Prediction of rutting potential of dense bituminous mixtures with polypropylene fibers via repeated creep testing by using neuro-fuzzy approach

This study investigates the potential use of the neuro-fuzzy (NF) approach to model the rutting prediction by the aid of repeated creep testing results for polypropylene modified asphalt mixtures. Marshall specimens, fabricated with M-03 type polypropylene fibers at optimum bitumen content have been tested in order to predict their rutting potential under different load values and loading patterns at 50°C. Throughout the testing phase, it has been clearly shown that the addition of polypropylene fibers results in improved Marshall stabilities and decrease in the flow values, providing an eminent increase of the service life of samples under repeated creep testing. The performance of the accuracy of proposed neuro-fuzzy model is observed to be quite satisfactory. In addition, to obtain the main effects plot, a wide range of detailed two and three dimensional parametric studies have been performed

Genetik Programlama Yardimi İle Kı’nin Açik Formülasyonu

Konferans Bildirisi -- Teorik ve Uygulamalı Mekanik Türk Milli Komitesi, 2008Conference Paper -- Theoretical and Applied Mechanical Turkish National Committee, 2008Bu çalışmada, kırılma mekaniğinde açılma moduna (Kı) göre Gerilme Şiddet Çarpanı’nın (GŞÇ) formülasyonu için Genetik Programlama (GP) kullanılmıştır. GP için eğitim setleri ANSYS paket programı kullanılarak hazırlanmıştır. GP modellenmesi için gerekli tüm işlemler hazır paket program kullanılarak yapılmıştır. Kırılma Mekaniğinde yaygın olarak kullanılan üç değişik geometri için bir GŞÇ formülasyonu elde edilmiştir. Elde edilen bu açık formülasyonun sonuçlarının, ANSYS sonuçları ile oldukça uyumlu sonuçlar verdiği görülmüştür.In this study, Genetic Programming (GP) is used for the analysis and the formulation of the Stress Intensity Factor (SIF) for the opening mode (KI) of fracture mechanics. The training patterns for Genetic Programming are prepared using ANSYS. All necessary processes for Genetic Programming are conducted using ready package software. A SIF formulation for the three different geometries which are commonly used in fracture mechanics has been obtained. It is shown that the results of the explicit formulation are in good agreement with, ANSYS results

Modelling Marshall Design Test Results of Polypropylene Modified Asphalt by Genetic Programming Techniques

Determining Marshall design test results is time consuming. If the researchers can obtain stability and flow values by mechanical testing, rest of the calculations will just be mathematical manipulations. Marshall stability and flow tests were carried out on specimens fabricated with dierent type of polypropylene fibers. It has been shown that addition of polypropylene fibers improved Marshall stabilities and Marshall quotient values in a considerable manner. Input variables in the developed genetic programming model use the physical properties of standard Marshall specimens such as polypropylene type, polypropylene percentage, bitumen percentage, specimen height, calculated unit weight, voids in mineral aggregate, voids filled with asphalt and air voids. Performance of the genetic programming model is quite satisfactory. Besides, to obtain main eects plot, a wide range of parametric studies have been performed.The presented closed form solution will also help further researchers willing to perform similar studies, without carrying out destructive tests

A Detailed Investigation of the Bond Performance of Basalt Fiber-Reinforced Polymer Bars in Geopolymer Concrete

This comprehensive experimental study aimed to determine the bond performance of basalt fiber reinforced polymer (BFRP) bars in geopolymer concrete (GC). The study examined the bond performance of BFRP bars and GC by considering several parameters, including bar diameters of 8, 10, and 12 mm, embedment lengths of 4, 8, and 12 db mm (where db is the diameter of the bar), concrete covers of 20, 40, and 70 mm and compressive strengths of 21.7 and 34.4 MPa. The study also compared the effect of the bar surface and bar type on GC bond performance. Eventually, the results were compared with ordinary concrete (OC). The obtained results indicated that an increase in the BFRP bar diameter results in a decrease in the average bond stress. Similarly, an increase in the length of the bond leads to a reduction in the bond stress. The specimen possessing a short embedment length failed due to bar pullout, while the specimens with a longer embedment length failed as a result of concrete splitting. The outcomes also showed that the strength of bond increases with an increase in compressive strength and cover thickness. Furthermore, the results also indicated that BFRP-reinforced GC has comparable bond performance to steel-reinforced GC and BFRP-reinforced OC and performed better than OC. Last, Comparisons between the existing bond-slip models were offered to demonstrate the best bond stress-slip model for FRP bars and GC for ascending branch up to ultimate bond stress of the bond slip curves and for whole curves

The Effects of Recycled Tire Rubbers and Steel Fibers on the Performance of Self-compacting Alkali Activated Concrete

In this study, the effects of recycled tire rubbers (RTR) and steel fiber (SF) on the fresh and hardened state properties of the self-compacted alkali activated concrete (SCAAC) were investigated. The ground granulated blast furnace slag, 1 % hooked-end SF, and two types of RTR were utilized. The crumb rubbers (CR) and tire rubber chips (TCR) were used as a substation to natural aggregates at substation levels of 10 % and 15 %. The fresh state performances were evaluated by T50 value, slump flow, V-funnel, and L-Box tests, while mechanical performances were assessed through compressive, flexural, and splitting tensile strength tests. Also, detailed crack and microstructural analyses were conducted. The RTR adversely affected the fresh state properties, which reduced more with SF inclusions. Among the RTR, the TR specimens exhibited lower fresh state performance than the CR specimens. Similar mechanical strengths were obtained on the TR and CR specimens under the same replacement ratios. However, TR specimens exhibited higher deformation capacities than the CR specimens, when SF was utilized. The SCAAC specimens with 1 % SF and 15 % RTR showed more and wider flexural cracks, higher mechanical strength, and deformation capacity, which can be utilized in structural applications, particularly in high seismic zones

Support vector machines in structural engineering: a review

Recent development in data processing systems had directed study and research of engineering towards the creation of intelligent systems to evolve models for a wide range of engineering problems. In this respect, several modeling techniques have been created to simulate various civil engineering systems. This study aims to review the studies on support vector machines (SVM) in structural engineering and investigate the usability of this machine learning based approach by providing three case studies focusing on structural engineering problems. Firstly, the concept of SVM is explained and then, the recent studies on the application of SVM in structural engineering are summarized and discussed. Next, we performed three case studies using the experimental studies provided. Applicability of SVM in structural engineering is confirmed by these case studies. The results showed that SVM is superior to various other learning techniques considering the generalization capability of produced model