Infrastructures, Volume 2, Issue 3 (September 2017) – 3 articles

Infiltrative rain gardens can add retention capacity to sewersheds, yet factors contributing to their capacity for detention and redistribution of stormwater runoff are dynamic and often unverified. Over a four-year period, we tracked whole-system water fluxes in a two-tier rain garden network and assessed near-surface hydrology and soil development across construction and operational phases. The monitoring data provided a quantitative basis for determining effectiveness of this stormwater control measure. Based on 233 monitored warm-season rainfall events, nearly half of total inflow volume was detained, with 90 percent of all events producing no flow to the combined sewer. For the events that did result in flow to the combined sewer system, the rain garden delayed flows for an average of 5.5 h. Multivariate analysis of hydrologic fluxes indicated that total event rainfall depth was a predominant hydrologic driver for network outflow during both phases, with average event intensity and daily evapotranspiration as additional, independent factors in regulating retention in the operational phase. Despite sediment loads that can clog the rooting zone, and overall lower-than-design infiltration rates, tradeoffs among soil profile development and hydrology apparently maintained relatively high overall retention effectiveness. Overall, our study identified factors relevant to regulation of retention capacity of a rain garden network. These factors may be generalizable, and guide improvement of new or existing rain garden designs. Full article

Retaining walls are critical geotechnical assets and their performance needs to be monitored in accordance to transportation asset management principles. Current practices for retaining wall monitoring consist mostly of qualitative approaches that provide limited engineering information or the methods include traditional geodetic surveying, which may provide high accuracy and reliability, but is costly and time-consuming. This study focuses on evaluating failure modes of a 2.43 m × 2.43 m retaining wall model using three-dimensional (3D) photogrammetry as a cost-effective quantitative alternative for retaining wall monitoring. As a remote sensing technique, photogrammetry integrates images collected from a camera and creates a 3D model from the measured data points commonly referred to as a point cloud. The results from this photogrammetric approach were compared to ground control points surveyed with a total station. The analysis indicates that the accuracy of the displacement measurements between the traditional total station survey and photogrammetry were within 1–3 cm. The results are encouraging for the adoption of photogrammetry as a cost-effective monitoring tool for the observation of spatial changes and failure modes for retaining wall condition assessment. Full article

Active contour models present a robust segmentation approach, which makes efficient use of specific information about objects in the input data rather than processing all of the data. They have been widely-used in many applications, including image segmentation, object boundary localisation, motion tracking, shape modelling, stereo matching and object reconstruction. In this paper, we investigate the potential of active contour models in extracting road edges from Mobile Laser Scanning (MLS) data. The categorisation of active contours based on their mathematical representation and implementation is discussed in detail. We discuss an integrated version in which active contour models are combined to overcome their limitations. We review various active contour-based methodologies, which have been developed to extract road features from LiDAR and digital imaging datasets. We present a case study in which an integrated version of active contour models is applied to extract road edges from MLS dataset. An accurate extraction of left and right edges from the tested road section validates the use of active contour models. The present study provides valuable insight into the potential of active contours for extracting roads from 3D LiDAR point cloud data. Full article