The characterization of ultra-soft clayey soil exhibits extreme challenges due to low shear strength of such material.Hence,inspecting the non-destructive electrical impedance behavior of untreated and treated ultra-s...The characterization of ultra-soft clayey soil exhibits extreme challenges due to low shear strength of such material.Hence,inspecting the non-destructive electrical impedance behavior of untreated and treated ultra-soft clayey soils gains more attention.Both shear strength and electrical impedance were measured experimentally for both untreated and treated ultra-soft clayey soils.The shear strength of untreated ultra-soft clayey soil reached 0.17 kPa for 10% bentonite content,while the shear strengths increased to 0.27 kPa and 6.7 kPa for 10% bentonite content treated with 2% lime and 10% polymer,respectively.The electrical impedance of the ultra-soft clayey soil has shown a significant decrease from 1.6 kΩ to 0.607 kΩ when the bentonite content increased from 2% to 10% at a frequency of 300 kHz.The10%lime and 10% polymer treatments have decreased the electrical impedances of ultra-soft clayey soil with 10%bentonite from 0.607 kΩ to 0.12 kΩ and 0.176 kΩ,respectively,at a frequency of 300 kHz.A new mathematical model has been accordingly proposed to model the non-destructive electrical impedancefrequency relationship for both untreated and treated ultra-soft clayey soils.The new model has shown a good agreement with experimental data with coefficient of determination（R;）up to 0.99 and root mean square error（RMSE） of 0.007 kΩ.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
基金supported by the Center for Innovative Grouting Materials and Technology (CIGMAT) at the University of Houston, Texas, USA
文摘The characterization of ultra-soft clayey soil exhibits extreme challenges due to low shear strength of such material.Hence,inspecting the non-destructive electrical impedance behavior of untreated and treated ultra-soft clayey soils gains more attention.Both shear strength and electrical impedance were measured experimentally for both untreated and treated ultra-soft clayey soils.The shear strength of untreated ultra-soft clayey soil reached 0.17 kPa for 10% bentonite content,while the shear strengths increased to 0.27 kPa and 6.7 kPa for 10% bentonite content treated with 2% lime and 10% polymer,respectively.The electrical impedance of the ultra-soft clayey soil has shown a significant decrease from 1.6 kΩ to 0.607 kΩ when the bentonite content increased from 2% to 10% at a frequency of 300 kHz.The10%lime and 10% polymer treatments have decreased the electrical impedances of ultra-soft clayey soil with 10%bentonite from 0.607 kΩ to 0.12 kΩ and 0.176 kΩ,respectively,at a frequency of 300 kHz.A new mathematical model has been accordingly proposed to model the non-destructive electrical impedancefrequency relationship for both untreated and treated ultra-soft clayey soils.The new model has shown a good agreement with experimental data with coefficient of determination（R;）up to 0.99 and root mean square error（RMSE） of 0.007 kΩ.
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.
