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Abstract: This study explores the challenges and methodologies involved in implementing bridge weigh-in-motion (B-WIM) system on a century-old steel riveted railway bridge. A unique aspect of this study, funded by the EU H2020 Shift2Rail Joint Undertaking, was the adaptation of B-WIM systems to the specific constraints of railway bridges since, traditionally, this technology is used to collect heavy gross vehicle loading data on road bridges. The paper details the experimental setup on an old steel-riveted railway bridge, including sensor placement and calibration processes. It highlights the complexities encountered, such as differences in bridge response due to passenger and other type of trains. It introduces the system calibration strategy using known axle loads from passenger locomotives. The study provides insights into the structural response of old steel bridges under traffic loads, contributing valuable data to the field of railway bridge monitoring and maintenance.
Keywords: bridge, B-WIM, bridge Weigh-in-Motion system, numerical model updating, bridge response measurements, railways
Published in DiRROS: 14.08.2024; Views: 26; Downloads: 49
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Abstract: In uneven-aged forests, trees of different diameters, heights, and ages are located in a small area, which is due to the felling of individual trees or groups of trees, as well as small-scale natural disturbances. In this article, we present an objective method for classifying forest stands into even- and uneven-aged stands based on freely available low-resolution (with an average recording density of 5 points/m2) national lidar data. The canopy closure, dominant height, and canopy height diversity from the canopy height model and the voxels derived from lidar data were used to classify the forest stands. Both approaches for determining forest structural diversity (canopy height diversity—CHDCHM and CHDV) yielded similar results, namely two clusters of even- and uneven-aged stands, although the differences in vertical diversity between even- and uneven-aged stands were greater when using CHM. The first analysis, using CHM for the CHD assessment, estimated the uneven-aged forest area as 49.3%, whereas the second analysis using voxels estimated it as 34.3%. We concluded that in areas with low laser scanner density, CHM analysis is a more appropriate method for assessing forest stand height heterogeneity. The advantage of detecting uneven-aged structures with voxels is that we were able to detect shade-tolerant species of varying age classes beneath a dense canopy of mature, dominant trees. The CHDCHM values were estimated to be 1.83 and 1.86 for uneven-aged forests, whereas they were 1.57 and 1.58 for mature even-aged forests. The CHDV values were estimated as 1.50 and 1.62 for uneven-aged forests, while they were 1.33 and 1.48 for mature even-aged forests. The classification of stands based on lidar data was validated with data from measurements on permanent sample plots. Statistically significantly lower average values of the homogeneity index and higher values of the Shannon–Wiener index from field measurements confirm the success of the classification of stands based on lidar data as uneven-aged forests.
Keywords: uneven-aged forest, lidar data, canopy height model, voxels, canopy height diversity
Published in DiRROS: 13.08.2024; Views: 40; Downloads: 495
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Abstract: We propose a new point-prediction model, the DEep Learning WAVe Emulating model (DELWAVE), which successfully emulates the behaviour of a numerical surface ocean wave model (Simulating WAves Nearshore, SWAN) at a sparse set of locations, thus enabling numerically cheap large-ensemble prediction over synoptic to climate timescales. DELWAVE was trained on COSMO-CLM (Climate Limited-area Model) and SWAN input data during the period of 1971–1998, tested during 1998–2000, and cross-evaluated over the far-future climate time window of 2071–2100. It is constructed from a convolutional atmospheric encoder block, followed by a temporal collapse block and, finally, a regression block. DELWAVE reproduces SWAN model significant wave heights with a mean absolute error (MAE) of between 5 and 10 cm, mean wave directions with a MAE of 10–25°, and a mean wave period with a MAE of 0.2 s. DELWAVE is able to accurately emulate multi-modal mean wave direction distributions related to dominant wind regimes in the basin. We use wave power analysis from linearised wave theory to explain prediction errors in the long-period limit during southeasterly conditions. We present a storm analysis of DELWAVE, employing threshold-based metrics of precision and recall to show that DELWAVE reaches a very high score (both metrics over 95 %) of storm detection. SWAN and DELWAVE time series are compared to each other in the end-of-century scenario (2071–2100) and compared to the control conditions in the 1971–2000 period. Good agreement between DELWAVE and SWAN is found when considering climatological statistics, with a small (≤ 5 %), though systematic, underestimate of 99th-percentile values. Compared to control climatology over all wind directions, the mismatch between DELWAVE and SWAN is generally small compared to the difference between scenario and control conditions, suggesting that the noise introduced by surrogate modelling is substantially weaker than the climate change signal.
