Standard self-similarity
Fractals are often constructed using a recursive procedure and self-similar sets in particular, are always constructed this way. I think that most folks working in fractal geometry would guess that your picture implies a recursive construction like so:
![enter image description here](https://cdn.statically.io/img/i.sstatic.net/YRt9v.png)
Note that the first set is an initial seed. The second set is composed of three copies of the first scaled by the factor $1/2$ (the three outer blue triangles) and one copy of the first scaled by the factor $1/4$ (the inner red-ish piece). This pattern can be analyzed as a self-similar set. In general, we have a single set composed of copies of itself scaled by the factors $r_1,r_2,\ldots,r_n$. Assuming the copies don't overlap, the dimension of the set is the unique number $s$ such that
$$\sum_{i=1}^n r_i^s = 1.$$
I actually answered a question today indicating why there is a unique such $s$ and how to solve for it in general.
The dimension is the unique number $s$ such that
$$\frac{3}{2^s} + \frac{1}{4^s} = 1.$$
This can be written
$$\left(\left(\frac{1}{2}\right)^s\right)^2 + 3\left(\frac{1}{2}\right)^s - 1 = 0.$$
Substituting $q=(1/2)^s$, we get
$$q^2+3q-1=0,$$
whose unique positive solution is
$$q=\frac{-3+\sqrt{13}}{2}.$$
The dimension of your set is therefore
$$\frac{\log(\frac{-3+\sqrt{13}}{2})}{\log(1/2)} \approx 1.72368.$$
As pointed out in the comments, we expect this to be larger than the fractal dimension of the Sierpinski gasket or
$$\frac{\log(3)}{\log(2)} \approx 1.58496.$$
Condensation
As you mention, the central triangle in your example stays fixed throughout from the first to the second step. While I don't think you've given a precise definition of exactly what's going on here, this is reminiscent of an Iterated Function System with condensation - a term coined by Michael Barnsley in his book Fractals Everywhere. In this scheme, the condensation set proliferates throughout the fractal further with each set. More precisely, we have a list of functions $(f_1,f_2,\ldots,f_m)$ and we define an operation $T$ on sets by
$$T(E) = \bigcup_{i=1}^m f_i(E).$$
At the $n^{\text{th}}$ stage of our construction, we include
$$\bigcup_{k=1}^n T^k(E)$$
in our picture. The sequence of approximations now looks more like so:
![enter image description here](https://cdn.statically.io/img/i.sstatic.net/SjRfv.png)
Note that the black triangles are exactly those in the standard approximation to the Sierpinski gasket. That is, if
\begin{align}
f_1(x) &= x/3 \\
f_2(x) &= x/3 + \langle 1/2,0 \rangle \\
f_3(x) &= x/3 + \langle 1/4, \sqrt{3}/4 \rangle
\end{align}
and $D$ is the equilateral triangle whose base is the unit interval, then the black portion at the $n^{\text{th}}$ stage is exactly $T^n(D)$.
The initial gray portion is the condensation set $C$ and all the gray throughout the $n^{\text{th}}$ approximation is
$$\bigcup_{i=1}^n T^k(C).$$
Note that the only difference between the generation of the attractor and the generator of the condensation portion is the union in the condensation is absent for the attractor and this union is why the condensation set persists throughout the construction.
Barnsley, it turns out, was primarily interested applications involving image compression so his introduction of condensation in iterated function systems was motivated largely by that application.
The dimensional analysis of such sets, by contrast, is not terribly exciting. Your example (if I've got it right), must have dimension two. It certainly can't have dimension greater than two, since it is contained in the plane, but it must have dimension at least two, since it contains a solid triangle. There's not much fancy work to be done.
More generally, the dimension of a self-similar set with condensation is the larger of the dimension of the condensation set and the dimension of the attractor.