Integrating machine learning and symbolic regression for predicting damage initiation in hybrid FRP bolted connections

Sherif Samy Sorour, Chahinaz Abdelrahman Saleh, Mostafa H. Shazly

Scientific Reports 2025 article cited by 15

Source Open-access PDF More in Engineering & Materials

importance 4/10 ai assisted Mechanical Behavior of Composites composite structures stage: research

Problem

Hybrid FRP bolted connections are rapidly adopted in industry but lack interpretable predictive models for damage initiation loads.

Why it matters

The abstract states these connections are rapidly adopted in industry yet insufficiently addressed in academic research.

What the AI did

Machine learning models analyzed experimental and finite element data to select key design parameters, and PySR symbolic regression derived explicit interpretable equations predicting damage initiation loads.

Results

not stated in source

Impact

An interpretable framework was developed and FEM results validated against fabricated specimen tests, though no numbers appear in the abstract.

Record

Venue	Scientific Reports
Published	2025
Work type	article
Authors	Sherif Samy Sorour, Chahinaz Abdelrahman Saleh, Mostafa H. Shazly
Citations	15 (OpenAlex, at import time)
References	50
Model or method	PySR symbolic regression plus machine learning models (types not stated)
Organization	not stated in source
Primary source	https://doi.org/10.1038/s41598-025-02390-4
Open access	gold
Keywords	symbolic regression, machine learning, FRP composites, bolted connections, damage prediction

Importance rating

4 / 10

real but narrower advance

Rapid industry adoption of hybrid FRP bolted connections is stated, but the advance is a niche interpretable design model, not a broad-impact problem.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

More in Engineering & Materials

#	Paper	Year	Citations	Importance
1	Scaling deep learning for materials discovery Amil Merchant, Simon Batzner, Samuel S. Schoenholz, Muratahan Aykol, G...	2023	1,471	8
2	Application of Machine Learning in Material Synthesis and Property Prediction Guannan Huang, Yani Guo, Ye Chen, Zhengwei Nie	2023	165	7
3	Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries Kai Li, Jifeng Wang, Yuanyuan Song, Ying Wang	2023	115	7
4	Machine Learning for Structural Health Monitoring of Aerospace Structures: A Review Gennaro Scarselli, Francesco Nicassio	2025	44	7
5	Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology Mihail D. Senyuk, Murodbek Safaraliev, Firuz Kamalov, Hana Sulieman	2023	42	7