I don't think my concern with the issue at end is much more related to automation and wages than it is related to the "engineering" side of things.
Most of what the "literature" recommends is based on individual lessons learned and ideas, that are indeed backed less by data than they are by anecdote.
Having that is better than nothing. But if you compare software "engineering" when talking about software architecture, to construction, electrical or such, it's puzzling indeed to see how much more craft and less actual science is involved.
There are metrics that seem to apply though. Lines of codes and bugs aren't good indeed. Cyclomatic complexity is more related to algorithmic than architecture (which if going that direction is very much a science).
Measuring architecture effectiveness is measuring how much the architecture achieves its goals. These are contextual, most commonly in business they will be related to things like quality (as in, the software does what it is supposed to), total cost, time of execution. Some goals you can derive from that are adaptability to change, ability to modify and extend, which you might derive in turn into qualities such as decouple-ness, non redundancy of code, readability, etc. Some metrics for these exist, such as the instability (fan-in / fan-out ratio) or the abstractness measure of packages.
I don't know of architecture books that present patterns or practices with this sort of justification though. There's a chapter on that in Clean Architecture, but most of the rest is only strongly worded opinion.