  Volumetric design, also called massing design, is the first and critical step
in professional building design which is sequential in nature. As the
volumetric design process is complex, the underlying sequential design process
encodes valuable information for designers. Many efforts have been made to
automatically generate reasonable volumetric designs, but the quality of the
generated design solutions varies, and evaluating a design solution requires
either a prohibitively comprehensive set of metrics or expensive human
expertise. While previous approaches focused on learning only the final design
instead of sequential design tasks, we propose to encode the design knowledge
from a collection of expert or high-performing design sequences and extract
useful representations using transformer-based models. Later we propose to
utilize the learned representations for crucial downstream applications such as
design preference evaluation and procedural design generation. We develop the
preference model by estimating the density of the learned representations
whereas we train an autoregressive transformer model for sequential design
generation. We demonstrate our ideas by leveraging a novel dataset of thousands
of sequential volumetric designs. Our preference model can compare two
arbitrarily given design sequences and is almost 90% accurate in evaluation
against random design sequences. Our autoregressive model is also capable of
autocompleting a volumetric design sequence from a partial design sequence.
