Abstract

This paper presents a methodology to understand the interface behavior of soil and pile under transient loading. Previous works by Trochanis (1991), Bently and Naggar (2000) and others gave an insight to the problem. But this paper illustrates the research methodology developed to understand the dynamic behavior of pile and soil by taking the soil yielding effects and also the interface effects. A detailed parametric study has been done to understand the behavior of the same by varying the length of the pile for various soil conditions. For the purpose of comparison, soil model size was taken as 15m x 10m x 11m with pile cross section as 0.5 X 0.5 m and length of pile as 10m. For this we have developed a program for modeling the soil pile structure interaction using three dimensional Finite Element Method in MATLAB R2009a, the details of the same have been given in this paper along with the validity of it with benchmark problems in the literature.

Abstract

extensive damage Occurred in non engineered structure..Many casualities occured in stone masonary buildings

Abstract

The objective of this paper is to compare pushover response of ductile and non-ductile frames using AEM (Applied Element Method). Pushover analysis is non linear static analysis, a tool for seismic evaluation of existing structures. Unlike FEM, AEM is a discrete method in which the elements are connected by pair of normal and shear springs which are distributed around the elements edges and each pair of springs totally represents stresses and deformation and plastic hinges location are formed automatically. In the present case study a 1 x 1 bay four storied building is modeled using AEM. Gravity loads and laterals loads as per IS 1893-2002 are applied on the structure and designed using IS 456 and IS 13920. Displacement control pushover analysis is carried out. The effect of ductile detailing, change in grade of concrete and bar sizes in columns is also compared.

Abstract

Construction practices in major part of rural as well as suburban areas in India uses brick masonry type of buildings; this may be due to easy availability of material, aesthetic appeal and cost effective. Such type of construction is also found in nearby areas like Nepal and Bangladesh. In India, these buildings are most commonly found in regions where good quality clay for brick production is abundantly available (World housing encyclopaedia report, 2003). In past studies it has been found that brick quality is comparatively better in north part of India than south, this is due to good quality of soil in north India. But, such type of buildings proved to be vulnerable during past history of earthquake. Though, past experimental and analytical studies stress upon understanding behaviour of brick masonry buildings, this numerical study has advantage over others that it can show initiation of crack, gradual propagation of cracks till the total collapse of building. In this parametric study, four cases have been considered and effect of considering infill and without infill on strength, stiffness and ductility of wall has been studied.

Abstract

Overall capacity of a structure depends on the strength and deformation capacities of the individual component of the structure. In order to determine capacities beyond the elastic limits some form of nonlinear analysis such as the pushover procedure is required. This procedure uses a series of sequential elastic analyses, superimposed to approximate a force-displacement capacity diagram of the overall structure. Usually this analysis is performed using FEM and that too with major assumption in defining hinge mechanisms and target displacement. In this paper, a new method for analysis nonlinear static large deformation analysis of bare frame subjected to lateral load is proposed. First we developed the method and later four frames were taken and they were pushed up to failure. Results clearly show the capability of the proposed method.

Abstract

Motion segmentation is an inevitable component for mobile robotic systems such as the case with robots performing SLAM and collision avoidance in dynamic worlds. This paper proposes an incremental motion segmentation system that efficiently segments multiple moving objects and simultaneously build the map of the environment using visual SLAM modules. Multiple cues based on optical flow and two view geometry are integrated to achieve this segmentation. A dense optical flow algorithm is used for dense tracking of features. Motion potentials based on geometry are computed for each of these dense tracks. These geometric potentials along with the optical flow potentials are used to form a graph like structure. A graph based segmentation algorithm then clusters together nodes of similar potentials to form the eventual motion segments. Experimental results of high quality segmentation on different publicly available datasets demonstrate the effectiveness of our method.

Abstract

In this paper we propose a framework for optimum steering input determination of all-wheel steer vehicles (AWSV) on rough terrains. The framework computes the steering input which minimizes the tracking error for a given trajectory. Unlike previous methodologies of computing steering inputs of car-like vehicles, the proposed methodology depends explicitly on the vehicle dynamics and can be extended to vehicle having arbitrary number of steering inputs. A fully generic framework has been used to derive the vehicle dynamics and a non-linear programming based constrained optimization approach has been used to compute the steering input considering the instantaneous vehicle dynamics, no-slip and contact constraints of the vehicle. All Wheel steer Vehicles have a special parallel steering ability where the instantaneous centre of rotation (ICR) is at infinity. The proposed framework automatically enables the vehicle to choose between parallel steer and normal operation depending on the error with respect to the desired trajectory. The efficacy of the proposed framework is proved by extensive uneven terrain simulations, for trajectories with continuous or discontinuous velocity profile.

Abstract

In this paper, we automate the traditional problem of Simultaneous Localization and Mapping (SLAM) by interleaving planning for exploring unknown environments by a mobile robot. We denote such planned SLAM systems as SPLAM (Simultaneous Planning Localization and Mapping). The main aim of SPLAM is to plan paths for the SLAM process such that the robot and map uncertainty upon execution of the path remains minimum and tractable. The planning is interleaved with SLAM and hence the terminology SPLAM. While typical SPLAM routines find paths when the robot traverses amidst known regions of the constructed map, herein we use the SPLAM formulation for an exploration like situation. Exploration is carried out through a frontier based approach where we identify multiple frontiers in the known map. Using Randomized Planning techniques we calculate various possible trajectories to all the known frontiers. We introduce a novel strategy for selecting frontiers which mimics Fast SLAM, selects a trajectory for robot motion that will minimize the map and robot state covariance. By using a Fast SLAM like approach for selecting frontiers we are able to decouple the robot and landmark covariance resulting in a faster selection of next best location, while maintaining the same kind of robustness of an EKF based SPLAM framework. We then compare our results with Shortest Path Algorithm and EKF based Planning. We show significant reduction in covariance when compared with shortest frontier first approach, while the uncertainties are comparable to EKF-SPLAM albeit at much faster planning times.

Abstract

Exploration is a core and important robotics area, whose applications include search and rescue robotics, planetary exploration etc. We know that this exploration task is best performed when using a multi-robot system. In this paper, we present an algorithm for multi-robot exploration of an unknown environment, taking into account the communication constraints between the robots. The aim of the robots is to explore the whole map as a pack, without losing communication throughout. The key task for us here is to allocate the target points for multiple robots so as to maximize the area explored and minimize the time and plan paths for the robots in such a way so as to avoid obstacles. A multi-robot exploration methodology is introduced similar to depth first strategy, that samples frontier points based on a metric function. This function aims to maximize the visibility gain or information gain while minimizing the distance to be travelled to the frontier points, such that the robots are within the limited communication distance of each other. The algorithm has been tested through simulation runs of various maps and results and evaluations have been presented based on it. The results effectively demonstrate that our algorithm allows robot pack to quickly accomplish the task of exploration and without the constraint ever breaking down. Here, we also present a comparative analysis of our algorithm with another exploration approach, which finds new areas based on population generation and utility calculation over the population. The results show tangible performance gain of this method over previous methods reported on exploration with limited communication constraints.

Abstract

In this paper we address the problem of detecting road intersections. We present two approaches to solve the problem of intersection detection in an unstructured outdoor setting. The first is a natural extension of the popular VFH* obstacle avoidance algorithm. It detects intersections and tracks, over a period of time, the angles at which gaps in the robot's certainty grid (CG) are first observed. The second approach uses techniques from image processing and computational geometry on the certainty grid image, to extract a skeleton of the navigable region, thus providing the intersections. We show experimental results portraying intersection detection due to both methods and show the results. On the whole, we found that the robot was able to detect all possible intersections.