ndian Standard guidelines have been used to prepare M30 concrete for computing control mix ratio. The literature study indicated that optimum percentage of quarry dust to be used could be 25% as replacement for fine aggregate. Polyethylene glycol has been used in the concrete mix for self-curing. Cubes and cylinders were casted as per the code specifications for test. From the test results, it is concluded that optimum percentage of lightweight aggregate for maximum compressive, tensile strength and modulus of rupture is about 25% for M30 grade concrete. With 25% lightweight aggregate as replacement, the optimum dose of polyethylene glyclol will be 1% for obtaining maximum concrete strength. When the percentage of lightweight aggregate increases in the mix, the slump value decreases. It is also observed that flexural behavior of self-curing concrete is the same as that of the conventional concrete.

The second paper, "Foamed Concrete with GGBS, Fly Ash, Polypropylene and Recron3s Fibers: An Experimental Study", by Suhas H B, Nikhil A Jambhale and K B Prakash, investigates the physical and mechanical properties of foamed concrete with addition of fly ash as replacement for fine aggregate. The foamed concrete is a lightweight concrete with density ranging from 400 to 1,600 kg/m3. Use of low density concrete decreases the self-weight of concrete, which further reduces the weight of column, beam and slabs, and thus a reduction in total weight of the structure could be achieved. But it may lead to other design problems as concrete will have low density. In the preliminary investigation, fly ash and ground granulated blast furnace slag are subjected to physical and mechanical analysis to decide whether the silica fumes are in compliance with the prescribed standard. Synthetic-based foaming agent is used in the study to produce foam. Recron3 fibers, an industrial waste, is added to concrete mix. Polypropylene is a non-synthetic thermoplastic polymer and when added to concrete, it improves the performance even after cracking. This polymer is rustproof, alkali-resistant, nonmagnetic and has low thermal conductivity. The study aimed at producing a foamed concrete with density of 1,200 kg/m3, and for this density all ingredients required were calculated. The concrete mixture was thoroughly mixed in rotating drum and specimens for tests were casted as cubes and cylinders. The results indicate that the maximum strength of concrete is achieved by addition of about 0.2% of recron3s and polypropylene fibers. Any further increase of fibers decreases the strength of foamed concrete. The study concludes that foamed concrete has less compressive strength than normal conventional concrete and thus not suitable to be used for main structural work but could be effectively utilized for partition walls, and hence reduction in weight of structure could be achieved.

The last paper, "ANN-Based Shear Capacity of Steel Fiber-Reinforced Concrete Beams Without Stirrups", by M Abambres and E Lantsoght, evaluates the shear capacity of steel fiber reinforced concrete beam without mild steel stirrups. The normal practice in reinforced beam construction is to provide stirrups for improving shear strength of beams, but the study evaluates the capacity without any stirrups using ANN technique. From the available literature, a database of 430 test results were used to develop artificial neural network-based formula to predict the shear capacity of beams without stirrups. It is well-known that concrete is strong in compression but weak in tension, hence addition of steel fibers to concrete mix could be a solution to increase its strength in tension to a limited value. Further, this addition also helps in crack prevention in concrete. The study exhaustively uses available publications in this regard. The whole analysis was done using MATLAB and making use of its neural network tool box. Parametric analysis was conducted to select the best ANN according to certain chosen criteria. Nine input variables were taken to describe the problem, whereas the maximum sectional shear force at collapse was the selected target variable. After several ANN-based parametric analyses, the resulting optimal model yielded maximum and mean relative errors of 0.0% for all the chosen data points. This proposed model outperforms the currently available formulas and code provisions. The study shows how ANN can be used to predict shear capacity of steel fiber reinforced concrete beams without stirrups. This study has limitations, and for generalizations, more work requires to be done using large sized beams.