Effect of polypropylene fibre and lime admixture on engineering properties of clayey soil

In order to reduce the brittleness of soil stabilized by lime only, a recent study of a newly proposed mixture of polypropylene fibre and lime for ground improvement is described and reported in the paper. To investigate and understand the influence of the mixture of polypropylene fibre and lime on the engineering properties of a clayey soil, nine groups of treated soil specimens were prepared and tested at three different percentages of fibre content (i.e. 0.05%, 0.15%, 0.25% by weight of the parent soil) and three different percentages of lime (i.e. 2%, 5%, 8% by weight of the parent soil). These treated specimens were subjected to unconfined compression, direct shear, swelling and shrinkage tests. Through scanning electron microscopy (SEM) analysis of the specimens after shearing, the improving mechanisms of polypropylene fibre and lime in the soil were discussed and the observed test results were explained. It was found that fibre content, lime content and curing duration had significant influence on the engineering properties of the fibre–lime treated soil. An increase in lime content resulted in an initial increase followed by a slight decrease in unconfined compressive strength, cohesion and angle of internal friction of the clayey soil. On the other hand, an increase in lime content led to a reduction of swelling and shrinkage potential. However, an increase in fibre content caused an increase in strength and shrinkage potential but brought on the reduction of swelling potential. An increase in curing duration improved the unconfined compressive strength and shear strength parameters of the stabilized soil significantly. Based on the SEM analysis, it was found that the presence of fibre contributed to physical interaction between fibre and soil whereas the use of lime produced chemical reaction between lime and soil and changed soil fabric significantly.     

Radiomagnetotelluric mapping, groundwater numerical modelling and 18-Oxygen isotopic data as combined tools to determine the hydrogeological system of a landslide prone area

Three methods were combined to determine the groundwater recharge and transfer processes of a landslide prone area. First, the radiomagnetotelluric method was used to investigate the distribution of electrical resistivity (ρ) of the subsurface and build a three-dimensional model of permeability (k), through an experimental relation between ρ and k. Second, this structural model of permeability and additional climatologic data were used to fix boundary and recharge conditions to perform a three-dimensional and transient numerical simulation of the groundwater flow. Finally 18-Oxygen time series observed at the main springs were used to validate the model. This association of methods led to an improved characterization of the groundwater flow system at local scale and a better understanding of the role of this system on the landslide phenomenon. This structured approach is thought to be useful to design specific remediation strategies to drain the unstable mass.     

Bayesian Spatial Modelling of Gamma Ray Count Data

Gamma ray logging is a method routinely employed by geophysicists and environmental engineers in site geology evaluations. Modelling of gamma ray data from individual boreholes assists in the local identification of major lithological changes; modelling these data from a network of boreholes assists with lithological mapping and spatial stratigraphic correlation. In this paper we employ Bayesian spatial partition models to analyse gamma ray data spatially. In particular, a spatial partition is defined via a Voronoi tessellation and the mean intensity is assumed constant in each cell of the partition. The number of vertices generating the tessellation as well as the locations of vertices are assumed unknown, and uncertainty about these quantities is described via a hierarchical prior distribution. We describe the advantages of the spatial partition modelling approach in the context of smoothing gamma ray count data and describe an implementation that may be extended to the fitting of a more general model than a constant mean within each cell of the partition. As an illustration of the methodology we consider a data set collected from a network of eight boreholes, which is part of a geophysical study to assist in mapping the lithology of a site. Gamma ray logs are linked with geological information from cores and the spatial analysis of log data assists with predicting the lithology at unsampled locations.     

Site Effects Study in the Serravalle Village (NE Italy) by Means of Spectral Ratios Analyses Relatively to Multiple Reference Sites

The area of Serravalle, sited in the northern part of the town of Vittorio Veneto (TV), NE Italy, has been the target of a seismic microzonation campaign. 10 seismic stations have been deployed for a 7 months period to record in continuous mode. Three stations were installed on bedrock outcrops and seven on sedimentary sites with variable cover thickness. Spectral analyses have been performed on the collected data-set using the Generalized Inversion Technique (GIT, e.g. Andrews, 1986). In particular, spectral ratios have been calculated for each station relatively to the average of the three reference, bedrock sites. The spectral ratios provide quantitative estimates of the seismic motion amplifications which occur in each of the monitored sites. Two sites show high values of amplification, 5 times larger than signal amplitude at the reference sites, in correspondence of well discernible peak frequencies of 5 Hz. Results for the other stations show smaller amounts of site amplification spreading over a broad range of frequencies. Sites where the highest amplifications were recorded all lie on the left bank of the Meschio River and in areas farther away from its outlet into the plain correlating with the presence of thick layers of Quaternary deposits.     

Driving forces of tropical deforestation: The role of remote sensing and spatial models

Remote sensing technologies are increasingly used to monitor landscape change in many parts of the world. While the availability of extensive and timely imagery from various satellite sensors can aid in identifying the rates and patterns of deforestation, modelling techniques can evaluate the socioeconomic and biophysical forces driving deforestation processes. This paper briefly reviews some emerging spatial methodologies aimed at identifying driving forces of land use change and applies one such methodology to understand deforestation in Mexico. Satellite image classification, change analysis and econometric modelling are used to identify the rates, hotspots and drivers of deforestation in a case study of the southern Yucatán peninsular region, an enumerated global hotspot of biodiversity and tropical deforestation. In particular, the relative roles of biophysical and socioeconomic factors in driving regional deforestation rates are evaluated. Such methodological approaches can be applied to other regions of the forested tropics and contribute insights to conservation planning and policy.