# Web Crippling of Pultruded GFRP Profiles: A Review of Experimental, Numerical, and Theoretical Analyses

## TL;DR

This paper reviews how pultruded GFRP profiles fail under local loading, focusing on web crippling and ways to improve design and prediction methods.

## Contribution

The paper provides a comprehensive review of web-crippling behavior in GFRP profiles, highlighting design factors and new modeling techniques.

## Key findings

- Profile geometry and flange–web joint detailing significantly influence web-crippling behavior.
- Finite element analysis and artificial intelligence offer improved predictive modeling for structural applications.
- Current design standards have limitations, and damage types like joint fractures and buckling require further study.

## Abstract

Glass fiber reinforced polymer (GFRP) composite profiles produced by pultrusion method are widely used as an alternative to traditional building materials due to their lightness and corrosion resistance. However, these materials are susceptible to crushing type fractures known as “web crippling” especially under local loading due to their anisotropic structure and limited mechanical strength. Understanding web-crippling behavior is crucial for the safe and efficient structural application of pultruded GFRP profiles. This study report narrated the review of experimental, numerical, and analytical investigations of web-crippling behavior of pultruded GFRP profiles. Highlights of the major findings include profile geometry and detailing of the flange–web joint, loading types (end-two-flange (ETF), interior-two-flange (ITF), end bearing with ground (EG), interior bearing with ground (IG)), bearing plate dimensions, presence of web openings, and elevated temperatures. It also considers the limitations of current standards, along with new modeling techniques that incorporate finite element analysis as well as artificial intelligence. Damage types such as web–flange joint fractures, crushing, and buckling were comparatively analyzed; design approaches based on finite element modeling and artificial intelligence-supported prediction models were also included. These insights provide guidance for optimizing profile design and improving predictive models for structural engineering applications. Gaps in current design standards and modeling approaches are highlighted to guide future research.