Modeling wrap faced reinforced soil retaining walls subjected to dynamic excitation

This paper describes some of the physical and numerical model tests of reinforced soil retaining walls subjected to dynamic excitation through uni-axial shaking tests. Models of retaining walls are constructed in a perspex box with geotextile reinforcement using the wrap around technique with dry sand backfill and instrumented with displacement sensors, accelerometers and soil pressure sensors. Numerical modelling of these shaking table tests is carried using FLAC. Numerical model is validated by comparing physical model results. Responses of wrap faced walls with different number of reinforcement layers are discussed from both the physical and numerical model tests. Results showed that the displacements are decreasing with the increase in number of reinforcement layers while acceleration amplifications are not affected significantly.

Reliability based design optimization of gravity retaining walls

In this paper the use of probability theory in reliability based optimum design of reinforced gravity retaining wall is described. The formulation for computing system reliability index is presented. A parametric study is conducted using advanced first order second moment method (AFOSM) developed by Hasofer-Lind and Rackwitz-Fiessler (HL-RF) to asses the effect of uncertainties in design parameters on the probability of failure of reinforced gravity retaining wall. Totally 8 modes of failure are considered, viz overturning, sliding, eccentricity, bearing capacity failure, shear and moment failure in the toe slab and heel slab. The analysis is performed by treating back fill soil properties, foundation soil properties, geometric properties of wall, reinforcement properties and concrete properties as random variables. These results are used to investigate optimum wall proportions for different coefficients of variation of φ (5% and 10%) and targeting system reliability index (βt) in the range of 3 – 3.2.

Probabilistic design of retaining walls.

Retaining walls are one of the important structures in nearshore environment and are generally designed based on deterministic approaches. The present paper focuses on the reliability assessment of cantilever retaining walls with due consideration to the uncertainties in soil parameters. Reliability analysis quantifies the level of reliability associated with designs and the associated risk. It also gives the formalisation of a design situation that is normally recognised by experienced designers and provides a greater level of consistency in design. The results are also examined in terms of a simple cost function. The study shows that sliding mode is the critical failure mode and the consequent failure costs are also higher. The study also shows that provision of shear key results in improved reliability and reduction in expected costs.

Modeling the dynamic response of wrap-faced reinforced soil retaining walls

This paper describes the development of a numerical model for simulating the shaking table tests on wrap-faced reinforced soil retaining walls. Some of the physical model tests carried out on reinforced soil retaining walls subjected to dynamic excitation through uniaxial shaking tests are briefly discussed. Models of retaining walls are constructed in a perspex box with geotextile reinforcement using the wraparound technique with dry sand backfill and instrumented with displacement sensors, accelerometers, and soil pressure sensors. Results showed that the displacements decrease with the increase in number of reinforcement layers, whereas acceleration amplifications were not affected significantly. Numerical modeling of these shaking table tests is carried out using the Fast Lagrangian Analysis of Continua program. The numerical model is validated by comparing the results with experiments on physical models. Responses of wrap-faced walls with varying numbers of reinforcement layers are compared. Sensitivity analysis performed on the numerical models showed that the friction and dilation angle of backfill material and stiffness properties of the geotextile-soil interface are the most affecting parameters for the model response.

Multi Label Classification of Discrete Data

The paper describes an algorithm for multi-label classification. Since a pattern can belong to more than one class, the task of classifying a test pattern is a challenging one. We propose a new algorithm to carry out multi-label classification which works for discrete data. We have implemented the algorithm and presented the results for different multi-label data sets. The results have been compared with the algorithm multi-label KNN or ML-KNN and found to give good results.

