Influence of earthquake ground motion incoherency on multi-support structures

A linear response history analysis method is used to determine the influence of three factors: geometric incoherency, wave-passage, and local site characteristics on the response of multi-support structures subjected to differential ground motions. A one-span frame and a reduced model of a 24-span bridge, located in Las Vegas, Nevada are studied, in which the influence of each of the three factors and their combinations are analyzed. It is revealed that the incoherency of earthquake ground motion can have a dramatic influence on structural response by modifying the dynamics response to uniform excitation and inducing pseudo-static response, which does not exist in structures subjected to uniform excitation. The total response when all three sources of ground motion incoherency are included is generally larger than that of uniform excitation.     

River suspended sediment load prediction based on river discharge information: application of newly developed data mining models

ABSTRACT

Suspended sediment load (SSL) is one of the essential hydrological processes that affects river engineering sustainability. Sediment has a major influence on the operation of dams and reservoir capacity. This investigation is aimed at exploring a new version of machine learning models (i.e. data mining), including M5P, attribute selected classifier (AS M5P), M5Rule (M5R), and K Star (KS) models for SSL prediction at the Trenton meteorological station on the Delaware River, USA. Different input scenarios were examined based on the river flow discharge and sediment load database. The performance of the applied data mining models was evaluated using various statistical metrics and graphical presentation. Among the applied data mining models, the M5P model gave a superior prediction result. The current and one-day lead time river flow and sediment load were the influential predictors for one-day-ahead SSL prediction. Overall, the applied data mining models achieved excellent predictions of the SSL process.     

Integrative stochastic model standardization with genetic algorithm for rainfall pattern forecasting in tropical and semi-arid environments

ABSTRACT

Climate patterns, including rainfall prediction, is one of the most complex problems for hydrologist. It is inherited by its natural and stochastic phenomena. In this study, a new approach for rainfall time series forecasting is introduced based on the integration of three stochastic modelling methods, including the seasonal differencing, seasonal standardization and spectral analysis, associated with the genetic algorithm (GA). This approach is specially tailored to eradicate the periodic pattern effects notable on the rainfall time series stationarity behaviour. Two different climates are selected to evaluate the proposed methodology, in tropical and semi-arid regions (Malaysia and Iraq). The results show that the predictive model registered an acceptable result for the forecasting of rainfall for both the investigated regions. The attained determination coefficient (R²) for the investigated stations was approx. 0.91, 0.90 and 0.089 for Mosul, Baghdad and Basrah (Iraq), and 0.80, 0.87 and 0.94 for Selangor, Negeri Sembilan and Johor (Malaysia).     

Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis

ABSTRACT

Ensemble machine learning models have been widely used in hydro-systems modeling as robust prediction tools that combine multiple decision trees. In this study, three newly developed ensemble machine learning models, namely gradient boost regression (GBR), AdaBoost regression (ABR) and random forest regression (RFR) are proposed for prediction of suspended sediment load (SSL), and their prediction performance and related uncertainty are assessed. The SSL of the Mississippi River, which is one of the major world rivers and is significantly affected by sedimentation, is predicted based on daily values of river discharge (Q) and suspended sediment concentration (SSC). Based on performance metrics and visualization, the RFR model shows a slight lead in prediction performance. The uncertainty analysis also indicates that the input variable combination has more impact on the obtained predictions than the model structure selection.     

Groundwater vulnerability assessment and evaluation of human activity impact (HAI) within the Dead Sea groundwater basin,Jordan

Groundwater vulnerability to contamination was determined within the Dead Sea groundwater basin, Jordan, using the DRASTIC model and evaluation of human activity impact (HAI). DRASTIC is an index model composed of several hydrogeological parameters and, in this study, the recharge parameter component was calculated as a function of rainfall, soil permeability, slope percentage, fault system, and the intersection locations between the fault system and the drainage system, based on the hydrogeologic characteristics of hard-rock terrain in an arid region. To evaluate the HAI index, a land use/cover map was produced using an ASTER VNIR image, acquired for September 2004, and combined with the resultant DRASTIC model. By comparing the DRASTIC and HAI indices, it is found that human activity is affecting the groundwater quality and increasing its pollution risk. The land use/cover map was verified using the average nitrate concentrations in groundwater associated with land in each class. A sensitivity analysis was carried out in order to study the model sensitivity. The analyses showed that the depth to water table and hydraulic conductivity parameters have no significant impact on the model, whereas the impact of vadose zone, aquifer media, and recharge parameters have a significant impact on the DRASTIC model.     

Three-Dimensional Wideband Beamforming for Imaging Through a Single Wall

Through-the-wall imaging and urban sensing is an emerging area of research and development. The incorporation of the effects of signal propagation through wall material in producing an indoor image is important for reliable through-the-wall mission operations. We have previously analyzed wall effects, such as refraction and change in propagation speed, and designed a wideband beamformer for 2D imaging using line arrays. In this letter, we extend the analysis to 3D imaging via delay-and-sum beamforming in the presence of a single uniform wall. The third dimension provides valuable information on target heights that can be used for enhancing target discrimination/identification. Supporting simulation results are provided.     

Analysis of liquefaction susceptibility of nearly saturated sands

A new constitutive formulation for simulating the behaviour of nearly saturated sands under seismic loads is presented. The formulation is based on combining the Henry's law for dissolution of gas in water, the ideal or perfect gas law and the law of conservation of mass. The effects of transient air dissolution in water on the compressibility of partially saturated soils are also taken into account. The model was calibrated based on numerical simulations of isotropically consolidated cyclic triaxial tests conducted on partially saturated samples of Toyoura sand. A multi‐yield plasticity soil constitutive model implemented in the finite element code DYNAFLOW was used for these numerical simulations. It is shown that the formulation proposed here is able to reasonably predict the soil cyclic undrained behaviour at various degrees of saturation (95% and higher). Copyright © 2006 John Wiley & Sons, Ltd.