Advanced Geotechnical Solutions for Soft Soils: FEM Analysis and Hybrid Reinforcement in the Semarang-Demak Toll Road Project
Bibliographic record
Abstract
This paper presents an analysis of the data collected from the full-scale trial embankment implemented in the Semarang-Demak Toll Road project, part of a national strategic project in Central Java, Indonesia. The project integrates a sea dike with a road embankment. The main challenge of the project lies in constructing the embankment on soft soil layers up to 40 meters thick. The road embankment is designed to reach a height of 6 meters above Mean Sea Level (MSL) or 8 meters above the average seabed level. In the absence of ground improvement techniques—such as bamboo mattress, prefabricated vertical drains (PVD), or high-strength geotextiles—very soft soil with an undrained shear strength of approximately 6.5 kPa can support only a critical embankment height of about 3 meters. A hybrid reinforcement that combines bamboo mattresses, high-strength woven geotextile, PVD and staged embankment construction is selected to solve the shear strength and settlement problem. Mattress acts as platform and designed to deform as much as the soft-soil to spread initial embankment load uniformly and increase bearing capacity, PVD accelerates consolidation, and high-strength woven geotextile provides tension capacity. To achieve a load ratio of 1.3 in accordance with SNI 8460-2017, a 13.5-meter soil preloading needs to be constructed. Several monitoring systems were installed to monitor the behavior of the entire embankment system. Complementary investigations, including CPT, CPTu, and Deep Boring, were conducted. Finite element analysis (FEA) was then performed, revealing improved performance, achieving safety factors of 1.25 during construction and 1.55 long-term.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".