In this paper, the probabilistic behavior of plain concrete beams subjected to flexure is studied using a continuous mesoscale model. The model is two-dimensional where aggregate and mortar are treated as separate constituents having their own characteristic properties. The aggregate is represented as ellipses and generated under prescribed grading curves. Ellipses are randomly placed so it requires probabilistic analysis for model using the Monte Carlo simulation with 20 realizations to represent geometry uncertainty. The nonlinear behavior is simulated with an isotropic damage model for the mortar, while the aggregate is assumed to be elastic. The isotropic damage model softening be
Practically, torsion is normally combined with flexure and shear actions. Even though, the behavior of reinforced concrete continuous beams under pure torsion is investigated in this study. It was performed on four RC continuous beams under pure torsion. In order to produce torsional moment on the external supports, an eccentric load was applied at various distances from the longitudinal axis of the RC beams until failure.
Variables considered in this study are absolute vertical displacement of the external supports, torsional moment’s capacity, angle of twist and first cracks occurrences. According to experimental results; when load eccentricity increased from 30cm to 60cm, the absolute vertical displacement i
... Show MoreThe present work divided into two parts, first the experimental side which included the
measuring of the first natural frequency for the notched and unnotched cantilever composite beams
which consisted of four symmetrical layers and made of Kevlar- epoxy reinforced. A numerical
study covers the effect of notches on the natural frequencies of the same specimen used in the
experimental part. The mathematical model for the beam contains two open edges on the upper
surface. The effect of the location of cracks relative to the restricted end, depth of cracks, volume
fraction of fibers and orientation of the fiber on the natural frequencies are explored. The results
were calculated using the known engineering program (ANSY
The study focused on examining the behavior of six concrete beams that were reinforced with glass fiber-reinforced polymer (GFRP) bars to evaluate their performance in terms of their load-carrying capacity, deflection, and other mechanical properties. The experimental investigation would provide insights into the feasibility and effectiveness of GFRP bars as an alternative to traditional reinforcement materials like steel bars in concrete structures. The GFRP bars were used in both the longitudinal and transverse directions. Each beam in the study shared the following specifications: an overall length of 2,400 mm, a clear span of 2,100 mm, and a rectangular cross-section measuring
HS Saeed, SS Abdul-Jabbar, SG Mohammed, EA Abed, HS Ibrahem, Solid State Technology, 2020
Directional control valves are designed to control direction of flow, while actuators maintain required speeds and precise positions. Magnetorheological (MR) fluid is a controllable fluid. Utilizing the MR fluid properties, direct interface between magnetic fields and fluid power is possible, without the need for mechanical moving parts like spools. This study proposes a design of a four-way three-position MR directional control valve, presents a method of building, and explains the working principle of the valve. An analysis of the design and finite elements using finite element method of magnetism (FEMM) software was performed on each valve. The magnetic circuit of the MR valve was analyzed and the performance was simulated. The
... Show MoreThe Internet of Things (IoT) has significantly transformed modern systems through extensive connectivity but has also concurrently introduced considerable cybersecurity risks. Traditional rule-based methods are becoming increasingly insufficient in the face of evolving cyber threats. This study proposes an enhanced methodology utilizing a hybrid machine-learning framework for IoT cyber-attack detection. The framework integrates a Grey Wolf Optimizer (GWO) for optimal feature selection, a customized synthetic minority oversampling technique (SMOTE) for data balancing, and a systematic approach to hyperparameter tuning of ensemble algorithms: Random Forest (RF), XGBoost, and CatBoost. Evaluations on the RT-IoT2022 dataset demonstrat
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