Abstract:
An Artificial Neural Network (ANN) approach was explored for supporting construction bid
decisions, since such decisions are heavily dependent on practitioner expertise, which in turn
is generally encapsulated in case histories. One of the ANNs described here was trained on
knowledge from a sample of the entire Sri Lankan construction industry, and was used to
predict the preferred job sizes for firms of differing characteristics; such information could
help firms in their bid/no-bid decisions. The other ANN was trained on case histories elicited
from a single contractor, and was used to predict the percentage mark-up. The network
outputs were obtained in both binary output and continuous valued output formats. The
former format had some distinct advantages over the latter, as it provided greater information
for decision making instead of being a "black box" output. The influences of the middle layer
size, output format and allowable error during training, on the training duration and accuracy
of prediction were studied.
