To be fair, the timeline proposes to get to a human brain not in a decade, but about 2030 -- or two decades.
I think it's impossible to say right now that the project will fail. Lab-based neuroscientists talk a lot about what we don't know, and how this is all pie-in-the-sky, but so what? The people in the labs will never by themselves figure it all out because they don't have a unifying architecture for understanding their data, generating new hypotheses and testing them. In short, the theory component is lacking.
For example, there are research papers where the authors have disabled the ability of an enzyme to become phosphorylated at a particular site (CaMKII at Thr 286) and then had these rats perform a standard test for finding their way through a maze. The result was that they had a harder time remembering where they were. Now, what can you really learn from that? CaMKII's ability to become autophosphorylated is important for forming memories..., and that's about it. There are so many layers in between cause and effect: The success rate of the gene transfer, gene expression, unknown variability in the formation of rat brains, and maybe, hey, maybe the rat was just having a shitty day and that's why it couldn't find the pad under the water. How does this help you generate new ideas for experiments? I don't know.
At any rate, when people say that there's so much that we don't know, this is partly what they're referring to: We have tons and tons of information, but no framework to hold it together and make sense of it.
The vision is that HBP will fill that role as a site for the assimilation of data into models that are constantly being refined and tested. They've spent years building the infrastructure necessary to gather standardized* data, build the models, run them, and analyze them. The idea now is to start taking in even more data, and just build things up one little bit at a time. Build a better cortical column, build several and hook them up together, build a rat cortex, a rat brain, a cat brain, a macaque brain, and then a human brain, learning along the way how to get it right and what the patterns are.
Make no mistake: The people working there are some of the brightest in the world. I see no technical reason why they won't succeed; I think it's only a matter of time and effort.
* Standardized data is very important because every lab tends to do things a little differently and there are so many variables that have to be pinned down. You might be amazed at what a difference 5 deg C can make in protein phosphorylation rates (about 20%, actually.)