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AbstractThere is no doubt that the tremendous development of information technology was one of the driving factors behind the great growth of surveying and geodesy science. This has spawned modern geospatial techniques for data capturing, acquisition, and visualization tools. Digital elevation model (DEM) is the 3D depiction of continuous elevation data over the Earth’s surface that is produced through many procedures such as remote sensing, photogrammetry, and land surveying. DEMs are essential for various surveying and civil engineering applications to generate topographic maps for construction projects at a scale that varies from 1:500 to 1:2,000. GIS offers a powerful tool to create a DEM with high resolution from accurate land survey measurements using interpolation methods. The aim of this research is to investigate the impact of estimation techniques on generating a reliable and accurate DEM suitable for large-scale mapping. As a part of this study, the deterministic interpolation algorithms such as ANUDEM (Topo to Raster), inverse distance weighted (IDW), and triangulated irregular network (TIN) were tested using the ArcGIS desktop for elevation data obtained from real total station readings, with different landforms to show the effect of terrain roughness, data density, and interpolation process on DEM accuracy. Furthermore, comparison and validation of each interpolator were carried out through the cross-validation method and numerous graphical representations of the DEM. Finally, the results of the investigations showed that ANUDEM and TIN models are similar and significantly better than those attained from IDW.

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In this study, a multi-linear regression model for potential fishing zone (PFZ) mapping along the Saudi Arabian Red Sea coasts of Yanbu’ al Bahr and Jeddah was developed, using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data derived parameters, such as sea surface salinity (SSS), sea surface temperature (SST), and chlorophyll-a (Chl-a). MODIS data was also used to validate the model. The model expanded on previous models by taking seasonal variances in PFZs into account, examining the impact of the summer, winter, monsoon, and inter-monsoon season on the selected oceanographic parameters in order to gain a deeper understanding of fish aggregation patterns. MODIS images were used to effectively extract SSS, SST, and Chl-a data for PFZ mapping. MODIS data were then used to perform multiple linear regression analysis in order to generate SSS, SST, and Chl-a estimates, with the estimates validated against in-situ data obtained from field visits completed at the time of the satellite passes. The proposed model demonstrates high potential for use in the Red Sea region, with a high level of congruence found between mapped PFZ areas and fish catch data (R2 = 0.91). Based on the results of this research, it is suggested that the proposed PFZ model is used to support fisheries in determining high potential fishing zones, allowing large areas of the Red Sea to be utilized over a short period. The proposed PFZ model can contribute significantly to the understanding of seasonal fishing activity and support the efficient, effective, and responsible use of resources within the fishing industry.

The palm oil industry generates a significant amount of wastes which their managing has been a major environmental concern in producing countries. The utilization of these wastes as an aggregate source for concrete production will help to sanitize the environment and provides a cheaper and renewable aggregates source for construction industries. This paper presents the results of the experimental program conducted on fly-ash based geopolymer concrete containing Palm Oil Clinker Aggregate (POCA). Several geopolymer concrete mixes were prepared in which POC was used as a replacement to both fine and coarse aggregates at different percentages starting from 25% to 100%. Mix proportioning was done in accordance with ACI 211.1-91. Geopolymer concrete specimens were cast, cured at ambient conditions and tested for the slump, density, water absorption and compressive, shear and flexural strengths. Overall, the use of fly ashbased geopolymer binder and POCA can enhance the sustainability aspects in concrete production as well as produce a high strength concrete. A concrete mixture containing 100% POCA can produce a structural lightweight concrete having a compressive strength of more than 30 MPa and a density of 1821 kg/m 3 . The use of a geopolymer binder promotes the workability and strength of POCA concrete and reduces its water absorbability. Incorporation of POCA up to 75% did not much change the structural efficiency of the produced concrete, while it was reduced by 32% when the POCA fully replaced the natural aggregate. Nevertheless, the benefits in terms of cost, energy, and environmental savings cannot be overlooked.

The development of geospatial technologies has opened a new era in terms of data collection techniques and analysis procedures. Digital elevation models as 3D visualization of the Earth’s surface have many mapping and spatial analysis applications. The primary terrain factors derived from the raster dataset are usually less critical than secondary ones, e.g., ruggedness index, which plays a vital role in engineering, hydrological information derivation, and geomorphological processes. Surface ruggedness is a significant predictor of topographic heterogeneity by calculating the absolute value of elevation differences within a specified neighborhood surrounding a central pixel. The current study investigates the impacts of various topographic metrics obtained from a digital elevation model on characterizing terrain ruggedness utilizing stepwise principal component analysis. This popular multivariate statistical technique is applied to conduct a comprehensive assessment and treat the information redundancy of terrain parameters. Simultaneously, the standard deviation of elevation is also proposed as an alternative approach to quantifying topographic ruggedness. Besides, quantitative and qualitative method is espoused to validate the algorithms and compare their capabilities to the previously introduced models in the literature. The findings have shown that principal component analysis provides superior performance against other models. Furthermore, they indicated that the standard deviation of elevation could be used instead of the available ones.

Concrete is indeed one of the most consumed construction materials all over the world. In spite of that, its behavior towards absolute volume change is still faced with uncertainties in terms of chemical and physical reactions at different stages of its life span, starting from the early time of hydration process, which depends on various factors including water/cement ratio, concrete proportioning and surrounding environmental conditions. This interest in understanding and defining the different types of shrinkage and the factors impacting each one is driven by the importance of these volumetric variations in determining the concrete permeability, which ultimately controls its durability. Many studies have shown that the total prevention of concrete from undergoing shrinkage is impractical. However, different practices have been used to control various types of shrinkage in concrete and limit its magnitude. This paper provides a detailed review of the major and latest findings regarding concrete shrinkage types, influencing parameters, and their impacts on concrete properties. Also, it discusses the efficiency of the available chemical and mineral admixtures in controlling the shrinkage of concrete.