Mopah airport has one runway that is supported by an apron which is located at a distance of 472 m from touch down to the size of 18.560 m2. Apron between the runway with two lines connected to the taxiway. One of the airside facilities Mopah Merauke airport is the runway strip that serves as a countermeasure state of emergency when the air out of the runway failure when landing or taking off. the objective of this study is to redesign the runway strip by evaluating geometric, cut and fill and CBR in Merauke Mopah Airportusing KP. 39 in 2015, KP. 93 The year 2015 and KP. 576 in 2011. The data taken is the carrying capacity of the soil and ground elevation using test equipment Theodolite and Dynamic Cone Parameter. Results of testing the design of the runway strip Merauke Mopah Airport with dimensions of the runway with a length of 2500 m and a width of 45 m with the code type “4.C”, obtained the runway strip width of 150 m plan right side and the left side of 150 m. CBR value of research in getting 9.93% which comply with minimum CBR value that is 6%, and a transverse slope of the runway strip elevation plan by 0.8%, to meet the slope of the runway strip elevation plan of each cross-section the importance of the volume of cut and fill with amounting to 174,205.00 m3 to 3008.80 m3 excavation and embankments.

This study aims to predict the compressive strength of existing concrete without using destructive tests which can damage the surface of the concrete. Destructive testing has the disadvantage of damaging the surface of the concrete, requires a long time and need expensive cost, while the Non Destructive Test (NDT) has the advantage of not damaging the surface of the concrete and faster when combined with the Artificial Neural Network (ANN) method. In this research, the Non Destructive Test (NDT) result such as hammer test and UPV were combined with concrete mix design properties and used to predict the compressive strength of concrete at three and 28 days. The Artificial Neural Network (ANN) method is used to make correlation of mix design properties data and NDT. In this study experimental tests were performed using variation of design parameters such as water per cement ratio and weight ratio of fly ash. The water per cement ratio used in this research was in range 0.45 until 0.55. Furthermore, the weight ratio of fly ash was in range 0% until 25%. Based on the modeling result using ANN method, it found that that the neural network method successfully predicts the compressive strength of concrete at three and 28 days with the mean square error (MSE) value and regression of concrete at three days are5.83 and 0.89 respectively. While at 28 days the MSE and regression value are 4.7 and 0.87 respectively.