The influence and hazard of fire flame are one of the most important parameters that affecting the durability and strength of structural members. This research studied the influence of fire flame on the behavior of reinforced concrete beams affected by repeated load. Nine self- compacted reinforced concrete beams were castellated, all have the same geometric layout (0.15x0.15x1.00) m, reinforcement details and compressive strength (50 Mpa). To estimate the effect of fire flame disaster, four temperatures were adopted (200, 300, 400 and 500) oC and two method of cooling were used (graduated and sudden). In the first cooling method, graduated, the tested beams were leaved to cool in air while in the second method, sudden, water splash was used to reduce the temperature. Eight of the tested beams were divided in to four groups, each were burned to one of the adopted temperature for about half an hour and cooled by the adopted cooling methods (one by sudden cooling and the other by graduated cooling). After burning and cooling the beams were tested under the effect of repeated load (loading – unloading) for five cycle and then up to failure. As a compared with the non- burned beam, the results indicated that the ultimate load capacity of the tested beams were reduced by (16, 23, 54 and 71)% after being burned to (200, 300, 400 and 500) oC , respectively, for a case of sudden cooling and by (8, 14, 36 and 64)% , respectively, for a case of graduated cooling. It was also found that the effect of sudden cooling was greater than that in a case of graduated cooling. Regarding the failure mode, there was a different between the non-burred beam and the other ones even that all of them had the same geometric layout, compressive strength and reinforcement details. The failure mode for all burned beams was combined shear- flexure failure which was belong to the reduction in the compressive strength of the concrete due to the effect of the temperature rising , while the failure mode of the non-burned beam was flexure failure which was compatible with the preliminary design. It was also detected that the residual deflection proportion directly with the temperature, as the temperature increase to (200, 300, 400 and 500) oC the residual deflection compared with the non-burned beam increased by (32, 48, 326 and 358)% for a case of sudden cooling and by (13, 29, 303 and 332)% for a case of graduated cooling. Another effect was appear represented by the method of cooling, the results showed that the sudden cooling had more effect on the residual deflection than the graduated cooling by (15-6)% approximately. To vanish the residual deflection, numbers of cycle (loading-unloading) were required. It was found that this number increase as the temperature of burning increased and it’s also larger in a case of sudden cooling.
Until recently, researchers have utilized and applied various techniques for intrusion detection system (IDS), including DNA encoding and clustering that are widely used for this purpose. In addition to the other two major techniques for detection are anomaly and misuse detection, where anomaly detection is done based on user behavior, while misuse detection is done based on known attacks signatures. However, both techniques have some drawbacks, such as a high false alarm rate. Therefore, hybrid IDS takes advantage of combining the strength of both techniques to overcome their limitations. In this paper, a hybrid IDS is proposed based on the DNA encoding and clustering method. The proposed DNA encoding is done based on the UNSW-NB15
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This paper describes a newly modified wind turbine ventilator that can achieve highly efficient ventilation. The new modification on the conventional wind turbine ventilator system may be achieved by adding a Savonius wind turbine above the conventional turbine to make it work more efficiently and help spinning faster. Three models of the Savonius wind turbine with 2, 3, and 4 blades' semicircular arcs are proposed to be placed above the conventional turbine of wind ventilator to build a hybrid ventilation turbine. A prototype of room model has been constructed and the hybrid turbine is placed on the head of the room roof. Performance's tests for the hybrid turbine with a different number of blades and different values o
... Show MoreThis paper interest to estimation the unknown parameters for generalized Rayleigh distribution model based on censored samples of singly type one . In this paper the probability density function for generalized Rayleigh is defined with its properties . The maximum likelihood estimator method is used to derive the point estimation for all unknown parameters based on iterative method , as Newton – Raphson method , then derive confidence interval estimation which based on Fisher information matrix . Finally , testing whether the current model ( GRD ) fits to a set of real data , then compute the survival function and hazard function for this real data.
The current research deals with spatial relations as a tool to link urban landmarks in a homogeneous composition with monumental sculptures, by identifying these landmarks and the extent of their impact on them, which constitutes an urgent need to evaluate the appropriate place and its effects on them, so that this analytical study is a critical approach adopted in artistic studies of monumental models in Arabcapitals .The current research came in four chapters, the first chapter of which dealt with the research problem, its importance and the need for it, then its objectives that were determined in revealing the spatial relations and their impact on
... Show MoreAn adaptive nonlinear neural controller to reduce the nonlinear flutter in 2-D wing is proposed in the paper. The nonlinearities in the system come from the quasi steady aerodynamic model and torsional spring in pitch direction. Time domain simulations are used to examine the dynamic aero elastic instabilities of the system (e.g. the onset of flutter and limit cycle oscillation, LCO). The structure of the controller consists of two models :the modified Elman neural network (MENN) and the feed forward multi-layer Perceptron (MLP). The MENN model is trained with off-line and on-line stages to guarantee that the outputs of the model accurately represent the plunge and pitch motion of the wing and this neural model acts as the identifier. Th
... Show MoreThere has been a growing interest in the use of chaotic techniques for enabling secure communication in recent years. This need has been motivated by the emergence of a number of wireless services which require the channel to provide very low bit error rates (BER) along with information security. This paper investigates the feasibility of using chaotic communications over Multiple-Input Multiple-Output (MIMO) channels by combining chaos modulation with a suitable Space Time Block Code (STBC). It is well known that the use of Chaotic Modulation techniques can enhance communication security. However, the performance of systems using Chaos modulation has been observed to be inferior in BER performance as compared to conventional communication
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In this research, we use fuzzy nonparametric methods based on some smoothing techniques, were applied to real data on the Iraqi stock market especially the data about Baghdad company for soft drinks for the year (2016) for the period (1/1/2016-31/12/2016) .A sample of (148) observations was obtained in order to construct a model of the relationship between the stock prices (Low, high, modal) and the traded value by comparing the results of the criterion (G.O.F.) for three techniques , we note that the lowest value for this criterion was for the K-Nearest Neighbor at Gaussian function .
Diagnosing heart disease has become a very important topic for researchers specializing in artificial intelligence, because intelligence is involved in most diseases, especially after the Corona pandemic, which forced the world to turn to intelligence. Therefore, the basic idea in this research was to shed light on the diagnosis of heart diseases by relying on deep learning of a pre-trained model (Efficient b3) under the premise of using the electrical signals of the electrocardiogram and resample the signal in order to introduce it to the neural network with only trimming processing operations because it is an electrical signal whose parameters cannot be changed. The data set (China Physiological Signal Challenge -cspsc2018) was ad
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The concepts of higher Bi- homomorphism and Jordan higher Bi- homomorphism have been introduced and studied the relation between Jordan and ordinary higher Bi- homomorphism also the concepts of Co- higher Bi- homomorphism and Co- Jordan higher Bi- homomorphism introduced and the relation between them in Banach algebra have also been studied.
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Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye
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