The ConversationSelf-driving cars, as researchers are finding, are no different from teens who need to build up experience before becoming reliably safe drivers. But at least the cars won’t have to learn every single thing for themselves – instead, they’ll talk to each other and share a pool of experience.

Johanna Zmud, Senior Research Scientist, Texas A&M Transportation Institute, Texas A&M University .

This article was originally published on The Conversation. Read the original article.