Geotechnical Engineering Journal of the SEAGS & AGSSEA

Analysis and optimisation of influencing factors in the performance of cement stabilised marine clay using Response Surface Methodology

REJIN P — 2024-09-26

Stabilisation of marine clays pivotal in providing favourable condition for construction, especially in rapidly developing coastal
regions. Cement stabilisation is a go-to technique for improving the weak engineering characteristics of marine clays. However, the effectiveness of stabilisation is dependent on certain factors. As per previous studies there are numerous factors influencing the degree of cement stabilisation. Due to the complex dynamics among these factors, there is a critical need to understand the interplay between the controlling factors to achieve the optimal performance in cement stabilization. The cement content, moulding water conditions and the curing periods are such controlling factors. The paper adopts an analytical approach to quantify the impact of cement content, moulding water content and curing days in the strength gain of marine clays using unconfined compressive strength (UCS) data. Experimental design using Design Expert was employed to minimise the experimental runs. The ranges of factors were fixed in accordance to previous studies and OMC conditions. Cement
content CC (5-15%), Moulding water content MWD, (15 to 21%), and Curing Days CD, (0-14 days). The study used the Response surface methodology to optimise these factors. The results produced a statistically significant quadratic model using ANOVA (Analysis of Variance). A quadratic equation for UCS was generated depicting the factor's individual and interactive influence. Optimisation results showed a maximum unconfined compressive strength value of 487.488 kPa for a cement content of 15%, curing days-14 days, and a moulding water content of 19.675%

The Failure of Road Embankment Along the Canal During Driven Piles Construction in Thickness of Soft Sensitive Clay

Salisa Chaiyaput — 2024-09-25

The pile-retaining wall, which was reinforced concrete piles or driven piles combined with concrete retaining wall, was designed and constructed to increase the stability of the road along the canal at the Nonthaburi rural road no. 5036. During 18-m driven piles construction, the failure of driven piles occurred. The resistivity survey and screw driving sounding test were used to investigate the thickness of soft clay layers and unexpected hard soil layers at the failure area. The field vane shear test was used to investigate the sensitivity of the soft clay layer. Moreover, the finite element model was analyzed to confirm the failure behavior of the driven pile during construction. As a result, it was found that the subsoil at the failure area was a very soft clay layer to medium stiff clay layer (from 2 m to 10 m depth below the ground surface) with sensitive values, while stiff clay was found from 10 m depth below the ground surface. Due to the 18-m driven pile, the tip of the driven pile was installed at the hard soil layer, which led to disturbance above the soft sensitive clay layer to reduce the strength of soft clay and affect to the displacement of the driven pile during construction. This result was in accordance with the analysis results by the finite element software.

Comparative Study: Simplified vs. FEM for Seismic Forces in Circular Tunnels

Achouri Abderrahim — 2024-09-26

A comparative seismic study between simplified approaches and the finite element method (FEM) was conducted to estimate the internal forces of a circular tunnel. Plaxis2D software© was used for the FE analysis, employing the contraction method in phasing the 2D FE model. The study focused on the Algiers Metro as a practical case study, considering the impact of the horizontal and vertical components of the Boumerdes earthquake in 2003. The purpose of this study is also to use the maximum shear strain rate to determine the maximum vertical strain rate subsequently apply it in existing simplified approaches to calculate the internal forces under the impact of the vertical seismic component. The effect of the volume loss coefficient (VL) was considered. Results demonstrate that increasing VL values initially reduced axial thrust, followed by an increase. Shear force and bending moment proportionally increased with the VL ratio, remaining within the practical range of simplified solutions. Additionally, the total principal stresses around the tunnel increased with the VL ratio. The study underscores the crucial role of selecting the appropriate VL ratio in achieving accurate results in FE analysis.

Stability Analysis of Embankment Using Finite Element Method Constructed Over Treated Soil with Anionic Polyacrylamide

Lindung Zalbuin Mase — 2024-09-26

This paper presents a method for deformation analysis of soil treated with anionic polyacrylamide (APAM) as embankment fill material. The stability analyses of the embankment have been done by two-dimensional finite element analysis. t has been carried out for different conditions considering the geometry of the embankment and material properties. Several models were analysed to determine the safe height and side slope required to stabilize the embankment in addition to that of treated soils for the embankment. Numerical results show that with increasing slope height and angle, the factor of safety decreases. Conversely, if the height and slope of the slope decrease, the factor of safety increases. Also, the deformation decreased along with the increase in APAM percentage. It is observed that the high percentage of 1% APAM stabilisation, the highest shear strength parameters and the lowest deformation occurred. This study found that APAM-treated saturated soils could be used as embankment fill, but with potentially more extensive failures.

