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
Basic magnesium sulfate cement coral aggregate concrete(MCAC)is a new type of concrete consisting of basic magnesium sulfate cement,coarse coral aggregate,coral reef sand and seawater.The rebound hammer(RH),the ultras...Basic magnesium sulfate cement coral aggregate concrete(MCAC)is a new type of concrete consisting of basic magnesium sulfate cement,coarse coral aggregate,coral reef sand and seawater.The rebound hammer(RH),the ultrasonic pulse velocity(UPV)and the compressive strength(fcu)tests of 14 sets of cube specimens of the MCAC after 28 d of aging were conducted.The impact of the content and length of sisal fiber on the relationship between the fcu-RH and the fcu-UPV was determined.A mathematical model was established to predict the strength of the MCAC using the UPV,RH,and comprehensive UPV/RH methods and to obtain the curves of test strength.The applicability of the test strength curves of ordinary portland concrete(OPC),light-weight aggregate concrete(LAC),and coral aggregate concrete(CAC)to MCAC was assessed.The results showed that the test strength curves of OPC,LAC and CAC were inappropriate to determine the strength of MCAC using non-destructive method.The relative standard error of the curves of test strength of the RH method and the comprehensive method met the specifications,whereas that of the UPV method did not.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
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
The Sonreb and Core (SRC) combined method is proposed to assess the concrete compression strength of mass concrete structures.Artificial neural network is employed together with the SRC combined method to obtain the o...The Sonreb and Core (SRC) combined method is proposed to assess the concrete compression strength of mass concrete structures.Artificial neural network is employed together with the SRC combined method to obtain the optimal core number.The artificial neural network is trained based on data from different testing methods.The procedure of using artificial neural network to assess the concrete strength is described.It proves that the SRC combined method is superior in many aspects and artificial the presented neural network has a high efficiency and reliability.The combined method using artificial intelligence is promising in the strength assessment of mass concrete structures such as the dam,the anchor of the suspension bridge,etc.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.
基金Funded by National Natural Science Foundation of China(Nos.51878350,11832013,52078250)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX21_0236)。
文摘Basic magnesium sulfate cement coral aggregate concrete(MCAC)is a new type of concrete consisting of basic magnesium sulfate cement,coarse coral aggregate,coral reef sand and seawater.The rebound hammer(RH),the ultrasonic pulse velocity(UPV)and the compressive strength(fcu)tests of 14 sets of cube specimens of the MCAC after 28 d of aging were conducted.The impact of the content and length of sisal fiber on the relationship between the fcu-RH and the fcu-UPV was determined.A mathematical model was established to predict the strength of the MCAC using the UPV,RH,and comprehensive UPV/RH methods and to obtain the curves of test strength.The applicability of the test strength curves of ordinary portland concrete(OPC),light-weight aggregate concrete(LAC),and coral aggregate concrete(CAC)to MCAC was assessed.The results showed that the test strength curves of OPC,LAC and CAC were inappropriate to determine the strength of MCAC using non-destructive method.The relative standard error of the curves of test strength of the RH method and the comprehensive method met the specifications,whereas that of the UPV method did not.
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.
基金Sponsored by the Priority Academic Program Development Foundation of Jiangsu Higher Education Institute(Grant No. CE01-3)the NSFC for Outstanding Youth Fund (Grant No. 50725828),the NSFC for Young Scholars (Grant No. 50908046)+1 种基金the Ph. D. Programs Foundation of Ministry of Education of China (Grant No. 200802861012)the Basic Scientific & Research Fund of Southeast University (Grant No. Seucx201106)
文摘The Sonreb and Core (SRC) combined method is proposed to assess the concrete compression strength of mass concrete structures.Artificial neural network is employed together with the SRC combined method to obtain the optimal core number.The artificial neural network is trained based on data from different testing methods.The procedure of using artificial neural network to assess the concrete strength is described.It proves that the SRC combined method is superior in many aspects and artificial the presented neural network has a high efficiency and reliability.The combined method using artificial intelligence is promising in the strength assessment of mass concrete structures such as the dam,the anchor of the suspension bridge,etc.
