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جستجوی مقالات مرتبط با کلیدواژه

computational methods

در نشریات گروه عمران
تکرار جستجوی کلیدواژه computational methods در نشریات گروه فنی و مهندسی
تکرار جستجوی کلیدواژه computational methods در مقالات مجلات علمی
  • Parankush Koul*

    In this review paper, the applications of machine learning, computational methods, and robotics to bridge design are considered to help improve structure integrity and resilience. It describes a variety of computational methods, including finite element analysis (FEA) and computational fluid dynamics (CFD), that have been used to calculate failure modes and evaluate the dynamic behavior of bridge structures in extreme conditions, such as earthquakes and floods. It also highlights robotics’ potential to streamline inspection techniques, showing new robotic systems for effective bridge monitoring. Additionally, it points out issues related to data shortages and implementation difficulty and presents future research priorities, such as the need for powerful machine learning algorithms and the use of Internet of Things (IoT) solutions for real-time monitoring. In summary, the paper highlights the life-changing impact of these technologies on the safety and reliability of bridge systems.

    Keywords: Machine Learning, Computational Methods, Robotics, Structural Integrity, Resilience
  • Asma Hasheminezhad, Abbasali Sadeghi *
    Uniaxial compressive strength (UCS) is a critical geomechanical property of rocks that is frequently required during the preliminary stage of civil engineering design. To obtain the UCS value needs a time consuming and costly process of samples collection and preparation. There are alternate methods for determining UCS that can be conducted in situ. In this study, an attempt has been made to predict the UCS of limestone from some simple and inexpensive rock index tests such as block punch index (BPI), ultrasonic wave velocity test (Vp), Schmidt's hammer rebound number (SHR), and point load index tests (I_s50). For indirect estimation of the UCS as a function of BPI, Vp, SHR, and I_s50, block samples of limestone were collected from a quarry site in Birjand, the center of Southern Khorasan province in Iran. Then, the number of 70 core samples and 210 bulk samples were prepared and tested based on available standards. According to extensive experimental results, a database was established for estimation of the UCS via three computational methods such as support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), and multi layer perceptron (MLP). After developing the models and considering several performance indices including the coefficient of determination R^2, variance account for (VAF), root mean squared error (RMSE), and using simple ranking method, the predictive models were applied to obtain the best model. Consequently, SVM approach predicted the UCS of limestone with higher accuracy in comparison to other studied computational methods.
    Keywords: Limestone, Uniaxial compressive strength (UCS), Rock index tests, Computational Methods
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