Artificial Neural Network Analysis for Cost Estimation of Building Projects in India - Ankita Gupta; Piyali Debnath (Proceedings of the International Conference of Contemporary Affairs in Architecture and Urbanism-ICCAUA)
In Construction Management, it is difficult to predict the cost estimate during the preliminary stage of the project because of limited information and unknown factors. Artificial Neural Networks can help in the prediction of estimate because of their simplicity and adaptability to non-linear problems. Due to their self-organizing nature they can be used to solve the problems even with low level programming. This makes them useful in interpreting and generalizing inadequate input information. ANN’s are crude derivatives of the biological neural network with single layered or multi-layered neuron in the form of input layer, hidden layer and output layer. The neural network first has to undergo training from historical data in order to make predictions or show results.  A problem was formulated based on these drivers with numerical and categorical data. The data set was trained with a neural network using the MATLAB software using feed forward backpropagation. Training was carried out till the greatest correlation and least Mean Squared Error was obtained after multiple iterations. This trained data was used to predict the cost for a new project. The output of the testing was 87% accurate despite the small data set used.