Estimating the acceptability of new formwork systems using neural networks

Continual development in construction techniques results in emergence of specialized formwork systems. A new system will have to compete with in-use systems for adoption in a target operation. Thus, it is essential that decision makers anticipate the acceptability of new systems before making decisions to acquire them. Estimating acceptability basically assesses how features of a new system are comparable to that of in-use systems. Therefore, analogy is a focal factor for the acceptability estimating process. Neural networks (NNs) are more suitable to model construction problems requiring analogy-based solutions. A NN-based approach was employed to anticipate the acceptability of new formwork systems. The study collected data from a group of 40 users in Egypt. A set of six performance characteristics that mostly pertain to acceptability estimating were identified. The study used the analytical hierarchy process to produce pairs of a performance characteristics' vector and the corresponding acceptability value, and utilized the developed pairs to train NNs. Finally, tests on trained NNs using unseen data indicated satisfactory performance. CE Database subject headings: Neural networks; Decision making; Artificial intelligence: Data collection: Construction management; Egypt.