Design of rectangular concrete-filled steel tubular （CFT） columns has been a big concern owing to their complex constraint mechanism. Generally, most existing methods are based on simplified mechanical model with li...Design of rectangular concrete-filled steel tubular （CFT） columns has been a big concern owing to their complex constraint mechanism. Generally, most existing methods are based on simplified mechanical model with limited experimental data, which is not reliable under many conditions, e.g., columns using high strength materials. Artificial neural network （ANN） models have shown the effectiveness to solve complex problems in many areas of civil engineering in recent years. In this paper, ANN models were employed to predict the axial bearing capacity of rectangular CFT columns based on the experimental data. 305 experimental data from articles were collected, and 275 experimental samples were chosen to train the ANN models while 30 experimental samples were used for testing. Based on the comparison among different models, artificial neural network modell （ANN1） and artificial neural network model2 （ANN2） with a 20- neuron hidden layer were chosen as the fit prediction models. ANN1 has five inputs： the length （D） and width （B） of cross section, the thickness of steel （t）, the yield strength of steel （fy）, the cylinder strength of concrete （fc＇）- ANN2 has ten inputs： D, B, t, fy, f′, the length to width ratio （D/B）, the length to thickness ratio （D/t）, the width to thickness ratio （B/t）, restraint coefficient （ξ）, the steel ratio （α）. The axial beating capacity is the output data for both models.The outputs from ANN1 and ANN2 were verified and compared with those from EC4, ACI, GJB4142 and AISC360-10. The results show that the implemented models have good prediction and generalization capacity. Parametric study was conducted using ANN1 and ANN2 which indicates that effect law of basic parameters of columns on the axial bearing capacity of rectangular CFT columns differs from design codes.The results also provide convincing design reference to rectangular CFT columns.展开更多
Based on the field destructive test of six rock-socketed piles with shallow overburden,three prediction models are used to quantitatively analyze and predict the intact load−displacement curve.The predicted values of ...Based on the field destructive test of six rock-socketed piles with shallow overburden,three prediction models are used to quantitatively analyze and predict the intact load−displacement curve.The predicted values of ultimate uplift capacity were further determined by four methods(displacement controlling method(DCM),reduction coefficient method(RCM),maximum curvature method(MCM),and critical stiffness method(CSM))and compared with the measured value.Through the analysis of the relationship between the change rate of pullout stiffness and displacement,a method used to determine the ultimate uplift capacity via non-intact load−displacement curve was proposed.The results show that the predicted value determined by DCM is more conservative,while the predicted value determined by MCM is larger than the measured value.This suggests that RCM and CSM in engineering applications can be preferentially applied.Moreover,the development law of the change rate of pullout stiffness with displacement agrees well with the attenuation form of power function.The theoretical predicted results of ultimate uplift capacity based on the change rate of pullout stiffness will not be affected by the integrity of the curve.The method is simple and applicable for the piles that are not loaded to failure state,and thus provides new insights into ultimate uplift capacity determination of test piles.展开更多
非洲某工程桥梁基础穿越深厚砂性土,为了研究砂性地层钻孔桩承载特性,选取了主桥的TP-1和TP-2两根2.0 m直径的桩基进行现场试验及对临近钻孔进行旁压试验。试验结果表明,深厚砂性土会对成桩质量产生影响,砂性土越厚,桩端沉渣越显著。桩...非洲某工程桥梁基础穿越深厚砂性土,为了研究砂性地层钻孔桩承载特性,选取了主桥的TP-1和TP-2两根2.0 m直径的桩基进行现场试验及对临近钻孔进行旁压试验。试验结果表明,深厚砂性土会对成桩质量产生影响,砂性土越厚,桩端沉渣越显著。桩端沉渣会显著影响桩基沉降,使桩端阻力产生弱化和强化特性。此外,基于欧洲规范(BS EN 1997—2)旁压试验预测桩基承载力,会高估95%~140%桩基承载力,而公路桥涵规范计算的承载力也明显大于实测值。通过对旁压试验进行三段线性分类,可得到一种直接利用旁压试验预测桩基承载力的方法,相较于欧洲规范和公路桥涵规范更适用于深厚砂性土桩基承载力预测。展开更多
Rock masses are commonly used as the underlying layer of important structures such as bridges, dams and transportation constructions. The success of a foundation design for such structures mainly depends on the accura...Rock masses are commonly used as the underlying layer of important structures such as bridges, dams and transportation constructions. The success of a foundation design for such structures mainly depends on the accuracy of estimating the bearing capacity of rock beneath them. Several traditional numerical approaches are proposed for the estimation of the bearing capacity of foundations resting on rock masses to avoid performing elaborate and expensive experimental studies. Despite this fact, there still exists a serious need to develop more robust predictive models. This paper proposes new nonlinear prediction models for the ultimate bearing capacity of shallow foundations resting on non-fractured rock masses using a novel evolutionary computational approach, called linear genetic programming. A comprehensive set of rock socket, centrifuge rock socket, plate load and large-scaled footing load test results is used to develop the models. In order to verify the validity of the models, the sensitivity analysis is conducted and discussed. The results indicate that the proposed models accurately characterize the bearing capacity of shallow foundations. The correlation coefficients between the experimental and predicted bearing capacity values are equal to 0.95 and 0.96 for the best LGP models. Moreover, the derived models reach a notably better prediction performance than the traditional equations.展开更多
基金Acknowledgements This work was sponsored by the National Natural Science Foundation of China （Grant No. 61272264）.
