ArticleScientific reports2026
Machine learning model optimization with optuna for accurate prediction of strength and crack behavior in prestressed concrete beams.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Deep Learning Optimisation Strategies for Uveal Melanoma Detection Using Ultra-Widefield Photography.Research square · 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
Prestressed concrete beams are widely used in bridge and building structures, and their performance is directly related to the overall safety and durability. To predict the performance of prestressed concrete beams, machine learning (ML) has been widely applied. However, the selection of model hyperparameters remains a significant challenge in achieving efficient and accurate predictions. To address this issue, this paper investigates the application of Optuna for hyperparameter optimization to four machine learning models: XGBoost, Decision Trees (DT), Random Forests (RF), and LightGBM (LGBM). This study demonstrates that adopting this optimization method significantly improves the efficiency of the prediction process and successfully identifies the optimal hyperparameter combinations for each model. The performance of the final models was validated using a set of performance evaluation metrics. The results show that the LGBM model achieved an R
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.