Browsing by Author "B. Alhaji"

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    Analysis of Statically Determinate Trusses using Exact Method (Joint Resolution Method) and Matrix Stiffness Method
    (USEP: Journal of Research Information in Civil Engineering, 2017-10-10) A. Abdullah; I. T. Yusuf; M. Abubakar; H. O. Aminulai; YUSUF, Abdulazeez; B. Alhaji
    Matrix Stiffness Method (MSM) as a tool for static analysis of structures is premised on the principle of Finite Element Method (FEM), which in itself is a numerical/approximate method capable of giving only approximate results. However, Joint Resolution Method (JRM) is one of the most popular classical/exact methods of static analysis capable of giving exact results. This paper presents an analysis of a statically determinate 2-D truss using Exact/Joint Resolution Method (JRM) and Matric Stiffness Method (MSM) to ascertain the validity of the latter against the former. In the JRM, the support reactions and internal member forces were obtained from considerations of the equilibrium conditions of the entire truss and isolated joints respectively. On the other hand, a computer program was written in MATLAB 7.8.0 (R2009a) based on the principles of MSM for ease of computation and increased accuracy to solve for member forces and reactions of the same truss. The element properties were obtained and employed to calculate the element stiffness matrices, these were then assembled into the global stiffness matrix, from which the unknown displacements, member forces and support reactions were calculated. The results obtained from using both JRM and MSM were found to be exactly the same or very close, with percentage errors ranging between 0% and 3%. Hence MSM results as compared to JRM have 97% accuracy and above, and can therefore be relied upon.
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    Development of an Android Based Mobile Application for Design and Detailing of Pad Foundations to BS8110
    (Epistemics in Science, Engineering and Technology, 2017-12-10) YUSUF, Abdulazeez; H. O. Aminulai; A. Abdullahi; M. Abubakar; B. Alhaji
    Many innovative computer software have been developed to perform the task of designing and detailing structural elements such as beams, columns, slabs and foundations. This design and detailing can be done using mobile devices but software developed to operate on such devices have not been fully developed. However, this research is aimed at developing an android based mobile application for the design of pad foundations to Bs8110. The mobile application developed designs isolated axially loaded-only; axially loaded with moment pad footings as well as combined pad footings. The mobile application developed was tested using three typical test parameters and results compared to the manual computations. There was no significant variation in the steel sections required and provided for the manual design and that generated by the mobile application. The steel required by manual design for the axially loaded pad footing was 835mm2/m and that generated by the application was 837.2mm2/m. That of the axially loaded with moment gave required steel section as 1019mm2/m using manual design. This android based mobile application would thus give the structural engineer the leverage to design pad footings anywhere and anytime
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    Modelling Slump of Concrete Containing Natural Coarse Aggregate from Bida Environs Using Artificial Neural Network
    (Journal of Soft Computing in Civil Engineering, 2021-05-02) YUSUF, Abdulazeez; M. Abdullahi; S. Sadiku; J.I. Aguwa; B. Alhaji; T.A. Folorunso
    Consumption of crushed granite as coarse aggregate in concrete has led to devastating environmental and ecological consequences. In order to preserve local and urban ecology therefore, substitute aggregate such as naturally occurring stone with the propensity of reducing this problem was studied. Furthermore, artificial Neural Network (ANN) models have become the preferred modeling approach due to their accuracy. Thus, in this paper, MATLAB software was used to develop ANN models for predicting slump of concrete made using Bida Natural Gravel (BNG). Four model architectures (5:5:1; 5:10:1; 5:15:1 and 5:20:1) were tried using a back-propagation algorithm with a tansig activation function. The performance of the developed models was examined using Mean Square Error (MSE), Correlation Coefficient (R) and Nash-Sutcliffe Efficiency (NSE). Results showed that 5:20:1 model architecture with MSE of 8.33e-27, R value of 98% and NSE of 0.96 was the best model. The chosen 5:20:1 ANN model also out performed Multiple Linear Regression (MLR) model which recorded MSE of 0.83, R value of 88.68% and NSE of 0.87. The study concluded that the higher the neuron in hidden layer of ANN slump model for concrete containing BNG, the better the model.