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    Exploring the effect of basalt fibers on maximum deviator stress and failure deformation of silty soils using ANN, SVM and FL supported by experimental data
    (Elsevier Sci Ltd, 2022) Ndepete, Cyrille Prosper; Sert, Sedat; Beycioglu, Ahmet; Katanalp, Burak Yigit; Eren, Ezgi; Bagriacik, Baki; Topolinski, Syzmon
    Because the experimental trials in civil engineering field are difficult and time-consuming, the application of artificial intelligence (AI) techniques is attracting considerable attention, with their use enabling successful results to be more easily obtained. In this study, we investigated the effect of fiber size, fiber amount, water content, and cell pressure on maximum deviator stress (MDS) and failure deformation (FD) of basalt fiber (BF) -reinforced, unsaturated silty soils using three AI techniques: the artificial neural network (ANN), support vector machine (SVM), and fuzzy logic (FL). The numerical analyses and experiments were conducted using varying amounts (1, 1.5, and 2%) and lengths (6, 12, and 24 mm) of BF, and a total of 180 samples were prepared for the detailed investigation. In order to compare model performances, R-2 and MAPE goodness-of-fit metrics were used. The experimental results revealed that the addition of BF generally increased the MDS of the soils, which corresponds to the shearing resistance. According to AI models result, FL outperformed the SVM and ANN, with a R-2 value of 0.938, especially in FD prediction. The sensitivity analysis was performed to ascertain the effect of the inputs on the MDS and FD response variables. Results revealed that fiber length and cell pressure have substantial influence in MDS estimations.
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    Recycling the electric arc furnace waste after geopolymerization in bitumen: experimental analyses and LCA study
    (Springer, 2024) Katanalp, Burak Yigit; Tastan, Murat; Ahmedzade, Perviz
    The conversion of solid waste materials into cleaner products for road paving. applications appears to be a promising and sustainable option. However, there is still a lack of attention given to quantifying the potential environmental benefits of recycling solid wastes in asphalt pavements, regarding the impact on asphalt performance. To address this gap, the present study investigates the effects of recycling electric arc waste based geopolymers on asphalt binder and mixture characteristics, as well as environmental outputs. For this purpose, geopolymers were incorporated into both neat and SBS-modified binders. A comprehensive rheological investigation was conducted using cutting-edge multiple stress creep recovery (MSCR) and linear amplitude sweep (LAS) analyses. Stability, Marshall quotient, and flow values, as well as dry and wetconditioned tensile strength were considered, to determine asphalt mixture properties. In the Life Cycle Assessment (LCA), greenhouse gases resulting from fuel and energy consumption in each inventory phase were determined. The varying service lifetimes, maintenance and rehabilitation plans, and production and construction requirements of the different asphalt schemes were taken into account. Subsequently, the environmental impacts of the asphalt mixtures, including global warming potential, acidification, eutrophication, and smog formation potential, along with the total energy demand, were calculated across different stages of the LCA. The results show that the geopolymerization process results in important contributions in terms of both environmental savings and pavement performance.
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    The novel approaches to classify cyclist accident injury-severity: Hybrid fuzzy decision mechanisms
    (Pergamon-Elsevier Science Ltd, 2020) Katanalp, Burak Yigit; Eren, Ezgi
    In this study, two novel fuzzy decision approaches, where the fuzzy logic (FL) model was revised with the C4.5 decision tree (DT) algorithm, were applied to the classification of cyclist injury-severity in bicycle-vehicle accidents. The study aims to evaluate two main research topics. The first one is investigation of the effect of road infrastructure, road geometry, street, accident, atmospheric and cyclist related parameters on the classification of cyclist injury-severity similarly to other studies in the literature. The second one is examination of the performance of the new fuzzy decision approaches described in detail in this study for the classification of cyclist injury-severity. For this purpose, the data set containing bicycle-vehicle accidents in 2013-2017 was analyzed with the classic C4.5 algorithm and two different hybrid fuzzy decision mechanisms, namely DT-based converted FL (DT-CFL) and novel DT-based revised FL (DT-RFL). The model performances were compared according to their accuracy, precision, recall, and F-measure values. The results indicated that the parameters that have the greatest effect on the injury-severity in bicycle-vehicle accidents are gender, vehicle damage-extent, road-type as well as the highly effective parameters such as pavement type, accident type, and vehicle-movement. The most successful classification performance among the three models was achieved by the DT-RFL model with 72.0 % F measure and 69.96 % Accuracy. With 59.22 % accuracy and %57.5 F-measure values, the DT-CFL model, rules of which were created according to the splitting criteria of C4.5 algorithm, gave worse results in the classification of the injury-severity in bicycle-vehicle accidents than the classical C4.5 algorithm. In light of these results, the use of fuzzy decision mechanism models presented in this study on more comprehensive datasets is recommended for further studies.