Manjriker Gunaratne

Vision–IMU Integration Using a Slow-Frame-Rate Monocular Vision System in an Actual Roadway Setting

We present results of an effort where position and orientation data from vision and inertial sensors are integrated and validated using data from an actual roadway. Information from a sequence of images, which were captured by a monocular camera attached to a survey vehicle at a maximum frequency of 3 frames/s, is fused with position and orientation estimates from the inertial system to correct for inherent error accumulation in such integral-based systems. The rotations and translations are estimated from point correspondences tracked through a sequence of images. To reduce unsuitable correspondences, we used constraints such as epipolar lines and correspondence flow directions. The vision algorithm automatically operates and involves the identification of point correspondences, the pruning of correspondences, and the estimation of motion parameters. To simply obtain the geodetic coordinates, i.e., latitude, longitude, and altitude, from the translation-direction estimates from the vision sensor, we expand the Kalman filter space to incorporate distance. Hence, it was possible to extract the translational vector from the available translational direction estimate of the vision system. Finally, a decentralized Kalman filter is used to integrate the position estimates based on the vision sensor with those of the inertial system. The fusion of the two sensors was carried out at the system level in the model. The comparison of integrated vision-inertial-measuring-unit (IMU) position estimates with those from inertial-GPS system output and actual survey demonstrates that vision sensing can be used to reduce errors in inertial measurements during potential GPS outages.

Modeling Crack Deterioration of Flexible Pavements: Comparison of Recurrent Markov Chains and Artificial Neural Networks

Pavement cracking and rutting are two of the most critical distress types manifested on flexible pavements, and they often govern the overall pavement condition. Hence, many models have been developed for forecasting the deterioration of the crack condition accurately, with the traditionally preferred technique being the use of a regression relationship developed from laboratory or field statistical data, or both. However, it becomes tedious for regression techniques to predict crack performance accurately and robustly in the presence of the multitude of tributary factors, material nonlinearity, and uncertainty involved in the cracking process. With the advancement of modeling techniques, two innovative breeds of models, neural networks and recurrent Markov chains, have drawn increasing attention from researchers for their use in modeling complex phenomena such as pavement cracking. This paper compares the ability of neural networks and recurrent Markov chains to model crack performance, using the Florida...

Response of a layered elastic medium to a moving strip load

Analytical determination of stresses and deformations caused by moving loads is vital to foundation and pavement designs. In current applications, moving loads are often approximated to be vertical impact loads. In this work, however, a live load is modelled as a uniform distribution of normal or shear stresses in actual motion. Then, a layer stiffness approach utilizing linear elasticity is followed in determining the surface and interior deformations due to the live load. By superimposing the two solutions for normal and shear surface stresses, the new approach can be made to provide an approximate solution to the problem of evaluating stresses and deformations caused by a wide wheel load rolling on a layered elastic system. Although elastic solutions in general are inadequate to explain the more significant consequences of pore pressure generation and dissipation in the soil subgrade, these results can certainly be useful to examine the shearing effects of wide rolling wheels on the asphalt layer and immediate settlement of the subgrade. It is found that the dynamic effects of a smoothly rolling wide load are significant at relatively low wheel velocities compared to those of shear waves in the subgrade and base.

Modeling of Crack Depths in Digital Images of Concrete Pavements Using Optical Reflection Properties

Digital image-based automated pavement crack detection and classification technology has seen vast improvements in the recent years. Although crack lengths and widths can be evaluated using state-of-the-art software with a reasonable accuracy, no reported evidence is found in extending this technology to evaluate crack depths. As a supplement to the existing technology, additional information relevant to pavement crack severity could be revealed by the optical modeling of the image formation process and the subsequent analysis of the variation in pixel intensity profiles within images. A preliminary study was carried out to model the digital image formation of cracked concrete pavements based on the bidirectional reflection distribution function. This study was specifically focused on the optical modeling of shallow longitudinal and transverse cracks as well as joints of concrete pavements using the variation of reflection properties at surface discontinuities. Surface discontinuities were considered to b...

Forecasting Overall Pavement Condition with Neural Networks: Application on Florida Highway Network

Timely identification of undesirable crack, ride, and rut conditions is a critical issue in pavement management systems at the network level. The overall pavement surface condition is determined by these individual pavement surface conditions. A research project was carried out to implement an overall methodology for pavement condition prediction that uses artificial neural networks (ANNs). In the research, three ANN models were developed to predict the three key indices—crack rating, ride rating, and rut rating—used by the Florida Department of Transportation (FDOT) for pavement evaluation. The ANN models for each index were trained and tested by using the FDOT pavement condition database. In addition to the three key indices, FDOT uses a composite index called pavement condition rating (PCR), which is the minimum of the three key indices, to summarize overall pavement surface condition for pavement management. PCR is forecast with a combination of the three ANN models. Results of the research suggest that the ANN models are more accurate than the traditional regression models. These ANN models can be expected to have a significant effect on FDOTs pavement management system.

