Predicting construction crew productivity by using Self Organizing Maps

摘要: Abstract A Self Organizing Map (SOM), is a machine learning method that represents high-dimensional data in low-dimensional form without losing topological relations of the data. After an unsupervised process, it organizes on basis similarity. In current study, SOM based algorithm has been developed which not only produces 2-D maps to analyze relationship between various factors and crew productivity, but also predicts productivity under given conditions. Validation model achieved both by using artificial set from 144 concrete pouring, 101 formwork reinforcement crews. The results show are produced satisfactory clustering prediction performance superior similar neural network models.