Comparative Analysis of Simplified and Finite Element Method Approaches for Seismic Forces in Circular Tunnels
A. Achouri and M. N. Amrane
ABSTRACT: This paper presents a comparative analysis between simplified and Finite Element Method (FEM) approaches for evaluating seismic forces in circular tunnels, with a specific focus on the Algiers Metro as a practical case study, considering the Boumerdes earthquake in 2003. The FE modeling was carried out under plane strain conditions, using the contraction method to phase the performed model and incorporating the Volume Loss coefficient (VL). The behavior of soil and tunnel elements was considered linear elastic. Based on the maximum strain rate of the soil medium, various simplified approaches existing in the literature were adopted in this study, including solutions proposed by Wang (1993), Penzien (2000), Bobet (2003, 2010), and Park et al. (2009). The maximum shear strain rate was determined by plotting the cumulative horizontal displacement of the soil profile and then using this value to deduce the vertical strain rate. Results indicate that increasing VL values initially reduce 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. The best agreement between the simplified and FEM approaches was observed when VL ranged between 1 and 2. Additionally, the total principal stresses around the tunnel increased with the VL ratio. This study highlights the importance of estimating the appropriate maximum strain rate and VL ratio to achieve accurate results while using both simplified and FEM approaches.
KEYWORDS: Seismic Forces, Circular Tunnels, Simplified Approaches, FE Modeling, and Volume Loss Coefficient.
Stability Analysis of Embankment using Finite Element Method Constructed over Treated Soil with Anionic Polyacrylamide
Lindung Zalbuin Mase, Dewi Amalia, and Anna Dewi
ABSTRACT: Landfill materials composed of weak soil are closely related to stability problems. To minimise this problem, various stabilisation techniques are often used, one of which is the addition of polyacrylamide anionic polymer (APAM). In this paper, soil behaviour before and after adding APAM in the West Bandung area, Indonesia, has been analysed. It has been done with different variables by considering the landfill’s geometry and the soil material’s properties. Several models were analysed to determine the slope height and angle that are safe for soil stabilisation. The modelling was done using the finite element method based on the soil hardening criteria model and Mohr-Coulomb. The analysis results show that with the increase in height and slope, the safety factor (FS) decreases, and the deformation increases. Conversely, if the height and slope decrease, the FS increases, and the deformation decreases. It is observed that the soil with the highest percentage of APAM (1%) has produced the highest shear strength parameters and the lowest deformation. This study found that weak soil treated with APAM can be used as a backfill material, but the potential for collapse is more significant.
KEYWORDS: Anionic Polyacrylamide, Embankment, Finite Element Method, and Deformation.
Advancing Tunnel Boring Machine Performance Prediction in Massive and Highly Fractured Granite: Integrating Innovative Deep Learning and Block Model Techniques
N. Monthanopparat and T. Tanchaisawat
ABSTRACT: 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 the accuracy of ROP prediction. 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 ranging between 50 and 900 percent. Integrating deep learning techniques proves valuable, offering a pathway for more reliable and cost-effective tunnel construction endeavors.
KEYWORDS: Tunnel Boring Machine, Deep Learning Technique, Hard Rock Tunneling, and TBM Performance Prediction Model.
Ground Improvement of Mongla Container Yard in Bangladesh
Mahabub Sadiq, Md. Rokonuzzaman, Firoz Mahmud, and Md. Hasan Tareq
ABSTRACT: 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.
KEYWORDS: Prefabricated Vertical Drain, Soft Clay, Observational Method, and Consolidation Settlement.
Analysis and Optimisation of Influencing Factors on the Performance of Cement Stabilised Marine Clay Using Response Surface Methodology
Rejin Raj P., Vandana Sreedharan, and Abdul Nazar K. P.
