Tong Qiu

Remote and real-time displacement measurements are crucial for a successful bridge health monitoring program. Researchers have attempted to monitor the deformation of bridges using remote sensing techniques such as an accelerometer when a static reference frame is not available. However, errors accumulate throughout the double-integration process, significantly reducing the reliability and accuracy of the displacement measurements. To obtain accurate reference-free bridge displacement measurements, this paper aims to develop a real-time computing algorithm based on hybrid sensor data fusion and implement the algorithm via smart sensing technology. By combining the accelerometer and strain gauge measurements in real time, the proposed algorithm can overcome the limitations of the existing methods (such as integration errors, sensor drifts, and environmental disturbances) and provide real-time pseud-static and dynamic displacement measurements of bridges under loads. A wireless sensor, SmartRock, containing multiple sensing units (i.e., triaxial accelerometer and strain gauges) and a Micro Controlling Unit (MCU) were utilized for remote data acquisition and signal processing. A remote sensing system (with SmartRocks, an antenna, an industrial computer, a Wi-Fi hotspot, etc.) was deployed, and a laboratory truss bridge experiment was conducted to demonstrate the implementation of the algorithm. The results show that the proposed algorithm can estimate a bridge displacement with sufficient accuracy, and the remote system is capable of the real-time monitoring of bridge deformations compared to using only one type of sensor. This research represents a significant advancement in the field of bridge displacement monitoring, offering a reliable and reference-free approach for remote and real-time measurements.

Recent small-scale studies for pixel-wise labeling of potential landslide areas in remotely-sensed images using deep learning (DL) showed potential but were based on data from very small, homogeneous regions with unproven model transferability. In this paper we consider a more realistic and practical setting for large-scale heterogeneous landslide data collection and DL-based labeling. In this setting, remotely sensed images are collected sequentially in temporal batches, where each batch focuses on images from a particular ecoregion, but different batches can focus on different ecoregions with distinct landscape characteristics. For such a scenario, we study the following questions: (1) How well do DL models trained in homogeneous regions perform when they are transferred to different ecoregions? (2) Does increasing the spatial coverage in the data improve model performance in a given ecoregion? and (3) Can a landslide pixel labeling model be incrementally updated with new data, but without access to the old data and without losing performance on the old data? We address these questions by developing a mechanism for incremental training of semantic segmentation models. We call the resulting extension task-specific model updates (TSMU). A national compilation of landslide inventories by the U.S. Geological Survey (USGS) was used to develop a global database for this study. Our results indicate that the TSMU framework can be used to aid in the creation of new landslide inventories or expanding existing datasets, and also to rapidly develop hazard maps for situational awareness following a widespread landslide event.

Road embankments in the mountainous terrains are typically constructed using locally available stones through dynamic compaction (DC) because of the relatively low cost; however, the filling quality of large stones is difficult to control with limited research and field performance data available. This article presents the results of a field study for estimating the construction parameters including effective improvement depth, number of tampering points, and lateral influence distance for a DC of a stone-filled embankment. The pilot field-testing program consisted of roller compaction and DC. The field test data included tamping settlement and surface uplift after each hammer drop. Static and dynamic soil pressures were measured, and the former was used to correlate with compacted fill density. The field data show a strong correlation between the static soil pressure increment and compacted fill density. Based on this correlation, it was found that (1) in order for each DC point to achieve the target compacted fill density, it was necessary for tampering to be done a total of eight times; (2) the effective improvement depth was approximately 5 m; and (3) the lateral influence distance was approximately 2.5 m and, hence, a horizonal interval of 5 m was determined for the layout of tampering points in the field construction. The good in-service performance of the completed embankment and paved road suggests that the pilot field-testing program was a success in determining the construction parameters.

Soil conditioning plays an important role in reducing soil abrasivity and wear of cutting tools in earth pressure balance shield tunneling, leading to service life extension of cutting tools. In this article, an experimental study to assess the abrasivity of conditioned soil with different conditioners, namely foam and slurry, was carried out using a soil abrasion tester. The abrasive characteristics of the tested soil samples were quantitatively assessed through the measurement of weight loss of the steel cubes fixed on the propeller. Preliminary test results indicate that both foam and slurry can modify the sandy gravel to a suitable state according to slump test and decrease soil abrasivity significantly. The torque, which reflects the interaction between the propeller and tested materials, is used to provide insights into the soil-tool contact. Potential relationships between tool wear and tool position are also investigated.