This paper investigates the experimental response of composite reinforced concrete with GFRP and steel I-sections under limited cycles of repeated load. The practical work included testing four beams. A reference beam, two composite beams with pultruded GFRP I-sections, and a composite beam with a steel I-beam were subjected to repeated loading. The repeated loading test started by loading gradually up to a maximum of 75% of the ultimate static failure load for five loading and unloading cycles. After that, the specimens were reloaded gradually until failure. All test specimens were tested under a three-point load. Experimental results showed that the ductility index increased for the composite beams relative to the refe

Optimization is essentially the art, science and mathematics of choosing the best among a given set of finite or infinite alternatives. Though currently optimization is an interdisciplinary subject cutting through the boundaries of mathematics, economics, engineering, natural sciences, and many other fields of human Endeavour it had its root in antiquity. In modern day language the problem mathematically is as follows - Among all closed curves of a given length find the one that closes maximum area. This is called the Isoperimetric problem. This problem is now mentioned in a regular fashion in any course in the Calculus of Variations. However, most problems of antiquity came from geometry and since there were no general methods to solve suc

 The logistic regression model of the most important regression models a non-linear which aim getting estimators have a high of efficiency, taking character more advanced in the process of statistical analysis for being a models appropriate form of Binary Data.                                                          

Among the problems that appear as a result of the use of some statistical methods I

A substantial percentage of the world’s energy consumption (almost 40%) and carbon dioxide (CO2) emissions (around 37%) come from the construction industry, especially schools. This work presents a new hybrid artificial intelligence (AI) engineering model that aims to maximize energy performance on campuses in a holistic way. Modules for data-driven forecasting, metaheuristic optimization, and real-time adaptive control are all part of the concept. A thorough energy simulation of a university campus building is used in conjunction with the AI model to assess its performance through a co-simulation framework. Findings show that yearly peak electricity demand may be reduced by 18.7% and total site energy consumption by 22.4% when co

       In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial

Nuclear emission rates for nucleon-induced reactions are theoretically calculated based on the one-component exciton model that uses state density with non-Equidistance Spacing Model (non-ESM). Fair comparison is made from different state density values that assumed various degrees of approximation formulae, beside the zeroth-order formula corresponding to the ESM. Calculations were made for 96Mo nucleus subjected to (N,N) reaction at Emax=50 MeV. The results showed that the non-ESM treatment for the state density will significantly improve the emission rates calculated for various exciton configurations. Three terms might suffice a proper calculation, but the results kept changing even for ten terms. However, five terms is found to give

Aerial manipulation of objects has a number of advantages as it is not limited by the morphology of the terrain. One of the main problems of the aerial payload process is the lack of real-time prediction of the interaction between the gripper of the aerial robot and the payload. This paper introduces a digital twin (DT) approach based on impedance control of the aerial payload transmission process. The impedance control technique is implemented to develop the target impedance based on emerging the mass of the payload and the model of the gripper fingers. Tracking the position of the interactional point between the fingers of gripper and payload, inside the impedance control, is achieved using model predictive control (MPD) approach.