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In this paper the use of ER measurements in soils in the context of biocementation, is investigated considering its use as an indicator of treatment efficiency measured trough the presence of biocement, or to provide information about the presence of chemical by-products during or after final washing operations. The electrical resistivity of a slightly biocemented sand was analyzed on a laboratory scale test conceived to reproduce field measurements conditions such as varying degrees of saturation and the presence of chemical by-products in the pore fluid. The results confirmed that, independently from the degree of saturation, the nature of the pore fluid was the parameter that affected most the electrical resistivity measurements, and the presence of calcium carbonate affecting soil structure was detected only after removal of the treatment by-products by a complete final soil washing using water. From a practical point of view this non-destructive technique is more adequate to be used mainly at the end of the treatment, to check that complete soil washing has occurred, rather than checking the presence of bio-cement specially if the amount precipitated is not very high as it was the case of the tests performed. • During the biocementation process by-products are generated (ammonium & chlorides). • By-products must be removed from the soil to ensure environmental sustainability. • ER measurements allow checking that all by-products were removed from the soil. • When removed, is possible to detect the effect of biocement on ER measurements. [ABSTRACT FROM AUTHOR]

Classical clustering (CC) normally divides a database into a predetermined number of fixed clusters before assigning each target site into one of these clusters to build a quasi-local model. Because the range of target sites is much larger than the range of clusters, it is possible that the identified cluster contains database sites that are weakly correlated to some assigned target sites, especially when these target sites are located near the cluster boundaries. The existence of weakly correlated sites in the cluster may make the quasi-local model less effective in predicting soil properties at the target site. This paper proposes a Tailored Clustering Enabled Regionalization (TCER) framework that can minimize the inference uncertainty at a target site. It mainly consists of two parts: (1) a site similarity measure to quantify the similarity between database sites and the target site and (2) a novel tailored clustering (TC) approach to identify the optimal cluster (called quasi-regional cluster) of database sites with the highest site similarity measures. In comparison to the traditional TC, the novel TC does not require users to specify a site similarity threshold that divides database sites into those within the cluster and those outside the cluster. Finally, a hierarchical Bayesian model is applied to the quasi-regional cluster (rather than the entire database) to infer geotechnical/geological properties at the target site. The capability of TCER is verified using synthetic and real examples. It is shown that the proposed TCER is computationally efficient and can reduce inference uncertainty. • An inter-dataset similarity measure is modified to make it suitable for comparing the similarity between two sites. • A novel clustering approach is proposed for identifying a cluster that is highly correlated to the target site. • A tailored clustering enabled regionalization framework is proposed for reducing inference uncertainty of a target site. [ABSTRACT FROM AUTHOR]

In geotechnical engineering, an appreciation of local geological conditions from similar sites is beneficial and can support informed decision-making during site characterization. This practice is known as "site recognition", which necessitates a rational quantification of site similarity. This paper proposes a data-driven method to quantify the similarity between two cross-sections based on the spatial variability of one soil property from a spectral perspective. Bayesian compressive sensing (BCS) is first used to obtain the discrete cosine transform (DCT) spectrum for a cross-section. Then DCT-based auto-correlation function (ACF) is calculated based on the obtained DCT spectrum using a set of newly derived ACF calculation equations. The cross-sectional similarity is subsequently reformulated as the cosine similarity of DCT-based ACFs between cross-sections. In contrast to the existing methods, the proposed method explicitly takes soil property spatial variability into account in an innovative way. The challenges of sparse investigation data, non-stationary and anisotropic spatial variability, and inconsistent spatial dimensions of different cross-sections are tackled effectively. Both numerical examples and real data examples from New Zealand are provided for illustration. Results show that the proposed method can rationally quantify cross-sectional similarity and associated statistical uncertainty from sparse investigation data. The proposed method advances data-driven site characterization, a core application area in data-centric geotechnics. • A novel method is proposed for quantifying 2D cross-sectional similarity of soil property spatial variability • An efficient formulation of 2D auto-correlation function is derived for a unified representation of 2D spatial variability • The challenges of sparse investigation data, non-stationarity, and inconsistent cross-sectional dimensions are tackled • The proposed method facilitates the quasi-regional clustering of geotechnical data [ABSTRACT FROM AUTHOR]

