Prediction of punching shear strength in flat slabs: ensemble learning models and practical implementation

Nguyen, Khuong Le; Trinh, Hoa Thi; Pham, Thong M.

doi:10.1007/s00521-023-09296-0

Cited by 2 publications

(1 citation statement)

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“…When a concentrated load is exerted onto an RC slab, this leads to the creation of a shear cone around the column, ultimately causing punching failure (see Figure 1) [1]. Punching failure happens in a way that makes the slab crack along critical planes that go through the slab from its compression to tension surfaces in an oblique direction [2,3]. This failure is immense, sudden, and detrimental; no apparent signs were observed before the collapse.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Learning Models for Prediction of Punching Shear Strength in RC Slab-Column Connections

Habibi,

Youssef,

Naseri

et al. 2024

Civ Eng J

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In reinforced concrete (RC) structures, accurate prediction of the punching shear strength (PSS) of slab-column connections is imperative for ensuring safety. The existing equations in the literature show variability in defining parameters influencing PSS. They neglect potential variable interactions and rely on a limited dataset. This study aims to develop an accurate and reliable model to predict the PSS of slab-column connections. An extensive dataset, including 616 experimental results, was collected from earlier studies. Six robust ensemble machine learning techniques—random forest, gradient boosting, extreme gradient boosting, adaptive boosting, gradient boosting with categorical feature support, and light gradient boosting machines—are employed to predict the PSS. The findings indicate that gradient boosting stands out as the most accurate method compared to other prediction models and existing equations in the literature, achieving a coefficient of determination of 0.986. Moreover, this study utilizes techniques to explain machine learning predictions. A feature importance analysis is conducted, wherein it is observed that the reinforcement ratio and compressive strength of concrete demonstrate the highest influence on the PSS output. SHapley Additive exPlanation is conducted to represent the influence of variables on PSS. A graphical user interface for PSS prediction was developed for users’ convenience. Doi: 10.28991/CEJ-SP2024-010-01 Full Text: PDF

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Section: Introductionmentioning

confidence: 99%