Data-based modeling of the punching shear capacity of concrete structures

Mellios, Nikolaos; Uz, Okan; Spyridis, Panagiotis

doi:10.1016/j.engstruct.2022.115195

Cited by 8 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast, the ultimate load or punching shear capacity, which led to punching shear failure in the area of internal supports under axial symmetric loading during the experiments, was used as the only output value. Further details regarding the splitting of the database in splitting and training can be found in [11].…”

Section: Development Of the ML Modelmentioning

confidence: 99%

Data Cleansing & Overfitting Check for Interpretable ML in Concrete Design – a punching shear paradigm

Mellios,

Spyrdis

2023

ce papers

Self Cite

View full text Add to dashboard Cite

Evaluation of shear capacity is crucial for existing concrete buildings and bridges. Shear failure can cause an abrupt and brittle loss of load‐carrying capacity, leading to load redistribution and progressive collapse. An internationally research and scietific discourse is actively ongoing on the best prediction model for shear, torsion, and punching shear capacity, with no clear consensus. A machine learning‐based strength model is suggested for accurate prediction of punching shear capacity, using Gaussian Process Regression and Support Vector Regression models. Data elaboration and thorough evaluation are needed to enhance the model's performance and interpretability. The paper discusses critical methodological steps based on the authors' experience of developing ML‐models for structural concrete design against punching shear failure using 544 full‐scale tests.

show abstract

Section: Development Of the ML Modelmentioning

confidence: 99%

Data Cleansing & Overfitting Check for Interpretable ML in Concrete Design – a punching shear paradigm

Mellios,

Spyrdis

2023

ce papers

Self Cite

View full text Add to dashboard Cite

show abstract

“…Consequently, potential interactions between different variables were not taken into account [7]. A significant debate surrounds the identification of the optimal predictive model for PSC in RC slab-column connections [8,9]. Notably, design codes showed considerable disparities when compared to experimentally measured PSC [9].…”

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

View full text Add to dashboard Cite

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

show abstract

Engineering punching shear strength of flat slabs predicted by nature-inspired metaheuristic optimized regression system

Truong,

To,

Truong

et al. 2024

Front. Struct. Civ. Eng.

View full text Add to dashboard Cite

Reinforced concrete (RC) flat slabs, a popular choice in construction due to their flexibility, are susceptible to sudden and brittle punching shear failure. Existing design methods often exhibit significant bias and variability. Accurate estimation of punching shear strength in RC flat slabs is crucial for effective concrete structure design and management. This study introduces a novel computation method, the jellyfish-least square support vector machine (JS-LSSVR) hybrid model, to predict punching shear strength. By combining machine learning (LSSVR) with jellyfish swarm (JS) intelligence, this hybrid model ensures precise and reliable predictions. The model’s development utilizes a real-world experimental data set. Comparison with seven established optimizers, including artificial bee colony (ABC), differential evolution (DE), genetic algorithm (GA), and others, as well as existing machine learning (ML)-based models and design codes, validates the superiority of the JS-LSSVR hybrid model. This innovative approach significantly enhances prediction accuracy, providing valuable support for civil engineers in estimating RC flat slab punching shear strength.

show abstract

Data-based modeling of the punching shear capacity of concrete structures

Cited by 8 publications

References 15 publications

Data Cleansing & Overfitting Check for Interpretable ML in Concrete Design – a punching shear paradigm

Data Cleansing & Overfitting Check for Interpretable ML in Concrete Design – a punching shear paradigm

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

Engineering punching shear strength of flat slabs predicted by nature-inspired metaheuristic optimized regression system

Contact Info

Product

Resources

About