Unmanned Aerial Vehicle (UAV) as data acquisition tools are becoming more affordable for many civil engineering applications. However, the accuracy of the output is influenced by many parameters. The main objective of this study was to investigate the effect of flight altitude toward the final output measurement accuracy without using Ground Control Point (GCP). Altitude is a parameter that is very important in flying UAV that has to be taken into consideration. Notably, the flight altitude depends on the ground condition, surrounding obstruction, Ground Sample Distance (GSD) and camera monitoring. The UAV should fly in a lower condition when GSD is better. However, this approach rarely can succeed because different site conditions such as flat terrain nor hilly terrain required different flight planning. Therefore, a field experiment will be carried out to investigate the optimum flight altitude to obtain acceptable accuracy of orthomap at hilly type of terrain. This study evaluates both the qualitative of the image and the quantitative aspect of the orthomap. The actual measurement of selected features was made and compared with the on screen measurement. An orthophoto will be generated by using Pix4Dmapper on a selected slope of the hilly terrain in UPNM Campus. Based on the results, different accuracy obtain on flat surface is 0.14% and slope surface is 2.77%, which needed further study to identify the method to reduce error. It is found that the accuracy without GCP is not having large error of more than 1% for flat area. Due to distortion of image on slope surface, the error is larger and needed GCP calibration. This study shows that UAV is a feasible platform for mapping of small area with acceptable accuracy.
Road surface condition of a pavement is one of the most important features as it affect driving comfort and safety. A good road surface condition could reduce the risk of traffic accidents and injuries. Pavement Condition Index (PCI) is one of the important tools to measure the pavement performance. By conducting pavement evaluation, civil engineers could prioritize the maintenance and rehabilitation which usually incurred a huge cost. In University Pertahanan Nasional Malaysia (UPNM), there was no proper maintenance and rehabilitation scheduled for the roads as no performance evaluation tool available to measure the pavement condition. Thus, the objective of this study was to develop a Composite Pavement Performance Index (CPPI) to monitor the pavement condition and to rank the roads in UPNM. To develop the CPPI, road defects data were collected from 6 internal roads in UPNM. From the data collected, 4 major distresses were identified: longitudinal cracking, crocodile cracking, potholes and ravelling were found more likely to affect the pavement’s condition in UPNM. By measuring the growth of the distresses over a period of 6 months, modelling was conducted using simple linear regression. The growth of the distresses were compared, and odds ratios were computed to calculate the weightage of each distress for the determination of the CPPI value. The CPPI value developed could be used to rank the roads in UPNM. This study demonstrated that the road connecting to the library building in UPNM experienced the worst pavement deterioration with a PCI of 24 or a CPPI value of 1.1915. The level of severity was classified as “SERIOUS” in accordance to ASTM D6433. This road was recommended for reconstruction to increase the comfort and safety for road users