Stabilization of expansive soils using chemical additives: A review

Volume instability of expansive soils due to moisture fluctuations is often disastrous,causing severe damages and distortions in the supported structures.It is,therefore,necessary to adequately improve the performance of such soils that they can favorably fulfil the post-construction stability requirements.This can be achieved through chemical stabilization using additives such as lime,cement and fly ash.In this paper,suitability of such additives under various conditions and their mechanisms are reviewed in detail.It is observed that the stabilization process primarily involves hydration,cation exchange,flocculation and pozzolanic reactions.The degree of stabilization is controlled by several factors such as additive type,additive content,soil type,soil mineralogy,curing period,curing temperature,delay in compaction,pH of soil matrix,and molding water content,including presence of nano-silica,organic matter and sulfate compounds.Provision of nano-silica not only improves soil packing but also accelerates the pozzolanic reaction.However,presence of deleterious compounds such as sulfate or organic matter can turn the treated soils unfavorable at times even worser than the unstabilized ones.

Short-term mixed electricity demand and price forecasting using adaptive autoregressive moving average and functional link neural network

A new hybrid adaptive autoregressive moving average(ARMA)and functional link neural network(FLNN)trained by adaptive cubature Kalman filter(ACKF)is presented in this paper for forecasting day-ahead mixed short-term demand and electricity prices in smart grids.The hybrid forecasting framework is intended to capture the dynamic interaction between the electricity consumers and the forecasted prices resulting in the shift of demand curve in electricity market.The proposed model comprises a linear ARMA-FLNN obtained by using a nonlinear expansion of the weighted inputs.The nonlinear functional block helps introduce nonlinearity by expanding the input space to higher dimensional space through basis functions.To train the ARMA-FLNN,an ACKF is used to obtain faster convergence and higher forecasting accuracy.The proposed method is tested on several electricity markets,and the performance metrics such as the mean average percentage error(MAPE)and error variance are compared with other forecasting methods,indicating the improved accuracy of the approach and its suitability for a real-time forecasting.