ABSTRACT: Cement stabilization is a go-to technique for improving the engineering characteristics of marine clays. As per the previous studies, numerous factors influence the effectiveness of cement stabilization. It is well established that the cement content, molding water, and curing periods are the major controlling factors. Due to the complex dynamics among such factors, there is a critical need to understand the interplay between these factors to achieve optimal performance in cement stabilization of marine clays. The paper adopts an analytical approach to quantify the impact of controlling factors using unconfined compressive strength (UCS) data. Design Expert 13 was employed for the experimental design and the response surface study. A central composite design (CCD) was adopted for the analysis, and the ranges of factors were fixed in accordance with the previous studies and the respective optimum moisture conditions. The ranges of cement content (CC), molding water content (MWC), and curing days (CD) were fixed as 5 to 15%,15 to 21%, and 0 to 14 days, respectively. The statistical analysis using ANOVA was used to arrive at a statistically significant quadratic model. A quadratic equation was generated depicting each factor’s individual and interactive influence on the unconfined compressive strength of the cement-stabilized marine clay. The optimization results showed a maximum unconfined compressive strength value of 487.49 kPa for a cement content of 15%, curing days-14 days, and a molding water content of 19.67%. The study aids in understanding the extent of influence of binder content, molding, and curing conditions on the performance of cement-stabilized marine clay.
KEYWORDS: Response Surface Methodology, Cement Stabilization, Marine Clay, Marine Geotechnics, Soil Stabilization, and Clays.
Prediction of Stone Column Bearing Capacity Using Artificial Neural Network Model (ANNs)
Maryam Gaber and Jamal M. A. Alsharef
ABSTRACT: In the area of ground improvement, the stone columns (SCs) play a definite role. The ground treatment technique has been demonstrated to be effective in improving the embankments’ stability and natural slopes by raising the bearing capacity and decreasing settlements. The objectives of this study are to develop models for predicting the performance of SCs-supported embankment foundations utilizing artificial neural networks (ANN). For the aim of creating ANN models, training, testing, and validation sets comprising 70%, 15%, and 15% of the data, respectively steps were done, making use of available numerical results obtained from the 2D finite element analysis. A dataset including about 200 cases is involved, and the mean square error (MSE) with R-squared value is used as performance metrics of the system. The applied data in ANN models are arranged in the component of 4 input parameters, which cover column diameter d, centre-to-centre 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 the 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 used, the forecasted data from the model had a good agreement with the actual datum, where the high regression coefficient (R2) was equal to 0.995 and 0.891 for BC and SF models, respectively. Furthermore, the relative importance of influential variables is examined, which shows that the column diameter is the most effective parameter in the two study models with a significance score of 32.9%. Finally, the outcomes clearly demonstrated that the ANN method is reliable for modelling and optimizing of the SC behaviour.
KEYWORDS: Stone Column, Bearing Capacity, Safety Factor, and Artificial Neural Network (ANN).
The Failure of Road Embankment Along the Canal during Driven Piles Construction in Thickness of Soft Sensitive Clay
Salisa Chaiyaput, Taweephong Suksawat, Jakkaphong Wongkumchun, Jiratchaya Ayawanna, and Thanadol Kongsomboon
ABSTRACT: The pile-retaining wall at Nonthaburi rural road no. 5036 was constructed using reinforced concrete piles or driven piles combined with a concrete retaining wall. The purpose of this structure was to enhance the slope stability of the canal-side road (road embankment along the canal). The damage to the driven piles occurred during the pile construction at 18 m depth below the ground surface. The resistivity survey and screw driving sounding test were employed to investigate the thickness of soft clay layers and unexpected stiff soil layers at the failure area. The field vane shear test was employed to investigate the sensitivity of the soft clay layer. Furthermore, the finite element model was analyzed to verify the failure behaviour of the road embankment during the driven pile’s construction. Consequently, the investigation revealed that the subsoil in the failure area exhibited sensitivity values. The subsoil consisted of a layer of soft clay to medium stiff clay, ranging from 2-10 m below the ground surface, while the subsoil consisted of stiff clay below a depth of 10 m. The installation of the 18-m driven pile caused a disturbance in the soft sensitive clay layer above the stiff soil layer, resulting in a reduction in the strength of the soft clay and affecting the displacement of the driven pile during construction. Furthermore, the occurrence of rapid drawdown causes water seepage to continue to flow toward the canal side. This phenomenon produces active forces on the slope of the road embankment along the canal. As a result, the road embankment along the canal side can collapse due to a disturbance in the sensitive clay layer with rapid drawdown. The result was agreed with the study findings obtained by the finite element model.
KEYWORDS: Clay Thickness, Driven Pile, Sensitive Clay, Soft Clay, and Pile Damage.
