A novel demountable shear connector for precast steel‐concrete composite bridges is presented. The connector uses high‐strength steel bolts, which are fastened to the top flange of the steel beam with the aid of a special locking nut configuration that prevents slip of bolts within their holes. Moreover, the connector promotes accelerated construction and overcomes typical construction tolerances issues of precast structures. Most importantly, the connector allows bridge disassembly, and therefore, can address different bridge deterioration scenarios with minimum disturbance to traffic flow, i.e. (i) precast deck panels can be rapidly uplifted and replaced; (ii) connectors can be rapidly removed and replaced; and (iii) steel beams can b

This paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm n

The objective of this study was tointroduce a recursive least squares (RLS) parameter estimatorenhanced by using a neural network (NN) to facilitate the computing of a bit error rate (BER) (error reduction) during channels estimation of a multiple input-multiple output orthogonal frequency division multiplexing (MIMO-OFDM) system over a Rayleigh multipath fading channel.Recursive least square is an efficient approach to neural network training:first, the neural network estimator learns to adapt to the channel variations then it estimates the channel frequency response. Simulation results show that the proposed method has better performance compared to the conventional methods least square (LS) and the original RLS and it is more robust a

The reduction of vibration properties for composite material (woven roving E-glass fiber plies in thermosetting polyester matrix) is investigated at the prediction time under varied combined temperatures (60  to -15) using three types of boundary conditions like (CFCF, CCCF, and CFCC). The vibration properties are the amplitude, natural frequency, dynamic elastic moduli (young modulus in x, y directions and shear modulus in 1, 2 plane) and damping factor. The natural frequency of a system is a function of its elastic properties, dimensions, and mass. The woven roving glass fiber has been especially engineered for polymer reinforcement; but the unsaturated thermosetting polyester is widely used, offering a good balance of vibration p

Machine learning is considered a powerful technique in many applications such as classification, clustering, recognition and prediction. Deep learning is a modern, vital and superior machine learning that gives stunning performance, especially with huge data. Stock market price prediction is the process of determining the future value of a prospect of a financial instrument traded in the market, to gain a great profit a successful prediction must be conducted, in order to achieve that machine learning is used, in this article, two approaches are proposed to predict the stock market prices and movement using two datasets, the first approach employs two machine learning models (J48 & logistic regression) while the second approach based on rec

The research aims to conduct a comparative study between the events (800) m and (1500) m track in the types of muscular strength and to identify the differences between them among Iraqi sports club players. The sample represented the players participating in the Iraqi Athletics Championship for the period between (02/11/2023) and (04 /11/2023), and their number was (16) players (8) for each event, as the selection was made intentionally.  The researchers used the descriptive approach to achieve the goal of the research and used the statistical package (SPSS) to process the data statistically. According to the results collected, it was found that there was superiority among the intermediate track players (800) m in explosive power and the s

Reactive Powder Concrete (RPC) could be considered as the furthermost significant modern high compressive strength concrete. In this study, an experimental investigation on the impact of micro steel fiber volume fraction ratio and gamma ray irradiation duration influence upon the compressive strength of RPC is presented. Three volume fraction ratios (0.0, 1.0 and 1.5) % was implemented. For each percentage of the adopted fiber ratios, six different irradiation duration was considered; these are (1, 2, 3, 4, 5 and 6) days. Gamma source (Cs-137) of energy (0.662) MeV and activity (6) mci was used. In a case of zero volume fraction ratio, the experimental results showed that gamma ray had a significant influence on the reducing of the

Background: Failure of resin bases were a major disadvantage recorded in the constructed dentures. Reinforcements of the repair joint with nano fillers represent an attempt to enhance the strength and durability. The purpose of the research was to estimate the influence of nano fillers reinforcement with (ZrO2 and Al2O3) on impact and transverse strength of denture bases repaired with either cold or hot processing acrylic resin. Materials and methods: A hundred and forty (140) samples were processed with hot cured resin and organized in subgroups depending on the repair materials and condition (without repair (control), repair with hot cure, cold cure, hot and cold cure reinforced with either (5% Zr2O or 0.5% Al2O3). The samples in these