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Energy consumption is consecutively increasing nowadays in residential sectors. So as to overcome this issue, window openings are provided in the masonry units. The window openings will diminish the energy consumption in the buildings. The increase in opening size will directly improves the ventilation and enrich in the comfort of the buildings. However, as the size of the window increases, the thermal comfort is improved but, the structural properties of the masonry unit will be diminished. To rectify this current issue, the window opening size has been optimized to improve the structural properties without compromising the thermal aspects. The percentage of window opening sizes are 10%, 20%, 30% up to 100% from the masonry infill area. The window openings are made in single bay single storey reinforced concrete Infilled frame model. The static lateral loading is applied to the RC Infilled frame. A finite element analysis software was used for analysing this issue. By analysing the models, the window opening size of 50% from the masonry infill attains a better performance when comparing all the other models. The soft computing approaches using ANN modelling to predict the Stiffness of the frame. The ANN results indicate R2values of 0.98466 in 0%, 0.9674 in 20%, 0.9762 in 40%, 0.94987 in 60%, 0.95577 in 80% and 0.99421 in 100% in every instance, ANN model is a highly effective prediction of stiffness of the frame with openings. Therefore, the ANN model is the most effective machine-learning algorithm for the stiffness of the frame.

Waste glass of the same property replaces coarse aggregate in this project. Waste glass will be replaced at 5%, 10%, 15%, 20%, and 25%, and the cubes will be cast and tested for compressive strength and workability at 7, 14, and 28 days. The project’s goal is to compare the strength of normal concrete to concrete with varied percentages of discarded glass. This research article presents a comprehensive analysis of the replacement of coarse aggregate in concrete, employing Response Surface Methodology (RSM), Multiple Linear Regression (MLR), and Artificial Neural Networks (ANN) as predictive models. To determine physical qualities, cement, fine aggregate, and coarse aggregate are tested. Slump cone and compressive strength tests are performed on fresh and hardened concrete, respectively. This casted M20 grade cube of 150 mm side undergoes all the aforesaid tests according to Indian standards. 5%, 10%, 15%, 20%, and 25% waste glass is added to M20 grade concrete. The investigation showed that the proposed equations generated more accurate compressive strength in optimized RSM and forecasts than MLR and ANN. The proposed equations have lower MAE, RMSE, mean, and R2 values than the LRM and ANN. The compressive strength testing machine measures cube compressive strength. The data's variability was captured by the ANN model's effective R2threshold.

An Eulerian multi-fluid computational fluid dynamics (CFD) model was used to simulate a gas-solid-liquid fluidised bed to analyse the behaviour of fluidised bed with varying inputs on the pressure drop, bed expansion and also gas hold up in the column. Simulated predictions were also plotted against the experimental results which showed they agree with each other. Results plotted for velocity vector of solid particles, i.e., glass beads reveal that the direction of magnitude is upwards in the centre column and near the wall zone there is downward movement due to absence of bubble regime. The velocity vector behaviour of gas and liquid has also been explained. In addition to this, investigations were also carried out to analyse the bed expansion in the column; it shows that bed expansion increases as liquid velocity increases. Gas hold up and pressure drop distribution were also simulated for the three phase fluidised bed; results reveal that pressure drop decreases as air velocity is increased and vice-versa for gas hold up, i.e., gas hold up increases as air velocity is increased.

In this article, a method is proposed for network restoration using a centralized, static restoration after failure, where the restoration initiated at the local node or at the source uses a hybrid strategy. © 1998 John Wiley & Sons, Ltd.

In this article, a method is proposed for network restoration using a centralized, static restoration after failure, where the restoration initiated at the local node or at the source uses a hybrid strategy. © 1998 John Wiley & Sons, Ltd.