This paper presents the development, evaluation, calibration, and field application of a novel, relatively inexpensive, vision-based sensor system employing commercially available off-the-shelf devices, for enabling the autonomous data acquisition of road surface conditions. Detailed evaluations and enhancements of a variety of technical approaches and algorithms for overcoming vision-based measurement distortions induced by the motion of the monitoring platform were conducted. It is shown that the proposed multi-sensor system, by capitalizing on powerful data-fusion approaches of the type developed in this paper, can provide a robust cost-effective road surface monitoring system with sufficient accuracy to satisfy typical maintenance needs, in regard to the detection, localization, and quantification of potholes and similar qualitative deterioration features where the measurements are acquired via a vehicle moving at normal speeds on typical city streets. The proposed system is ideal to be used for crowdsourcing where several vehicles would be equipped with this cost-effective system for more frequent data collection of road surfaces. Suggestions for future research needs to enhance the capabilities of the proposed system are included.

The advent of inexpensive digital cameras with depth sensing capabilities (RGB-D cameras) has opened the door to numerous useful applications that need quantitative measures of dynamic fields whose simultaneous time history quantification (at many points as dictated by the resolution of the camera) provides capabilities that were previously accessible only through expensive sensors (e.g., laser scanners). This paper presents a comprehensive experimental and computational study to evaluate the performance envelope of a representative RGB-D sensor (the first generation of Kinect sensor) with the aim of assessing its suitability for the class of problems encountered in the structural dynamics field, where reasonably accurate information of evolving displacement fields (as opposed to few discrete locations) that have simultaneous dynamic planar translational motion with significant rotational (torsional) components. This study investigated the influence of key system parameters of concern in selecting an appropriate sensor for such structural dynamic applications, such as amplitude range, spectral content of the dynamic displacements, location and orientation of sensors relative to target structure, fusing of measurements from multiple sensors, sensor noise effects, rolling-shutter effects, etc. The calibration results show that if the observed displacement field generates discrete (pixel) sensor measurements with sufficient resolution (observed displacements more than 10 mm) beyond the sensor noise floor, then the subject sensors can typically provide reasonable accuracy for transnational motion (about 5%) when the frequency range of the evolving field is within about 10 Hz. However, the expected error for torsional measurements is around 6% for static motion and 10% for dynamic rotation for measurements greater than 5°.

A 3D finite element model (FEM) for predicting the distribution of lateral pressure in a square flexible walled steel silo during the filling phase was analyzed in this study. The numerical approach, developed using Abaqus software, predicts the stress state in bulk solids, as well as the pressures exerted on the silo walls. An elasto-plastic model using the Drucker–Prager criterion was employed to simulate the behavior of the granular materials. The FEM simulates the behavior of the bulk solid and its interaction with the silo’s wall and base using a surface-to-surface discretization model. The model’s predictions were validated by previous experimental measurements. The results revealed good agreement between the FEM predictions and the experimental measurements. The research confirms that the lateral pressure distribution is not uniform at any silo level. This highlights the fact that many available theories and current design codes are not accurate for flexible steel walls. As a result of the wall’s deformability, pressure regimes on the silo wall change significantly in the horizontal direction at any level. The results showed that the horizontal variations of lateral pressure change drastically with regard to wall stiffness. The FEM has been used to investigate the effect of critical parameters on wall pressure distribution, such as properties of bulk solids, wall thickness, and silo type, whether deep or flat.