Tensile Strain Capacity Prediction of Engineered Cementitious Composites (ECC) Using Soft Computing Techniques

Plain concrete is strong in compression but brittle in tension,having a low tensile strain capacity that can significantly degrade the long-term performance of concrete structures,even when steel reinforcing is present.In order to address these challenges,short polymer fibers are randomly dispersed in a cement-based matrix to forma highly ductile engineered cementitious composite(ECC).Thismaterial exhibits high ductility under tensile forces,with its tensile strain being several hundred times greater than conventional concrete.Since concrete is inherently weak in tension,the tensile strain capacity(TSC)has become one of the most extensively researched properties.As a result,developing a model to predict the TSC of the ECC and to optimize the mixture proportions becomes challenging.Meanwhile,the effort required for laboratory trial batches to determine the TSC is reduced.To achieve the research objectives,five distinct models,artificial neural network(ANN),nonlinear model(NLR),linear relationship model(LR),multi-logistic model(MLR),and M5P-tree model(M5P),are investigated and employed to predict the TSCof ECCmixtures containing fly ash.Data from115 mixtures are gathered and analyzed to develop a new model.The input variables include mixture proportions,fiber length and diameter,and the time required for curing the various mixtures.The model’s effectiveness is evaluated and verified based on statistical parameters such as R2,mean absolute error(MAE),scatter index(SI),root mean squared error(RMSE),and objective function(OBJ)value.Consequently,the ANN model outperforms the others in predicting the TSC of the ECC,with RMSE,MAE,OBJ,SI,and R2 values of 0.42%,0.3%,0.33%,0.135%,and 0.98,respectively.

Numerical study on the strain capacity of girth-welded X80 grade pipes

Strain capacity is an important performance indicator for designing and evaluating high-grade steel pipelines.Due to the inhomogeneity of material properties in welded structures,girth welds are one of the main factors that restrict the strain capacity of pipelines.In this paper,girth-welded pipes with cracks in the inner surface of the weld have been studied,and the ductile crack initiation and propagation behavior have been simulated using the Gurson model.The corresponding nominal strain at the onset of crack initiation was defined as the characteristic value of strain capacity.The influencing factors on the strain concentration area,strain concentration factor,and strain capacity of girth-welded pipes were quantitatively analyzed.A semiempirical calculation formula for the strain capacity of typical girthwelded X80 grade pipes has been proposed as a function of the crack size,mismatch coefficient of the weld,and softening degree of the heat affected zone(HAZ).This study can facilitate the defect assessment of girth-welded pipes.

Bending Failure Mode and Prediction Method of the Compressive Strain Capacity of A Submarine Pipeline with Dent Defects

A dent is a common type of defects for submarine pipeline.For submarine pipelines,high hydrostatic pressure and internal pressure are the main loads.Once pipelines bend due to complex subsea conditions,the compression strain capacity may be exceeded.Research into the local buckling failure and accurate prediction of the compressive strain capacity are important.A finite element model of a pipeline with a dent is established.Local buckling failure under a bending moment is investigated,and the compressive strain capacity is calculated.The effects of different parameters on pipeline local buckling are analyzed.The results show that the dent depth,external pressure and internal pressure lead to different local buckling failure modes of the pipeline.A higher internal pressure indicates a larger compressive strain capacity,and the opposite is true for external pressure.When the ratio of external pressure to collapse pressure of intact pipeline is greater than 0.1,the deeper the dent,the greater the compressive strain capacity of the pipeline.And as the ratio is less than 0.1,the opposite is true.On the basis of these results,a regression equation for predicting the compressive strain capacity of a dented submarine pipeline is proposed,which can be referred to during the integrity assessment of a submarine pipeline.

Strength, strain capacity and toughness of five dual-phase pipeline steels

The effect of microstructures on strength,strain capacity and low temperature toughness of a micro-alloyed pipeline steel was elucidated.Five various dual-phase microstructures,namely,acicular ferrite and a small amount of(around 2 vol.%)polygonal ferrite(AF+PF),polygonal ferrite and bainite(PF+B),polygonal ferrite and martensite/austenite islands(PF+M/A),polygonal ferrite and martensite(PF+M)and elongated polygonal ferrite and martensite(ePF+M),have been studied.Experimental results show that AF+PF microstructure has high yield strength and excellent low temperature toughness,whereas its yield ratio is the highest.Polygonal ferrite-based dual-phase steels,PF+B,PF+M/A and PF+M microstructures show better strain capacity and low temperature toughness.The strain capacity and low temperature toughness of ePF+M microstructure are the worst due to its high strength.The relationship between microstructure,strength,strain capacity and toughness has been established.Based on the results,the optimum microstructure for a better combination of strength,strain capacity and toughness is suggested to be the one having appropriate polygonal ferrite as second phase in an acicular ferrite matrix.

Seismic Liquefaction Resistance Based on Strain Energy Concept Considering Fine Content Value Effect and Performance Parametric Sensitivity Analysis

Liquefaction is one of the most destructive phenomena caused by earthquakes,which has been studied in the issues of potential,triggering and hazard analysis.The strain energy approach is a common method to investigate liquefaction potential.In this study,two Artificial Neural Network(ANN)models were developed to estimate the liquefaction resistance of sandy soil based on the capacity strain energy concept(W)by using laboratory test data.A large database was collected from the literature.One group of the dataset was utilized for validating the process in order to prevent overtraining the presented model.To investigate the complex influence of fine content(FC)on liquefaction resistance,according to previous studies,the second database was arranged by samples with FC of less than 28%and was used to train the second ANN model.Then,two presented ANN models in this study,in addition to four extra available models,were applied to an additional 20 new samples for comparing their results to show the capability and accuracy of the presented models herein.Furthermore,a parametric sensitivity analysis was performed through Monte Carlo Simulation(MCS)to evaluate the effects of parameters and their uncertainties on the liquefaction resistance of soils.According to the results,the developed models provide a higher accuracy prediction performance than the previously publishedmodels.The sensitivity analysis illustrated that the uncertainties of grading parameters significantly affect the liquefaction resistance of soils.