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Abstract
The Jordan Museum is the most important modern archeological museum in Jordan. This paper assesses the quality of design and the visitors' satisfaction with the Jordan Museum. Also, it investigates which of the selected performance elements contribute most to visitors' satisfaction. Investigative Post-Occupancy Evaluation (POE) was used within a mixed methodology approach. A questionnaire was used for the quantitative part, complemented by other qualitative methods. Descriptive statistics were used to determine the design quality and visitors' satisfaction. Stepwise regression was used to detect which performance elements predict overall visitors' satisfaction. The design quality of the museum was rated as good, and its permanent galleries were exceptionally rated as excellent. The visitors were generally satisfied with the museum. Visitors were satisfied with the interior and exterior finish but were not impressed with the heavy use of stone in the façade. The simplicity of the museum layout made navigation easy, and visitors were satisfied with wayfinding. This paper is the first to use POE to assess the Jordan Museum. The findings can be used to enhance the management and operation of the Jordan Museum. Also, important performance elements can be developed into guidelines to guarantee effective museum designs in the future.
Abstract
This study explores the impact of corrosion on Ground Penetrating Radar (GPR) responses through practical experiments and numerical modelling, focusing on rebar diameter reduction, corrosion product layer thickness, crack formation and corrosion product filling in vertical and transverse crack. Practical experiments involved GPR testing of reinforced concrete slab. By analyzing B-scans we identify areas with moderate and severe corrosion. Numerical modelling using the Finite Difference Time Domain (FDTD) Method to model GPR signal propagation in a concrete bridge deck with corrosion is applied. Key finding includes a significant 26.70% increase in reflected wave amplitude when corrosion product filling in vertical crack increased by 400%, highlighting its extensive effect on signal GPR propagation. Reduced rebar diameter led to a 9.79% amplitude decrease and a 0.06 ns arrival time delay. Increased corrosion product layer thickness primarily affected arrival time with a 0.06 ns extension but significantly amplified GPR signal amplitude. These findings offer insights for improving GPR based corrosion detection and assessment methods, leading to more robust systems for concrete bridge deck inspection and maintenance. This paper contributes to understanding how corrosion affects the signal that is detected by GPR. This information can be used to improve the way that we manage and assess corrosion in concrete bridge deck.
Abstract
Direct current (DC) motors have superior features such as operating at different speeds, being affordable and easily controllable. Therefore, DC motors have many uses, such as machine tools and robotic systems in many factories up to the textile industry. The PID controller is one of the most common methods used to control DC motors. PID is a feedback controller with the terms Proportional, Integral, and Derivative. The proper selection of P, I, and D parameters is critical for achieving the desired control in the PID controller. In this study, the transfer function of a DC motor is first obtained, and the speed of the DC motor is controlled by the PID controller using this transfer function. Then, Particle Swarm Optimization (PSO), an optimization method based on swarm intelligence, is used to adjust the P, I, and D parameters. By using the obtained P, I, and D coefficients, the speed of the DC motor is tried to be controlled, and the effect of the filter coefficient on the system output is examined. The performance of the proposed PSO-PID controller with successful results is given in tables and graphics. Control and optimization studies are carried out with MATLAB Simulink.
Abstract
Green wall systems have been introduced all over the world as a sustainable solution to combat the hot environment inside buildings and provide thermal comfort by improving the thermal efficiency of the buildings. This study aims to find out whether green walls can be used to manage the inside thermal conditions of Aqaba buildings. It is intended to lessen the impact of Aqaba's harsh warm climate on internal building spaces and achieve a thermal comfort level. A physical live experiment was used to detect the thermal impact of green walls on internal spaces. The thermal performances of two identical real-scale test rooms, one of which had a fixed green facade, were compared. This study concludes that green facades have a significant potential to promote buildings' thermal behaviour in the hot summer of Aqaba and thermally similar regions.
Abstract
The compressive behavior of self-consolidating concrete columns strengthened by hybrid confinement of polypropylene fiber rope and glass fiber-reinforced polymer sheet was investigated experimentally. We cast and tested eight column specimens under axial compression load. (Six confined SCC columns and two conventional SCC columns.) The concrete grade utilized is SCC M40. For reinforcement, the SCC columns are enclosed with GFRP wrap and polypropylene fiber rope in various volumetric ratios. The compressive resistance of a confined SCC column, strength enhancement, stress-strain relationship, ductility ratio, and load deflection relationship are the parameters studied. The results are compared to establish the adequacy of the confinement. The wrapping of GFRP increases the load-carrying capacity and modulus of elasticity.
