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A set of two to three prominent hardgrounds can be traced for more than 40 km from east to west within the Jurassic succession of the Jaisalmer Basin at the western margin of the Indian Craton. The hardgrounds started to form under subtidal conditions in a mixed carbonate-siliciclastic setting during the last phase of a transgressive systems tract, i.e. the maximum flooding zone. The age difference between the hardgrounds is very small, but they differ lithologically. Typically, the stratigraphically oldest hardground occurs at the top of a 1-m-thick calcareous sandstone. It is characterized by a spectacular megaripple surface encrusted with oysters and subsequently occasionally bored by bivalves. The hardground is overlain by 10-25 cm of biowackestone to biopackstone, at the top of which another hardground is developed. This second hardground is characterized by abundant bivalve (Gastrochaenolites isp.) and "worm" borings (Trypanites and Meandropolydora isp.) and occasional oyster encrustations. The third hardground can be found within the overlying 60-cm-thick, bioturbated, fossiliferous silty marly packstone. It shows common to abundant oyster encrustations and occasional borings together with reworked concretions. The individual hardground can be well recognized throughout the basin based on lithology and biotic components. The second hardground (biowackestone to biopackstone) with abundant bivalve and worm borings is most prominent and widespread. Lithostratigraphically, these three hardground surfaces belong to the uppermost part of the Bada Bag Member of the Jaisalmer Formation. Based on ammonites, such as Perisphinctes congener (Waagen), brachiopods, and corals, this interval of the Bada Bag Member has been assigned a late Bathonian age. The entire succession above the first hardground is bioturbated up to the overlying marly silt of the Kuldhar Member of the Jaisalmer Formation, which is already Callovian in age. The characteristic hardground lithologies, together with the ammonite record, allow long-distance correlations within the basin emphasizing their importance as valuable marker horizons. The biotic components associated with the hardgrounds and alternating sediments represent high diversity community relicts developed in shallow-water, open-marine environments.

Aggregate piers are extensively in use for increasing bearing pressure and diminish settlement under the footing. The ultimate bearing capacity of aggregate pier reinforced clay is majorly affected by soil strength (c u ), area replacement ratio (a r ) of piles, geometry, and slenderness ratio (λ) of piles. Various prediction models have been proposed to predict the ultimate bearing capacity of aggregate piers. However, existing models have shown a broad range of bias, variation, errors, and as such they are unsuitable for practical design. In this study, machine learning algorithms (linear and non-linear regression) and Artificial neural networks (ANNs) were performed using field loading test results to predict the ultimate bearing capacity of ground reinforced by aggregate piers. Sensitivity analysis was conducted to identify the influence of input variables. To fulfil this objective, 37 test results were used for training and testing of different models and compared with each other based on statistical parameters (mean absolute error, root mean squared error, and r 2 -score). Random Forest Regression model came out to be the best suitable for prediction of ultimate bearing capacity with minimum mean absolute error (MAE = 38.93 kPa) and r 2 -score equal to 0.98.

Ground improvement with granular piles increases the load-carrying capacity, reduces the settlement of foundations built on the reinforced ground and is also a good alternative to concrete pile. Granular piles or stone columns are composed of granular material, such as crushed stone or coarse dense sand. An analytical approach based on the continuum approach is presented for the non-linear behaviour of the granular pile. The formulation for pile element displacement is done considering the non-homogeneity of the granular pile as it reflects the true behaviour and also accounts for the changes in the state of the granular pile due to installation, stiffening and improvement effects. The present study shows that the settlement influence factor for an end-bearing granular pile decreases with increase in the relative stiffness of the bearing stratum. The settlement influence factor decreases with increase in linear and non-linear non-homogeneity parameters for all values of relative length. For a shorter pile, the rate of decrease of the settlement influence factor is greater in comparison to that for a longer pile. Shear stress at the soil–granular pile interface reduces in the upper compressible portion of the granular pile and increases in the lower stiffer portion of the granular pile due to the non-homogeneity of an end-bearing granular pile.

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