Volume estimation and earthworks calculation of borrow pits and roadway constructions are typical applications in civil Engineering. Although several methods for volume estimation were introduced, the average end area method still the common method used by owners and contractors. Average end area method is tedious and time consuming. Volume of terrains that do not have regular geometric structure can be obtained more accurately by using 3D models of surfaces with respect to developing technology such as GIS. The gridding method and point distribution are important factors in modeling earth surfaces used for volume estimation. In this study the credibility of 3D volume estimation based on raster GRID or Triangular Irregular Network (TIN) using GIS was investigated. The effects of interpolation method and point distribution in defining a terrain surface were also investigated. For this purpose, an artificial surface with a known volume that used by Chen and Lin in their paper is employed. The 3D surface and volume are calculated for both surfaces represented by TIN and GRIDs generated by using 6 different interpolation methods. The resultant volumes were compared to the exact volume and to that estimated by using average end area method. Moreover a comparison between cut and fill volumes needed for grading the study cases at a certain elevation was done. The results show that for gentle slope surface, TIN and all interpolation techniques gave results very close to the exact except Kriging and Trend interpolation. For steep slope terrain, Kriging interpolation gave the best results. Comparing earthwork volume to the average end area method, TIN surface, IDW, Topo to raster and Nearest Neighbor methods gave the best results.

The orthometric height has an essential role in a variety of civil engineering projects and it is defined as the length of the curved plumbline from a point (on the earth surface) to its intersection with the geoid surface. Leveling process is considered as the most accurate technique for obtaining these heights. However, regardless of its potentials, it is tedious, costly, and time consuming. Recently many organizations and research centers have developed multi Global Geopotential Models (GGMs) depending on several types of available gravity and height datasets to estimate orthometric heights from GNSS measurements. In this study, we present an evaluation and assessment of the accuracy of five of recent and popular GGMS: XGM2016, XGM2019e, EIGEN-6C4, GO_CONS_GCF_2_TIM_R6e, and EGM2008 using actual 145 GNSS/leveling points and 96 terrestrial gravity points. The goal of this research is to find the best fit model along the study area located along the coastal zones of Egypt with distances of about 1,970 km for further determination of geoid modeling at regional scale. The selection of these areas basically was due to their developmental, urban, and economical importance and their continuous need for protection works to fight against the coastal erosion caused by climate change and global warming. The results indicated that for geoid undulation, GO_CONS_GCF_2_TIM_R6e model is the best fit GGM for the estimation of geoid model along Mediterranean Sea coastal line, while XGM2019e_2159 model is the best suitable for coastal line of the Red Sea. And regarding the gravity anomalies, the most reliable GGMs for this study area are XGM2019e_2159 and EIGEN-6C4 for Bouguer and free-air gravity anomaly, respectively.

Flash flood in urban area strikes mainly the road network. In fact, the streets during flood act as streams or overland flow paths. This jams the traffic, stops the public services, and interrupts the economic activities. Previous studies have treated floods in urban areas as if they were occurring in rural areas. This study presents a new approach that treats the road network as the path of the flash flood water. The new approach uses a 3D city model as the basis for hydrology analysis. This approach regards the building and the streets as part of the terrain that results in water flowing through the streets as it does in reality. The depth of flood water in the streets is calculated and used as a risk factor. Remote Sensing (RS) and Geographical Information System (GIS) technologies are used to obtain and prepare the required input data for the hydraulic model. Various flood scenarios were investigated for different return periods and flood risk code maps for the road network were generated. The obtained results showed that 41.2% of the road network in the study area is under high flood risk from fairly frequent rainfall events, and this percentage reaches 80% to 90% for low frequent flood events (50 years and 100 years flood). The new approach was evaluated by comparing the derived results with actual flood data and had an accuracy of 77%. The results of this study may help decision makers to take the necessary actions to protect people and property.