Category Archives: PhD Thesis

In this dissertation, three nonlinear techniques of prestressing, analysis, and preservation were developed based on the principles of the force method to address geometric nonlinearities in pin-jointed spatial structures. These techniques provide a comprehensive framework for accurately performing prestressing, analysing, and preserving spatial assemblies, validated through rigorous numerical and experimental investigations.

The research introduces direct nonlinear approaches especially for prestressing and preservation, overcoming the limitations of iterative and linear approximation-based methods. The derived nonlinear equations, expressed as functions of joint displacements, were efficiently solved using MATLAB’s fsolve function, demonstrating robust applicability to both simple and complex spatial systems. The proposed prestressing technique computes the desired prestress level by accurately accounting for nonlinear member alterations, preventing cable slack, and maintaining alignment with software solvers under predetermined actuation conditions.

The developed analysis method is efficient and precise, capable of calculating internal member stresses and axial forces for both rigid and flexible members while incorporating geometric nonlinearities under different loading conditions. Similarly, the preservation technique reliably restores disturbed geometries, nodal displacements, and internal forces, with targeted control of specific parameters. The effectiveness of the preservation process depends on actuator placement, bar sensitivity analysis, and the appropriate selection of actuation targets.

Validation of these techniques included numerical case studies and experimental testing on a hyperbolic paraboloid space cable net model with 64 members and 41 joints. The results demonstrated strong agreement, with maximum and minimum discrepancy ratios of 7% and 0%, respectively, between theoretical and experimental measurements. This dissertation presents a novel framework that significantly enhances the precision, efficiency, and control of structural response prediction, making substantial advancements in the field of pin-jointed spatial structures.

This dissertation investigated the effect arrangement of layers in glass and carbon composites, focusing on the effects of longitudinal fiber orientation, thickness, and the addition of SiO₂ nanoparticles. Three batches of composite plates were produced. The first category comprised unidirectional glass fiber sheets arranged in a stacking sequence of 0° [G/G/G/G]s  with and without 2% silica dioxide nanoparticles and carbon fiber sheets arranged in a stacking sequence of 0° [C/C/C/C]s with and without 2% silica dioxide nanoparticles. These sheets were specifically designed to assess the mechanical properties, namely the modulus of elasticity in the longitudinal and transverse directions E₁ and E₂, shear modulus (G₁₂), and Poisson's ratio (ν₁₂) for glass/epoxy and carbon/epoxy composites.

The second group, characterized by quasi-isotropic-balanced distinct stacking sequences, comprised three primary unidirectional fiber orientations: 0°, 45°, and 90°. These sequences varied in thickness, consisting of eight layers (2 mm), ten layers (2.5 mm), and twelve layers (3 mm). Additionally, SiO₂ nanoparticles were utilized as a reinforcing agent within the epoxy matrix. The third group consisted of cross-layer configurations, with various stacking sequences examined. These sequences involved two primary unidirectional fiber orientations: 0° and 90°, and they exhibited different thicknesses. Specifically, the group included eight layers (2 mm), twelve layers (3 mm), sixteen layers (4 mm), and twenty layers (5 mm).

Furthermore, SiO₂ nanoparticles were employed as a reinforcing agent within the epoxy matrix. The vacuum-assisted resin infusion process was utilized to fabricate fourteen combinations of fiber-reinforced epoxy composites, including those with and without the addition of 2% silicon dioxide nanoparticle composites, designated as QS1, QS1N, QS2, QS2N, QS3, QS3N, CS1, CS1N, CS2, CS2N, CS3, CS3N, CS4, and CS4N. The quasi-static mechanical properties (tensile and three-point bending test) and dynamic (axial and flexural fatigue test) behaviors of the material were examined through experimental analysis and validated using numerical simulations via the finite element method (ANSYS 2019/R3 Workbench).

The modulus of elasticity (E₁) and maximum stress for QS3 and QS3N increased by 20.97% and 18.65%, respectively. In comparison to CS1, which lacks SiO₂ nanoparticles, CS1N exhibited increases in E₁ and maximum stress of 2.5% and 12.7%, respectively. The incorporation of SiO₂ nanoparticles significantly enhanced the performance of glass/carbon hybrid composite materials. Axial fatigue tests demonstrated that the number of cycles of the hybrid composites (CS1 and CS1N), (CS2 and CS2N), and (CS3 and CS3N) increased by approximately 55%, 27%, and 58%, respectively, at a load level of 70%. In flexural fatigue testing, there was a stress increase of 17.4% between CS1 and CS₁N, and a similar increase of 13.11% between CS2 and CS2N. The sample pairs CS3 and CS3N showed a comparable percentage increase of 17.1%, while CS4 and CS4N exhibited an increase of 13.61%.

