UGBOWO CAMPUS

In Nigeria, road construction is often followed by neglect, with little or no provision for maintenance. This accelerates pavement deterioration due to factors such as rain fall, traffic loads, poor drainage, and harsh environmental conditions. This study aims to evaluate the condition of a 0.25 km road connecting Ethiope River Road and the University of Benin Back Gate (Ugbowo Campus). The study involved a field survey to document visible deterioration, followed by an assessment using the Pavement Condition Index (PCI) and Pavement Condition Rating (PCR) methods. The procedure adhered to ASTM D6433-07 standards, with the roadway divided into 5 sample units. Each unit was inspected for distress type, severity, and extent, and PCI values were calculated and categorized from “Failed” to “Excellent.” The results were analyzed and mapped using ArcGIS software for spatial visualization. The calculated PCI values for the five sample units, S1, S2, S3, S4 and S5 were 75, 49, 62, 55, and 32 respectively. These values correspond to pavement condition ratings (PCR) spanning from Satisfactory to Very Poor. The overall PCI for the road section was determined by computing the average of the individual unit PCI values, yielding an overall PCI of 54.6 for the Ekosodin Road, which classifies the pavement as being in a poor condition according to ASTM D6433-07 standards. The GIS-based spatial analysis revealed a concentration of severe pavement distresses toward the mid and lower portions of the road, suggesting localized structural and drainage-related problems. The results (Overall PCI of 54.6 and the digitized road section) indicates that while the pavement remains marginally serviceable, it requires urgent maintenance and partial rehabilitation to restore its functionality and prevent further deterioration. The combined use of PCI, PCR, and GIS tools proved to be a reliable and effective approach for comprehensive pavement condition assessment and should be adopted for routine pavement management within the University of Benin and similar environments

This study investigates the influence of satellite constellation configurations on the positional accuracy of post-processed static GNSS data at the University of Benin, Ugbowo Campus. Static observations were collected at five known control points using a Tersus David30 receiver. Data was processed in Tersus Geomatics Office across seven constellation setups: GPS-only, GLONASS-only, BEIDOU-only, and their combinations. A detailed epoch-based analysis was also conducted at one control point using RTKLIB. Accuracy was assessed using coordinate residuals (∆E, ∆N, ∆H), RMSE, standard deviation, CEP, and 2DRMS, supplemented by classical
and robust statistics and time-series analysis. Results demonstrated that GPS-based solutions consistently delivered superior performance. The GPS+BEIDOU combination achieved the best accuracy (2DRMS = 0.160 m, CEP = 0.067 m), closely followed by GPS-only. In contrast, BEIDOU-only yielded the poorest results (2DRMS = 0.587 m), while GLONASS-only was notably weak and unstable. RTKLIB processing confirmed that multi-constellation setups, particularly GPS+GLONASS+BEIDOU, produced highly precise solutions with sub-centimeter standard deviations. Conversely, the GLONASS-only solution exhibited severe instability, with significant errors and outliers. Time-series analysis revealed that stable constellations maintained narrow error bands, while error spikes in other configurations corresponded directly to drops in satellite visibility