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RISS 인기검색어
- 검색키워드
- 저자 : Khalifa Al-Alawi (검색결과 3 건)
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Khalifa S. Al-Jabri,M. Al-Alawi2 한국강구조학회 2010 International Journal of Steel Structures Vol.10 No.4
This paper describes an artificial neural networking (ANN) model developed to predict the behaviour of semi-rigid composite joints at elevated temperature. Three different semi-rigid composite joints were selected, two flexible end-plates and one flush end-plate. Seventeen different parameters were selected as input parameters representing the geometrical and mechanical properties of the joints as well as the joint’s temperature and the applied loading, and used to model the rotational capacity of the joints with increasing temperatures. Data from experimental fire tests were used for training and testing the ANN model. Results from nine experimental fire tests were evaluated with a total of 280 experimental cases. The results showed that the R2 value for the training and testing sets were 0.998 and 0.97, respectively. This indicates that results from the ANN model compared well with the experimental results demonstrating the capability of the ANN simulation techniques in predicting the behaviour of semi-rigid composite joints in fire. The described model can be modified to study other important parameters that can have considerable effect on the behaviour of joints at elevated temperatures such as temperature gradient, axial restraints,etc.
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Question mark ear deformity: a case study and surgical technique
Sondus Al Jadeedi,Khalifa Al-Alawi,Said Al-Busaidi 대한미용성형외과학회 2024 Archives of Aesthetic Plastic Surgery Vol.30 No.2
Clefts of the ear are rare and typically develop within the earlobe. They were first de scribed by Vincent et al. in association with urogenital anomalies. Subsequently, Cos man reported them in 1970, referring to the condition as “question mark ear,” which led to its naming. This deformity is infrequently reported and is not well-documented in the literature. In this paper, we present the first reported case in the Omani popula tion of bilateral Cosman ear, associated with a mild cranial deformity. We also discuss the social impact of this condition and introduce our corrective technique for moder ate forms of deformity.
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Predicting the Behaviour of Semi-rigid Joints in Fire Using an Artificial Neural Network
Khalifa S. Al-Jabri,Saleh M. Al-Alawi 한국강구조학회 2007 International Journal of Steel Structures Vol.7 No.3
In this paper, we describe an artificial neural networking (ANN) model developed to predict the moment-rotation responseof semi-rigid beam-to-column joints at elevated temperature. Five types of beam-to-column joints, which represent typicaljoints used in construction, were modelled. Three flush end-plate bare-steel joints, one flexible end-plate bare-stel joint andtwo flexible end-plate composite joints were considered. The aplied moment and joint’s temperatures were used as inputeused for training and testing and validating the neural network models. The model’s predicted values were compared with actualtest results. The results indicate that the models can predict the momentrotationtemperature behaviour of semi-rigid beam-to-column joints with very high accuracy. The developed model can be modified easily to investigate other parameters thatinfluence the performance of joints in fire.
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