Over the last year I have had the privilege of working on some of the largest tunneling projects in North America. Most recently, two of the first single-bore transit tunnels to be built here: Toronto’s Scarborough Subway Extension (technically, I have just been working on the stations, not the tunnel) and San Francisco’s Silicon Valley Extension. Involvement in these megaprojects has led me to two questions: Is all this worth it? If so, surely we can build them better, how? In one of my more recent pursuits of these questions, I have read two works that have improved my understanding of the problem (though still leading to more questions than answers): ...

In geotechnical engineering we commonly have to determine the load the ground exerts on a structure. Two of the main parameters used to determine that are the coefficient of lateral earth pressure ($K_0$) and the soil friction angle ($\phi$). In certain scenarios it is also often useful to relate these to the overconsolidation ($OCR$) ratio of the soil and the respective $K_{0,oc}$ (the lateral earth pressure coefficient for overconsolidated soils). I recently had to do this for a project, but was having a tough time visualizing these relationships, so I made a couple of contour plots to help me out. ...

The geotechnical data management software I use at work has report templates that perform SQL joins to relate test data to geologic strata. For example, if a test is performed at 20 feet, it looks at the strata data (borelogs) and determines what code (like sandstone or limestone) to assign. Unfortunately, the software only provides reports for limited data types and offer zero customization of the output. And, they do not provide a way for me to write my own SQL queries. Thankfully, I have been learning a little python (specifically the pandas library), and found a pretty good solution. ...

In 1971 two psychologists wrote a paper titled “Belief in the Law of Small Numbers”. Their thesis is that individuals (both trained scientists and laypeople) consider a sample (a set of data) drawn from a population to be more representative of the population than they should, especially when the sample is small. In other words, when dealing with small data sets, we will erroneously assume that our “mean” value is very representative of the population mean. ...

When I am not working at work, I am usually thinking about (and tinkering with) ways to adopt things from other industries to make us better. Most of these just sit on the shelves in my mind with little to no action taken (my excuse is that they would require too much behavior change from other people, the hardest thing in the world). Some of those: Use latex (through Overleaf) to collaborate on and produce engineering reports. Use GIT to comment / track / update the reports mentioned above. Use jupyter notebooks (with the help of connerferster’s handcalc library) for calculation packages instead of excel. Use documentation structures similar to software packages or wiki’s to manage project documents. Something like bookstack for a quick start, but it would eventually need a little more functionality. Better adoption of existing industry data “standards” (like DIGGS and AGS). And of course, there’s the never-ending effort to automate the boring stuff.