This research study includes an experimental and analytical study of steel fiber-reinforced concrete columns using natural and recycled aggregate (RA). To improve the structural application of recycled aggregate and to protect the environment and preserve natural resources, it is crucial to use recycled aggregate in construction. The recycled coarse aggregate reinforced concrete columns with the addition of steel fiber subjected to concentric and eccentric loadings for short and slender columns are examined experimentally and analytically in this research. Forty two column specimens were cast to examine the impact of steel fiber, recycled aggregate, slenderness, and eccentricity on the behavior of reinforced concrete columns. In addition to concentrically loaded columns, columns were loaded at 50% and 100% eccentricity, corresponding to e/h ratios of 0.5 and 1.0. Three different slenderness ratios were selected to examine the effects of height: 17.24 for short columns, 26 for moderately slender columns, and 34.5 for highly slender columns. The research examined the failure mode, maximum load-carrying capacity, strain in the concrete, and strain in the reinforcement, mid-height lateral displacement, vertical displacement and ductility. Based on the results of the current study, it can be concluded that employing recycled concrete aggregate is a potential approach that can meet design codes. Columns produced with recycled concrete aggregate behaved similarly to columns made with natural aggregate (NA). The addition of 1% steel fiber effectively prevented concrete from crushing and spalling. Steel fiber, however, improved the columns' ductility and strength. According to experimental results, the steel fiber addition narrowed the crack width which visually observed and had a comparable effect on columns constructed with recycled aggregate and columns constructed with natural aggregate. The experimental test maximum load carrying capacity agreed well with the results using ACI-318-19 equations. Furthermore, a model has been proposed for columns with both natural and recycled aggregate and accounts for eccentricity and slenderness to forecast the load-carrying capability. Additionally, the second-order effect due to the intentional such as given eccentricity and unintentional eccentricity such as alignment errors was investigated. The second-order effect is considered an excellent theoretical method to examine the behavior of columns. Using this method theoretical load path was drawn for each column tested as well as an experimental load path for comparison, later, using the same method, an axial load bending moment interaction diagram was plotted for all the tested columns. The outcomes demonstrated that the design principles were met well. Plots of load-moment interaction diagrams for short, slender columns prepared using the ACI-318-19 equations, 2nd order effect method, and proposed method. The experimental findings were added to the interaction diagrams for comparison.

Finally, it needs to be mentioned that recycling concrete waste cubes and other recycled materials decreases the amount of waste sent to landfills. This practice helps avoid the consumption of natural resources, thereby preventing their quick depletion and cutting down on the expenses and distractions linked to their extraction. Utilizing sustainable materials and creating a new pathway for their reuse, such as incorporating recycled aggregates, can reduce waste and conserve natural resources. 

The world's population has exponentially grown, which has an effect on usage of vehicles by individuals and leads to an increase in the number of cars in urbans. With the direct relationship between population and car usage, traffic management has become an important issue to be solved. For this purpose, an intelligent traffic signaling with a rapid urbanization is required to overcome the traffic congestions, and reduce cost and time of traveling. To overcome these problems, emerging computer vision and deep learning are vital candidates to handle this issue because they take an important role for managing and controlling traffic signals with great success. Nevertheless, detecting and distinguishing between objects are helpful for counting vehicles and other objects which avoid crowds and controlling signals in the traffic areas. Besides, detecting emergency vehicles and giving the priority to them is required for intelligent traffic signaling system.

The main objective of this study is to design and implement an efficient system for traffic signal systems based on custom vehicle detection.  Furthermore, the proposed system involves four phases; the first one is capturing images from both simulated and real time cameras from the roads. In the second phase, different image preprocessing algorithms are performed to the captured images as a pre-processing step. In addition, the deep learning techniques are applied to detect objects such as (regular car, police car, ambulance, and firefighter, etc..). In the last phase, the proposed system is tested to evaluate the performance accuracy of the detected vehicles.

A modified transfer learning approach has been applied to the DenseNet201 model for multiple classifications, including non-emergency cars, ambulances, police, and firefighters. The approach involves freezing the architecture of the model's layers. A high accuracy rate is obtained with this model and reaches 98.6%. Also, various optimization methods, including (Adam, Adamax, Nadam, and RMSprob) are used to improve the detection performance based on the best optimizer selection and yielded an accuracy of 98.84%. In addition, a modified version of YOLOv5 was proposed for vehicle detection, which aims to enhance the mean average precision (mAP) detection by 3%. Finally, the proposed system was simulated to reduce the waiting time at traffic signal. The experimental results demonstrate a significant reduction in waiting time, ranging from 30 to 100 seconds depending on the status.