TY  - JOUR
T1  - Claim Causing Assessment in Construction Projects in Iran Using
Artificial Neural Networks Model: Radial Basis Function (RBF)
AU - Gholhaki, Majid AU - Kheiroddin, Ali AU - Ghorbani, Ali 
JO  - Journal of Engineering and Applied Sciences
VL  - 11
IS  - 5
SP  - 1122
EP  - 1127
PY  - 2016
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2016.1122.1127
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2016.1122.1127
KW  - Claims
KW  -artificial neural networks
KW  -radial basis function
KW  -claim causes
KW  -assessment
AB  - This study presents the model that uses Radial Basis Function (RBF) from Artificial Neural Networks (ANNs) to predict and decision about claim causes and their responsibility that helps project organizations such as owners and consultants in their construction project decisions to control and minimize claims. The model is composed of twenty ANNs that work based on radial basis function that predict the percent of owner, consultant and contractor in twenty mail claims that occurs in Iran construction projects. The framework is implemented using actual data from civil projects and Literature review, interviews with pertinent experts with filling questionnaire. Different weights were assigned as variables to the different layers of each ANN and the total square error was used as the objective function to be minimized. Data was collected from 138 construction projects in order to train and test the model which predicted relationships between contracting parties. It implements an intelligent input interface which helps project parties in their decisions on the project’s for claim causing assessment.
ER  -