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Indonesian people traditionally use Gigantochloa apus bamboo as structural materials. For modern building design, structural grading should be conducted on bamboo culm to get its design characteristic values. Non-destructive assesment on each culm condition, dimension, and geometric were conducted to predict the strength and capacity. Wall density ( ρ w ) and Modulus of Elasticity measured by fixed load deflection using Panter machine ( E p ) have moderate correlation with apparent and true modulus of elasticity ( E app , E true ) and Modulus of Rupture (MOR). Additional predictors in multiple regression improved the adj- R 2 , but it was not reliable enough for estimating E app , E true , and MOR of G. apus culm, thus bamboo structural grading should better use capacity grading rather than strength grading. Diameter ( D ) is a potential grading parameter that provides strong evidence to predict flexural capacity. Non destructive test which deals with measuring linear mass ratio ( q ) is a simple method that refers to dimension and density, and it has strong correlation with flexural rigidity ( EI app , EI true ) and capacity ( M max ). The measurements of both D and q are proper indicating variables for predicting the capacity of G. apus. The grade of structural bamboo could be classified by D , q, and combination of both measurement based on ISO 22156 and confident band method. Confident band method resulted in a more conventional value than that from ISO 22156, thus it proved to be safer and more reliable. The combination of linear mass and square of diameter ( qD 2 ) was the best predictor for estimating the stiffness, while qD was the best one for estimating M max . In capacity grading, additional measurement of eccentricity ( E c ), culm density ( ρ c ) , wall density ( ρ w ) , moisture content ( M c ) , and ovality ( O v ) predictors significantly improved the model rather than a single predictor, but fixed load bending stiffness measured using panter machine ( EI p ), taper ( t a ), and out of straightness ( s o ) addition into the model did not give significant contribution. [ABSTRACT FROM AUTHOR]