Artificial Neural Networks aided Conceptual Stage Design of Water Harvesting Structures

Publication date: Available online 23 April 2016 Source:Perspectives in Science Author(s): Vinay Chandwani, Naveen Kumar Gupta, Ravindra Nagar, Vinay Agrawal, Ajay Singh Jethoo The paper presents an Artificial Neural Networks (ANN) based methodology for ascertaining the structural parameters of Water Harvesting Structures (WHS) at the conceptual stage of design. The ANN is trained using exemplar patterns generated using an in-house MSExcel based design program, to draw a functional relationship between the five inputs design parameters namely, peak flood discharge, safe bearing capacity of strata, length of structure, height of structure and silt factor and four outputs namely, top width, bottom width, foundation depth and flood lift representing the structural parameters of WHS. The results of the study show that, the structural parameters of the WHS predicted using ANN model are in close agreement with the actual field parameters. The versatility of ANN to map complex or complex unknown relationships has been proven in the study. A parametric sensitivity study is also performed to assess the most significant design parameter. The study holistically presents a neural network based decision support tool that can be used to accurately estimate the major design parameters of the WHS at the conceptual stage of design in quick time, aiding the engineer-in-charge to conveniently forecast the budget requirements and minimize the labor involved during the subsequent ph...

Source: Perspectives in Science - April 23, 2016 Category: Science Source Type: research