Abstract: Prefabrication technology (prefab is the practice of assembling components of a structure in a factory or other manufacturing siteand then transporting the assembled components to the construction site where the structure is to be located. The term is used to differentiate between the process of the conventional construction practice, i.e., transporting the basic materials to the construction site. Cost and time savings emerge to be the chief benefits which makes us adopt the new technique. The objective of the research is to enclose an Artificial Neural Network (ANN) with the aid of optimization techniques. In order to predict the time and cost performance parameters of the prefabrication technology process the ANN is utilized. There are different types of optimization techniques such as Grey Wolf Optimization (GWO), Harmony Search (HS) and Social Spider Optimization (SSO) algorithm which are utilized to arrive at the optimal weight of the ANN process. The optimum results demonstrate the attained error values between the output of the experimental values and the predicted values which are closely equal to zero in the designed network. It is gathered from the results that, the minimum error of time performance and cost performance is 81.2 and 76.28% determined by the ANN as attained by the Social Spider Optimization (SSO) algorithm.
S. Ashok Manikandan and K.C. Pazhani, 2017. Estimation of Time and Cost in Prefabrication Construction AID of ANN with SSO. Asian Journal of Information Technology, 16: 650-659.