Abstract
Whenever a new material is replaced in concrete, some other tests other than strength and durability need to be carried out to validate the viability of the material replaced. This study aims to investigate the sustaining capacity of the light weight concrete manufactured with compost and Ground Granulated Blast Furnace Slag (GGBS) for M sand and cement respectively subjected to high temperature. Four concrete samples are tested, which includes the control specimen and three specimens are opted based on the optimum mix arrived from the strength and durability studies. Thermogravimetric Analysis (TGA) is done on the samples and they are heated up to 1,000 °C. For the specimens tested, the loss in mass with respect to the temperature is obtained. It is noted that the mass loss of the concrete samples with 15% GGBS along with compost at 0 & 10% is found lower than the control specimen. Also, from the loss in mass, the loss of chemically bound water and free CH content can be found, which aids in contributing strength to the concrete. For the concrete to be sustainable, compost can be replaced at 10% and GGBS at 15%.
Abstract
This paper aims to investigate the effect of liquid heterogeneity on sloshing in a two-dimensional rectangular tank. The container is excited horizontally, and the density of the liquid at equilibrium can almost be considered a linear dependence of the depth. The linearized equations representing the sloshing phenomenon are solved numerically using ANSYS 2020 R2 software and analytically using the separation method of variables in conjunction with the Fourier analysis. On the other hand, a comparison study has been carried out between the obtained results and other works related to the same phenomenon. Overall, the results exhibit considerable effects of variation in the Heterogeneity Coefficient β on the free surface's motion and the pressure distribution within the liquid. Furthermore, the free surface of liquid heterogeneity is also affected by the variation of Excitation Frequency Ω and the Filling Rate h of the liquid in the tank.
Abstract
The redundant manipulators have more DOFs (degree of freedoms) than it requires to perform specified task. The inverse kinematic (IK) of such robots are complex and high nonlinear with multiple solutions and singularities. As such, modern Artificial Intelligence (AI) techniques have been used to address these problems. This study proposed two AI techniques based on Neural Network Genetic Algorithm (NNGA) and Particle Swarm Optimization (PSO) algorithm to solve the inverse kinematics (IK) problem of 3DOF redundant robot arm. Firstly, the forward kinematics for 3 DOF redundant manipulator has been established. Secondly, the proposed schemes based on NNGA and PSO algorithm have been presented and discussed for solving the inverse kinematics of the suggested robot. Thirdly, numerical simulations have been implemented to verify the effectiveness of the proposed methods. Three scenarios based on triangle, circular, and sine-wave trajectories have been used to evaluate the performances of the proposed techniques in terms of accuracy measure. A comparison study in performance has been conducted and the simulated results showed that the PSO algorithm gives 7% improvement compared to NNGA technique for triangle trajectory, while 2% improvement has been achieved by the PSO algorithm for circular and sine-wave trajectories.
Abstract
Various developing countries are confronted with serious environmental difficulties due to excessive resource utilization and insufficient waste management system. In particular, construction and demolition waste poses a grave threat to the environment, contributing to escalating energy consumption, the depletion of landfill capacities, and the generation of harmful noise and dust pollution. Consequently, the research community is tasked with the daunting challenge of devising effective strategies to incorporate this waste material in producing concrete, without compromising the critical strength and durability characteristics. The investigation aims to attain the aforementioned objective by examining the effects of using recycled aggregates as a distinct partial replacement of 0%, 5%, 10%, 15%, and 20% on the compressive and split tensile strength traits, contingent upon 7 and 28 days of age of curing. Experimental test results show that the optimal concrete production is achieved when 10% of coarse aggregate is replaced with recycled aggregate, maintaining 98% of the materials compressive and split tensile strength. To further validate the obtained experimental data, model equations were derived through regression analysis and the framed model equation is further assessed for accuracy using error analysis. In this study, a MATLAB program was utilized for prediction of compressive and split tensile strength with five distinct network types and the Levenberg-Marquardt algorithm is used for optimization. A comparative analysis was conducted between the regression analysis values and the performance of the ANN modelling. The findings demonstrate that the Artificial Neural Network (ANN) serves as a highly effective model, offering significantly improved accuracy in predicting the optimal correlation between compressive strength and split tensile strength of concrete.