Reduced graphene oxide-titania based platform for label-free biosensor

A label-free biosensor has been fabricated using a reduced graphene oxide (RGO) and anatase titania (ant-TiO2) nanocomposite, electrophoretically deposited onto an indium tin oxide coated glass substrate. The RGO-ant-TiO2 nanocomposite has been functionalized with protein (horseradish peroxidase) conjugated antibodies for the specific recognition and detection of Vibrio cholerae. The presence of Ab-Vc on the RGO-ant-TiO2 nanocomposite has been confirmed using electron microscopy, Fourier transform infrared spectroscopy and electrochemical techniques. Electrochemical studies relating to the fabricated Ab-Vc/RGO-ant-TiO2/ITO immunoelectrode have been conducted to investigate the binding kinetics. This immunosensor exhibits improved biosensing properties in the detection of Vibrio cholerae, with a sensitivity of 18.17 x 10(6) F mol(-1) L-1 m(-2) in the detection range of 0.12-5.4 nmol L-1, and a low detection limit of 0.12 nmol L-1. The association (k(a)), dissociation (k(d)) and equilibrium rate constants have been estimated to be 0.07 nM, 0.002 nM and 0.41 nM, respectively. This Ab-Vc/RGO-ant-TiO2/ITO immunoelectrode could be a suitable platform for the development of compact diagnostic devices.

Making risk minimization tolerant to label noise

In many applications, the training data, from which one needs to learn a classifier, is corrupted with label noise. Many standard algorithms such as SVM perform poorly in the presence of label noise. In this paper we investigate the robustness of risk minimization to label noise. We prove a sufficient condition on a loss function for the risk minimization under that loss to be tolerant to uniform label noise. We show that the 0-1 loss, sigmoid loss, ramp loss and probit loss satisfy this condition though none of the standard convex loss functions satisfy it. We also prove that, by choosing a sufficiently large value of a parameter in the loss function, the sigmoid loss, ramp loss and probit loss can be made tolerant to nonuniform label noise also if we can assume the classes to be separable under noise-free data distribution. Through extensive empirical studies, we show that risk minimization under the 0-1 loss, the sigmoid loss and the ramp loss has much better robustness to label noise when compared to the SVM algorithm. (C) 2015 Elsevier B.V. All rights reserved.

A Simple Label Switching Algorithm for Semisupervised Structural SVMs

In structured output learning, obtaining labeled data for real-world applications is usually costly, while unlabeled examples are available in abundance. Semisupervised structured classification deals with a small number of labeled examples and a large number of unlabeled structured data. In this work, we consider semisupervised structural support vector machines with domain constraints. The optimization problem, which in general is not convex, contains the loss terms associated with the labeled and unlabeled examples, along with the domain constraints. We propose a simple optimization approach that alternates between solving a supervised learning problem and a constraint matching problem. Solving the constraint matching problem is difficult for structured prediction, and we propose an efficient and effective label switching method to solve it. The alternating optimization is carried out within a deterministic annealing framework, which helps in effective constraint matching and avoiding poor local minima, which are not very useful. The algorithm is simple and easy to implement. Further, it is suitable for any structured output learning problem where exact inference is available. Experiments on benchmark sequence labeling data sets and a natural language parsing data set show that the proposed approach, though simple, achieves comparable generalization performance.

A Label-Free Multisensing Immunosensor Based on Imaging Ellipsometry

An immunosensor based on imaging ellipsometry and its potential applications was demonstrated in this paper. It has been proven a fast, reliable, and convenient method to quantify the thickness distribution of protein layers or detect protein concentration in solution. Combined with a protein chip, the immunosensor was able to detect multiple analytes simultaneously without any labeling. Preliminary results demonstrated how this immunosensor could be used to monitor several independent biospecific binding processes in real-time and in situ conditions.

A Label-Free Protein Microfluidic Array for Parallel Immunoassays

A label-free protein microfluidic array for immunoassays based on the combination of imaging ellipsometry and an integrated microfluidic system is presented. Proteins can be patterned homogeneously on substrate in array format by the microfluidic system simultaneously. After preparation, the protein array can be packed in the microfluidic system which is full of buffer so that proteins are not exposed to denaturing conditions. With simple microfluidic channel junction, the protein microfluidic array can be used in serial or parallel format to analyze single or multiple samples simultaneously. Imaging ellipsometry is used for the protein array reading with a label-free format. The biological and medical applications of the label-free protein microfluidic array are demonstrated by screening for antibody–antigen interactions, measuring the concentration of the protein solution and detecting five markers of hepatitis B.