The effect of basalt and polypropylene fibers on the flexural behavior of reinforced concrete (RC) beams is investigated in this paper. The compressive and tensile behaviors of the basalt concrete and polypropylene concrete cylinders are also investigated. Eight beams and 28 cylinders were made with different percentages of basalt and polypropylene fibers. The dosages of fiber were selected as 0.6%, 1.3%, and 2.5% of the total cement weight. Each type of fiber was mixed solely with the concrete mix. Basalt and polypropylene fibers are modern and cheap materials that can be used to improve the structural behavior of RC members. This research is designed to find the optimum percentage of basalt and polypropylene fibers for enhancing the flexural behavior of RC beams. Test results showed that the addition of basalt and polypropylene fibers in any dosage (0.6%, 1.3%, and 2.5%) can increase the flexural strength and displacement ductility index of the beams where the maximum enhancement was measured with 1.3% fibers. The maximum increments in the flexural strength and the displacement ductility index were 30.39% and 260% for the basalt fiber case, while the maximum improvement for the polypropylene fibers case was 55.5% and 230% compared to the control specimen. Finite element (FE) models were then developed in ABAQUS to predict the numerical behaviour of the tested beams. The FE models were able to predict the experimental behaviour with reasonable accuracy. This research confirms the efficiency of basalt and polypropylene fibers in enhancing the flexural behavior of RC beams, and it also suggests the optimum dosage of fibers.

To study the working mechanism and size effect of an innovative dovetail UHPC joint originated from the 5th Nanjing Yangtze River Bridge, a large-scale testing subject to negative bending moment was conducted and compared with the previous scaled specimens. The static responses, i.e., the crack pattern, failure mode, ductility and stiffness degradation were analyzed. It was found that the scaled specimens presented similar working stages and working mechanism with the large-scale ones. However, the post-cracking ductility and relative stiffness degradation all decrease with the enlarged length/scale, apart from the relative stiffness after flexural cracking. The slab stiffness at the flexural cracking stage is 90% of the initial stiffness while only 24% of the initial stiffness reserved in the ultimate stage. Finite element model (FEM) was established and compared with the experiments to verify its effectiveness in exploring the working mechanism of the innovative joint. Based on this effective method, a series of FEMs were established to further study the influence of material strength, pre-stressing level and ratio of reinforcement on its deflection-load relationship. It is found that the ratio of reinforcement can significantly improve its load-carrying capacity among the three major-influenced factors.

In this paper, the dynamic characteristics of a composite cylindrical beam made of a mechanism of carbon dioxide absorption coated on the tube core are investigated based on the classical beam theory coupled with the modified couple stress theory. The composite tube structures are assumed to be uniform along the tube length, and the energy method regarding the Hamilton principle is utilized for generating the governing equations. A powerful numerical solution, the generalized differential quadrature method (GDQM), is employed to solve the differential equations. The carbon dioxide trapping mechanism is a composite consisting of a polyacrylonitrile substrate and a cross-link polydimethylsiloxane gutter layer. Methacrylate, poly (ethylene glycol), methyl ether methacrylate, and three pedant methacrylates are all taken into account as potential mechanisms for capturing carbon dioxide. The application of the present study is helpful in the design and production of microelectromechanical systems (MEMS) and the different valuable parameters, such as the length-scale parameter, rate of section change, aspect ratio, etc., are presented in detail.

Population growth in cities increases the need for service facilities and different urban spaces, and the organism of the city undergoes profound changes. One of the main problems that endanger the physical environment of the city due to this turmoil is the lack of public spaces and cultural complexes that increase individual and social pollution and on the other hand make leisure facilities available to the public. It severely limits people and, ultimately, the flourishing of individual and social artistic tastes. Thus, dealing with an issue called cultural complex has special importance and is one of the most basic categories in the field of architecture and urban planning, so dealing with it must be done in a measured, comprehensive and accurate manner. Cultural shock results from the immersion of an unprepared traveler in a foreign culture. In other words, human connection with people, objects, places, organizations and institutions, thoughts and the world of information will be constantly becoming more unstable and diverse. As a result, there is a need to create places for information or, in a central sense, to acquire up-to-date knowledge that requires information in the fields of human individual and social life. Spaces and places are all kinds of media tools from gramophone records to cassettes, CDs, newspapers, magazines, Internet books, etc. Each person can use them according to his needs and work.

In this paper, the artificial neural network (ANN) is employed to predict the flexural behavior of reinforced concrete (RC) beams retrofitted with carbon fiber/epoxy composites modified by carbon nanotubes (CNTs). Multiple techniques are used to improve the accuracy of the ANN prediction, as the data represents a multivalued function. These techniques include static ANN modeling, ANN modeling with load history, and ANN modeling with double load history. The developed ANN models are used to predict the load-displacement profiles of beams retrofitted with either CFRP or CNTs modified CFRP, flexural capacity, and maximum displacement of the beams. The results demonstrate that the ANN is able to predict the flexural behavior of the retrofitted RC beams as well as the effect of each parameter including the type of the used epoxy and the presence of the CNTs.

In this article, Multi-Gene Genetic Programming (MGGP) is proposed for the estimation of the compressive strength of concrete. MGGP is known to be a powerful algorithm able to find a relationship between certain input space features and a desired output vector. With respect to most conventional machine learning algorithms, which are often used as "black boxes" that do not provide a mathematical formulation of the output-input relationship, MGGP is able to identify a closed-form formula for the input-output relationship. In the study presented in this article, MGPP was used to predict the compressive strength of plain concrete, concrete with fly ash, and concrete with furnace slag. A formula was extracted for each mixture and the performance and the accuracy of the predictions were compared to the results of Artificial Neural Network (ANN) and Extreme Learning Machine (ELM) algorithms, which are conventional and well-established machine learning techniques. The results of the study showed that MGGP can achieve a desirable performance, as the coefficients of determination for plain concrete, concrete with ash, and concrete with slag from the testing phase were equal to 0.928, 0.906, 0.890, respectively. In addition, it was found that MGGP outperforms ELM in all cases and its' accuracy is slightly less than ANN's accuracy. However, MGGP models are practical and easy-to-use since they extract closed-form formulas that may be implemented and used for the prediction of compressive strength.