Design of an Artificial Neural Network (BPNN) to Predict the Content of Silicon Oxide (SiO2) based on the Values of the Rock Main Oxides: Glass Factory Feed Case Study

Artificial neural network (ANN) is one of the practical methods for prediction in various sciences. In this study, which was carried out on Glass and Crystal Factory in Isfahan, the amount of silica purification used in industry has been investigated according to its analyses. In this discussion, according to the artificial neural network algorithm back propagation neural network (BPNN), the amount of silica (SiO2) was predicted according to rock main oxides in chemical analysis. These studies can be used as a criterion for estimating the purity for use in the factory due to the high accuracy obtained.

Geospatial Technologies in Waste Management: A Case Study of Rivers State Waste Management Agency (RIWAMA)

Waste Management can be quite challenging, especially in the developing world. The challenge becomes even more complex with the growing population. City planners and decision makers are turning to technology to improve the efficiency of the waste management process. Geospatial technologies have offered a range of solutions, which have been deployed with success in waste management. This paper highlights the challenges of waste management in Port Harcourt, Nigeria, and how the Rivers State Waste Management Agency (RIWAMA) deployed the use of GIS in solving most of the challenges of identification, planning, evacuation, and transportation of wastes within the Port Harcourt metropolis. This paper looked at how this solution was deployed to solve key challenges as well as stimulate citizen participation in the waste management process. The paper concludes with a set of recommendations for expanding the potentials of its application.

Freeze-Thaw Behavior of Stabilized Clayey Soil with Red Mud and Cement

The clayey soils in areas with seasonal frost are exposed to at least one freeze-thaw cycle every year and worsen their engineering properties. To prevent the engineering properties of clayey soils, it is necessary to improve the freeze-thaw resistance of them. In this study, the clayey soil was stabilized by using red mud and cement additive materials. Prepared samples of clayey soil and stabilized clayey soil were subjected to the unconfined compressive test. To investigate the effects of red mud and cement additive materials on the freeze-thaw resistance of clayey soil, the natural and stabilized expansive soil samples were exposed to the freeze-thaw cycles under laboratory conditions. The obtained results showed that the red mud and cement additive materials increased the freeze-thaw resistance of clayey soil. Consequently, it was concluded that red mud and cement additive materials can be successfully used to improve the freeze-thaw resistance of clayey soils.

Abnormal Driving Behavior Recognition Method Based on Smart Phone Sensor and CNN-LSTM

Accurate identification of abnormal driving behavior is very important to improve driver safety. Aiming at the problem that threshold or traditional machine learning methods are mostly used in existing studies, it is difficult to accurately identify abnormal driving behavior of vehicles, a method of abnormal driving behavior recognition based on smartphone sensor data and convolutional neural network (CNN) combined with long and short-term memory (LSTM) was proposed. Smartphone sensors are used to collect vehicle driving data, and data sets of various driving behaviors are constructed by preprocessing the data. A recognition model based on a convolutional neural network combined with a long short-term memory network was constructed to extract depth features from data sets and recognize abnormal driving behaviors. The test results show that the accuracy of the model based on CNN-LSTM can reach 95.22%, and the performance indexes can reach more than 94%. Compared with the recognition model constructed only by CNN or LSTM, this model has higher recognition accuracy.

Effects of Quartzite on the Desiccation Cracks of Clayey Soils Exposed to Wetting-Drying Cycles

The compacted clayey soils crack on drying because of their high swelling potential, and their hydraulic conductivities increase. To solve this problem, it is essential to stabilize the clayey soils using additive materials. The aim of this study is to examine the suitability of quartzite as a stabilization material to reduce the development of desiccation cracks in compacted clayey liner and cover systems. Experimental study was conducted to investigate the effect of wetting-drying cycles on the initiation and evolution of cracks in compacted clayey soils. For experimental studies, seven samples were prepared stabilized by using 0%, 2.5%, 5%, 7,5%, 10%, 12,5% and 15% quartzite and then they were subjected to four subsequent wetting-drying cycles. The results show that quartzite decreases the development of desiccation cracks on the surface of compacted samples. It is concluded that quartzite as a geological material can be successfully used to reduce the development of desiccation cracks in compacted clayey liner and cover systems exposed wetting-drying cycles.

