Danial Jahed Armaghani - Ph.D Student, University Technology Malaysia, UTM
Mohsen Hajihassani - Ph.D Student, University Technology Malaysia, UTM
Koohyar Faizi - Postgraduate Student, University Technology Malaysia, UTM
Edy Tonnizam Mohammad - Assoc. Prof. Dr. University Technology Malaysia, UTM

Proper determination of Unconfined Compressive Strength (UCS) of rocks is a crucial subject in designof geotechnical structures. Although direct determination of UCS through laboratory test appears to berelatively simple, obtaining proper core segments specifically for weathered rocks is difficult andexpensive. It is well established that UCS can be estimated indirectly using rock index properties. Incomparison to the direct test, indirect prediction of UCS is relatively easier and cheaper. This studyinvolves extensive laboratory tests on 32 datasets of shale and sandstone in various weathering gradesobtained from excavation site in Johor, Malaysia. The laboratory tests include UCS test, BrazilianTensile Strength (BTS) test, Point Load Index Test (Is(50)), P-wave velocity (Vp) test Schmidt HammerRebound Number (Rn) and Dry Density (DD) measurement. The application of Artificial Neural Network(ANN) in UCS prediction is highlighted in this study. For this reason, BTS, Is(50), Vp, Rn and DD wereconsidered as input parameters while the UCS was set to be the output. The ANN results shows thesuperiority of ANN in UCS prediction

Unconfined Compressive Strength UCS, Laboratory Tests, Artificial Neural Network