Performance evaluation of steel reduced flange plate moment connections

This study details a new moment connection that overcomes difficulties in achieving field‐weld quality and eliminates steel beam buckling encountered in steel moment connections. This study presents cyclic test and finite element analysis results of full‐scale subassemblies using steel reduced flange plates (RFPs) to connect steel beam flanges and the column without any other direct connection. Since the RFP connection is designed as strong column‐strong beam‐weak RFPs, the RFP functions as a structural fuse that eliminates weld fractures and beam buckling. Test and analytical results show that (1) the connections transferred the entire beam flexural strength to the column and reached an interstorey drift of 4% with minor strength degradation, (2) failure of the connections was owing to buckling or fracturing of the RFP and not of the beam, and (3) the RFP connection subassembly, modelled using the nonlinear finite element computer program ABAQUS, exhibited hysteretic behaviour similar to that of the flange plate (FP) moment connection subassembly. The inelastic buckling force of the RFP was also evaluated by nonlinear regression analyses performed on a nonlinear model that relates buckling force to RFP geometries. Copyright © 2007 John Wiley & Sons, Ltd.     

Parameter identification for leaky aquifers using global optimization methods

In the past, graphical or computer methods were usually employed to determine the aquifer parameters of the observed data obtained from field pumping tests. Since we employed the computer methods to determine the aquifer parameters, an analytical aquifer model was required to estimate the predicted drawdown. Following this, the gradient‐type approach was used to solve the nonlinear least‐squares equations to obtain the aquifer parameters. This paper proposes a novel approach based on a drawdown model and a global optimization method of simulated annealing (SA) or a genetic algorithm (GA) to determine the best‐fit aquifer parameters for leaky aquifer systems. The aquifer parameters obtained from SA and the GA almost agree with those obtained from the extended Kalman filter and gradient‐type method. Moreover, all results indicate that the SA and GA are robust and yield consistent results when dealing with the parameter identification problems. Copyright © 2006 John Wiley & Sons, Ltd.     

Online conservative generalized multiscale finite element method for highly heterogeneous flow models

Computational Geosciences - In this work, we consider an online enrichment procedure in the context of the Generalized Multiscale Finite Element Method (GMsFEM) for the two-phase flow model in...     

Surveillance of waste disposal activity at sea using satellite ocean color imagers: GOCI and MODIS

Korean Geostationary Ocean Color Imager (GOCI) and Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua observations of the variation in ocean color at the sea surface were utilized to monitor the impact of nutrient-rich sewage sludge disposal in the oligotrophic area of the Yellow Sea. MODIS revealed that algal blooms persisted in the spring annually at the dump site in the Yellow Sea since year 2000 to the present. A number of implications of using products of the satellite ocean color imagers were exploited here based on the measurements in the Yellow Sea. GOCI observes almost every hour during the daylight period, every day since June 2011. Therefore, GOCI provides a powerful tool to monitor waste disposal at sea in real time. Tracking of disposal activity from a large tanker was possible hour by hour from the GOCI timeseries images compared to MODIS. Smaller changes in the color of the ocean surface can be easily observed, as GOCI resolves images at smaller scales in space and time in comparison to polar orbiting satellites, e.g., MODIS. GOCI may be widely used to monitor various marine activities in the sea, including waste disposal activity from ships.     

