Three-Dimensional Site Characterization Model Using Artificial Neural Networks

This research is designed to develop a new technique for site characterization in a three-dimensional domain. Site characterization is a fundamental task in geotechnical engineering practice, as well as a very challenging process, with the ultimate goal of estimating soil properties based on limited tests at any half-space subsurface point in a site.In this research, the sandy site at the Texas A&M University's National Geotechnical Experimentation Site is selected as an example to develop the new technique for site characterization, which is based on Artificial Neural Networks (ANN) technology. In this study, a sequential approach is used to demonstrate the applicability of ANN to site characterization. To verify its robustness, the proposed new technique is compared with other commonly used approaches for site characterization. In addition, an artificial site is created, wherein soil property values at any half-space point are assumed, and thus the predicted values can compare directly with their corresponding actual values, as a means of validation. Since the three-dimensional model has the capability of estimating the soil property at any location in a site, it could have many potential applications, especially in such case, wherein the soil properties within a zone are of interest rather than at a single point. Examples of soil properties of zonal interest include soil type classification and liquefaction potential evaluation. In this regard, the present study also addresses this type of applications based on a site located in Taiwan, which experienced liquefaction during the 1999 Chi-Chi, Taiwan, Earthquake.

Modeling of wheeled mobile robots using dextrous manipulation kinematics

Abstract—This document introduces a new kinematic simulation of a wheeled mobile robot operating on uneven terrain. Our modeling method borrows concepts from dextrous manipulation. This allows for an accurate simulation of the way 3-dimensional wheels roll over a smooth ground surface. The purpose of the simulation is to validate a new concept for design of off-road wheel suspensions, called Passive Variable Camber (PVC). We show that PVC eliminates kinematic slip for an outdoor robot. Both forward and inverse kinematics are discussed and simulation results are presented.

Predictive distribution modeling for rare Himalayan medicinal plant Berberis aristata DC

Predictive distribution modelling of Berberis aristata DC, a rare threatened plant with high medicinal values has been done with an aim to understand its potential distribution zones in Indian Himalayan region. Bioclimatic and topographic variables were used to develop the distribution model with the help of three different algorithms viz. GeneticAlgorithm for Rule-set Production (GARP), Bioclim and Maximum entroys(MaxEnt). Maximum entropy has predicted wider potential distribution (10.36%) compared to GARP (4.63%) and Bioclim (2.44%). Validation confirms that these outputs are comparable to the present distribution pattern of the B. atistata. This exercise highlights that this species favours Western Himalaya. However, GARP and MaxEnt's prediction of Eastern Himalayan states (i.e. Arunachal Pradesh, Nagaland and Manipur) are also identified as potential occurrence places require further exploration.

Spectral Finite Element Modeling of Complex Structures for Applications in Real-time Structural Health Monitoring

In this paper we discuss the recent progresses in spectral finite element modeling of complex structures and its application in real-time structural health monitoring system based on sensor-actuator network and near real-time computation of Damage Force Indicator (DFI) vector. A waveguide network formalism is developed by mapping the original variational problem into the variational problem involving product spaces of 1D waveguides. Numerical convergence is studied using a h()-refinement scheme, where is the wavelength of interest. Computational issues towards successful implementation of this method with SHM system are discussed.

Modeling of active fiber composite for delamination sensing

In order to demonstrate the feasibility of Active Fiber Composites (AFC) as sensors for detecting damage, a pretwisted strip made of AFC with symmetric free-edge delamination is considered in this paper. The strain developed on the top/bottom of the strip is measured to detect and assess delamination. Variational Asymptotic Method (VAM) is used in the development of a non-classical non-linear cross sectional model of the strip. The original three dimensional (3D) problem is simplified by the decomposition into two simpler problems: a two-dimensional (2D) problem, which provides in a compact form the cross-sectional properties using VAM, and a non-linear one-dimensional (1D) problem along the length of the beam. This procedure gives the non-linear stiffnesses, which are very sensitive to damage, at any given cross-section of the strip. The developed model is used to study a special case of cantilevered laminated strip with antisymmetric layup, loaded only by an axial force at the tip. The charge generated in the AFC lamina is derived in closed form in terms of the 1D strain measures. It is observed that delamination length and location have a definite influence on the charge developed in the AFC lamina. Also, sensor voltage output distribution along the length of the beam is obtained using evenly distributed electrode strip. These data could in turn be used to detect the presence of damage.