Parameter identification for lined tunnels in a viscoplastic medium

This paper is dedicated to the identification of constitutive parameters of elasto‐viscoplastic constitutive law from measurements performed on deep underground cavities (typically tunnels). This inverse problem is solved by the minimization of a cost functional of least‐squares type. The exact gradient is computed by the direct differentiation method and the descent is done using the Levenberg–Marquardt algorithm. The method is presented for lined or unlined structures and is applied for an elastoviscoplastic constitutive law of the Perzyna class. Several identification problems are presented in one and two dimensions for different tunnel geometries. The used measurements have been obtained by a preliminary numerical simulation and perturbed with a white noise. The identified responses match the measurements. We also discuss the usage of the sensitivity analysis of the system, provided by the direct differentiation method, for the optimization of in situ monitoring. The sensitivity distribution in space and time assess the location of the measurements points as well as the time of observation needed for reliable identification. Copyright © 2002 John Wiley & Sons, Ltd.     

Sea cliffs resulting from late Miocene extensional tectonics: the Serra Gelada case study (Betic Cordillera, Spain)

The Serra Gelada sea cliffs are carved in Mesozoic carbonate rocks belonging to the External Zones of the eastern Betic Cordillera (Alicante, SE Spain). Several normal faults with vertical slips of more than a hundred metres have played an important role in the origin of this coastline. Some previous studies propose that the present cliff morphology was mainly originated by Quaternary fault activity. However, the integration of geomorphological features, stratigraphical and sedimentological data, together with the results of the tectonic analysis of fractures occurring in Serra Gelada, and a detailed study of seismic reflection profiles carried out in the adjacent continental shelf, indicate that these normal faults were active mainly during the late Miocene. Therefore, the Serra Gelada sea cliffs represent a tectonically controlled long-term landscape. Thus, normal faults have not significantly modified the Serra Gelada relief since then. Furthermore, the northern part of the Serra Gelada cliff may be considered as an inherited pre-Quaternary relict palaeocliff since it has only undergone very little erosive recession.     

Use of Noise to Augment Training Data: A Neural Network Method of Mineral–Potential Mapping in Regions of Limited Known Deposit Examples

One of the main factors that affects the performance of MLP neural networks trained using the backpropagation algorithm in mineral-potential mapping isthe paucity of deposit relative to barren training patterns. To overcome this problem, random noise is added to the original training patterns in order to create additional synthetic deposit training data. Experiments on the effect of the number of deposits available for training in the Kalgoorlie Terrane orogenic gold province show that both the classification performance of a trained network and the quality of the resultant prospectivity map increasesignificantly with increased numbers of deposit patterns. Experiments are conducted to determine the optimum amount of noise using both uniform and normally distributed random noise. Through the addition of noise to the original deposit training data, the number of deposit training patterns is increased from approximately 50 to 1000. The percentage of correct classifications significantly improves for the independent test set as well as for deposit patterns in the test set. For example, using ±40% uniform random noise, the test-set classification performance increases from 67.9% and 68.0% to 72.8% and 77.1% (for test-set overall and test-set deposit patterns, respectively). Indices for the quality of the resultant prospectivity map, (i.e. D/A, D × (D/A), where D is the percentage of deposits and A is the percentage of the total area for the highest prospectivity map-class, and area under an ROC curve) also increase from 8.2, 105, 0.79 to 17.9, 226, 0.87, respectively. Increasing the size of the training-stop data set results in a further increase in classification performance to 73.5%, 77.4%, 14.7, 296, 0.87 for test-set overall and test-set deposit patterns, D/A, D × (D/A), and area under the ROC curve, respectively.     

Use of Fuzzy Membership Input Layers to Combine Subjective Geological Knowledge and Empirical Data in a Neural Network Method for Mineral-Potential Mapping

Use of GIS layers, in which the cell values represent fuzzy membership variables, is an effective method of combining subjective geological knowledge with empirical data in a neural network approach to mineral-prospectivity mapping. In this study, multilayer perceptron (MLP), neural networks are used to combine up to 17 regional exploration variables to predict the potential for orogenic gold deposits in the form of prospectivity maps in the Archean Kalgoorlie Terrane of Western Australia. Two types of fuzzy membership layers are used. In the first type of layer, the statistical relationships between known gold deposits and variables in the GIS thematic layer are used to determine fuzzy membership values. For example, GIS layers depicting solid geology and rock-type combinations of categorical data at the nearest lithological boundary for each cell are converted to fuzzy membership layers representing favorable lithologies and favorable lithological boundaries, respectively. This type of fuzzy-membership input is a useful alternative to the 1-of-N coding used for categorical inputs, particularly if there are a large number of classes. Rheological contrast at lithological boundaries is modeled using a second type of fuzzy membership layer, in which the assignment of fuzzy membership value, although based on geological field data, is subjective. The methods used here could be applied to a large range of subjective data (e.g., favorability of tectonic environment, host stratigraphy, or reactivation along major faults) currently used in regional exploration programs, but which normally would not be included as inputs in an empirical neural network approach.     

Distribution and effectiveness of nivation in Mediterranean mountains: Peñalara (Spain)

Snow accumulation is responsible for geomorphic and biogeographic processes taking place in the southern sector of the Peñalara massif in central Spain (40°51′N, 3°57′W; max. altitude 2428 m at Pico de Peñalara). This work compares the intensity of nivation on the eastern slope, leeward of the prevailing westerly winds and heavily eroded by glacial activity during the Pleistocene, to that of the western slope on the windward side, unaffected by glacial erosion and completely covered by a thick weathering mantle. On the eastern slope, nivation is effective only where the weathering mantle is exposed or on morainic formations. It does not occur on the landforms derived from glacial erosion. In contrast, the western side shows almost no evidence of snow action except where catastrophic mass movements have altered the regularity of the slope. During the post-glacial epoch, nivation cirques formed in the scars left by mass wasting. In the last 30 years, spring temperatures have increased and this activity has diminished. The pattern of evolution observed at Peñalara can be extrapolated to other Mediterranean mountains with similar characteristics such as marginal glacial activity during the Pleistocene, unconsolidated formations on the summits caused by chemical weathering, and dry, hot summers that can increase the effectiveness of nivation.     

Wind erosion from military training lands in the Mojave Desert, California, U.S.A.

Military training activities reduce vegetation cover, disturb crusts, and degrade soil aggregates, making the land more vulnerable to wind erosion. The objective of this study was to quantify wind erosion rates for typical conditions at the Marine Corps Air Ground Combat Center, Twentynine Palms, CA, U.S.A. Five Big Spring Number Eight (BSNE) sampler stations were installed at each of five sites. Each BSNE station consisted of five BSNE samplers with the lowest sampler at 0·05 m and the highest sampler at 1·0 m above the soil surface. Once a month, sediment was collected from the samplers for analysis. Occurrence of saltating soil aggregates was recorded every hour using Sensits, one at each site. The site with the most erosion had a sediment discharge of 311 kg m⁻¹ over a period of 17 months. Other sites eroded much less because of significant rock cover or the presence of a crust. Hourly sediment discharge was estimated combining hourly Sensit count and monthly sediment discharge measured using BSNE samplers. More simultaneously measured data are needed to better characterize the relationship between these two and reconstruct a detailed time-series of wind erosion. This measured time-series can then be used for comparison with simulation results from process-based wind erosion models such as the Wind Erosion Prediction System (WEPS), once it has been adapted to the unique aspects of military lands.