Performance assessment of fly ash-based geopolymer concrete pipes and its comparison with cement reinforced concrete pipes
Bibliographic record
Abstract
The use of precast concrete pipes for water and sewage transportation systems is an important asset for maintaining a healthy and sustainable environment. This study aimed to investigate the mechanical performance of full-scale sustainable cementless geopolymer concrete pipes (GPC1) and compare them with conventional reinforced cement concrete pipes (RC1) and cementless geopolymer concrete pipes incorporating 50 kg/m 3 of steel fibers (GPC1-SF) using non-destructive and destructive techniques. In cementless concrete mixtures, fly ash used as a binder, procured from a local power plant and steel fibers were locally made by cutting the long steel wires. Initially, a rebound hammer test was performed on all the casted pipes (RC1, GPC1 and GPC1-SF) on the inside and outside of the pipe’s wall. Afterwards, cores were retrieved from all pipes and ultrasonic pulse velocity, core compressive strength, volume of permeable voids and sorptivity tests were conducted. Moreover, a three-edge bearing test was also conducted to evaluate the flexural performance of full-scale pipes. No issues were encountered in the casting process of pipes using the spun-cast methodology for incorporating the steel fibers and fly ash in cementless concrete pipes, indicating the adequacy of the already available precast unit for casting fiber-reinforced cementless concrete pipes. Results showed that the outer wall of tested pipes exhibited higher compressive strength than the inner walls, with a maximum outer to inner wall compressive strength ratio of 1.24. The core compressive strength was higher than 30 MPa for all the tested cores. The rebound hammer, ultrasonic pulse velocity and core compressive strength results of tested pipes can be correlated with each other. Under three-edge bearing, the GPC1-SF pipe showed D-load at 0.30 mm crack width and at an ultimate load of around 83 kN/m/m and 125 kN/m/m respectively, higher than the RC1 and GPC1 pipes, indicating improved structural performance. The overall cost of GPC pipes was comparable to conventional cement concrete pipes. This demonstrated GPC pipes as a sustainable option for reducing the carbon footprint due to the huge consumption of cement in sewer networks, making it an economically feasible option, supporting the accomplishment of Sustainable Development Goals (6, 9, and 11) by endorsing sustainable robust infrastructure and generating environmentally friendly municipal solutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".