A Novel Feature Selection Approach Based on Tree Models for Evaluating the Punching Shear Capacity of Steel Fiber-Reinforced Concrete Flat Slabs

Abstract

When designing flat slabs made of steel fiber-reinforced concrete (SFRC), it is very important to predict their punching shear capacity accurately. The use of machine learning seems to be a great way to improve the accuracy of empirical equations currently used in this field. Accordingly, this study utilized tree predictive models (i.e., random forest (RF), random tree (RT), and classification and regression trees (CART)) as well as a novel feature selection (FS) technique to introduce a new model capable of estimating the punching shear capacity of the SFRC flat slabs. Furthermore, to automatically create the structure of the predictive models, the current study employed a sequential algorithm of the FS model. In order to perform the training stage for the proposed models, a dataset consisting of 140 samples with six influential components (i.e., the depth of the slab, the effective depth of the slab, the length of the column, the compressive strength of the concrete, the reinforcement ratio, and the fiber volume) were collected from the relevant literature. Afterward, the sequential FS models were trained and verified using the above-mentioned database. To evaluate the accuracy of the proposed models for both testing and training datasets, various statistical indices, including the coefficient of determination (R2) and root mean square error (RMSE), were utilized. The results obtained from the experiments indicated that the FS-RT model outperformed FS-RF and FS-CART models in terms of prediction accuracy. The range of R2 and RMSE values were obtained as 0.9476–0.9831 and 14.4965–24.9310, respectively; in this regard, the FS-RT hybrid technique demonstrated the best performance. It was concluded that the three hybrid techniques proposed in this paper, i.e., FS-RT, FS-RF, and FS-CART, could be applied to predicting SFRC flat slabs.

Mechanical and Physical Characterization of Papercrete as New Eco-Friendly Construction Material

“…For instance, in References [14,15], integrated interventions are investigated in upgrading at the same time thermal and structural performance of a building. Moreover, many researches have been also addressed to propose materials with different recycled components, such as: reinforced concrete with recycled steel fibers from tires waste [16,17], or nylon fibers from waste fishing nets [18]; sawdust-reinforced ice-filled flax FRP circular columns [19].…”

Predicting axial load capacity of CFST columns using machine learning

“…22,23 Different ML algorithms including random forests (RF), artificial neural networks (ANN), decision trees (DT), deep learning (DL), and support vector machines (SVM), have been developed for modeling structural members. In civil structural engineering field, ANN have been the most frequently used technique to estimate the CS and other mechanical properties of concrete, [24][25][26] timedependent feature of structures, 27,28 bond strength of fiber reinforced polymer and concrete, 29 strength of concrete flat slabs, 30,31 fire resistance, 32 and ultimate strength 33 of CFST columns. With the recent development of ML methods and the availability of experimental datasets, it has become possible to develop additional alternative ML-based predictive models to address the limitations of existing design equations recommended by international standards once high-strength steel and concrete materials and/or slender section CFST are used.…”

Intelligent Design of Construction Materials: A Comparative Study of AI Approaches for Predicting the Strength of Concrete with Blast Furnace Slag

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