ArticleGels (Basel, Switzerland)2026
Interpretable Machine Learning for Thermal Conductivity Prediction of Silica Aerogel-Incorporated Cementitious Composites.
Article in Gels (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Silica aerogel-incorporated cementitious composites possess low density and thermal conductivity. Their thermal conductivity is influenced by the interplay of mix composition, pore structure, mineral admixtures, and environmental testing conditions. In this study, a literature-derived database containing 208 data records was established for thermal conductivity prediction. Eight variables were utilized as inputs: aerogel content, water-to-cement ratio (W/C), sand content, foam content, silica fume content, fly ash content, testing temperature, and testing relative humidity. Thermal conductivity was designated as the output. The models developed for this study included XGBoost, random forest (RF), support vector regression (SVR), and their counterparts optimized using particle swarm optimization (PSO), which were subsequently compared. Among the optimized models, PSO-SVR showed the most balanced predictive performance, with test-set
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.