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Machine learning for predicting long-term deflections in reinforce concrete flexural structures
Anh-Duc Pham,Ngoc-Tri Ngo,Thi-Kha Nguyen 한국CDE학회 2020 Journal of computational design and engineering Vol.7 No.1
Prediction of deflections of reinforced concrete (RC) flexural structures is vital to evaluate the workability and safety of structures during its life cycle. Empirical methods are limited to predict a long-term deflection of RC structures because they are difficult to consider all influencing factors. This study presents data-driven machine learning (ML) models to early predict the long-term deflections in RC structures. An experimental dataset was used to build and evaluate single and ensemble ML models. The models were trained and tested using the stratified 10-fold cross-validation algorithm. Analytical results revealed that the ML model is effective in predicting the deflection of RC structures with good accuracy of 0.972 in correlation coefficient (R), 8.190 mm in root mean square error (RMSE), 4.597 mm in mean absolute error (MAE), and 16.749% in mean absolute percentage error (MAPE). In performance comparison against with empirical methods, the prediction accuracy of the ML model improved significantly up to 66.41% in the RMSE and up to 82.04% in the MAE. As a contribution, this study proposed the effective ML model to facilitate designers in early forecasting long-term deflections in RC structures and evaluating their long-term serviceability and safety.
Evaluating Load-Carrying Capacity of Short Composite Beam Using Strain-Hardening HPFRC
Duy-Liem Nguyen,Vu-Tu Tran,Ngoc-Thanh Tran,Tri-Thuong Ngo,Manh-Tuan Nguyen 대한토목학회 2021 KSCE JOURNAL OF CIVIL ENGINEERING Vol.25 No.4
The main goal of this study was to develop the short composite beams using strain-hardening fiber-reinforced concrete (S_HPFRC) and conventional concrete (CC) together. Firstly, the sensitivity of the hybrid fiber system to the enhancement of mechanical properties of plain high performance concrete (P_mortar) was experimentally studied. The ranking of the mechanical properties in terms of sensitive coefficient was observed as follows: direct tensile > splitting tensile > compressive. Next, the responses of short composite beams with various thicknesses of S_HPFRC were investigated under a three-point bending test (3PBT). Six beams with no reinforcement (type A) and six beams with reinforcement were tested (type B) with their identical dimensions of 150 × 150 × 300 mm (depth × width × span length). The compressive strength of S_HPFRC and CC were about 80.65 MPa and 21.12 MPa, respectively. Most of the composite beams were observed to fail in shear mode. And, there was a favorable effect on enhancing the load-carrying capacity of a beam as S_HPFRC was placed at a critical tensile zone. Finally, based on the test data, the analytical equations were proposed for the purpose of predicting shear resistance of the S_HPFRC - CC beam.