# Data-driven approach for rapid prediction of strength scatter in brittle ceramics using deep learning and swarm optimization

https://mdr.nims.go.jp/datasets/a8f222da-d5ce-423d-ab30-767827bd45dc

## File

- [Data-driven approach for rapid prediction of strength scatter in brittle ceramics using deep learning and swarm optimization.pdf](https://mdr.nims.go.jp/filesets/71243fea-947c-42b4-b190-04f4909396d9/download) ([Detail](https://mdr.nims.go.jp/filesets/71243fea-947c-42b4-b190-04f4909396d9.md))

## Id

a8f222da-d5ce-423d-ab30-767827bd45dc

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-04-22T05:11:58.918342Z

## Updated at

2026-04-22T07:01:04.770719Z

## Published at

2026-04-22T09:24:14.946265Z

## Doi



## First published url

https://doi.org/10.1080/27660400.2026.2656050

## Date published

2026-12-31

## Recorded date published

2026-12-31

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Data-driven approach for rapid prediction of strength scatter in brittle
    ceramics using deep learning and swarm optimization
  title_type: original
  lang: en

## Description

- description: The intrinsic brittleness and high defect sensitivity of ceramics result
    in significant strength variability, presenting substantial challenges for structural
    reliability assessments. Experimental characterization of strength scatter in
    ceramic components is both time-consuming and costly. Conventional physics-based
    (forward) analyses can model strength scatter based on microstructural data; these
    methods are computationally intensive. This study introduces a deep learning–based
    surrogate model that directly predicts the Weibull distribution parameters of
    ceramic bending strength from equivalent crack length distributions, achieving
    substantial reductions in computational cost without compromising predictive accuracy.
    Additionally, an inverse analysis framework is developed by integrating the surrogate
    model with intelligent swarm optimization, enabling the estimation of defect distributions
    from reference strength measurements. The proposed approach demonstrates high
    accuracy and efficiency, achieving over 200-fold speedup in forward analysis and
    18,000-fold in inverse analysis. It facilitates a rapid and reliable evaluation
    of the relationship between defect distributions and strength scatter as characterized
    by Weibull parameters. This methodology provides a robust tool for accelerating
    the design and development of high-performance ceramic materials.
  description_type: abstract
  lang: eng

## Creator

- name: Taiyo Maeda
  role: author
  organization: Graduate School of Yokohama National University
- name: Muhammad Aiman bin Musa
  role: author
  organization: Graduate School of Yokohama National University
- name: Toshio Osada
  role: author
  orcid: https://orcid.org/0000-0003-1539-9264
  organization: National Institute for Materials Science
  department: Research Center for Structural Materials/Materials Manufacturing Field/High-Reliability
    Heat-Resistant Materials Group
- name: Shingo Ozaki
  role: author
  organization: Faculty of Engineering, Yokohama National University

## Contact agent



## Publisher

organization: Informa UK Limited

## Managing organization



## Keyword

- subject: Ceramics
  schema: not_defined
- subject: Fracture statistics
  schema: not_defined
- subject: Defect distribusion
  schema: not_defined
- subject: Multilayer perceptron
  schema: not_defined
- subject: Inverse analysis
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Science and Technology of Advanced Materials-Methods
  issn: '27660400'
  volume: '6'
  issue: '1'

## Conference



## Related item



## Funding

- funder_name: Japan Society for the Promotion of Science

## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



## Energy level/transition state



## Software



## Custom property



## Fileset

- id: 71243fea-947c-42b4-b190-04f4909396d9
  filename: Data-driven approach for rapid prediction of strength scatter in brittle
    ceramics using deep learning and swarm optimization.pdf
  content_type: application/pdf
  size: 3861816
  md5: c0ca21b2ffd84b51a75b5ec3331dbceb

## Thumbnail

fileset_id: 71243fea-947c-42b4-b190-04f4909396d9
filename: Data-driven approach for rapid prediction of strength scatter in brittle
  ceramics using deep learning and swarm optimization.pdf