Loading rate effect on fracture behavior of fiber reinforced high strength concrete using a semi-circular bending test

© 2019 Elsevier Ltd Adding different types of fiber is one of the most common ways to enhance high strength concrete's mechanical behavior. In this paper, the effect of the loading rate and different type of fibers including glass, polypropylene, and steel were studied using the semi-circular bending (SCB) test method. It was evaluated that the SCB test can be used as a rapid and simple method to measure fracture properties of fiber reinforced high strength concrete (HSC) including ductility, energy absorption, and loading capacity by considering the effect of the loading rate on the parameters mentioned above. Specimens with glass fibers showed the most ductile behavior among all specimens with different types of fiber. On the other hand, steel fibers provided higher strength and higher energy absorption among the specimens. While specimens with steel fibers are highly sensitive to the loading rate in terms of peak load, this effect is not significant for specimens with glass and polypropylene fibers

Optimized machine learning approaches for the prediction of viscoelastic behavior of modified asphalt binders

Additives are commonly used in pavement engineering to improve the original bitumen's rheological and mechanical characteristics to meet severe loading and climatic condition requirements. To select the optimum dosage of an additive for modifying the original bitumen, it is essential to predict the viscoelastic behavior of modified bitumens, which can be performed by implementing the constitutive viscoelastic parameters of the complex shear modulus (G*) and phase angle (δ). In this work, a comprehensive experimental database consisting of the results of the frequency sweep mode of a dynamic shear rheometer (DSR) at seven test temperatures (−22 ~ 22 °C) was used. Prediction models for the viscoelastic behavior of bitumen modified with different dosages of crumb rubber, styrene–butadienestyrene (SBS), and polyphosphoric acid (PPA) were developed by optimizing and applying different machine learning approaches, including Artificial Neural Networks (ANN), Robust Linear Regression, Linear Support Vector Regression, Decision Tree Regression, Gaussian Process Regression (GPR), and Ensemble Regression, on the data. By comparing the various studied model outputs in terms of performance measurements, such as the correlation of coefficients, relative root mean square error, scatter index, relative error, and Nash-Sutcliffe efficiency coefficient, it was determined that the Ensemble Regression method has the highest performance in predictions