Probabilistic failure envelopes of strip foundations on soils with non-stationary characteristics of undrained shear strength

This paper investigates probabilistic failure envelopes of strip foundations on spatially variable soils with profiles of undrained shear strength su linearly increasing with depth using the lower bound random finite element limit analysis. The spatially variable su is characterised by a non-stationary random field with linearly increasing mean and constant coefficient of variation (COV) with depth. The deterministic uniaxial capacities and failure envelopes are firstly derived to validate numerical models and provide a reference for the subsequent probabilistic analysis. Results indicate that the random field parameters COVsu (COV of su) and Δ (dimensionless autocorrelation distance) have a considerable effect on the probabilistic normalised uniaxial capacities which alters the size of probabilistic failure envelopes. However, COVsu and Δ have an insignificant effect on the shape of probabilistic failure envelopes is observed in the V-H, V-M and H-M loading spaces, such that failure envelopes for different soil variabilities can be simply scaled by the uniaxial capacities. In contrast to COVsu and Δ, the soil strength heterogeneity index κ = μkB/μsu0 has the lowest effect on the probabilistic normalised uniaxial capacity factors but the highest effect on the shape of the probabilistic failure envelopes. A series of expressions are proposed to describe the shape of deterministic and probabilistic failure envelopes for strip foundations under combined vertical, horizontal and moment (V-H-M) loading.

Spatial scale structure soil bacterial communities across an Arctic landscape

Bacterial community composition is largely influenced by environmental factors, and this applies to the Arctic region. However, little is known about the role of spatial factors in structuring such communities. In this study, we evaluated the influence of spatial scale on bacterial community structure across an Arctic landscape. Our results showed that spatial factors accounted for approximately 10% of the variation at the landscape scale, equivalent to observations across the whole Arctic region, suggesting that while the role and magnitude of other processes involved in community structure may vary, the role of dispersal may be stable globally in the region. We assessed dispersal limitation by identifying the spatial autocorrelation distance, standing at approximately 60 m, which would be required in order to obtain fully independent samples and may inform future sampling strategies in the region. Finally, indicator taxa with strong statistical correlations with environment variables were identified. However, we showed that these strong taxa-environment associations may not always be reflected in the geographical distribution of these taxa. IMPORTANCE The significance of this study is threefold. It investigated the influence of spatial scale on the soil bacterial community composition across a typical Arctic landscape and demonstrated that conclusions reached when examining the influence of specific environmental variables on bacterial community composition are dependent upon the spatial scales over which they are investigated. This study identified a dispersal limitation (spatial autocorrelation) distance of approximately 60 m, required to obtain samples with fully independent bacterial communities, and therefore, should serve to inform future sampling strategies in the region and potentially elsewhere. The work also showed that strong taxa-environment statistical associations may not be reflected in the observed landscape distribution of the indicator taxa.

Clustering stationary and non-stationary time series based on autocorrelation distance of hierarchical and k-means algorithms

Observing large dimension time series could be time-consuming. One identification and classification approach is a time series clustering. This study aimed to compare the accuracy of two algorithms, hierarchical cluster and K-Means cluster, using ACF’s distance for clustering stationary and non-stationary time series data. This research uses both simulation and real datasets. The simulation generates 7 stationary data models and another 7 of non-stationary data models. On the other hands, the real dataset is the daily temperature data in 34 cities in Indonesia. As a result, K-Means algorithm has the highest accuracy for both data models.

Imaging of photo-occlusion colour films for quantitative strain and stress analysis

Digital image analysis and processing techniques have been applied to colour films obtained by a photo-occlusion method to quantitatively describe strain and stress distributions. The objective here is to outline the different colour areas (training areas) on each image accurately and consistently in order for a quantitative comparison of the induced stress on each film to be made based on the size of each colour area. To ensure that the outlined colour areas are uniform in terms of grey levels (isochromatic areas), a statistical analysis was pursued. To ensure consistency of the outline process on each image, an autocorrelation distance was used. The technique described allows direct quantitative judgement of strain fields obtained ‘before’ and ‘after’ treatment. The technique can be applied in any research area where a sequence of isochromatic areas can be recorded on a photographic film.