To minimize the environmental risks and for sustainable development, the utilization of recycled aggregate (RA) is gaining popularity all over the world. The use of recycled coarse aggregate (RCA) in concrete is an effective way to minimize environmental pollution. RCA does not gain more attraction because of the availability of adhered mortar on its surface, which poses a harmful effect on the properties of concrete. However, a suitable mix design for RCA enables it to reach the targeted strength and be applicable for a wide range of construction projects. The targeted strength achievement from the proposed mix design at a laboratory is also a time-consuming task, which may cause a delay in the construction work. To overcome this flaw, the application of supervised machine learning (ML) algorithms, gene expression programming (GEP), and artificial neural network (ANN) was employed in this study to predict the compressive strength of RCA-based concrete. The linear coefficient correlation (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were evaluated to investigate the performance of the models. The k-fold cross-validation method was also adopted for the confirmation of the model’s performance. In comparison, the GEP model was more effective in terms of prediction by giving a higher correlation (R2) value of 0.95 as compared to ANN, which gave a value of R2 equal to 0.92. In addition, a sensitivity analysis was conducted to know about the contribution level of each parameter used to run the models. Moreover, the increment in data points and the use of other supervised ML approaches like boosting, gradient boosting, and bagging to forecast the compressive strength, would give a better response.

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Reinforced concrete (RC) Pile cap, a thick reinforced concrete block, is constructed to provide a connection between a structure and multiple single piles. In the existing design methods, the behavior of RC pile cap is usually considered as a rigid body and resulting forces or reactions on piles are assumed equal. However, in actual conditions, there is a possibility of bending of pile cap and unequal reactions or forces on piles. This study represents a detailed finite element analysis of RC eight-pile cap by using a computer program ATENA. ATENA serve as rational tools to explain the behavior of RC structures. Research parameters included different types of loading conditions, analysis types such as linear and non-linear, support conditions, length of pile, strength of concrete and thickness of pile cap. The finite element analysis results indicate that the behavior of RC pile cap is not a rigid body and resulting forces or reactions are unequal. Also, it was observed that, support conditions, analysis type and thickness of RC pile cap had a significant effect on the global behavior of RC pile caps.

In the last few decades, many studies have been conducted on the flexural strengthening of reinforced concrete (RC) beams using different strengthening techniques such as concrete, steel and artificial fiber reinforced polymer composites (FRP). Among artificial FRPs, mainly glass, carbon and aramid fibers have been considered extensively. This study presents an experimental investigation on the flexural strengthening of small scale RC beams using natural fibers such as jute fiber reinforced polymer composites (JFRP) and basalt fiber reinforced polymer (BFRP) composites. A total number of five RC beams were constructed and tested under three point bending loading scheme to investigate the flexural response of both un-strengthened and FRP strengthened RC beams. Two types of strengthening techniques were adopted to strengthen RC beams. In strengthening technique A, the fiber was applied only at the tension side of the RC beams whereas in strengthening technique B, the fiber was applied both at sides and at the bottom in the form of U shape. The results indicate that use of both strengthening materials such as JFRP and BFRP is very effective to enhance ultimate load carrying capacity of RC beams. Further it was found that strengthening technique B is more efficient as compared with the strengthening technique A.

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