J.L. Justo

Pattern recognition to forecast seismic time series

Earthquakes arrive without previous warning and can destroy a whole city in a few seconds, causing numerous deaths and economical losses. Nowadays, a great effort is being made to develop techniques that forecast these unpredictable natural disasters in order to take precautionary measures. In this paper, clustering techniques are used to obtain patterns which model the behavior of seismic temporal data and can help to predict medium-large earthquakes. First, earthquakes are classified into different groups and the optimal number of groups, a priori unknown, is determined. Then, patterns are discovered when medium-large earthquakes happen. Results from the Spanish seismic temporal data provided by the Spanish Geographical Institute and non-parametric statistical tests are presented and discussed, showing a remarkable performance and the significance of the obtained results.

Settlement-time behaviour of granular embankments

Creep settlements are the main cause of deterioration of road pavement and impervious elements of dams, and therefore a method to calculate them is needed. Viscoelastic models (e.g. the standard linear solid) have been chosen to represent the creep of granular materials (Figure 1). Finite element calculations show that quasi-oedometric conditions exist near the centre of embankments. Explicit expressions for one-dimensional viscoelastic settlements of an embankment during and after construction have been obtained for any loading law and drawn for a linear load. The three viscoelastic parameters, Eo, Rc and Tr can be determined through laboratory or field testing, and the results can be adjusted by using settlement records. Good agreement has been found between measured and calculated settlements at several dams. Copyright

The dry closure of the Almagrera tailings dam: detailed modelling, monitoring results and environmental aspects

The Aznalcóllar tailings dam failure moved the Spanish authorities to pay attention to tailings deposits. The Almagrera tailings dam holds one of the largest mining waste deposits in Andalucía. The dry closure of this dam has been detailed in this manuscript. Some serious difficulties had to be solved. Firstly, the dam had undergone up to five raisings before the closure operations started, and this process had not been properly documented. Secondly, the reservoir water was contaminated by the toxic tailings placed several metres below and, due to the high acidity of this water, the geotechnical characterization of the tailings deposit has been really challenging. Thirdly, the definition of the model itself has been a complex task due to the consideration of many phases and different hypotheses. In the finite element calculation, a constitutive model of perfect non-associated plasticity has been used for the dam and a soft soil creep model for the tailings. Next, it has been decided to decontaminate a closed mine by placing its abandoned material—Las Viñas fill—on top of the tailings deposit inside the reservoir. This operation generated important settlements on the tailings deposit. These settlements had to be accelerated by placing drainage wells to avoid the cracking of the final cap. The safety factor during the dry closure operations under dynamic loading was insufficient and a compacted rockfill reinforcement had to be laid on the downstream slope of the dam. Very few papers describe a successful dry closure of a tailings dam as is done here.ResumeLa rupture du barrage de stériles d’Aznalcóllar a poussé les autorités espagnoles à prêter attention aux dépôts de résidus. Le barrage de stériles d’Almagrera abrite l’un des plus importants dépôts de déchets miniers d’Andalousie. La fermeture à sec de ce barrage est présentée dans ce manuscrit. Certaines difficultés sérieuses ont dû être résolues. Premièrement, le barrage avait connu jusqu’à cinq surélévations avant le début des opérations de fermeture et ce processus n’avait pas été correctement documenté. Deuxièmement, l’eau du réservoir a été contaminée par les résidus toxiques placés plusieurs mètres plus bas et, en raison de la forte acidité de cette eau, la caractérisation géotechnique du gisement de résidus a été très difficile. Troisièmement, la définition du modèle lui-même a été une tâche complexe en raison de la prise en compte de nombreuses phases et d’hypothèses différentes. Dans le calcul par des éléments finis (EF), un modèle constitutif de plasticité parfaite non associée a été introduit pour le barrage et un modèle visqueux pour les déchets miniers. Ensuite, il a été décidé de décontaminer une mine à proximité—Las Viñas—en plaçant son matériau abandonné au-dessus du dépôt de résidus à l’intérieur du réservoir. Cette opération a généré d’importants tassements sur le dépôt de résidus. Ces tassements ont dû être accélérés en plaçant des puits de drainage pour éviter la fissuration de la couverture finale. Le coefficient de sécurité (CS) à la fin des opérations de fermeture à sec sous chargement dynamique était insuffisant et des renforts d’enrochement compactés devaient être posés sur la pente aval. Pas beaucoup d’articles décrivent une fermeture à sec réussie d’un barrage de stériles comme cela est. fait ici.

The Effect of Treatment with Fly Ash and Cement upon the Characteristic Curve of a Collapsing Soil

The soil-water characteristic curve (SWCC) of collapsing silty clay is studied, both with and without addition of cement and fly ash from coal combustion. Collapse (decrease in volume upon wetting) is a major cause of damage to buildings and routes of communication founded on low-density soils. The material analysed is a mixture of sand, silt and clay, collected from the Guadalquivir basin in the province of Seville (Spain). In this paper, the characteristic curves of the collapsing silty clay with 2% by weight of cement or fly ash are studied through pressure membrane and vacuum desiccator techniques. The results are analysed with the model developed by Fredlund & Xing (1994).

Probabilistic Method to Estimate Design Accelerograms in Seville and Granada Based on Uniform Seismic Hazard Response Spectra

The response of a structure affected by an earthquake is the result of “filtering” the seismic signal through the structure. A dynamic analysis of a structure requires the previous definition of the accelerogram and the structure characteristics. A complete calculation implies working out the seismic response in all points of the structure; that is, calculating the seismic response in an infinite number of points and in an infinite number of instants. (Meirovitch, 1985) has demonstrated that, with an infinite number of points and instants, the problem has no numerical solution. To solve the numerical problem, models with a finite number of predeterminated points are defined. The response of a structure subject to a seismic movement can be determined by two methods: either using the accelerograms recorded near the site, or using visco-elastic response spectra. The first method can only be used in places where many accelerograms have been recorded, and needs a probabilistic calculation to ascertain the design accelerograms. This procedure can be used for linear and non-linear analyses. In both cases various records of a frequency similar to that expected at the location of the structure, may be used to obtain realistic calculation results. A structural analysis for all the accelerograms considered must be carried out in order to obtain a calculation envelope or carry out the probabilistic study. This procedure implies a significant work. This procedure has the difficulty of finding accelerograms at the location of the structure. In some regions, with a vast history of large earthquakes, such as Japan and California, a wide network of recording stations is available and provides many records for large earthquakes, for different type of soils and for a wide range of distances. In regions of minor seismicity, the network of recording stations is not so wide, or is not old enough, so that the number of records is insufficient. For the analysis of minor seismic activity regions, records from other regions are used, or artificial accelerograms are generated. Artificial accelerograms have the advantage that, from a minimum number of parameters, accelerograms can be obtained.