(Sprayed) concrete production in life cycle assessments: a systematic literature review
Bibliographic record
Abstract
Purpose The carbon intensity that accompanies concrete manufacturing has been widely investigated. However, depending on the intended use, concrete’s embedded materials’ quantities can change significantly, affecting its environmental performance. Seldom investigated, sprayed concrete’s impact differs from that of typical ready mixed concrete, which justifies a differentiated inspection. Our goals are (i) to prove that sprayed concrete’s environmental impacts are under-investigated and (ii) to provide an overview on how concrete’s components’ production cycles are typically modelled in LCAs. Methods We performed a systematic literature review (SLR) to gather the widest possible sample of papers in a replicable and transparent manner, aiming to answer two research questions: ‘What is the life cycle performance of sprayed concrete?’ and ‘What are the most frequent methodological choices made to perform an LCA of concrete’s constituents?’. We used eight different keyword strings for each of concrete’s most used components and searched for documents in databases Springer and ScienceDirect. After 3 conservative filtering rounds, 282 papers were thoroughly and collectively assessed to feed the outcome herein documented. Results and discussion The investigated literature not only showed a gap in sprayed concrete’s environmental impacts documentation but also allowed us to build a literary dossier to ground researches aiming to calculate typical concrete mixes’ impact through LCA, assuring comparability with the ecological status quo for that construction material. Practitioners’ most frequent methodological choices were documented, along with common standard breaches and limitations in investigated studies. Conclusions By systematically structuring our research protocol, we covered enough papers to provide a sound overview and to make collective conclusions regarding available literature. We make two main recommendations for LCA practitioners: non-carbon correlated impact categories ought to be investigated—especially as we move towards more carbon-friendly technologies in concrete/cement manufacturing. Second, practitioners should always comply with the transparency requirements of an LCA. Our outcome pointed to an alarming number of published papers that failed to declare basic methodological choices such as data sources, assessment methods used and impact distribution strategies in multifunctional processes’ modelling.
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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 | 0.023 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.030 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".