Exploring the synergy of ECCs and SMAs in creating resilient civil infrastructure
Bibliographic record
Abstract
Extreme loading events such as blasts, impacts and earthquakes often lead to the partial or total collapse of reinforced-concrete structures, resulting in economic and human life losses. Civil engineers have therefore been seeking innovative materials and systems that would allow the design of resilient and smart structures that can withstand such catastrophic events. Recently, engineered cementitious composites (ECCs) and shape memory alloys (SMAs) have emerged as strong contenders in the production of smart and resilient structural systems. This paper examines recent research work into the performance of structural members produced with ECCs and/or SMAs for applications in new structures as well as in strengthening and retrofitting work. The constraints on wider implementation of these materials in structural applications are discussed. It is shown that the superior performance of SMA-reinforced ECC elements under static and dynamic loading could allow the development of novel resilient composites with exceptional impact performance to protect infrastructure of paramount importance for homeland security against explosive, dynamic and impact loading.
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 | 0.002 | 0.001 |
| 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 | 0.001 | 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".