Masonry compressive strength prediction using artificial neural networks

The masonry is not only included among the oldest building materials, but it is also the most widely used material due to its simple construction and low cost compared to the other modern building materials. Nevertheless, there is not yet a robust quantitative method, available in the literature, which can reliably predict its strength, based on the geometrical and mechanical characteristics of its components. This limitation is due to the highly nonlinear relation between the compressive strength of masonry and the geometrical and mechanical properties of the components of the masonry. In this paper, the application of artificial neural networks for predicting the compressive strength of masonry has been investigated. Specifically, back-propagation neural network models have been used for predicting the compressive strength of masonry prism based on experimental data available in the literature. The comparison of the derived results with the experimental findings demonstrates the ability of artificial neural networks to approximate the compressive strength of masonry walls in a reliable and robust manner.- (undefined

Experimental and theoretical studies of the interaction of Penicillamine with SWCNT (6,0) as a drug delivery system

In this paper the interaction of Penicillamine (PCA) with single-walled carbon nanotube (SWCNT)(6,0) has been studied experimentally and theoretically. The element mapping (MAP) analysis has been indicated that the drug was adsorbed and distributed throughout the SWCNT (6,0) surface. The obtained FTIR spectrum confirms the presence of PCA on the surface of nanotube. The images of Field Emission Scanning Electron Microscope (FE-SEM) clearly demonstrate that the compound is fused in irregular films of nanometer size in which composed of very small spherical particles. The results of Energy Dispersive X-ray Analysis (EDAX) also, have been confirmed the combination of PCA with nanotube. TEM images have been clearly indicated the possibility of physical interaction between the CNT and the functional group of drug. These results have confirmed that the interaction of PCA with nanotube can be considered as a physical adsorption. The results obtained by the computational have confirmed the experimental data. © 2022 Taylor & Francis Group, LLC

Applications of Particle Swarm Optimization in Geotechnical Engineering: A Comprehensive Review

Particle swarm optimization (PSO) is an evolutionary computation approach to solve nonlinear global optimization problems. The PSO idea was made based on simulation of a simplified social system, the graceful but unpredictable choreography of birds flock. This system is initialized with a population of random solutions that are updated during iterations. Over the last few years, PSO has been extensively applied in various geotechnical engineering aspects such as slope stability analysis, pile and foundation engineering, rock and soil mechanics, and tunneling and underground space design. A review on the literature shows that PSO has utilized more widely in geotechnical engineering compared with other civil engineering disciplines. This is due to comprehensive uncertainty and complexity of problems in geotechnical engineering which can be solved by using the PSO abilities in solving the complex and multi-dimensional problems. This paper provides a comprehensive review on the applicability, advantages and limitation of PSO in different disciplines of geotechnical engineering to provide an insight to an alternative and superior optimization method compared with the conventional optimization techniques for geotechnical engineers