The ink-bottle effect, resulting from the non-uniformity of pore structure, plays a significant role in the hydraulic behaviour of soils. In this paper, a series of laboratory tests including standard mercury intrusion porosimetry (MIP), intrusion-extrusion cyclic MIP (IEC-MIP), and scanning electron microscopy (SEM) were conducted on two soils (a Nanyang expansive clay and a Kaolin mixture) to examine the evolution pattern of ink-bottle effect in soils with different initial void ratios. To evaluate the impact of such evolution on the soil-water retention behaviour, the axis translation and vapour equilibrium techniques were utilised to measure the water retention curve of the tested soils (SWRC). A key part of the analysis involved a separation between pore bodies (or ink-bottle pores) and pore throats to facilitate a more thorough examination of the pore structure. The test results indicated that the reduction in soil volume during compression is mainly attributed to the reduction in pore body size, whereas the pore throat size remains relatively constant. Therefore, the ink-bottle effect would weaken with a decreasing initial void ratio, which will reduce the hysteresis potential of soils, manifesting as a contraction of the SWRC hysteresis loop. Additionally, under a fixed void ratio but with decreasing pore size, the ink-bottle effect will also weaken, which will reduce the distance between the drying and wetting curves with increasing matric suction. Finally, the mechanisms regarding how the evolution of ink-bottle effect during soil deformation influences water retention hysteresis were addressed. • Soil-water retention curves (SWRCs) of two soils under wetting-drying cycles were measured. • The ink-bottle effect was investigated using the intrusion-extrusion cyclic MIP test. • The weakening of the ink-bottle effect occurs as the initial void ratio of soils decreases. • Mechanisms for the contraction of SWRC hysteresis loop as the ink-bottle effect weakens are revealed. [ABSTRACT FROM AUTHOR]

Accurately predicting slope reliability under a rainfall/rainstorm event is an important prerequisite for preventing rainfall-induced landslide hazards. However, the predicted probability of slope failure under the rainfall/rainstorm event is often larger than the observed frequency of slope instability. The spatial variability of multiple soil parameters was rarely accounted for. To address this issue, this paper proposes an efficient sequential probabilistic back analyses approach for learning multiple spatially varying soil parameters using Bayesian Updating with Subset simulation (BUS) method. Two survival records of a real slope in India (i.e., the slope stays stable before the rainfall and the slope keeps stable after a 57-day weak rainfall) are successively used in the sequential probabilistic back analyses of soil parameters. The results indicate that the proposed sequential probabilistic back analyses approach can effectively update the distributions of multiple spatially variable soil parameters by the fusion of slope survival records. More accurate statistics of soil parameters can be obtained when additional slope survival records are used in the probabilistic back analyses. Furthermore, two slope failure records under a 3-day heavy rainfall event and a rainfall event ranging from May 1, 2016 to June 30, 2016 in Chibo, India are, respectively, used to predict the slope reliability and further validate the effectiveness of the proposed approach. The predicted probabilities of slope failure under the target rainfall events are well consistent with the actual observation frequency. The proposed approach can provide a powerful and versatile tool for determining the statistics of soil parameters and early warning of landslide hazards under the future rainfall events. • An efficient back analysis approach is proposed for learning multiple spatially variable soil parameters under rainfall. • The BUS method and surrogate models greatly facilitate the sequential probabilistic back analyses of soil parameters. • A real unsaturated slope is investigated to illustrate the effectiveness of the proposed approach. • The proposed approach can effectively update the distributions of soil parameters and slope reliability prediction. • The using of field observations can reduce the uncertainties of soil parameters and enhance the slope reliability prediction. [ABSTRACT FROM AUTHOR]