Invasive Weed Optimization Algorithm for Prediction of Compression Index of Lime-Treated Expansive Clays
With the recent emphasis on large-scale civil engineering constructions, artificial intelligence in the construction activities has received importance. Compressibility behavior is an important property in fine soils to find out the settlements in foundation designs. However, compression index (Cc) from one-dimensional swell-consolidation test is time consuming and laborious. Many traditional prediction-stimulated models rely on simplified assumptions, leading to inaccurate Cc estimations. This paper explores, by comparison, the application of invasive weed optimization (IWO) algorithm and particle swarm optimization (PSO) to predict Cc via multiple linear regression models. The predicted model equations have been developed, uses four input parameters namely plasticity index, free swell index, rate of heave and swell potential in both methods. The results confirm that the developed models using IWO provides accurate prediction than standard particle swarm optimization (PSO) algorithm.
KeywordsCompression index Expansive clays IWO PSO Swelling
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