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Estimating the acceptability of new formwork systems using neural networks
Faculty
Science
Year:
2005
Type of Publication:
Article
Pages:
33-41
Authors:
Elazouni, AM, Ali, AE, Abdel-Razek, RH
DOI:
10.1061/(ASCE)0733-9364(2005)131:1(33)
Journal:
JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT-ASCE ASCE-AMER SOC CIVIL ENGINEERS
Volume:
131
Research Area:
Construction \& Building Technology; Engineering
ISSN
ISI:000225896800004
Keywords :
Estimating , acceptability , , formwork systems using neural
Abstract:
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.
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