Scour hole depth prediction around pile groups: review,comparison of existing methods,and proposition of a new approach

Flooding is the most costly natural hazard event worldwide and can severely impact communities, both through economic losses and social disruption. To predict and reduce the flood risk facing a community, a reliable model is needed to estimate the cost of repairing flood-damaged buildings. In this paper, we describe the development and assessment of two models for predicting direct economic losses for single-family residential buildings, based on the experience of the 2013 Boulder, Colorado riverine floods. The first model is based on regression analyses on empirical data from over 3000 residential building damage inspections conducted by the Federal Emergency Management Agency (FEMA). This model enables a probabilistic assessment of loss (in terms of FEMA grants paid to homeowners for post-flood repairs) as a function of key building and flood hazard parameters, considering uncertainties in structural properties, building contents, and damage characteristics at a given flood depth. The second model is an assembly-based prediction of loss considering unit prices for damaged building components to predict mean repair costs borne by the homeowner, which is based on typical Boulder construction practices and local construction and material costs. Comparison of the two proposed models illustrates benefits that arise from each of the two approaches, while also serving to validate both models. These models can be used as predictive tools in the future, in Boulder and other US communities, due to adaptability of the model for other context, and similarities in home characteristics across the country. The assembly-based model quantifies the difference between the FEMA grants and true losses, providing a quantification of out-of-pocket homeowner expenses.     

Magmatic and metamorphic history of the Deh-Salm metamorphic Complex, Eastern Lut block, (Eastern Iran), from U–Pb geochronology

The Deh-Salm metamorphic Complex (DMC) of the Lut block in East Iran consists of metapelites, amphibolites, marbles, and metasandstones intruded by granite and pegmatites. U–Pb dating of zircon, monazite, xenotime, and titanite by ID-TIMS show that the granitic rocks were emplaced at 166–163 Ma, confirming that the high temperature metamorphism was synchronous with the intrusive activity, and that the region cooled rapidly thereafter. Late- to post-magmatic hydrothermal activity was probably responsible for the late crystallization, at 159.5 Ma, of zircon and titanite in an amphibolite and of monazite in granite. Xenocrystic zircons yield indications for a Carboniferous component in the source, together with a variety of Precambrian ages, which indicate a provenance of the sedimentary protolith from mature continental crust. The timing and rapidity of the events are consistent with evolution of the DMC in a back-arc environment during the Jurassic subduction of the Neotethys Ocean.     

Influence of Boundary Condition Types on Unstable Density‐Dependent Flow

Boundary conditions are required to close the mathematical formulation of unstable density‐dependent flow systems. Proper implementation of boundary conditions, for both flow and transport equations, in numerical simulation are critical. In this paper, numerical simulations using the FEFLOW model are employed to study the influence of the different boundary conditions for unstable density‐dependent flow systems. A similar set up to the Elder problem is studied. It is well known that the numerical simulation results of the standard Elder problem are strongly dependent on spatial discretization. This work shows that for the cases where a solute mass flux boundary condition is employed instead of a specified concentration boundary condition at the solute source, the numerical simulation results do not vary between different convective solution modes (i.e., plume configurations) due to the spatial discretization. Also, the influence of various boundary condition types for nonsource boundaries was studied. It is shown that in addition to other factors such as spatial and temporal discretization, the forms of the solute transport equation such as divergent and convective forms as well as the type of boundary condition employed in the nonsource boundary conditions influence the convective solution mode in coarser meshes. On basis of the numerical experiments performed here, higher sensitivities regarding the numerical solution stability are observed for the Adams‐Bashford/Backward Trapezoidal time integration approach in comparison to the Euler‐Backward/Euler‐Forward time marching approach. The results of this study emphasize the significant consequences of boundary condition choice in the numerical modeling of unstable density‐dependent flow.     

Mineral chemistry,petrogenesis and evolution of the Ghorveh-Seranjic skarn,Northern Sanandaj Sirjan Zone,Iran

Mineralogy and Petrology - The Ghorveh-Seranjic (GS) skarn is located in the northern part of the Sanandaj-Sirjan zone, NW Iran, which is part of Alpine-Himalaya orogenic belt. The GS metamorphic...     

