Indonesian Geotechnical Journal

Fiber-reinforcement MICP for Durability Improvements

2024-01-24T01:53:25+00:00

Microbially Induced Carbonate Precipitation (MICP) technology, a method for soil enhancement, has recently garnered considerable attention within geotechnical communities. This study places a significant focus on addressing the paramount concern pertaining to the endurance of MICP-treated specimens. The research centers on MICP-treated samples fortified with plant-derived natural fibers, specifically jute. It evaluates their robustness when subjected to exposure to both distilled water (DW) and artificial seawater (ASW). The primary objectives encompass acquiring a comprehensive understanding of their prolonged performance under varied conditions, appraising the consequences of fiber reinforcement, and augmenting the suitability of MICP-treated samples for applications in the safeguarding of coastal regions against erosion. The investigation subjected these specimens to 12 wetting-drying cycles utilizing artificial seawater following treatment periods of 5 days, 7 days, and 14 days. The findings unveiled an approximate 8.5% diminution in sample mass, with the fibers constituting 2% of the sand's total weight. Moreover, the study underscores the adeptness of the integrated fiber in withstanding the wetting-drying (WD) cyclic process, amplifying the mechanical and physical attributes of the fiber-reinforced MICP-treated specimens, thus contributing significantly to their overall durability.

Investigation on the Effects of Different Concentration of CO2 Gas Injected into Fresh Cement Paste Along with the Addition of Superplasticizer on the Mechanical Properties of Cement

2024-06-13T09:43:18+00:00

This paper focused on examining the impact of injecting varying concentrations of carbon dioxide (CO₂) gas on the mechanical characteristics of both the fresh and hardened states of cement paste. This study also considered the influence of the presence or absence of polycarboxylate superplasticizer in cement mixture on these properties. Many researchers discovered the benefit of CO₂ gas utilization in cement mixture to accelerate the early strength of cement or concrete hydration by its carbonation that form calcium carbonate (CaCO₃). The application method is to directly inject CO₂ gas in a curing chamber for air-curing of precast concrete. Alternatively, carbonated water is mixed with cement during concrete mixing. However, the use of CO₂ gas does not significantly improve the 28-day strength of concrete. This study explores how to improve the carbonation impact on mechanical properties of cement paste and apply it to ground improvement. In this study, the method adopted is direct injection of CO₂ gas during cement slurry mixing with different injection duration. The influence of CO₂ in the presence of superplasticizer (SP) in cement slurry was also studied as SP is generally used for grouting. The results showed that the carbonation of cement paste with additional of superplasticizer significantly affect its flow, viscosity and bleeding properties. Unlike samples with SP addition, the samples without SP addition showed higher compressive strength after 28 days of curing up to certain CO₂ injection time. For all CO₂ gas injection time, smaller porosity rates were observed for 7-day cured samples with SP addition compared to those without SP addition. This is due to accelerated carbonation due to SP presence in cement mixture. From the results, the optimum of CO₂ gas injection time for one liter mixture of cement paste to improve its compressive strength (up to 123% increase) have been discovered. It can be inferred that the addition of superplasticizer in cement slurry reduces the amount of CO₃^2- ions and Ca²⁺ ions during carbonation process of cement hydration products, which are strongly related to the pH level in pore solution. These ions play a significant roles in determining the mechanical properties of cement slurry.

Seismic Safety Evaluation of Mechanically Stabilized Earth (MSE) Wall for Highway Construction

2024-07-18T04:14:53+00:00

The usage of Mechanically Stabilized Earth (MSE) Walls has grown in popularity over the last few decades and has been widely used in many countries for highway construction, including Indonesia. As a country with a high risk against seismic hazards, a considerable stability analysis against earthquakes for construction must be conducted. This paper is directed to evaluate the static and seismic stability of MSE wall by adopting design criteria from SNI 8460:2017 using the pseudo-static approach with Limit Equilibrium Method (LEM) which modelling earthquake as a seismic coefficient, a one-way constant load and dynamic response approach with Finite Element Method (FEM) which modelling earthquake as ground motion, a fluctuating load which varies in time. The stability analysis is performed by considering three failure mechanisms that mostly occurred in MSE Walls; base sliding, tensile overstress, and slope failure. The earthquake load is modelled based 1000-year return period earthquake. Based on the analysis result, the most potential failure mechanism that may occur in the MSE wall is tensile overstress, while the least potential failure is base sliding. The analysis result also shows that the finite element method obtained higher safety factors compared to limit equilibrium, while on the other hand, the remaining two failure mechanisms shows the different result, with the finite element method obtaining lower safety factors than limit equilibrium. Modelling seismic load as an accelerogram indicates that earthquakes have higher impacts on structure stability compared to seismic coefficient, based on seismic safety factor reduction of each method. Although show differences in the value of the safety factor, the minimum safety factor required still complies with both methods.

Spatial Variability of Sliding Plane on Volcanic Region. Case Study of Sta 21+200 Cisumdawu Toll Road

2023-09-04T03:44:09+00:00

Landslide can be caused either by natural phenomenon or due to human intervention. Regardless of the triggering factor, once landslide occurs, sliding plane in the form of discontinuity or sometimes referred as sliding plane is formed. Identifying the location and geometry of the sliding plane is important in determining the location of the reinforcement to rehabilitate slope failure. However, it is often difficult to locate the location and geometry of the sliding plane as the sliding direction is also difficult to ascertain. In this paper, slope failure which occurred in STA 21+200 of Cisumdawu toll road is used as case study. Variability of the sliding planes are investigated based on the location of the concrete overbreak that occurred during the bored pile construction on Tuff (volcanic soils) in Sumedang Region. Sliding planes are also estimated based on the soil investigation and pile boring records. The proposed solution is to reinforce the bored piles that did not penetrate into the hard layer with ground anchors installed at the pile cap.

Deep Neural Networks for Predicting the Settlement of Earth Dams Based on the InSAR Outputs

2024-08-07T15:48:31+00:00

Earth dams play a crucial role in water resource management, necessitating effective maintenance to ensure prolonged functionality and safety. Monitoring these dams traditionally involves methods such as surveying and instrumentation. However, challenges arise from equipment malfunctions and absences, especially in dams affected by environmental changes. In response to these challenges, the cost-effective and adaptable nature of Interferometric Synthetic Aperture Radar (InSAR) has made it a preferred choice for monitoring. Despite their advantages, traditional numerical models like finite element methods have limitations in predicting deformation comprehensively, particularly due to intricate, non-linear correlations involving material type and environmental conditions. To overcome these limitations, this research employs deep learning techniques, specifically Long Short-Term Memory (LSTM) network, to capture intricate relationships and accurately predict dam behavior. Time series data from InSAR, representing settlement, are decomposed into trend and seasonal components using Artificial Neural Networks (ANN) for trend prediction. Furthermore, an LSTM network is utilized to handle the complexity of the seasonal component and its correlation with environmental factors. This network incorporates settlement, precipitation, temperature, and reservoir water level time series as inputs, thereby enhancing prediction accuracy. The research outcome presents a robust solution that holds the promise of increased accuracy and efficiency in predicting, monitoring, and serving as an early warning system for earth dam deformations over time. Such advancements are crucial for ensuring the safety and integrity of critical infrastructure in the face of evolving environmental conditions.