Assessment of corrective measures for alleviating slope instabilities in carbonatic Flysch formations: Alicante (SE of Spain) case study

As is well known, in order to select remediation measures to correct or prevent slope instabilities, it is essential to identify and characterize the instability mechanisms. This task is especially complex for heterogeneous rock masses such as Flysch formations. This paper addresses the assessment of corrective measures used in carbonate Flysch formations by classifying and grouping field data reported in an available database in order to associate this data with various instability mechanisms and stratigraphic column types as well as with the corrective measures taken to stabilise them. For this purpose, 194 slopes have been geomechanically characterized, mainly by considering the observed instability mechanisms. The corrective measures that were applied have been evaluated for their suitability and performance, and, if applicable, the causes of their malfunction have been also studied. As a result, some guidelines based on the observed behaviour and the suitability of the correction measure as a function of instability type are proposed for similar slopes.

Improving classification using a Confidence Matrix based on weak classifiers applied to OCR

This paper proposes a new feature representation method based on the construction of a Confidence Matrix (CM). This representation consists of posterior probability values provided by several weak classifiers, each one trained and used in different sets of features from the original sample. The CM allows the final classifier to abstract itself from discovering underlying groups of features. In this work the CM is applied to isolated character image recognition, for which several set of features can be extracted from each sample. Experimentation has shown that the use of CM permits a significant improvement in accuracy in most cases, while the others remain the same. The results were obtained after experimenting with four well-known corpora, using evolved meta-classifiers with the k-Nearest Neighbor rule as a weak classifier and by applying statistical significance tests.

Characterization of rock slopes through slope mass rating using 3D point clouds

Rock mass classification systems are widely used tools for assessing the stability of rock slopes. Their calculation requires the prior quantification of several parameters during conventional fieldwork campaigns, such as the orientation of the discontinuity sets, the main properties of the existing discontinuities and the geo-mechanical characterization of the intact rock mass, which can be time-consuming and an often risky task. Conversely, the use of relatively new remote sensing data for modelling the rock mass surface by means of 3D point clouds is changing the current investigation strategies in different rock slope engineering applications. In this paper, the main practical issues affecting the application of Slope Mass Rating (SMR) for the characterization of rock slopes from 3D point clouds are reviewed, using three case studies from an end-user point of view. To this end, the SMR adjustment factors, which were calculated from different sources of information and processes, using the different softwares, are compared with those calculated using conventional fieldwork data. In the presented analysis, special attention is paid to the differences between the SMR indexes derived from the 3D point cloud and conventional field work approaches, the main factors that determine the quality of the data and some recognized practical issues. Finally, the reliability of Slope Mass Rating for the characterization of rocky slopes is highlighted.