The strength curves of lightweight aggregate concrete （LWAC） were tested based on detecting LWAC with density of 1 400-1 900 kg/m3 and LWAC with strength grade of LC15-LC50 by rebound method and ultrasonic-rebound c...The strength curves of lightweight aggregate concrete （LWAC） were tested based on detecting LWAC with density of 1 400-1 900 kg/m3 and LWAC with strength grade of LC15-LC50 by rebound method and ultrasonic-rebound combined method.The results show that the common measured strength curves tested by above two methods can not satisfy the required accuracy of LWAC strength test.In addition,specified compressive strength curves of testing LWAC by rebound method and ultrasonic-rebound combined method are obtained,respectively.展开更多
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
文摘The strength curves of lightweight aggregate concrete （LWAC） were tested based on detecting LWAC with density of 1 400-1 900 kg/m3 and LWAC with strength grade of LC15-LC50 by rebound method and ultrasonic-rebound combined method.The results show that the common measured strength curves tested by above two methods can not satisfy the required accuracy of LWAC strength test.In addition,specified compressive strength curves of testing LWAC by rebound method and ultrasonic-rebound combined method are obtained,respectively.
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.
