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Before using concrete for a particular purpose, its strength must be determined because its physical properties vary depending on the type of supplementary cementitious material (SCMs). Initially, researchers anticipated it with statistical processes; more recently, they started implementing deep learning (DL) and machine learning (ML) models. This research employed ensemble machine learning (EML) approaches to predict the compressive strength (CS) of concrete developed for ground-granulated blast furnace slag (GGBFS) along with Alccofine 1203 (AF). Ensemble regressions of Random Forest Regression (RFR) and AdaBoost Regression (ABR) were considered for model prediction using the Jupyter Notebook. The models were created based on the outcome of the compressive strength under the 90 experimental conditions. Moreover, a comparative study was performed between the RFR and ABR to benchmark the proposed model against a given combination of features and CS. The experimental conditions were optimized using a genetic algorithm (GA) after the model was created by ANN. With the two EML algorithms, the smallest errors, along with the largest coefficient of determination, were observed in the RFR algorithm. It was recognized that the optimized value of CS is 48.606 MPa, and their optimized process elements such as cement, alccofine, GGBS, water, and days were 261.506 kg/m3, 0.055 kg/m3, 52.468 kg/m3, 141.007 L, and 27.999 days, respectively.

Performance based analysis is conducted on a structure to know the performance of building under severe earthquake loads with limited and well-distributed damage. To do this analysis a non-linear static analysis called pushover analysis had conducted on the structure. In this paper, an RC building with both 5 storey and 10 storey is designed for both gravity loads and earthquake resistant loads using SAP2000 software. Analysis is done in both X and Y direction to get a damage curve (pushover curve). By studying the damage curve, the results that obtained are earthquake resistant designed building had more strength when compared to gravity load designed building and it is better to consider earthquake in building design , because gravity loads alone cannot give the adequate results.

Non-linear static analysis or pushover analysis is now-a –days generally preferred by many researchers to analyse the non-linear behaviour of the structure. Present study includes the effect of joint rigidity on the behaviour of the structure. A building is modeled with 5 and 10 story and designed for gravity and earthquake resistant loads by considering joint rigidity factor as 0 and 1. The analysis is done in both X and Y directions by using pushover analysis in SAP2000 software. Comparison is made between then capacity curves obtained from designed models of rigidity and non-rigidity models. From the results it is concluded that structure designed using joint rigidity has more strength when compared non-rigid joints.

Among all the techniques available for ground improvement, stone columns are the most preferred elements used for supporting a wide variety of flexible structures such as road, railway embankments, and oil storage tanks. In this study, several laboratory tests have been conducted to improve the soft marine clay with end bearing stone columns by using Silica-Manganese slag as stone column material. Further, these stone columns were reinforced with circular geotextile discs by placing them laterally within the column. The improvement in load carrying capacity was studied and compared to the clay bed and unreinforced i.e. plain stone column. Circular geotextile discs of two different spacings (D and D/2, where "D" is the diameter of the stone column) with varied reinforcement depths, such as D, 2D, 3D and 4D, were tried. It was found that the soil reinforced with a spacing of D/2 to the embedment length of 3D shows better performance than a spacing of D. It was also observed that the bulging diameter was reduced by incorporation of the geotextile reinforcement and found below the reinforcement. [ABSTRACT FROM AUTHOR]

An efficient protocol has been developed for the preparation of a library of benzoxazole, benzimidazole, and benzothiazole derivatives from reactions of acyl chlorides with o-substituted aminoaromatics in the presence of catalytic amount of silica-supported sodium hydrogen sulphate under solvent-free conditions. Simple workup procedure, high yield, easy availability, reusability, and use of ecofriendly catalyst are some of the striking features of the present protocol. [ABSTRACT FROM AUTHOR]