The Foundation Engineering Handbook

Review of Soil Mechanics Concepts and Analytical Techniques Used in Foundation Engineering Manjriker Gunaratne In Situ Soil Testing Austin Gray Mullins Spread Footings: Analysis and Design Manjriker Gunaratne Geotechnical Design of Combined Spread Footings Manjriker Gunaratne Structural Design of Foundations Panchy Arumugasaamy Design of Driven Piles and Pile Groups Manjriker Gunaratne Design of Drilled Shafts Austin Gray Mullins Design of Laterally Loaded Piles Manjriker Gunaratne Construction Monitoring and Testing Methods of Driven Piles Manjriker Gunaratne and Austin Gray Mullins Retaining Walls: Analysis and Design Alaa Ashmawy Stability Analysis and Design of Slopes Manjriker Gunaratne Methods of Soft Ground Improvement James D. Hussin Impact of Groundwater on the Design of Earthen Structures Manjriker Gunaratne Index

Experimental Evaluation of a Pavement Imaging System: Florida Department of Transportation’s Multipurpose Survey Vehicle

The Florida Department of Transportation (FDOT) has acquired and validated a multifunctional survey vehicle for the collection of highway pavement-related data at normal operating speeds. With its ability to collect pavement, right-of-way, and side-view images together with position location, cross-slope, grade, curvature, rutting, and roughness data, this state-of-the-art vehicle enables the rapid and automated evaluation of roadway performance and identification of hazardous conditions. Manual surveys that involve a high degree of subjectivity, a low production rate, and exposure to hazardous conditions are still the most widely used means for evaluating pavement distress. The pavement evaluation subsystem of the FDOT survey vehicle represents an efficient, cost-effective, and safe alternative for the collection and evaluation of pavement distress data. In the research reported in this paper, the precision and accuracy of the pavement imaging subsystem was tested under different lighting conditions, spe...

Evaluation of the Effect of Pavement Roughness on Skid Resistance

Roughness of a pavement surface is commonly correlated to its serviceability. On the other hand, on many occasions, investigators have attributed pavement roughness to inadequate skid resistance (friction) as well. However, current pavement friction evaluation and standardization models have yet to incorporate effects of pavement roughness. Hence a study was conducted to investigate and quantify the effects of pavement roughness on the skid number (SN) (or 100 coefficient of friction). First, an experimental program was executed to evaluate SN measured from a locked wheel tester (LWT) on pavement sections with similar micro- and macrotexture conditions but different levels of roughness. The measured average SN was seen to be significantly lower on relatively rougher pavement sections. To explain the above observations, a second set of experiments was conducted to study the effect of the normal load on the LWT tire on SN. Statistical analysis including regression and ANOVA was used to validate the nonlinear reciprocal relationship found between SN and the normal load which contradicts the general perception of constant SN with respect to the normal load. Then, a one-dimensional two-degrees-of-freedom vibration model was formulated to incorporate the significant dynamic fluctuations of the normal load of the LWT induced by pavement roughness and the vehicle speed. The variation of the normal load and its nonlinear relation to SN was used to explain lower SN values measured on relatively rougher surfaces. The feasibility of using the international roughness index and the dynamic load coefficient as predictors of the reduction in SN due to pavement roughness was also investigated. Assurance of adequate skid resistance is a vital factor considered in allocating pavement rehabilitation funds at the network level. Since excessively rough pavements also create skid hazards, it is concluded that roughness effects must be considered in pavement management systems not only for serviceability purposes, but also in safety evaluations.

Investigation of impact stresses induced in laboratory dynamic compaction of soft soils

The majority of currently available analytical tools to predict ground stresses due to impact are based on linear spring-dashpot dynamic models. Although these simple models adequately represent stiff ground possessing linear visco-elastic behaviour, they suffer from two striking limitations when applied to relatively softer ground; (1) the inability to account for the permanent deformation resulting from impact, (2) failure to incorporate stiffness changes of softer soil within the impact duration. In this paper, the authors present an improved analytical approach formulated on the basis of a series of laboratory impact tests, to address the shortcomings of the current dynamic models in relation to soft soils. In this procedure, the impact zone is modelled as three distinct zones; (1) a zone beneath the falling weight undergoing non-linear axial deformation while being in vertical motion, (2) an inner zone immediately surrounding zone 1 with non-linear shear deformation, and (3) an outer zone undergoing a relatively lower degree of (linear) shear deformation. The soil constitutive parameters pertinent to the model are obtained from a modified dynamic compression test that simulates the impact conditions. It is shown that analytical predictions of the impact stress history and penetration are in agreement with test results. The findings are useful in the exploration of dynamic compaction techniques that will be effective in soft soil improvement.

Study of pore pressures induced in laboratory dynamic consolidation

Abstract Knowledge of the pore pressure behavior during dynamic impacts on moderately low permeability soils is essential in averting possible liquefaction and effective field implementation of dynamic consolidation. Although field observations of dynamically induced pore pressures are abundant in the literature, analytical or numerical approaches for pore pressure prediction are scarce. Herein, the authors advance a simple technique to analytically model the laboratory dynamic consolidation by modifying the classical Terzaghis static consolidation theory. Since the analytical prediction of dynamic surface stress and experimental verification are performed in a companion paper, the surface stress due to a dynamic impact is assumed to be known in this work. Then, the time dependent stress pulse is de-synthesized into a number of constant load steps to predict the subsequent pore pressure behavior. Since this methodology accounts for even the dynamic stress attenuation within the soil sample, a rigorous closed form solution has to be replaced by a numerical solution. It is shown how this solution rapidly converges when the load steps are made sufficiently small. Finally, the analytical predictions of dynamic pore pressure are verified by well controlled laboratory experiments performed on a special apparatus set up at the University of South Florida.