文摘Design of rectangular concrete-filled steel tubular （CFT） columns has been a big concern owing to their complex constraint mechanism. Generally, most existing methods are based on simplified mechanical model with limited experimental data, which is not reliable under many conditions, e.g., columns using high strength materials. Artificial neural network （ANN） models have shown the effectiveness to solve complex problems in many areas of civil engineering in recent years. In this paper, ANN models were employed to predict the axial bearing capacity of rectangular CFT columns based on the experimental data. 305 experimental data from articles were collected, and 275 experimental samples were chosen to train the ANN models while 30 experimental samples were used for testing. Based on the comparison among different models, artificial neural network modell （ANN1） and artificial neural network model2 （ANN2） with a 20- neuron hidden layer were chosen as the fit prediction models. ANN1 has five inputs： the length （D） and width （B） of cross section, the thickness of steel （t）, the yield strength of steel （fy）, the cylinder strength of concrete （fc＇）- ANN2 has ten inputs： D, B, t, fy, f′, the length to width ratio （D/B）, the length to thickness ratio （D/t）, the width to thickness ratio （B/t）, restraint coefficient （ξ）, the steel ratio （α）. The axial beating capacity is the output data for both models.The outputs from ANN1 and ANN2 were verified and compared with those from EC4, ACI, GJB4142 and AISC360-10. The results show that the implemented models have good prediction and generalization capacity. Parametric study was conducted using ANN1 and ANN2 which indicates that effect law of basic parameters of columns on the axial bearing capacity of rectangular CFT columns differs from design codes.The results also provide convincing design reference to rectangular CFT columns.
基金Project(2016YFC0802203)supported by the National Key R&D Program of ChinaProject(2013G001-A-2)supported by the Science and Technology Research and Development Program of China Railway CorporationProject(SKLGDUEK2011)supported by the State Key Laboratory for GeoMechanics and Deep Underground Engineering,China University of Mining&Technology。
文摘Based on the field destructive test of six rock-socketed piles with shallow overburden,three prediction models are used to quantitatively analyze and predict the intact load−displacement curve.The predicted values of ultimate uplift capacity were further determined by four methods(displacement controlling method(DCM),reduction coefficient method(RCM),maximum curvature method(MCM),and critical stiffness method(CSM))and compared with the measured value.Through the analysis of the relationship between the change rate of pullout stiffness and displacement,a method used to determine the ultimate uplift capacity via non-intact load−displacement curve was proposed.The results show that the predicted value determined by DCM is more conservative,while the predicted value determined by MCM is larger than the measured value.This suggests that RCM and CSM in engineering applications can be preferentially applied.Moreover,the development law of the change rate of pullout stiffness with displacement agrees well with the attenuation form of power function.The theoretical predicted results of ultimate uplift capacity based on the change rate of pullout stiffness will not be affected by the integrity of the curve.The method is simple and applicable for the piles that are not loaded to failure state,and thus provides new insights into ultimate uplift capacity determination of test piles.
文摘非洲某工程桥梁基础穿越深厚砂性土,为了研究砂性地层钻孔桩承载特性,选取了主桥的TP-1和TP-2两根2.0 m直径的桩基进行现场试验及对临近钻孔进行旁压试验。试验结果表明,深厚砂性土会对成桩质量产生影响,砂性土越厚,桩端沉渣越显著。桩端沉渣会显著影响桩基沉降,使桩端阻力产生弱化和强化特性。此外,基于欧洲规范(BS EN 1997—2)旁压试验预测桩基承载力,会高估95%~140%桩基承载力,而公路桥涵规范计算的承载力也明显大于实测值。通过对旁压试验进行三段线性分类,可得到一种直接利用旁压试验预测桩基承载力的方法,相较于欧洲规范和公路桥涵规范更适用于深厚砂性土桩基承载力预测。
文摘Rock masses are commonly used as the underlying layer of important structures such as bridges, dams and transportation constructions. The success of a foundation design for such structures mainly depends on the accuracy of estimating the bearing capacity of rock beneath them. Several traditional numerical approaches are proposed for the estimation of the bearing capacity of foundations resting on rock masses to avoid performing elaborate and expensive experimental studies. Despite this fact, there still exists a serious need to develop more robust predictive models. This paper proposes new nonlinear prediction models for the ultimate bearing capacity of shallow foundations resting on non-fractured rock masses using a novel evolutionary computational approach, called linear genetic programming. A comprehensive set of rock socket, centrifuge rock socket, plate load and large-scaled footing load test results is used to develop the models. In order to verify the validity of the models, the sensitivity analysis is conducted and discussed. The results indicate that the proposed models accurately characterize the bearing capacity of shallow foundations. The correlation coefficients between the experimental and predicted bearing capacity values are equal to 0.95 and 0.96 for the best LGP models. Moreover, the derived models reach a notably better prediction performance than the traditional equations.