Advancing Tunnel Boring Machine Performance Prediction in Massive and Highly Fractured Granite: Integrating Innovative Deep Learning and Block Model Techniques

Nantapol Monthanopparat — 2024-09-26

Tunneling projects encounter challenges in predicting Rate of Penetration (ROP), often leading to cost overruns. This study introduces a deep learning approach, combining Deep Feed Forward (DFF) and Long-Short Term Memory (LSTM) techniques, to enhance accuracy. Focused on the Mae Tang - Mae Ngad Project and its geological complexities in massive and highly fractured granite rock conditions, the research aims to improve ROP predictions. The study demonstrates substantial improvements, revealing Root Mean Square Error (RMSE) values of 0.162 (m/h) for DFF and 0.216 (m/h) for LSTM. Notably, the models exhibit enhanced performance in massive rock conditions with an RMSE of 0.110 (m/h), while highly fractured granite shows an RMSE of 0.261 (m/h). These findings underscore the potential for more precise predictions, addressing historical inaccuracies that often lead to cost overruns. Integrating deep learning techniques proves valuable, offering a pathway for more reliable and cost-effective tunnel construction endeavors.

Ground Improvement of Mongla Container Yard in Bangladesh

Mahabub Sadiq — 2024-09-26

In the context of constructing container yards on soft soil layers, it often becomes necessary to undertake ground improvement works to mitigate potential settlement caused by anticipated dead and live loads. In situations involving substantial accumulations of soft and compressible clay deposits, it becomes imperative to expedite the process of consolidation. The utilization of prefabricated vertical drains in conjunction with preloading is a commonly employed technique for ground improvement in such scenarios. In the context of ground improvement projects involving soft soil, it is necessary to determine the extent of improvement accomplished in the soft, compressible clay. This assessment assists in verifying if the soil has reached the desired level of consolidation, hence allowing for the removal of preloading measures. The analysis can be conducted using observational methods, wherein continuous records of ground behavior are monitored starting from the date of equipment installation. Field instruments are employed to validate the efficacy of soil improvement activities and to guarantee that the prescribed level of consolidation resulting from the sandfill and surcharge loading has been attained before the removal of the preloading. This paper presents a comparative analysis of different approaches used to assess the degree of consolidation in a case study conducted at the Mongla Port container yard project in Bangladesh

Prediction of Stone Column Bearing Capacity Using Artificial Neural Network Model (ANNs)

Maryam Gaber — 2024-09-25

In the area of ground improvement the stone columns (SCs) play a deﬁnite role. The ground treatment technique has demonstrated to be effective in improving the embankments stability and natural slopes by rising the bearing capacity and decreasing settlements. The objectives of this study are to develop models for predicting the performance of SCs supported embankment foundation utilizing artificial neural network (ANN). For the aim of creating ANN models, training; testing and validation set comprising 70%, 15%, and 15% of the data, respectively steps were done, making use of available numerical results were obtained from the 2D finite element analysis. A dataset including of about 200 cases is involved and the mean square error (MSE) with R-squared value are used as performance metrics of the system. The applied data in ANN models are arranged in component of 4 input parameters which cover column diameter d, center to center spacing S, the internal friction angle of columns material ϕ, and embankment high H. Relating to these input parameters, the selected responses were; the bearing capacity of the SC (BC) and safety factor against the stability (SF). Based on the simulated results, an ideal 4-14-1 ANN architecture has been settled for the direct prediction. According to the technique was used, the forecasted data from the model had a good agreement with the actual datum, where the high regression coefficient (R2) was equals to 0.995 and 0.891 for BC and SF models, respectively. Furthermore, the relative importance of inﬂuential variables are examined, which shows that the column diameter is the most effective parameter in two study models with an significance score of 32.9%. Finally, the outcomes clearly demonstrated that the ANN method is reliable for modelling and optimizing of the SC behaviour.