Assessment of steel-fiber-reinforced segmental lining of Chamshir water conveyance tunnel, Iran: Integrating laboratory experiments, field observations, and numerical analysis
Bibliographic record
Abstract
The conventional rebar-reinforced concrete (RRC) segmented lining of the Chamshir water conveyance tunnel (CWCT) in Bushehr Province, Iran, has experienced significant damage issues. This paper presents a comprehensive study involving laboratory experiments, numerical simulations and statistical survey analysis aimed at evaluating the mechanical behaviors and structural integrity of steel fiber-reinforced concrete (FRC) and fiber-rebar-reinforced concrete (FRRC) segment types, as compared to the traditional RRC segments. Laboratory compressive, tensile and flexural strength testing provided insights into the impacts of the fiber diameter and content on the mechanical and structural properties of the FRC test specimens. Numerical simulations scrutinized beam and segment models reinforced by the explicit inclusion of randomly orientated and evenly distributed steel fibers. The segment simulations were directed at investigating concentrated loading, representative of the jack thrust applied during the tunnel boring machine (TBM) installation phase. Additionally, a field-monitoring program of the CWCT lining quantified common damage patterns among 545 RRC and 416 FRC segments that had experienced damage during their installation and in-service phases. For FRC beams, increased fiber content correlated with enhanced strength, although the use of larger diameter fibers produced diminishing returns. Compared to the considerable damage expected near the applied TBM jack thrust for the RRC segments, significantly reduced damage was predicted for both the FRC and FRRC segment types. However, even modest jack misalignment (of 5–7.5°) led to cracking damage across the segment width for all models, although with the fibers included, the chipping damage was reduced substantially. The findings underscore the potential of FRC segments to achieve better structural integrity compared to traditional RRC segments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".