The Experts below are selected from a list of 15 Experts worldwide ranked by ideXlab platform
Yuan Gao - One of the best experts on this subject based on the ideXlab platform.
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strength prediction based on Ultrasonic Property of fractal gangue cemented rockfill reinforced by carbon nanotubes
Construction and Building Materials, 2021Co-Authors: Qian Yin, Hongwen Jing, Yuan GaoAbstract:Abstract Producing cemented rockfill for supplying building material and underground filling material is currently the most effective approach to recycle solid mineral waste. Investigating its Ultrasonic Property and constructing a model for predicting its strength are of great significance to its on-site assessment and engineering stability. Ultrasonic detection test was carried out on the fractal gangue cemented rockfill reinforced by carbon nanotubes (CNTs) to study the effects of the curing time, CNT dosage and aggregate size distribution on its Ultrasonic Property. The relation between Ultrasonic pulse velocity (UPV) and uniaxial compressive strength (UCS) was established through uniaxial compression test, and the model I for predicting the strength of cemented rockfill was built based on this relationship. The coupling relationship between the curing time, CNT dosage, particle size distribution (PSD) fractal dimension of aggregates, UPV and the UCS of cemented rockfill was established by genetic algorithm (GA), and the strength prediction model II was constructed based on this coupling relationship. The difference between both prediction models and the matching degree between predictive strength and experimental data were discussed, a more accurate method for predicting the strength of cemented backfill was proposed. The results show that the relationship between CNT dosage and UPV can be described by the quadratic function, it believes that the optimal CNT dosage is between 0.05% and 0.10% for cemented rockfill. The relationship between PSD fractal dimension and UPV can be characterized by the quadratic function, it considers that the optimal PSD fractal dimension is in the range of 2.4150 and 2.6084 for the aggregates in cemented rockfill, and this optimal aggregate size distribution is easily affected by other conditions. The exponential function σ 1 c = ξ 10 e ξ 11 v - ξ 10 can be used to describe the relationship between UPV and UCS, the prediction model I based on this relationship for predicting the strength of cemented rockfill has the determination coefficient of 0.9164 matching with the experimental data. The prediction model II based on the coupling relationship combining material parameters with UPV correction by using GA performs the reliable prediction accuracy with 4.5223% of mean absolute percentage error (MAPE) and 0.9513 of determination coefficient better than model I.
Qian Yin - One of the best experts on this subject based on the ideXlab platform.
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strength prediction based on Ultrasonic Property of fractal gangue cemented rockfill reinforced by carbon nanotubes
Construction and Building Materials, 2021Co-Authors: Qian Yin, Hongwen Jing, Yuan GaoAbstract:Abstract Producing cemented rockfill for supplying building material and underground filling material is currently the most effective approach to recycle solid mineral waste. Investigating its Ultrasonic Property and constructing a model for predicting its strength are of great significance to its on-site assessment and engineering stability. Ultrasonic detection test was carried out on the fractal gangue cemented rockfill reinforced by carbon nanotubes (CNTs) to study the effects of the curing time, CNT dosage and aggregate size distribution on its Ultrasonic Property. The relation between Ultrasonic pulse velocity (UPV) and uniaxial compressive strength (UCS) was established through uniaxial compression test, and the model I for predicting the strength of cemented rockfill was built based on this relationship. The coupling relationship between the curing time, CNT dosage, particle size distribution (PSD) fractal dimension of aggregates, UPV and the UCS of cemented rockfill was established by genetic algorithm (GA), and the strength prediction model II was constructed based on this coupling relationship. The difference between both prediction models and the matching degree between predictive strength and experimental data were discussed, a more accurate method for predicting the strength of cemented backfill was proposed. The results show that the relationship between CNT dosage and UPV can be described by the quadratic function, it believes that the optimal CNT dosage is between 0.05% and 0.10% for cemented rockfill. The relationship between PSD fractal dimension and UPV can be characterized by the quadratic function, it considers that the optimal PSD fractal dimension is in the range of 2.4150 and 2.6084 for the aggregates in cemented rockfill, and this optimal aggregate size distribution is easily affected by other conditions. The exponential function σ 1 c = ξ 10 e ξ 11 v - ξ 10 can be used to describe the relationship between UPV and UCS, the prediction model I based on this relationship for predicting the strength of cemented rockfill has the determination coefficient of 0.9164 matching with the experimental data. The prediction model II based on the coupling relationship combining material parameters with UPV correction by using GA performs the reliable prediction accuracy with 4.5223% of mean absolute percentage error (MAPE) and 0.9513 of determination coefficient better than model I.