A Statistical Model for Analyzing Anthropometric Data and Developing Clothing Sizing Systems for Libyan School Children

A garment sizing system is essential for effective clothing design and production. A sizing system classifies a specific population into homogeneous subgroups based on some key dimensions. Persons of the same subgroup have the same body shape characteristics, and share the same garment size. Anthropometric data plays important role in creating clothing sizing system. The current work represents the sixth step towards the overall goal of developing the Libyan children’s clothing standards system based on physical measurements of the human body of Libyan schoolchildren. The objective of the current work is to study the physical measurements of students aged 6 to 17 years in the stages of primary, secondary. The body measurements of school children in Benghazi were collected and analyzed using simple statistics methods to understand the body ranges and current of student in all stages to develop the system sizing. The measurements were collected from previous projects. Some measurements were collected to complement a work of 90 (male and female) students between 6, 7 and 8 years old from a school in Benghazi. ANOVA test was used to determine differences between age groups.

A Vehicle and Pedestrian Detection Method Based on Improved YOLOv4-Tiny

Aiming at the problems of low detection accuracy and poor recognition effect of small-scale targets in traditional vehicle and pedestrian detection methods, a vehicle and pedestrian detection method based on improved YOLOv4-Tiny is proposed. On the basis of YOLOv4-Tiny, the 8-fold down sampling feature layer was added for feature fusion, the PANet structure was used to perform bidirectional fusion for the deep and shallow features from the output feature layer of backbone network, and the detection head for small targets was added. The results show that the mean average precision of the improved method has reached 85.93%, and the detection performance is similar to that of YOLOv4. Compared with the YOLOv4-Tiny, the mean average precision of the improved method is increased by 24.45%, and the detection speed reaches 67.83FPS, which means that the detection effect is significantly improved and can meet the real-time requirements.

Earthquake Performance Analysis of Steel Structures with A3 Plan Irregularities

In earthquake engineering, a performance-based design method is used to determine the level of the expected performance of the structures under the earthquake effect. The level of performance is related to the damage situation that could be occurred in the structure after the earthquake. In the performance-based structural design, it is predicted that more than one damage levels emerge under one certain earthquake effect. In this study, the seismic behavior of steel structures with plan irregularities in the Turkey Building Earthquake Code in the 2018 (TBEC-2018) is investigated by the nonlinear static analysis methods. The selected steel structures are located in İzmir, Turkey. The Turkey Earthquake Code in 2018 is considered for assessing seismic performance evaluation of the selected moment-resisting frame steel building. Four different A3 type irregularity was investigated. The steel building with no irregularity in its plan. was selected as the structure of the reference. The performance goals of the five different steel structures are evaluated by applying the pushover and procedures of the TBEC-2018. The steel structures were compared by obtaining pushover curves for both the X and Y directions. The results show that the effects of A3 type irregularity should be not considered in design and buildings without irregularities are safer.

Earthquake Analysis of a High-Rise Building Retrofitted with Support Braced Systems

The use of support braced systems represents one of the best solutions for retrofitting or upgrading the tall reinforced concrete buildings in areas with a high earthquake hazard. In this study, the behavior of a reinforced concrete tall structure under seismic loads is examined based on the Turkish Building Earthquake Code 2019 (TBEC-2019). Support braced systems were added to the 25-story structure on 0.4H and 0.8H levels (H is height of structure). For two different models, firstly, the Mode-Superposition Method for linear computational methods used within the scope of strength-based design is performed. In order to determinate more accurately the behavior of tall buildings, as in the earthquake regulations of other developed countries, the TBEC-2019 advises a nonlinear deformation-based design approach. In addition, the nonlinear time history analyses of these buildings were performed. As a result of these analyzes, it was determined whether the two models examined were within the targeted performance effects or not. In the model having support braced system, stiffness and shear forces in shear walls were increased. Thus, displacements, relative story drift, plastic rotations and bending moments of shear walls were decreased.

Modelling of Electric Grid to Enhance Generated Power Evacuation

The study of evacuation of power from the power plants in Rivers State Nigeria, connecting to the 330kV transmission network of the Transmission Company of Nigeria (TCN). The Power World Simulator Educational version was used in the modelling and simulation of the electric power grid. The study of load flow analysis, short circuit, transient and N-1 contingency analysis and their effect on the 330 kV/132kV transmission bus connected to the existing power plants in Rivers State Nigeria namely; Rivers IPP (180MW), Afam III (265.6MW), Afam IV & V (351.00 MW) and Afam VI G. S (650.00 MW) was carried out. From the short circuit study, it is observed that when a bus is faulted with a 3-phase fault, the three-phase voltages of the system drastically become zero in all the phases. The other buses of the network experience an increase in voltage and all the buses fed have the same effect as the bus under fault, though the effect is felt more on the buses. However, with the introduction of substation splitting at Afam III and ongoing Afam IV substations, the short circuit level will be reduced by 15%; leading to improvement in the overall system stability.