Numerical Modeling to Achieve Concrete Durability for New Waterfront Structures of 100 Years or More, but at What Price?
Bibliographic record
Abstract
Concrete durability design need not be black magic. Technologies and products exist in the market today to achieve durability goals of 100 or even 200 years. But the key questions that must be asked are: 1.What is the cost? 2. How can we convince ourselves that we can achieve these goals? 3. What quality control measures are needed to achieve these goals? The U.S. Navy has been making pioneering advances in concrete durability modeling techniques to ensure their extensive investments in new waterfront assets are sound investments. This technology is available to all and is being used on commercial waterfront projects as well. With the advances made in numerical modeling techniques, it is now possible to quantitatively predict durability with decent accuracy. The STADIUM® model, developed by Materials Service Life of Quebec, Canada, has been adopted by the U.S. Navy as their service life model of choice for predicting marine concrete durability. The numerical model relies on the use of four "transport properties" that are tested from actual concrete samples to determine how aggressive agents such as chloride ions move through the concrete over time. The advent of this new technology now allows engineers to place more emphasis on performance specifications rather than relying only on prescriptive methods. Engineers can specify strength and durability (in terms of required design life) requirements and allow contractors to innovate to achieve the desired results. There are numerous methods of achieving enhanced durability, including adding concrete cover distance of reinforcing steel, choosing a less corrosive or non-corrosive reinforcing steel, using fusion-bonded epoxy-coated rebars, using corrosion inhibitor admixtures, applying external barrier coatings, using supplementary cementitious materials such as fly ash and silica fume, and varying the concrete mix design parameters such as water cement ratio and cement content, for example. Numerical modeling allows the engineer to evaluate the myriad options on an even playing field to determine the optimal solution. The use of a standardized service-life model also allows suppliers to innovate and develop new products to improve durability performance, knowing that they will be subject to fair and unbiased evaluation. Such innovation is sorely lacking in the U.S. construction industry at present since it can often take 10 to 15 years to bring a new product to market. Durable concrete also requires effective quality control measures in the field to ensure the final product matches or exceeds performance in the laboratory. This paper will describe the beta-testing experience and lessons learned by the U.S. Navy on a major new wharf project in a corrosive tropical environment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".