Mercury intrusion porosimetry test was carried out to investigate the quality of thin concrete cover. Specimens with different water-to-cement ratios were prepared and exposed to different environmental conditions. The results show that the pore structure of the cover is coarser than that of the center portion when the cover is insufficient. With thinner cover, the pore structure becomes coarser. With decreasing water-to-cement ratio, the pore structure becomes finer, and the difference in the pore structure between the cover and the center portion becomes smaller. If the cover is insufficient, not only the distance needed for substances to reach the bar becomes shorter, but also the pore structure becomes coarser. At 91 days, the pore structures of outdoor specimens are almost the same as those of indoor specimens although the exposure conditions are different. To investigate the applicability of the air permeability test and electric resistivity test to evaluate the cover quality when the cover is insufficient, specimens with different water-to-cement ratios and cover depths were prepared. A combination of the air permeability test and electric resistivity test roughly pointed out the poor quality of the insufficient concrete cover and is a useful technique to evaluate the concrete cover in real structures.
<p>Suspended sediment concentration (SSC) is a crucial indicator for aquatic ecosystems and reservoir management but is challenging to predict at large scales. This study seeks to test the feasibility of deep-network-based models to predict SSC at basin outlets given basin-averaged forcings and basin-physiographic attributes as inputs and extract insights by interpreting the spatially-varying model performances. We trained long short-term memory (LSTM) deep networks either separately for each of the 371 sites across the conterminous United States (local models), or on all the sites collectively (Whole-CONUS). The local and Whole-CONUS models presented median Nash-Sutcliffe Efficiency (NSE) values of 0.72 and 0.57, respectively, which are state-of-the-art results. However, this comparison disagrees with our previous &#8220;data synergy&#8221; conclusion for LSTM models and suggests there are still important yet unavailable sediment-related attributes. Both local and Whole-CONUS models tended to be more successful where SSC-streamflow correlations (R<sub>s-q</sub>) were high - typically in the humid Eastern US - and with lower SSC. Low R<sub>s-q</sub> basins were often found in the arid Southwest with higher SSC. The highly-nonlinear SSC-streamflow relationship is arguably due to heterogeneity in land cover and rainfall or limitations in sediment supply, suggesting these basins need to be simulated at higher spatial resolution. The local models mostly outperformed the Whole-CONUS one due to the latter lacking critical attributes, but the latter can be competitive in high-SSC regions with enough flow events. Moreover, the Whole-CONUS model also performed well for basins not included in the training dataset (median NSE=0.55), supporting large-scale modeling.</p>
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