Culvert Prototype Made with Seawater Concrete: Materials Characterization, Monitoring, and Environmental Impact
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Recent developments in concrete research have considered the possible advantages related to the use of seawater as mixing water for concrete production. Specifically, the SEACON-INFRAVATION project investigated the performance of seawater concrete for the construction of sustainable and durable reinforced concrete structures. Besides laboratory activities aimed at characterizing the performance of seawater concrete and corrosion behavior of various types of embedded reinforcements, a demonstration project was executed in Italy. The prototype consisted of a concrete culvert built along the A1 motorway, close to the city of Piacenza, Italy. The demonstration activities led to testing of the on-site use of seawater and assessment of the corrosion conditions of the embedded reinforcements, allowing a thorough understanding of long-term durability and sustainability. For this purpose, the prototype culvert was divided into six segments; each segment was representative of a combination of type of concrete (reference, seawater, and recycled-asphalt-pavement concrete) and type of reinforcement (carbon steel, austenitic and duplex stainless steels, and glass fiber–reinforced polymer). This article presents concrete mix designs, materials characterization, embedded probes used to monitor the corrosion of the reinforcements, and test results obtained at various stages of execution and service conditions. In addition, a sampling campaign, one year from construction, is included. Finally, mention is made of the life cycle assessment and life cycle cost analyses performed to quantify the long-term benefits of seawater concrete combined with corrosion-resistant reinforcement.
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.
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 | 0.001 |
| 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 it