Comparing machine learning and deep learning approaches to predicting the seismic response of slab-column connections.

Mahmoud A El-Mandouh, Hassan Youssef, M S Elborlsy, Mostafa A Ebied

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 authors.

Mahmoud A El-MandouhCivil Engineering Department, Faculty of Engineering, Beni-Suef University, Beni-Suef, 62511, Egypt. m.abdel.aziz.mohsen@gmail.com.

Hassan YoussefCivil Construction Technology Department, Faculty of Technology and Education, Beni-Suef University, Beni-Suef, 62511, Egypt.

M S ElborlsyDepartment of Process Control Technology, Faculty of Technology and Education, Beni-Suef University, Beni-Suef, Egypt.

Mostafa A EbiedDepartment of Electronics Technology, Faculty of Technology and Education, Beni-Suef University, Beni-Suef, Egypt.

Funding

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6 · The paper itself

Abstract

Slab-column joints constitute the most sensitive elements of flat plate structures during seismic forces since they are likely to experience brittle failure. This paper examines the predictive accuracy of an extensive Machine Learning (ML) and Deep Learning (DL) solutions for predicting seismic performance of slab-column connections, such as punching moment (M) and Drift Ratio (dr). The ML models under study are Ridge Regression (RR), Linear Regression (LR), Lasso Regression (Lasso R), Elastic Net (EN), Support Vector Regression (SVR), Gradient Boosting (GB), random Forest (RF), and Extreme Gradient Boosting (XGBoost). The models of DL that were analyzed are Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and a hybrid version that incorporates CNN and LSTM (CNN-LSTM). The analysis shows that in (M) prediction, the performance of GB was the best with a coefficient of determination (R

Indexed as

CNN-LSTMDeep learningDrift ratio estimationGradient boostingHybrid modelMachine learningPunching moment predictionRandom forestStructural engineeringXGBoost algorithm

Identifiers

PMID42115248

DOI10.1038/s41598-026-50962-9

PMCPMC13161370

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