• Computers and Concrete
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• v.7 no.4
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• pp.347-364
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• 2010

Since its invention in the $19^{th}$ century, Reinforced Concrete (RC) has been widely used in the construction of a lot of different structures, as buildings, bridges, nuclear central plants, or even ships. The details of the mechanical response for this kind of structures depends directly upon the material behavior of each component: concrete and steel, as well as their interaction through the bond-slip, which makes a rigorous engineering analysis of RC structures quite complicated. Consequently, the practical calculation of RC structures is done by adopting a lot of simplifications and hypotheses validated in the elastic range. Nevertheless, as soon as any RC structural element is working in the inelastic range, it is possible to obtain the numerical prediction of its realistic behavior only through the use of non linear analysis. The aim of this work is to develop a new kind of Finite Element: the "Enhanced Solid Element (ESE)" which takes into account the complex composition of reinforced concrete, being able to handle each dissipative material behavior and their different deformations, and on the other hand, conserving a simplified shape for engineering applications. Based on the recent XFEM developments, we introduce the concept of nodal enrichment to represent kinematics of steel rebars as well as bonding. This enrichment allows to reproduce the strain incompatibility between concrete and steel that occurs because of the bond degradation and slip. This formulation was tested with a couple of simple examples and compared to the results obtained from other standard formulations.

• Composites Research
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• v.28 no.5
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• pp.260-264
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• 2015

In this paper, molecular dynamics (MD) simulation was carried to predict thermo-mechanical behaviors for carbon nanotube (CNT) reinforced epoxy composites and to analyze the trends. Total of six models having the volume fractions of CNT from 0 to 25% in epoxy were constructed. To predict thermal behaviors, temperature was increased constantly from 300 to 600 K, and the glass transition temperature ($T_g$) and coefficient of thermal expansion (CTE) analyzed using the relationship between temperature and specific volume. The elastic moduli that represented to the mechanical behaviors were also predicted by constant strain. Additionally, the effects of functionalization of CNT on mechanical behaviors of composite were analyzed. Models were constructed to represent CNTs functionalized by nitrogen doping and COOH groops, and interfacial behaviors and elastic moduli were analyzed. Results showed that the agglomerations of CNTs in epoxy cause by perturbations of thermo-mechanical behaviors, and the functionalization of CNTs improved the interfacial response as well as mechanical properties.

• Geomechanics and Engineering
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• v.28 no.4
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• pp.335-346
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• 2022

Buckling failure is a typical slope instability mode that should be paid more attention to. It is difficult to provide systematic guidance for the monitoring and management of such slopes due to unclear mechanism. Here we examine buckling failure as the potential instability mode for a slope above a railway tunnel in southwest China. A comprehensive model test system was developed that can be used to conduct buckling failure experiments. The displacement, stress, and strain of the slope were monitored to document the evolution of buckling failure during the experiment. Monitoring data reveal the deformation and stress characteristics of the slope with different slipping mass thicknesses and under different top loads. The test results show that the slipping mass is the main subject of the top load and is the key object of monitoring. Displacement and stress precede buckling failure, so maybe useful predictors of impending failure. However, the response of the stress variation is earlier than displacement variation during the failure process. It is also necessary to monitor the bedrock near the slip face because its stress evolution plays an important role in the early prediction of instability. The position near the slope foot is most prone to buckling failure, so it should be closely monitored.

• Structural Engineering and Mechanics
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• v.83 no.4
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• pp.515-535
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• 2022

The strength models for fiber-reinforced polymer (FRP)-confined normal strength concrete (NC) cylinders available in the literature have been suggested based on small databases using limited variables of such structural members portraying less accuracy. The artificial neural network (ANN) is an advanced technique for precisely predicting the response of composite structures by considering a large number of parameters. The main objective of the present investigation is to develop an ANN model for the axial strength of FRP-confined NC cylinders using various parameters to give the highest accuracy of the predictions. To secure this aim, a large experimental database of 313 FRP-confined NC cylinders has been constructed from previous research investigations. An evaluation of 33 different empirical strength models has been performed using various statistical parameters (root mean squared error RMSE, mean absolute error MAE, and coefficient of determination R²) over the developed database. Then, a new ANN model using the Group Method of Data Handling (GMDH) has been proposed based on the experimental database that portrayed the highest performance as compared with the previous models with R²=0.92, RMSE=0.27, and MAE=0.33. Therefore, the suggested ANN model can accurately capture the axial strength of FRP-confined NC cylinders that can be used for the further analysis and design of such members in the construction industry.

• International Journal of Highway Engineering
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• v.11 no.1
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• pp.165-175
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• 2009

With the current move towards adopting mechanistic-empirical concepts in the design of pavement structures, state-of-the-art mechanistic analysis methodologies are needed to determine accurate pavement responses, such as stress, strain, and deformation. Previous laboratory studies of pavement foundation geomaterials, i.e., unbound granular materials used in base/subbase layers and fine-grained soils of a prepared subgrade, have shown that the resilient responses followed by nonlinear, stress-dependent behavior under repeated wheel loading. This nonlinear behavior is commonly characterized by stress-dependent resilient modulus material models that need to be incorporated into finite element (FE) based mechanistic pavement analysis methods to predict more realistically predict pavement responses for a mechanistic pavement analysis. Developed user material subroutine using aforementioned resilient model with nonlinear solution technique and convergence scheme with proven performance were successfully employed in general-purpose FE program, ABAQUS. This numerical analysis was investigated in predicted critical responses and domain selection with specific mesh generation was implemented to evaluate better prediction of pavement responses. Results obtained from both axisymmetric and three-dimensional (3D) nonlinear FE analyses were compared and remarkable findings were described for nonlinear FE analysis. The UMAT subroutine performance was also validated with the instrumented full scale pavement test section study results from the Federal Aviation Administration's National Airport Pavement Test Facility (FAA's NAPTF).

• Food Science of Animal Resources
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• v.34 no.6
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• pp.736-741
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• 2014

This study developed probabilistic models to determine the initiation time of growth of Pseudomonas spp. in combinations with $NaNO_2$ and NaCl concentrations during storage at different temperatures. The combination of 8 NaCl concentrations (0, 0.25, 0.5, 0.75, 1, 1.25, 1.5, and 1.75%) and 9 $NaNO_2$ concentrations (0, 15, 30, 45, 60, 75, 90, 105, and 120 ppm) were prepared in a nutrient broth. The medium was placed in the wells of 96-well microtiter plates, followed by inoculation of a five-strain mixture of Pseudomonas in each well. All microtiter plates were incubated at 4, 7, 10, 12, and $15^{\circ}C$ for 528, 504, 504, 360 and 144 h, respectively. Growth (growth initiation; GI) or no growth was then determined by turbidity every 24 h. These growth response data were analyzed by a logistic regression to produce growth/no growth interface of Pseudomonas spp. and to calculate GI time. NaCl and $NaNO_2$ were significantly effective (p<0.05) on inhibiting Pseudomonas spp. growth when stored at $4-12^{\circ}C$. The developed model showed that at lower NaCl concentration, higher $NaNO_2$ level was required to inhibit Pseudomonas growth at $4-12^{\circ}C$. However, at $15^{\circ}C$, there was no significant effect of NaCl and $NaNO_2$. The model overestimated GI times by $58.2{\pm}17.5$ to $79.4{\pm}11%$. These results indicate that the probabilistic models developed in this study should be useful in calculating the GI times of Pseudomonas spp. in combination with NaCl and $NaNO_2$ concentrations, considering the over-prediction percentage.