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J. of Soft Computing in Civil Engineering     Open Access   (Followers: 1)
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Journal of Soft Computing in Civil Engineering
Number of Followers: 1  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2588-2872
Published by Pouyan Press Homepage  [1 journal]
  • Models Development for Asphalt Pavement Performance Index in Different
           Climate Regions Using Soft Computing Techniques

    • Abstract: The Pavement Condition Index (PCI) is one of the most critical pavement performance indicators and ride quality. This study aims to develop PCI models based on pavement distress parameters using conventional technique and artificial neural network (ANN) technique across two climate regions in the U.S. and Canada. The long-term pavement performance (LTPP) database was used to obtain pavement distress data, including pavement age, rutting, fatigue cracking, block cracking, longitudinal cracking, transverse cracking, potholes, patching, bleeding, and ravelling, as input variables for predicting PCI. Forty-three flexible pavement segments were considered with 333 observations. The type, severity, and extent of surface damage and the PCI were determined for each pavement segment. Two modelling techniques were conducted to predict the PCI, namely, multiple linear regression (MLR) and artificial neural network (ANN). The coefficient of determination ( R^2), Root mean squared error (RMSE), and mean absolute error (MAE) were used to examine the performance of the two techniques adopted in this study. The models' results determined that both ANN and MLR models could predict PCI with high accuracy; ANN models were more accurate and efficient. ANN provided the highest accuracy in predicting PCI of pavement for wet and wet no-freeze climate regions, with R^2 values of 99.8%, 98.3 %: RMSE values of 0.44%, 1.413%, and MAE values of 0.44%, 1.022%, respectively. Whereas in the MLR method, R^2 values of 86.8% and 89.4%: RMSE values of 7.195%, 7.324%, and MAE values of 5.616%, 5.79% for wet and wet no freeze climate regions, respectively.
       
  • Performance Based Review and Fine-Tuning of TRM-Concrete Bond Strength
           Existing Models

    • Abstract: Textile reinforced mortars (TRMs) are new composite materials which were considered as a proper alternative for fiber reinforced polymers (FRPs) to strengthen various structural elements. In comparison to FRPs, the TRMs have more fire resistance, more environmental consistency and are safer the structural elements because of their better bond to substrate and various failure modes. There are a lot of existing models to calculate the bond strength between TRMs and concrete substrate. But, most of them originated from the FRP-concrete bond models and are not accurate enough to estimate the TRM-concrete bond strength. In this paper, new TRM-concrete bond models were calibrated to predict the bond strength between various TRM composites and the concrete substrate. To achieve this goal, a database including 221 experimental direct shear tests were compiled and a simple existing model was selected to be calibrated via soft computing techniques. It was found that the presented novel models could be accurately utilized to anticipate the TRM-concrete bond strength with various types of fibers and different geometrical features with R value of 0.6909 and NMAE error value of 12.62%.
       
  • The optimization of mix proportion of hot mix asphalt for sustainable
           flexible pavements: Experimental Study and Grey Taguchi Relational
           Analysis

    • Abstract: Most of the Indian black topped roads have been damaged due to adverse weather and heavy load distresses. Many researchers have focused on improving durability of Hot Mix Asphalt (HMA) pavements. The factors influencing durability of HMA are Binder content, Combined aggregate gradation, type of Filler and addition of Fiber. In order to optimize the combination of variables used in HMA mix design, Grey relational analysis by using Taguchi technique is used, where many parameters can be analyzed at a time with more accuracy. Multiple performance measurements like Stability, Flow, Bulk specific gravity of the mix (Gmb), Theoretical maximum specific gravity of the mix (Gmm), Voids in Mineral Aggregate (VMA), Air voids (Va) and Voids Filled with Bitumen (VFB) are considered.Bituminous Concrete (BC) mix was optimized using L9 Orthogonal array considering four parameters such as Fiber content, Filler combination, Binder content and Combined Aggregate Gradation, with three levels having seven performance measurements. The most significant parameter and percent contribution of each parameter of BC mix are analyzed by Analysis of Variance (ANOVA) using Grey Taguchi technique. The analysis was done by considering two Gradation conditions (Coarse gradation and Fine gradation) based on voids in mix. From the analysis, it can be concluded that all parameters are significant except bitumen content for optimizing BC mix.
       
 
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