The chemistry of dicalcium silicate mineral

Dicalcium silicate is of vital importance in several fields of silicate science. It exists in several polymorphic forms, of which one (the-form) is stable at room temperature without any stabilizer. The-form is commonly found in ordinary portland cement (OPC) in association with stabilizing ions. Stabilization of other forms,,_L,_H and_m for structural and other studies have been reported. Theoretical structural analysis using topology has been reported to be of value in understanding the stabilization process of the polymorphs. The conversion of form is at times a problem in the cement industry, in addition to the formation of unwanted compounds, such as spurrite. The-form is low in hydraulic properties but in the presence of impurities such as excess CaO over the stoichiometric ratio, shows fairly high hydraulic properties. Of the other phases, the hydraulic properties of the a forms are quite encouraging but the choice of stabilizers etc. plays a dominant role. Correlation of hydraulicity with structural properties such as crystal defects, etc., has been reported but satisfactory explanation is yet to come. The hydration products of-C₂S are quite similar to those of C₃S but the kinetics are fairly slow. In the presence of active silica, and at elevated temperatures, even the-form hydrates at a faster rate. The influence of chemical accelerators on the hydration of C₂S at room temperature is well studied and NaF is found to be one of the best accelerators. The formation of reactive-C₂S by different preparative methods shows a quite interesting trend for potential manufacture of low-temperature inorganic cement or OPC with low C₃S; even utilization of low-grade limestone could be possible. The role of C₂S in the hydration of aluminous cements is being increasingly recognized and, in fact, a newer class of cements called alumina-belite cement, etc., are being developed in which C₂S is purposely maintained as a major phase.     

A multi-step linearization technique for a class of boundary value problems in non-linear mechanics

Non-linear finite element analyses of structures (such as beams) involve construction of weak solutions for the governing equations. While a weak approach weakens the differentiability requirements of the so-called shape functions, the governing equations are only satisfied in an integral sense and not point-wise, or, even path-wise. Moreover, use of a finite mesh leads to a stiffening of the numerical model. While strong solutions obtained through some of the existing mesh-free collocation methods overcomes some of these lacunae to an extent, the quality of the numerical solutions would be considerably improved if the computational algorithm were able to faithfully reproduce (or approximate or preserve) certain geometrical features of the response surfaces or manifolds. This paper takes the first step towards realizing this objective and proposes a multi-step transversal linearization (MTL) technique for a class of non-linear boundary value problems, which are treated as conditionally dynamical systems. Numerical explorations are performed, to a limited extent, through applications to large deflection analyses of planar beams with or without plastic deformations.     

Incorporating Ancestors' Influence in Genetic Algorithms

A new criterion of fitness evaluation for Genetic Algorithms is introduced where the fitness value of an individual is determined by considering its own fitness as well as those of its ancestors. Some guidelines for selecting the weighting coefficients for quantifying the importance to be given to the fitness of the individual and its ancestors are provided. This is done both heuristically and automatically under fixed and adaptive frameworks. The Schema Theorem corresponding to the proposed concept is derived. The effectiveness of this new methodology is demonstrated extensively on the problems of optimizing complex functions including a noisy one and selecting optimal neural network parameters.     

A mixture-of-experts framework for adaptive Kalman filtering

This paper proposes a modular and flexible approach to adaptive Kalman filtering using the framework of a mixture-of-experts regulated by a gating network. Each expert is a Kalman filter modeled with a different realization of the unknown system parameters such as process and measurement noise. The gating network performs on-line adaptation of the weights given to individual filter estimates based on performance. This scheme compares very favorably with the classical Magill filter bank, which is based on a Bayesian technique, in terms of: estimation accuracy; quicker response to changing environments; and numerical stability and computational demands. The proposed filter bank is further enhanced by periodically using a search algorithm in a feedback loop. Two search algorithms are considered. The first algorithm uses a recursive quadratic programming approach which extremizes a modified maximum likelihood function to update the parameters of the best performing filter in the bank. This particular approach to parameter adaptation allows a real-time implementation. The second algorithm uses a genetic algorithm to search for the parameter vector and is suited for post-processed data type applications. The workings and power of the overall filter bank and the suggested adaptation schemes are illustrated by a number of examples.     

Optimal design of unit hydrographs using probability distribution and genetic algorithms

A nonlinear optimization model is developed to transmute a unit hydrograph into a probability distribution function (PDF). The objective function is to minimize the sum of the square of the deviation between predicted and actual direct runoff hydrograph of a watershed. The predicted runoff hydrograph is estimated by using a PDF. In a unit hydrograph, the depth of rainfall excess must be unity and the ordinates must be positive. Incorporation of a PDF ensures that the depth of rainfall excess for the unit hydrograph is unity, and the ordinates are also positive. Unit hydrograph ordinates are in terms of intensity of rainfall excess on a discharge per unit catchment area basis, the unit area thus representing the unit rainfall excess. The proposed method does not have any constraint. The nonlinear optimization formulation is solved using binary-coded genetic algorithms. The number of variables to be estimated by optimization is the same as the number of probability distribution parameters; gamma and log-normal probability distributions are used. The existing nonlinear programming model for obtaining optimal unit hydrograph has also been solved using genetic algorithms, where the constrained nonlinear optimization problem is converted to an unconstrained problem using penalty parameter approach. The results obtained are compared with those obtained by the earlier LP model and are fairly similar.