The marble industry produces large amount of waste at almost every stage of marble processing. This waste is always uncontrollably discharged into open areas. Therefore, the consumption of Waste Marble Powder (WMP) in concrete is very important and will provide both an economic gain for concrete industries and an opportunity to achieve eco-efficient concrete production. Self-Compacting Concrete (SCC) is mostly preferred concrete type which uses the WMP as powdered material. Thus, to increase WMP usage in the production of eco-efficient SCC, it is aimed in this study to develop a model that can predict WMP demand. To develop this model, Artificial Neural Network (ANN), an artificial intelligence method, was preferred. An ANN model was developed using a comprehensive dataset that included eco-efficient SCC mixture compositions, workability measurements, and compressive strengths. The ANN model with seven inputs and one output as WMP was successfully trained and managed to produce the correct outputs to both validation and test datasets.Mix design of SCC with aimed properties can be a long process compared to conventional concrete. The proposed ANN model could reduce time loss in the production process. ANN's success is expected to facilitate the production of eco-efficient SCC with WMP.

Aggregate is an important ingredient of concrete that affects both fresh and hardened properties of concrete. The properties of concrete can be enhanced or worsened by changing properties of aggregate, especially particle size distribution of aggregate. However, in the literature, the effects of particle size distribution of aggregate on the properties of concrete have been neglected for particularly Steel-Fiber Reinforced Concrete (SFRC) although packing is a very important aspect for SFRC quality. Thus, this study investigates the effects of changing particle size distribution and maximum size (Dmax) of aggregate on the workability and mechanical properties of SFRC. With this aim, three gradations and two Dmax were used to produce SFRC mixtures with constant cement dosages and water/cement ratios. Totally, nine different concrete series were tested. To observe the properties of fresh concrete, Slump and Ve-Be tests were performed. The compressive, splitting tensile and flexural strengths of concretes were also evaluated, and the toughness of the SFRC specimens was calculated. Based on the test results , it can be concluded that the ratio of fine-to-coarse aggregate, the void content between the aggregate particles and Dmax have remarkable effects on the properties of both fresh and hardened SFRC. In addition, the toughness of the SFRC specimens which has constant cement dosage, and water/cement ratios were influenced by those aspects of the aggregates.

No abstract

Aggregate gradation is an essential concern in obtaining the intended properties in concrete production. The efficiency of this parameter dramatically increases for steel fiber–reinforced concrete (SFRC). Although steel fibers significantly improve the mechanical performance of concrete, they negatively affect the workability. Moreover, steel fibers, as expensive materials, drastically increase the cost of SFRC. For this reason, it is critical to correctly select the aggregate gradation and maximum aggregate size to be used in SFRC. In this study, the aim is to determine the effects of different aggregate gradations on the workability, mechanical performance, and cost of SFRCs. For this purpose, SFRC mixtures were produced with the maximum aggregate size of 16 and 31.5 mm. Vebe tests of fresh SFRCs were performed after the first mixing process and at the end of 30, 60, and 90-min intervals. The effects of test time and fine-to-coarse aggregate ratio on the workability were determined using response surface methodology. The compressive and flexural strengths and toughness performances of hardened SFRCs were also measured. In the last part of the study, to determine the effect of aggregate gradation on the cost, the unit costs of the SFRCs were calculated. The workability of SFRCs increased to a certain degree when finer aggregate gradation and smaller maximum aggregate size were preferred. Additionally, as finer aggregate gradation was used, it was observed that although the volume fraction of steel fibers did not change, the mechanical performance of SFRCs increased for a certain nominal aggregate size. Moreover, the unit cost of SFRCs decreased for a certain nominal aggregate size. As a result, by only selecting a finer aggregate gradation, it is possible to increase the workability and mechanical performance of SFRC and also produce more economic SFRCs for a constant volume fraction of steel fibers.

Bu çalışmanın amacı hafif beton üretilirken hedef mukavemet için gerekli olan hafif agrega miktarının tayin edilmesi ve gelecek çalışmalarda üretilecek hafif betonlar için pratik bir karışım tasarımı sunmaktır. Bu amaçla literatürde yer alan hafif beton ile ilgili çalışmalar detaylı bir şekilde incelenmiştir. Bu çalışmalarda bulunan hafif betonlara ait veriler sınıflandırılmış ve listelenmiştir. Literatürden hafif betonlara ait karışım bileşenleri ve hedef basınç dayanımı değerleri alınmıştır. Literatürden alınan deneysel veriler ile bir YSA modeli geliştirilmiştir. Bu modelde su, çimento, normal agrega, toz, kimyasal katkı, hedef basınç mukavemet ve hafif agrega tipi giriş olarak kullanılmıştır. Modelin çıkışı ise hafif agrega miktarı olarak belirlenmiştir. Düzenlenen veriler geliştirilen YSA modeli kullanılarak hafif beton bileşimindeki hafif agrega miktarının tahmininde kullanılmıştır. Geliştirilen model çıkışları ile literatürden alınmış deneysel veriler karşılaştırılmıştır. Geliştirilen YSA modeli ile elde edilen sonuçlar ile deneysel veriler arasındaki farklar uygun sınırlar içerisinde bulunmuştur. Sonuç olarak geliştirilen YSA modelinin hedeflenen çıkışı başarılı bir şekilde ve yüksek doğrulukta tahmin ettiği görülmektedir. Böylece hedef basınç dayanımı belirlenmiş olan bir hafif beton karışımı için hafif agrega miktarı hızlı, pratik ve yüksek doğrulukta tahmin edilmiş olacaktır.