Strong ground motion record selection for the reliable prediction of the mean seismic collapse capacity of a structure group

How to select a limited number of strong ground motion records (SGMRs) is an important challenge for the seismic collapse capacity assessment of structures. The collapse capacity is considered as the ground motion intensity measure corresponding to the drift‐related dynamic instability in the structural system. The goal of this paper is to select, from a general set of SGMRs, a small number of subsets such that each can be used for the reliable prediction of the mean collapse capacity of a particular group of structures, i.e. of single degree‐of‐freedom systems with a typical behaviour range. In order to achieve this goal, multivariate statistical analysis is first applied, to determine what degree of similarity exists between each selected small subset and the general set of SGMRs. Principal Component analysis is applied to identify the best way to group structures, resulting in a minimum number of SGMRs in a proposed subset. The structures were classified into six groups, and for each group a subset of eight SGMRs has been proposed. The methodology has been validated by analysing a first‐mode‐dominated three‐storey‐reinforced concrete structure by means of the proposed subsets, as well as the general set of SGMRs. The results of this analysis show that the mean seismic collapse capacity can be predicted by the proposed subsets with less dispersion than by the recently developed improved approach, which is based on scaling the response spectra of the records to match the conditional mean spectrum. Copyright © 2010 John Wiley & Sons, Ltd.     

Application of first-arrival tomography to characterize a quick clay landslide site in Southwest Sweden

First-arrival traveltime tomography was applied to high-resolution seismic data acquired over a known quick-clay landslide scar near the Göta River in southwest Sweden in order to reveal the geometry and physical properties of clay-related normally consolidated sediments. Investigated area proved to be a challenging environment for tomographic imaging because of large P-wave velocity variations, ranging from 500 to 6000 m/s, and relatively steeply-dipping bedrock. Despite these challenges, P-wave velocity models were obtained down to ca. 150 m for two key 2D seismic profiles (each about 500-m long) intersecting over the landslide scar. The models portrait the sandwich-like structure of marine clays and coarse-grained consolidated sediments, but the estimated resolution (20 m) is too small to distinguish thin layers within this structure. Modelled velocity structures match well the results of reflection seismic processing and resistivity tomography available along the same profiles.     

Mathematical modeling of competition for ammonium among Bacteria, Archaea and cyanobacteria within cyanobacterial mats: Can ammonia-oxidizers force nitrogen fixation?

Molecular analysis of cyanobacterial mat communities indicated that cyanobacteria, ammonia-oxidizing Archaea (AOA), and ammonia-oxidizing bacteria (AOB) coexist in those systems, competing for ammonium; this situation would imply competitive exclusion. We attempted to model how ammonia utilization niche partitioning occurs, and how ammonium levels can influence the interaction between those groups in a one-dimensional diffusionlimited system using Michaelis-Menten kinetics to describe ammonium consumption by each of those three groups. In our model, AOAs were able to dominate ammonium uptake by the community under most circumstances, except for unrealistically high (millimolar) levels of ammonium, where AOBs gained advantage. Cyanobacteria were unable to effectively compete for ammonium with either AOBs or AOAs throughout the mat at all ammonium concentrations and cell counts, suggesting that the presence of AOAs or AOBs forces cyanobacteria into nitrogen fixation mode. Such interaction can make cyanobacterial mats a net nitrogen source, as well as provide a carbon-independent energy transfer pathway from primary producers to the rest of the ecosystem.     

A geometric-based approach for road matching on multi-scale datasets using a genetic algorithm

Object matching is used in various applications including conflation, data quality assessment, updating, and multi-scale analysis. The objective of matching is to identify objects referring to the same entity. This article aims to present an optimization-based linear object-matching approach in multi-scale, multi-source datasets. By taking into account geometric criteria, the proposed approach uses real coded genetic algorithm (RCGA) and sensitivity analysis to identify corresponding objects. Moreover, in this approach, any initial dependency on empirical parameters such as buffer distance, threshold of spatial similarity degree, and weights of criteria is eliminated and, instead, the optimal values for these parameters are calculated for each dataset. Volunteered geographical information (VGI) and authoritative data with different scales and sources were used to assess the efficiency of the proposed approach. According to the results, in addition to an efficient performance in various datasets, the proposed approach was able to appropriately identify the corresponding objects in these datasets by achieving higher F-Score.     

Static and dynamic analysis on slope stability using a DFN-DEM approach on the right abutment of the Karun 4 dam

Uncertainty in input fracture geometric parameters during analysis of the stability of jointed rock slopes is inevitable and therefore the stochastic discrete fracture network (DFN) — distinct element method (DEM) is an efficient modeling tool. In this research, potentially unstable conditions are detected in the right abutment of the Karun 4 dam and downstream of the dam body as a case study. Two extreme states with small and relatively large block sizes are selected and a series of numerical DEM models are generated using a number of validated DFN models. Stability of the rock slope is assessed in both static and dynamic loading states. Based on the design basis earthquake (DBE) and maximum credible earthquake (MCE) expected in the dam site, histories of seismic waves are applied to analyze the stability of the slope in dynamic earthquake conditions. The results indicate that a MCE is likely to trigger sliding of rock blocks on the rock slope major joint. Furthermore, the dynamic analysis also shows a local block failure by the DBE, which can consequently lead to slope instability over the long term. According to the seismic behavior of the two models, larger blocks are prone to greater instability and are less safe against earthquakes.

     

Potential of legacy 2D seismic data for deep targeting and structural imaging at the Neves–Corvo massive sulphide-bearing deposit,Portugal

Seismic methods are becoming an established choice for deep mineral exploration after being extensively tested and employed for the past two decades. To investigate whether the early European mineral-exploration datasets had potential for seismic imaging that was overlooked, we recovered a low-fold legacy seismic dataset from the Neves–Corvo mine site in the Iberian Pyrite Belt in southern Portugal. This dataset comprises six 4–6 km long profiles acquired in 1996 for deep targeting. Using today's industry-scale processing algorithms, the world-class, ca. 150 Mt, Lombador massive sulphide and other smaller deposits were better imaged. Additionally, we also reveal a number of shallow but steeply dipping reflections that were absent in the original processing results. This study highlights that legacy seismic data are valuable and should be revisited regularly to take advantage of new processing algorithms and the experiences gained from processing such data in hard-rock environments elsewhere. Remembering that an initial processing job in hard rock should always aim to first obtain an overall image of the subsurface and make reflections visible, and then subsequent goals of the workflow could be set to, for example understanding relative amplitude ratios. The imaging of the known mineralization implies that this survey could likely have been among one of the pioneer studies in the world that demonstrated the capability of directly imaging massive sulphide deposits using the seismic method.     

Simultaneous estimation of shape factor and depth of subsurface cavities from residual gravity anomalies using feed-forward back-propagation neural networks

We develop a new method of using feed-forward back-propagation (FFBP) neural networks to simultaneously estimate shape factor and depth of gravity anomalies. The advantages compared to neural network methods are the following: no pre-assumptions are made on source shape, the FFBP neural network estimates both depth and shape factor of source bodies and, once trained, works well for any new data in the training space, without repeating the initial calculations.     

An alternative direct method towards mean dynamic topography computations

An alternative “direct method” to “mean dynamic topography” (MDT) computations using satellite altimetry-derived “mean sea surface” (MSS) and “global geopotential model” (GGM), without direct application of the geoid, is devised. The developed approach, which is based on derivation of an equipotential surface of the gravity field of the Earth that fits to global MSS in least squares sense, is formulated via a constrained optimization problem. The validity of our method is numerically tested by computing a global MDT model based on DNSC08 MSS model and EGM2008 GGM as input data.