The objective of this research is to study experimentally and theoretically the girder vertical load share of the curved I-Girder bridges subjected to the point load in addition to the self-weigh and supper imposed dead loads. The experimental program consist of manufacturing and testing the five simply supported bridge models was scaled down by (1/10) from a prototype of 30m central span. The models carriageway central radii are 30 m, 15m or 10m. The girder spacing of the first two models is 175 mm with an overall carriageway width of 650mm. The girder spacing of the other three bridge models is 200mm with the overall carriageway width of 700 mm. The overall depth of the composite section was 164 mm. To investigate the effect of live load

This paper presents a comparative study of two learning algorithms for the nonlinear PID neural trajectory tracking controller for mobile robot in order to follow a pre-defined path. As simple and fast tuning technique, genetic and particle swarm optimization algorithms are used to tune the nonlinear PID neural controller's parameters to find the best velocities control actions of the right wheel and left wheel for the real mobile robot. Polywog wavelet activation function is used in the structure of the nonlinear PID neural controller. Simulation results (Matlab) and experimental work (LabVIEW) show that the proposed nonlinear PID controller with PSO
learning algorithm is more effective and robust than genetic learning algorithm; thi

       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

Columns subjected to pure axial load rarely exist in practice. Reinforced concrete columns are usually subjected to combination of axial and lateral actions and  deformations, caused by  spatially‐complex loading patterns as during earthquakes causes lateral deflection that in turn affects the horizontal stiffness. In this study, a numerical model was developed in threedimensional nonlinear finite element and then validated against experimental results reported in the literatures,
to investigate the behavior of conventionally RC columns subjected to axial load and  . lateral reversal cyclic loading. To achieve this goal, numerical analysis was conducted by using finite element program ABAQUS/Explicit. The variables co

The steel jetty selected for strengthening is in Baghdad city, over Tigris River, consists of 55 short spans, each of approximately 4 meters and one naviga-tional opening of 12 m. The bridge is 224 meters length and 8 meters in width. The strengthening system was designed to remove overstresses that occurred when the bridge was subjected to abnormal loads of 380 tons. A strengthening system which installed in spring 2008 was used where the main concept is to depend on added side supporting elements which impose reversal forces on the bridge to counteract most of the loads expected from the abnormal heavy loads. The bridge was load tested before and after the strengthening system was activated. The load test results indicate that the strengt

The development of information systems in recent years has contributed to various methods of gathering information to evaluate IS performance. The most common approach used to collect information is called the survey system. This method, however, suffers one major drawback. The decision makers consume considerable time to transform data from survey sheets to analytical programs. As such, this paper proposes a method called ‘survey algorithm based on R programming language’ or SABR, for data transformation from the survey sheets inside R environments by treating the arrangement of data as a relational format. R and Relational data format provide excellent opportunity to manage and analyse the accumulated data. Moreover, a survey syste

      Internet of Vehicle (IoV) is one of the most basic branches of the Internet of Things (IoT), which provides many advantages for drivers and passengers to ensure safety and traffic efficiency. Most IoV applications are delay-sensitive and require resources for data storage and computation that cannot be afforded by vehicles. Thus, such tasks are always offloaded to more powerful nodes, like cloud or fog. Vehicular Fog Computing (VFC), which extends cloud computing and brings resources closer to the edge of the network, has the potential to reduce both traffic congestion and load on the cloud. Resources management and allocation process is very critical for satisfying both user and provider needs. However, th

     Recently, the Internet of Things has emerged as an encouraging technology that is scaling up new heights towards the modernization of real word physical objects into smarter devices in several domains. Internet of Things (IoT) based solutions in agriculture drives farming into a smart way through the proliferation of smart devices to enhanced production with minimal human involvement. This paper presents a comprehensive study of the role of IoT in prominent applications of farming, wireless communication protocols, and the role of sensors in precision farming. In this research article, the existing frameworks in IoT-based agriculture systems with relevant technologies are presented. Furthermore, the comparative analysis of the a