Improved Estimation of Shear-Wave Velocity by Ordered Weighted Averaging of Rock Physics Models in a Carbonate Reservoir

Natural Resources Research - The aim of this paper is to improve estimation of shear-wave velocity in carbonate rocks. The region being studied is an oil field located in southwest Iran, where...     

Earth surface deformation analysis of 2005 Qeshm earthquake based on three-dimensional displacement field derived from radar imagery measurements

We derived the 3D vector displacement field due to the 5.9 Mw Qeshm island (Iran) earthquake using ascending and descending interferograms and azimuth offsets obtained from ENVISAT ASAR data. The pick-to-pick estimated displacement was 10 cm in west, 69 cm in south and 22 cm in vertical directions. We then used strain analysis to study coseismic surface deformation of the earthquake. Finite differences and finite element as two numerical solutions were applied in order to compute the strain tensors. Furthermore, dilation and shear parameters were derived using the strain tensors. Finite differences results showed the maximum expansion of 0.002 and maximum contraction of 0.003. The amounts of maximum shear in xy, xz and yz planes were estimated using finite differences method as 0.05, 0.1 and 0.049, respectively. The maximum expansion and contraction were computed as 0.006 and 0.005, respectively, using finite element approach. Moreover, the maximum shear in xy, xz and yz planes obtained by finite element method was 0.2, 0.4 and 0.19, respectively.     

Machine learning for accelerating 2D flood models: Potential and challenges

Two-dimensional hydrodynamic models numerically solve full Shallow Water Equations (SWEs). Despite their high accuracy, these models have long simulation run times and therefore are of limited use for exploratory or real-time flood predictions. We investigated the possibility of improving flood modelling speed using Machine Learning (ML). We propose a new method that replaces the computationally expensive parts of the hydrodynamic models with simple and efficient data-driven approximations. Our hypothesis is that by integrating ML with physics-based numerical methods, we can achieve improved generalization performance: that is, the trained model for one case study can be used in other studies without the need for new training. We tested two ML approaches: for the first, we integrated curve fitting, and, for the second, artificial neural networks (ANN) with a finite volume scheme to solve the local inertial approximation of the SWEs. The data-driven models approximated the Momentum Equation, which explicitly solved the time derivative of flow rates. Water depths were then updated by applying a water balance equation. We also tested two different training datasets: the simulated dataset, generated from the results of hydrodynamic model, and the random dataset, generated by directly solving the momentum equation on randomly sampled input data. Various combinations of input features, for example, water slope and depth, were explored. The proposed models were trained in a small hypothetical case and tested in a different hypothetical and in two real case studies. Results showed that the curve-fitting method can be implemented successfully, given sufficient training and input data. The ANN model trained with a random dataset was substantially more accurate than that of the model trained with the simulated dataset. However, it was not successful in the real case studies. The curve-fitting method resulted in better generalization performance and increased the simulation speed of the local inertial model by 23%. Future research should test the performance of ML in terms of an increase in stable time step size and approximation of the full SWEs.     

Numerical simulation of wave generated by landslide incidents in dam reservoirs

In this work, a two-dimensional fourth-order Boussinesq-type numerical model is applied to estimate the impact of landslide-generated waves in dam reservoirs. This numerical model has recently been extended for simulating subaerial landslides. The extended model is validated using available three-dimensional experimental data, and a good agreement is obtained. The numerical model is then employed to investigate the impact of landslide-generated waves in two real cases, the Maku and Shafa-Roud dam reservoirs in the northwestern and the north of Iran, respectively. Generated wave heights, wave run-up, maximum wave height above dam crest, and dam overtopping volume have been estimated for each case. The amplitude of generated waves about 18 and 31 m and the volume of dam overtopping up to 80,000 m³ emphasize the importance of the estimation of the landslide-generated waves in dam sites.