Keywords: surrogate modelling, deep learning, DEep Learning WAVe Emulating model, DELWAVE, Simulating WAves Nearshore, SWAN
Published in DiRROS: 05.08.2024; Views: 106; Downloads: 78
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Abstract: For mitigation of the effects of pollution, the media, policy makers and, in turn, the scientific community and industry each provide contributions through development of a sense of urgency, and with guidelines and solutions. For non-indigenous species (NIS) that can frequently have negative impacts on the native biota, this is often conveyed in an emotive way to the general public, who are typically keen to help and to get personally involved. This might be through organization of cleaning campaigns, influence on the media, or collaboration with scientists, to inform them of the local presence and abundance of NIS. Alternatively, they might proactively develop technological solutions themselves. To assess the current state of affairs, we reviewed the presence and effects of NIS in the Mediterranean Sea. As so often, any well-planned and successful activity is directly linked to financing, or a lack thereof, and this leads to sometimes untargeted and sporadic measures that are developed within a project or over a limited timeframe, without any sustainability measures. Therefore, we also assessed the activities and strategies that have been financed in this area of NIS mitigation. Based on this review of the presence and impact of NIS, and previous and ongoing activities, we propose a new paradigm to mitigate such pollution: the 8Rs model (i.e., Recognize, Reduce, Replace, Reuse, Recycle, Recover/Restore, Remove, and Regulate). This model extends from the more traditional 3Rs model (i.e., Reduce, Reuse, and Recycle) that is often used and promoted for innovative waste management strategies. Importantly, the 8Rs model can be applied sequentially, for either prevention of NIS introduction, or preparation of mitigation measures. The 8Rs model was constructed based on Mediterranean NIS, although we believe it can be applied to other sources of pollution and other geographic areas. Importantly, the 8Rs model represents a general framework to organize and categorize future pollution mitigation strategies. This approach is essential for development of any action plan to influence the administrative and financial decision makers who essentially enable these activities, and therefore who have important roles in the guarantee of sustainability of these actions, and the creation of innovative societies.
Keywords: non-indigenous species, pollution mitigation, pollution mitigation, 8Rs model, quadruple helix, Mediterranean Sea, citizen science, communication, sustainability
Published in DiRROS: 22.07.2024; Views: 129; Downloads: 129
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Abstract: This study investigates the potential of ensemble forecasting using full realistic high-resolution nested atmosphere–ocean models for the prediction of meteotsunamis in Ciutadella (Menorca, Spain). The sensitivity of model results to the parameterizations of the atmospheric model is assessed considering the ten most significant recent meteotsunami events for which observations are available. Different schemes adapted to high-resolution Weather Research and Forecasting model simulations were used for the representation of cumulus, microphysics, planetary boundary layer and longwave and shortwave radiations. Results indicate a large spread of the ensemble simulations in terms of the final magnitude of the meteotsunamis. While the modeling system is shown to be able to realistically trigger tsunamigenic atmospheric disturbances in more than 90% of the situations, the small-scale characteristics of these disturbances are significantly modified with the change of parameterizations, leading to significant differences in the magnitude of the simulated sea-level response. No preferred set of parameterizations can be identified that leads to either the largest or the most realistic magnitudes in the majority of situations. Instead, the performance of a given set of parameterizations is found to change with the meteotsunami event under consideration. Importantly, the observed magnitude of the extreme sea-level oscillations lies within the range of a nine-member ensemble in 70% of the cases. This ensemble approach would then allow to generate a realistic range of possibilities in the majority of events, thus improving the realism of meteotsunami predictions compared to single deterministic forecasts.
Keywords: meteotsunamis prediction, atmosphere, ocean modeling, ensemble forecasting, atmospheric model parameterizations
Published in DiRROS: 19.07.2024; Views: 117; Downloads: 103
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