Previous studies indicate that traditional asphalt mixtures lack the ability to withstand the stresses caused by heavy traffic volumes under high temperatures. To enhance the rutting resistance of flexible pavement under high levels of temperature and loading, extensive laboratory experiments were carried out. A 60/70 grade bitumen was used as a neat sample for comparison. The study introduced three distinct polymers, polypropylene (PP), low-density polyethylene (LDPE), and acrylonitrile butadiene styrene (ABS), at varying concentrations by weight into the neat bitumen. Initially, conventional tests were performed to evaluate the conventional properties of both the neat and modified bitumen, while aggregate tests assessed the mechanical properties of the aggregates. Subsequently, a Marshall mix design was performed to determine the optimum bitumen content (OBC) in the asphalt mixture. Finally, wheel-tracking tests were performed under a specific load and temperature to investigate the rutting behavior of the modified asphalt mixtures. The results of this comprehensive study revealed that the modified asphalt mixtures displayed improved resistance to rutting compared to the neat asphalt mixture. Furthermore, it was also observed that the LDPE exhibited a superior performance against rutting, followed by the PP and ABS. At polymer contents of 3%, 5%, and 7%, the LDPE achieved reductions in rut depth of 13%, 24%, and 33%, respectively, outperforming both PP- and ABS-modified asphalt. These findings not only enhance our understanding of asphalt behavior under diverse conditions but also highlight the potential of plastic-modified asphalt as an effective solution for mitigating rutting problems in road pavements. By incorporating plastic modifiers into asphalt mixtures, this approach aligns with the principles of sustainable construction by reducing plastic waste while improving pavement durability and performance.

This study aims to determine the optimal location for a biodiesel power plant in the municipality of Jazan based on selection criteria by estimating energy consumption and using spatial analysis within the framework of GIS and Structured Query Language (SQL). Additionally, the study will calculate how much electricity each town needs. After Vision 2030, the user can combine the information from remote sensing (RS) and GIS mapping with electricity demand estimation to determine an appropriate location and evaluate the power plant's efficiency. A range of sources, including the Jazan city base map, satellite images, aerial photographs, landuse map, and demographic data, were used to collect descriptive and geographic data as part of the paper's methodology. This data was first entered into the digital model using the ArcGIS 10.4 program, and it was subsequently categorized according to the model's accuracy. Additionally, the data underwent a number of procedures, including scanning printed maps, georeferencing, and on-screen digitizing, to complete the logical design of the digital model. Additionally, the model was spatially assessed in compliance with the requirements for selecting the optimal location for the biodiesel power plant. The criteria and analysis were arranged using Structured Query Language (SQL) and spatial analysis, which determined the best location for a biodiesel power plant station with the greatest ability to model population increase. Mathematical model equations were used to estimate population size, growth rate, and electrical demands (Brauer and Castillo-Chavez in Mathematical models in population biology and epidemiology: Texts in Applied Mathematics, Springer-Verlag, New York USA., 2012). With a total electricity production capacity of 250,167,600 Kilowatt-hour (KWh), the study and investigation efforts concluded that the optimal site for the biodiesel power plant would be in the Dammed municipality. This study is interesting since it is the first to use GIS to determine the ideal location for a power plant station and produce electricity from biodiesel in Jazan Municipalities, KSA.

This study aims to establish a Comprehensive Spatial Data Framework (CSDF) utilizing GIS and road design software to enhance road design activities in Jazan City, aligning with the Kingdom's Vision 2030. The CSDF consists of soil tests, road design, and cost estimation. AutoCAD Civil 3D is employed for road geometric design in accordance with the American Association of State Highway and Transportation Officials (AASHTO) standards and the Saudi code. The CSDF model for the Jazan road network is developed using ArcGIS 10.4. This study focuses on the Alshawajra Road, which connects University Road with Alshawajra Road. The road spans 1000 meters in length and is 28 meters wide. The methodology outlined in this paper consists of four sections: the first discusses the literature review and road design principles; the second emphasizes the survey data required; the third addresses soil collection for testing, including California Bearing Ratio (CBR), Standard Proctor, and sieve analysis. The final section covers the design of the road network and the creation of a geodatabase. The findings indicate that the third version of the proposed design is the most effective, with the total construction cost amounting to 1,233,706.17 Saudi Riyals. Additionally, the CSDF model for Jazan city roads was established, which is vital for the simplicity of data recording and updating, serving as a reference for infrastructure planning and maintenance.