On Blind Tests and Spatial Prediction Models

This contribution discusses the usage of blind tests, BT, to cross-validate and interpret the results of predictions by statistical models applied to spatial databases. Models such as Bayesian probability, empirical likelihood ratio, fuzzy sets, or neural networks were and are being applied to identify areas likely to contain events such as undiscovered mineral resources, zones of high natural hazard, or sites with high potential environmental impact. By processing the information in a spatial database, the models establish the relationships between the distribution of known events and their contextual settings, described by both thematic and continuous data layers. The relationships are to locate situations where similar events are likely to occur. Maps of predicted relative resource potential or of relative hazard/impact levels are generated. They consist of relative values that need careful quantitative scrutiny to be interpreted for taking decisions on further action in exploration or on hazard/impact mitigation and avoidance. The only meaning of such relative values is their rank. Obviously, to assess the reliability of the predicted ranks, tests are indispensable. This is also a consequence of the impracticality of waiting for the future to reveal the goodness of our prediction. During the past decade only a few attempts have been made by some researchers to cross-validate the results of spatial predictions. Furthermore, assumptions and applications of cross-validations differ considerably in a number of recent case studies. A perspective for all such experiments is provided using two specific examples, one in mineral exploration and the other in landslide hazard, to answer the fundamental question: how good is my prediction?     

Development of a seaweed species-selection index for successful culture in a seaweed-based integrated aquaculture system

Integrated multi-trophic aquaculture (IMTA) has been proposed as a concept that combines the cultivation of fed aquaculture species (e.g., finfish/shrimp) with extractive aquaculture species (e.g., shellfish/seaweed). In seaweed-based integrated aquaculture, seaweeds have the capacity to reduce the environmental impact of nitrogen-rich effluents on coastal ecosystems. Thus, selection of optimal species for such aquaculture is of great importance. The present study aimed to develop a seaweed species-selection index for selecting suitable species in seaweed-based integrated aquaculture system. The index was synthesized using available literature-based information, reference data, and physiological seaweed experiments to identify and prioritize the desired species. Undaria pinnatifida, Porphyra yezoensis and Ulva compressa scored the highest according to a seaweed-based integrated aquaculture suitability index (SASI). Seaweed species with the highest scores were adjudged to fit the integrated aquaculture systems. Despite the application of this model limited by local aquaculture environment, it is considered to be a useful tool for selecting seaweed species in IMTA.     

Diagnosing Climate Feedbacks in Coupled Ocean–Atmosphere Models

We review the methodologies used to quantify climate feedbacks in coupled models. The method of radiative kernels is outlined and used to illustrate the dependence of lapse rate, water vapor, surface albedo, and cloud feedbacks on (1) the length of the time average used to define two projected climate states and (2) the time separation between the two climate states. Except for the shortwave component of water vapor feedback, all feedback processes exhibit significant high-frequency variations and intermodel variability of feedback strengths for sub-decadal time averages. It is also found that the uncertainty of lapse rate, water vapor, and cloud feedback decreases with the increase in the time separation. The results suggest that one can substantially reduce the uncertainty of cloud and other feedbacks with the accumulation of accurate, long-term records of satellite observations; however, several decades may be required.     

Interpolations of groundwater table elevation in dissected uplands

The variable elevation of the groundwater table in the St. Louis area was estimated using multiple linear regression (MLR), ordinary kriging, and cokriging as part of a regional program seeking to assess liquefaction potential. Surface water features were used to determine the minimum water table for MLR and supplement the principal variables for ordinary kriging and cokriging. By evaluating the known depth to the water and the minimum water table elevation, the MLR analysis approximates the groundwater elevation for a contiguous hydrologic system. Ordinary kriging and cokriging estimate values in unsampled areas by calculating the spatial relationships between the unsampled and sampled locations. In this study, ordinary kriging did not incorporate topographic variations as an independent variable, while cokriging included topography as a supporting covariable. Cross validation suggests that cokriging provides a more reliable estimate at known data points with less uncertainty than the other methods. Profiles extending through the dissected uplands terrain suggest that: (1) the groundwater table generated by MLR mimics the ground surface and elicits a exaggerated interpolation of groundwater elevation; (2) the groundwater table estimated by ordinary kriging tends to ignore local topography and exhibits oversmoothing of the actual undulations in the water table; and (3) cokriging appears to give the realistic water surface, which rises and falls in proportion to the overlying topography. The authors concluded that cokriging provided the most realistic estimate of the groundwater surface, which is the key variable in assessing soil liquefaction potential in unconsolidated sediments.