Modeling the ready mixed concrete delivery system with neural networks

The ready mixed concrete delivery system is a common construction process in a very wide range of construction projects. The ability of the planners and estimators of such projects to accurately determine the level of resources needed, and to estimate the output of an efficient and effective operation is highly important and thus modeling of the process can be useful. This paper presents a Neural Network methodology to the modeling problem and outlines the two main architectures employed: a feed-forward network and an Elman network. Many combinations of layers, training algorithms, number of neurons, activation functions and format of data were considered and the results were validated using an independent validation data set with five goodness-of-fit tests. The results indicate that two- and three-layer feed-forward networks provide the best estimates of concrete placing productivity and that the Elman network, not previously considered in this type of study, was less successful.