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BTS | HARDING MEMORIAL LECTURE


Construction process 7%


Gas 1%


Right, figure 1: Data interpretation for Human influence on failure


Water 13%


Human decision error 42%


Temporary works 4%


Structure 4%


Ground 29%


References 1 and 2 concentrate more upon the


New Austrian Tunnelling Method (NATM) and Sprayed Concrete Lined (SCL) tunnels, although some Tunnel Boring Machine (TBM) driven tunnels are also included. The majority of these incidents are related to some form of collapse. Reference 3 includes the projects from references 1 and 2 along with additional projects and details where these were found to be available. Reference 3 was published on the internet by the Civil Engineering and Development Department of Hong Kong, and is a fairly comprehensive list covering major incidents reported between 1964 and 2014. The author first analyzed this data in 2015 for a project


talk, and a year later a paper appeared at the World Tunnelling Conference in San Francisco (see reference 4), which also summarized and grouped the failures in a similar manner. The published results in reference 4 were similar to the ones that the author had established, although there were some minor differences in the precise naming of the failure categories, and within a few of the numbers, some of which can be explained by how each party may have individually interpreted and manipulated the data.


QUESTION THE ACCURACY OF THE DATA The accuracy of the data, and the way that it has been manipulated for presentation purposes should always be considered. For transparency, some of the reasons why the data and the assessed root causes of the incident may be questioned include: ● Not all incidents are recorded, as already noted, and therefore the data may be skewed.


● The original search criteria may have failed to identify some incidents.


● Reporting bias on the root cause - the ease of placing the blame against the most obvious cause, rather than forensically investigating the cause.


● Assessment bias on understanding of the implied root cause.


It was also considered necessary to be clear about how the data had been manipulated in order to provide


48 | Winter 2023


a presentation of the results. For clarity the following manipulation of the data was undertaken for this interpretation: ● Some of data had been ignored to avoid skewed results. For example, one project recorded 131 incidents, but this was assessed as one incident only otherwise it would overwhelm all of the other data. Where multiple incidents had occurred on a single project and the details were clear that they were separate, they had been counted as separate incidents. Where multiple incidents had occurred and separate details had not been provided, they were assessed as one occurrence along with the associated cause(s).


● Reporting of assessed results was based upon percentages, both here and within reference 4. Here, this is the percentage of the mention of the cause rather than the number of incidents, which may obscure some issues. Some incidents reported multiple causes contributing to the incident. For example, within the list of projects ‘the ground’ contributed to 42% of all reported causes, but it was a contributing cause in 65% of the incidents.


RESULTS OF ASSESSMENT OF DATA The ground and ground stability stand out as the most blamed cause of a failure. The authors original interpretation of the data resulted in 42% of the failures being blamed on the ground, while reference 4 resulted in 45%. The authors concern was that ‘design’ and ‘human influence’ appeared significantly less than anticipated based upon experience. The data was therefore examined in a different manner, deliberately looking for reference to, or contributions from, human decisions or influence. As a result, the modified interpretation is shown in figure 1. This assessment, although a little subjective, reduced the blame on the ground to 29% and introduced human error at 42%. It was noted that more than the established 42% might be blamed on human error, but there was no evidence within the data to support this in the details available.


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