Hongwen Jing - One of the best experts on this subject based on the ideXlab platform.
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strength prediction based on Ultrasonic Property of fractal gangue cemented rockfill reinforced by carbon nanotubes
Construction and Building Materials, 2021Co-Authors: Qian Yin, Hongwen Jing, Yuan GaoAbstract:Abstract Producing cemented rockfill for supplying building material and underground filling material is currently the most effective approach to recycle solid mineral waste. Investigating its Ultrasonic Property and constructing a model for predicting its strength are of great significance to its on-site assessment and engineering stability. Ultrasonic detection test was carried out on the fractal gangue cemented rockfill reinforced by carbon nanotubes (CNTs) to study the effects of the curing time, CNT dosage and aggregate size distribution on its Ultrasonic Property. The relation between Ultrasonic pulse velocity (UPV) and uniaxial compressive strength (UCS) was established through uniaxial compression test, and the model I for predicting the strength of cemented rockfill was built based on this relationship. The coupling relationship between the curing time, CNT dosage, particle size distribution (PSD) fractal dimension of aggregates, UPV and the UCS of cemented rockfill was established by genetic algorithm (GA), and the strength prediction model II was constructed based on this coupling relationship. The difference between both prediction models and the matching degree between predictive strength and experimental data were discussed, a more accurate method for predicting the strength of cemented backfill was proposed. The results show that the relationship between CNT dosage and UPV can be described by the quadratic function, it believes that the optimal CNT dosage is between 0.05% and 0.10% for cemented rockfill. The relationship between PSD fractal dimension and UPV can be characterized by the quadratic function, it considers that the optimal PSD fractal dimension is in the range of 2.4150 and 2.6084 for the aggregates in cemented rockfill, and this optimal aggregate size distribution is easily affected by other conditions. The exponential function σ 1 c = ξ 10 e ξ 11 v - ξ 10 can be used to describe the relationship between UPV and UCS, the prediction model I based on this relationship for predicting the strength of cemented rockfill has the determination coefficient of 0.9164 matching with the experimental data. The prediction model II based on the coupling relationship combining material parameters with UPV correction by using GA performs the reliable prediction accuracy with 4.5223% of mean absolute percentage error (MAPE) and 0.9513 of determination coefficient better than model I.
R B Thompson - One of the best experts on this subject based on the ideXlab platform.
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a study of Ultrasonic Property variations within jet engine nickel alloy billets
Quantitative Nondestructive Evaluation, 2002Co-Authors: P Haldipur, F J Margetan, R B ThompsonAbstract:A summary is presented of an ongoing project to measure the UT properties of jet-engine nickel alloy billets and to correlate their properties with the local billet microstructure. To date, measurements have been performed on four “strip” coupons cut from three different Nickel alloy billets (IN718 and Waspaloy). Longitudinal-wave velocities, attenuation, backscattered noise capacity (FOM) have been measured at selected sites for two propagation directions. The UT results are consistent with equiaxed microstructures in which the mean grain diameter varies with radial depth. The grain diameter at selected sites is determined from detailed metallographic studies and compared with that estimated from the measured attenuation.
P Haldipur - One of the best experts on this subject based on the ideXlab platform.
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a study of Ultrasonic Property variations within jet engine nickel alloy billets
Quantitative Nondestructive Evaluation, 2002Co-Authors: P Haldipur, F J Margetan, R B ThompsonAbstract:A summary is presented of an ongoing project to measure the UT properties of jet-engine nickel alloy billets and to correlate their properties with the local billet microstructure. To date, measurements have been performed on four “strip” coupons cut from three different Nickel alloy billets (IN718 and Waspaloy). Longitudinal-wave velocities, attenuation, backscattered noise capacity (FOM) have been measured at selected sites for two propagation directions. The UT results are consistent with equiaxed microstructures in which the mean grain diameter varies with radial depth. The grain diameter at selected sites is determined from detailed metallographic studies and compared with that estimated from the measured attenuation.
