Statistical Models in Engineering

The in-situ deep mixing technique has been established as a means to effect columnar inclusions into soft ground to enhance the bearing capacity and reduce settlement. Since the inception of this method, developments in the plant and machinery, as well as associated field techniques, have surpassed the basic understanding of strength developments in high water-content clays admixed with cementing agents.In this paper an attempt is made to identify the critical factors governing the engineering behavior of cement-stabilized clay, which helps not only to control the input of cementing agent to attain strength development with curing time and clay water content, but also to understand the subsequent engineering behavior. It is revealed that the clay-water/cement ratio, $wc/ c$ is the prime parameter for the above purposes .

Engineering Behavior of Cement Stabilized Clay at High Water Content (IS-YOKOHAMA 2000「沿岸域の地盤工学における理論と実際」特集号〔英文〕)

Stability of the soil slopes is one of the most challenging issues in civil engineering projects. Due to the complexity and non-linearity of this threat, utilizing simple predictive models does not satisfy the required accuracy in analysing the stability of the slopes. Hence, the main objective of this study is to introduce a novel metaheuristic optimization namely Harris hawks’ optimization (HHO) for enhancing the accuracy of the conventional multilayer perceptron technique in predicting the factor of safety in the presence of rigid foundations. In this way, four slope stability conditioning factors, namely slope angle, the position of the rigid foundation, the strength of the soil, and applied surcharge are considered. Remarkably, the main contribution of this algorithm to the problem of slope stability lies in adjusting the computational weights of these conditioning factors. The results showed that using the HHO increases the prediction accuracy of the ANN for analysing slopes with unseen conditions. In this regard, it led to reducing the root mean square error and mean absolute error criteria by 20.47% and 26.97%, respectively. Moreover, the correlation between the actual values of the safety factor and the outputs of the HHO–ANN (R2 = 0.9253) was more significant than the ANN (R2 = 0.8220). Finally, an HHO-based predictive formula is also presented to be used for similar applications.

A novel Harris hawks’ optimization and k-fold cross-validation predicting slope stability

Failure mechanism of geosynthetic-encased stone columns in soft soils under embankment

In this study, the objective was to develop a new and highly-accurate artificial intelligence model for slope failure prediction in open-pit mines For this purpose, the M5Rules algorithm was combined with a genetic algorithm (GA) in a novel hybrid technique, named M5Rules-GA model, for slope stability estimation and analysis and 450-slope observations in an open-pit mine in Vietnam were modeled using the Geo-Studio software based on essential parameters The factor of safety was used as the model outcome Artificial neural networks (ANN), support vector regression (SVR), and previously introduced models (such as FFA-SVR, ANN-PSO, ANN-ICA, ANN-GA, and ANN-ABC) were also developed for evaluating the proposed M5Rules-GA model The evaluation of the model performance involved applying and computing the determination coefficient, variance account for, and root mean square error, as well as a general ranking and color scale The results confirmed that the proposed M5Rules-GA model is a robust tool for analyzing slope stability The other investigated models yielded less robust performance under the evaluation metrics

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Prediction of slope failure in open-pit mines using a novel hybrid artificial intelligence model based on decision tree and evolution algorithm.

Theoretical and experimental studies have shown that stone columns can be used for accelerating the consolidation rate of soft soils by providing a drainage path, reducing foundation settlements, improving the bearing capacity of the soil, and limiting the risk of liquefaction due to seismic activity. This paper studies the influence of the main controlling parameters in the design of stone columns through a series of laboratory experiments. The effects of (a) the drainage conditions, (b) the grain size of the stone column material, (c) the confining pressure of the soil, and (d) the rate of deformation are investigated. Triaxial compression tests are performed on composite soil specimens of soft kaolin clay, reconstituted from a slurry with a central compacted reinforcement column. Two cases of reinforcement material are studied: sand and gravel. The behaviour of the reinforced soil compared with that of the unreinforced soil under the same loading and drainage conditions is analysed. The experimental re...

Experimental study on sand and gravel columns in clay

The paper describes the behavior of remolded kaolin clay reinforced by stone column as investigated in laboratory. The installation of stone column was simulated by performing lateral expansion at different rates within hollow cylindrical remolded kaolin specimens initially subjected to K0 consolidation path. These specimens were, then, subjected to classical consolidated undrained triaxial tests while recording excess pore pressure values. The objective of the experimental programme was to quantify the effects of consolidation stress and stone column on the undrained Young’s modulus and shear strength of kaolin clay. Obtained results showed a significant improvement in Young’s modulus when the cavity expansion ratio and the consolidation stress increase. It was also found that the increase in undrained shear strength of improved kaolin clay mainly occurred at lower consolidation stress. Another important finding of this study is that the ratio of undrained Young’s modulus to undrained shear stress increases when the consolidation stress decreases. Finally, the paper presents a model developed for the design of stone columns.

Observed Behaviour of Laterally Expanded Stone Column in Soft Soil

The installation of stone columns is widely adopted to prevent liquefaction. The main improvement mechanisms of stone columns as a liquefaction countermeasure are drainage, stiffening, and ...

Effects of Densification and Stiffening on Liquefaction Risk of Reinforced Soil by Stone Columns

This article addresses the problem of the stability of a slope within the framework of the yield design theory (YDT), which is a rigorous method that avoids assumptions that could affect th...

Wissem Frikha

Papers

Experimental study on sand and gravel columns in clay

Observed Behaviour of Laterally Expanded Stone Column in Soft Soil

Effects of Densification and Stiffening on Liquefaction Risk of Reinforced Soil by Stone Columns

Constrained Particle Swarm Optimization Algorithm Applied to Slope Stability

Experimental study for the mechanical characterization of Tunis soft soil reinforced by a group of sand columns.