ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2025
Bayesian machine learning for inverse design of ultra-high-performance concrete.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- An interpretable surrogate modeling framework for rice husk ash concrete using copula-based virtual sampling.Scientific reports · 2026Article
- Research on Early-Age Shrinkage and Prediction Model of Ultra-High-Performance Concrete Based on the BO-XGBoost Algorithm.Materials (Basel, Switzerland) · 2026Article
- Path and Bayesian network analyses in the complex design of a well-being survey via New Zealand's Integrated Data Infrastructure.Frontiers in psychiatry · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
The diversity of available material feedstocks, coupled with rigorous performance requirements, complicates the design of ultra-high-performance concrete (UHPC). Here, a Bayesian method for inverse design is first demonstrated from published UHPC data. Materials were represented in a framework of hierarchical machine learning; a fundamental goal in this study was to compare the accuracy and generalizability of models parameterized by compositional variables with those parameterized by latent variables based on empirical models. Data were first modelled by ensemble ridge regression, and miscalibration area (a Bayesian error metric) indicated improved generalizability for models parameterized by latent variables compared to those parameterized by composition. Then, Gaussian process regression based on an expanded feature set was used to predict strength that, counterintuitively, generated higher accuracy for models parameterized by compositional variables (test
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.