The compressive strength of concrete is one of most important mechanical parameters in the performance assessment of existing reinforced concrete structures.According to various international codes,core samples are dr...The compressive strength of concrete is one of most important mechanical parameters in the performance assessment of existing reinforced concrete structures.According to various international codes,core samples are drilled and tested to obtain the concrete compressive strengths.Non-destructive testing is an important alternative when destructive testing is not feasible without damaging the structure.The commonly used non-destructive testing(NDT)methods to estimate the in-situ values include the Rebound hammer test and the Ultrasonic Pulse Velocity test.The poor reliability of these tests due to different aspects could be partially contrasted by using both methods together,as proposed.in the SonReb method.There are three techniques that are commonly used to predict the compressive strength of concrete based on the SonReb measurements:computational modeling,artificial intelligence,and parametric multi-variable regression models.In a previous study the accuracy of the correlation formulas deduced from the last technique has been investigated in comparison with the effective compressive strengths based on destructive test results on core drilled in adjacent locations.The aim of this study is to verify the accuracy of Artificial Neural Approach comparing the estimated compressive strengths based on NDT measured parameters with the same effective compressive strengths.The comparisons show the best performance of ANN approach.展开更多
Ultrasonic pulse velocity (UPV) and rebound hammer (RH) tests are often used for assessing the quality of concrete and estimation of its compressive strength. Several parameters influence this property of concrete as ...Ultrasonic pulse velocity (UPV) and rebound hammer (RH) tests are often used for assessing the quality of concrete and estimation of its compressive strength. Several parameters influence this property of concrete as the type and size of aggregates, cement content, the implementation of concrete, etc. To account for these factors, both of the two tests are combined and their measurements are calibrated with the results of mechanical tests on cylindrical specimens cast on site and on cores taken from the existing structure in work progress at the new-city Massinissa El-Khroub Constantine in Algeria. In this study;the two tests cited above have been used to determine the concrete quality by applying regression analysis models between compressive strength of in situ concrete on existing structure and the nondestructive tests values, the combined method is used, equations are derived using statistical analysis (simple and multiple regression) to estimate compressive strength of concrete on site and the reliability of the technique for prediction of the strength is discussed for this case study.展开更多
文摘The compressive strength of concrete is one of most important mechanical parameters in the performance assessment of existing reinforced concrete structures.According to various international codes,core samples are drilled and tested to obtain the concrete compressive strengths.Non-destructive testing is an important alternative when destructive testing is not feasible without damaging the structure.The commonly used non-destructive testing(NDT)methods to estimate the in-situ values include the Rebound hammer test and the Ultrasonic Pulse Velocity test.The poor reliability of these tests due to different aspects could be partially contrasted by using both methods together,as proposed.in the SonReb method.There are three techniques that are commonly used to predict the compressive strength of concrete based on the SonReb measurements:computational modeling,artificial intelligence,and parametric multi-variable regression models.In a previous study the accuracy of the correlation formulas deduced from the last technique has been investigated in comparison with the effective compressive strengths based on destructive test results on core drilled in adjacent locations.The aim of this study is to verify the accuracy of Artificial Neural Approach comparing the estimated compressive strengths based on NDT measured parameters with the same effective compressive strengths.The comparisons show the best performance of ANN approach.
文摘Ultrasonic pulse velocity (UPV) and rebound hammer (RH) tests are often used for assessing the quality of concrete and estimation of its compressive strength. Several parameters influence this property of concrete as the type and size of aggregates, cement content, the implementation of concrete, etc. To account for these factors, both of the two tests are combined and their measurements are calibrated with the results of mechanical tests on cylindrical specimens cast on site and on cores taken from the existing structure in work progress at the new-city Massinissa El-Khroub Constantine in Algeria. In this study;the two tests cited above have been used to determine the concrete quality by applying regression analysis models between compressive strength of in situ concrete on existing structure and the nondestructive tests values, the combined method is used, equations are derived using statistical analysis (simple and multiple regression) to estimate compressive strength of concrete on site and the reliability of the technique for prediction of the strength is discussed for this case study.
