Construction project schedule delay is a worldwide concern and especially severe in the Ethiopian construction industry. This study developed a Construction Schedule Risk Assessment Model (CSRAM) and a management strategy for foreign general contractors (FGCs). 94 construction projects with schedule delay were collected and a questionnaire survey of 75 domain experts was conducted to systematically select 22 risk factors. In CSRAM, the artificial neural network (ANN) inference model was developed to predict the project schedule delay. Integrating it with the Garson algorithm (GA), the relative weights of risk factors with rankings were calculated and identified. For comparison, the Relative Importance Index (RII) method was also applied to rank the risk factors. Management strategies were developed to improve the three highest-ranked factors identified using the GA (change order, corruption/bribery, and delay in payment), and the RII (poor resource management, corruption/bribery, and delay in material delivery). Moreover, the improvement results were used as inputs for the trained ANN to conduct a sensitivity analysis. The findings of this study indicate that improvements in the factors that considerably affect the construction schedule can significantly reduce construction schedule delays. This study acts as an important reference for FGCs who plan to enter or work in the Ethiopian construction industry.

Accuracy requirements and data sources grow in tandem with advancements in GIS and remote sensing applications. In high advancement of GIS and Remote sensing application, the source of data is another problem going with parallel to them. The significance of geospatial data source is valuable worldwide. Especially some developing countries which don’t have their own satellite for image source have more faced the problem. This problem directly hinders the research that can be conducted by using this data. Ethiopia is one of the countries that countered with problems about this scarcity of geospatial and digital data sources. However, there is abundant of high resolution Google Earth imagery of different area temporal data used as an alternatives of geospatial data source, which requires examining its positional accuracy assessment, for further use as alternative geospatial data sources. In this paper Google Earth image of 2009 and IKONOS 2004 Satellite image of Adama city were tested horizontal positional accuracy level using the Orthophoto high accuracy image. The assessment includes points, lines and polygon samples for positional accuracy testing. For the point samples, Root Mean Square Error (RMSE) in X_ coordinate of Google Earth was 2.66ft/ 0.813m and IKONOS satellite image was 11.263ft/ 3.433m. The Y_coordinate Google Earth was 1.296 ft/0.395m and IKONOS satellite image was 3.806ft/1.160m. The horizontal line shift of Google Earth is 3.541ft/ 1.079m; IKONOS was 4.097m/ 13.442ft. The area difference of Google Earth was 4.219sqft/ 0.392sqm, and the IKONOS image was 13.347sqft/1.24sqm and all these samples are taken considering the slope characteristics of the study area.