Machine and deep learning-based prediction of flexural moment capacity of ultra-high performance concrete beams with/out steel fiber
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Nature
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This paper presents a study to develop the most appropriate machine learning (ML) and deep learning (DL) models for predicting the flexural moment capacity of ultra-high performance concrete beams (UHPC-Bs) with/out steel fibers. As prediction models, five different ML models (support vector regression (SVR), extreme gradient boosting (XGBoost), multilayer perceptron (MLP), random forest (RF), and extremely randomized trees (ERT)) and three different DL models (long short-term memory (LSTM), bidirectional LSTM (BILSTM), and gated recurrent unit (GRU)) are adopted in this study. To train and evaluate these prediction models, a total of 82 experimental results from the literature on rectangular UHPC-Bs with/out steel fibers are used as a database. On the other hand, the Shapley Additive Explanations (SHAP) analysis is used to evaluate the effects of input features on the prediction results. Furthermore, based on the best prediction model obtained in this study, a graphical user interface is designed to facilitate the use of the flexural moment capacity of UHPC-Bs with/out steel fibers in practical applications. The results obtained indicated that both ML and DL models can be effectively used to predict the flexural moment capacity of UHPC-Bs with/out steel fibers. Particularly, ML algorithm such as XGBoost and DL algorithm like BILSTM stand out for their ability to predict the flexural moment capacity with high accuracy. Furthermore, SHAP analysis revealed that the longitudinal tensile reinforcing steel bar area (As), effective depth (d), and fiber-reinforcing index (Vf × lf/df) are the most effective parameters for prediction performance, while the compressive strength (fc) of concrete is less effective. Consequently, the developed prediction models and graphical user interface (GUI) will help structural engineers and designers quickly predict the flexural moment capacity of UHPC-Bs with high accuracy and make the design process more efficient. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
Açıklama
Anahtar Kelimeler
Deep learning, Flexural performance, Machine learning, RC beams, Ultra-high performance concrete
Kaynak
Asian Journal of Civil Engineering
WoS Q Değeri
Scopus Q Değeri
Q3
Cilt
25
Sayı
6
