Empirical evaluations for predicting the damage of FRC wall subjected to close-in explosions
Duc-Kien Thai,Thai-Hoan Pham,Duy-Liem Nguyen,Tran Minh Tu,Phan Van Tien
Abstract
This paper presents a development of empirical evaluations, which can be used to evaluate the damage of fiberreinforced concrete composites (FRC) wall subjected to close-in blast loads. For this development, a combined application of numerical simulation and machine learning approaches are employed. First, finite element modeling of FRC wall under blast loading is developed and verified using experimental data. Numerical analyses are then carried out to investigate the dynamic behavior of the FRC wall under blast loading. In addition, a data set of 384 samples on the damage of FRC wall due to blast loads is then produced in order to develop machine learning models. Second, three robust machine learning models of Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) are employed to propose empirical evaluations for predicting the damage of FRC wall. The proposed empirical evaluations are very useful for practical evaluation and design of FRC wall subjected to blast loads.
Duc-Kien Thai — Department of Civil and Environmental Engineering, Sejong University, 98 Gunja-Dong, Gwangjin-Gu, Seoul, 143-747, South Korea
Thai-Hoan Pham — Department of Concrete Structures, National University of Civil Engineering, 55 Giai Phong, Hanoi, Vietnam
Duy-Liem Nguyen — Department of Civil Engineering and Applied Mechanics, Ho Chi Minh City University of Technology and Education, 1 Vo Van Ngan St., Thu Duc District, Ho Chi Minh City, Vietnam
Tran Minh Tu — Faculty of Industrial and Civil Engineering, National University of Civil Engineering, 55 Giai Phong, Hanoi, Vietnam
Phan Van Tien — Department of Civil Engineering, Vinh University, Vinh 461010, Vietnam
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