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This paper deals with the effect of using recycled concrete aggregate as a partial replacement of coarse aggregate in self-compacting concrete, on the structural behavior of reinforced concrete corbels. From the previous researches, there is no studies deals with the effect of using this type of aggregate on the structural behavior of corbels, and also the use of RCA has an economical and environmental benefits Three replacement ratios were considered 25%, 50% and 75%. All mixes (with and without RCA) have almost same compressive strength at age of 28days which is equal to (35MPa) with a tolerance of (±3MPa).For this purpose, an eleven reinforced concrete corbels were cast and divided in to three groups (A, Band C). Each group deals with specific problem. Different parameters which effect the behavior of corbels were studied and include replacement ratios of natural coarse aggregate by recycled concrete aggregate (RCA), amount of horizontal reinforcement (Ah) and amount of main tension reinforcement (Asmain). In order to get same compressive strength of concrete mixes made with natural and with recycled concrete aggregates, the quantity of cement was increased by (1.25%, 3.75% and 10%) for mixes containing (25%, 50% and 75%) recycled concrete aggregate respectively compared with SCC made with natural coarse aggregate. The experimental results of corbels show that the ultimate load capacity of corbels in group Atested with a/d of 0.34 and made from SCC with (25%, 50 and 75%) RCA was decreased by (2.22%, 7.4%, and 12.34%) respectively compared with corbel made from SCC without RCA. While in group B, all corbels casted with 50%RCA and have the same main tension reinforcement, a/d=0.34, corbel dimensions and concrete compressive strength and the only difference was in the amount of horizontal reinforcement. The results showed that when the amount of horizontal reinforcement (stirrups) was increased from zero to 2Ø6mm, the ultimate load increased

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Due to repeated wars against Iraq as well as the terrorist operations lead to demolition of many government and commercial buildings and thus necessitating the demolition of these buildings and throwing them as a rubble. Also any concrete structure reaches its end life, it is either repaired or demolished and transformed to waste. The waste materials were transferred to landfill and no longer used. These waste will increase with time and cause an environmental problem. So, this study was prepared to examine the ability of crushing and reusing of the demolition waste as a coarse aggregate in the production of new concrete known as a Recycled Aggregate Concrete. This study consists of two parts: experimental and theoretical parts. Both parts dealt with the effect of using recycled concrete aggregate (RCA) as a partial replacement of natural coarse aggregate in self-compacting concrete, on the structural behavior of reinforced concrete corbels. The replacement percentages were used in this study (0%, 25%, 50%, and 75%). The experimental part consists of casting and testing of ten self-compacting reinforced concrete corbels which made with either natural coarse aggregate or with a partial replacement of natural coarse aggregate by recycled concrete aggregate then the experimental results was compared with the analytical results (finite element results). The analytical part was carried out by using ANSYS program (version 18). In ANSYS program three types of elements were used in the modeling of each corbel specimen. The elements are Solid65, Link180 and Solid185. Solid 65 element was used to represent of concrete while link180 element was used to represent of steel

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Abstract: For the design or assessment of concrete structures that incorporate steel fiber in their elements, the accurate prediction of the shear strength of steel fiber reinforced concrete (SFRC) beams is critical. Unfortunately, traditional empirical methods are based on a small and limited dataset, and their abilities to accurately estimate the shear strength of SFRC beams are arguable. This drawback can be reduced by developing an accurate machine learning based model. The problem with using a high accuracy machine learning (ML) model is its interpretation since it works as a black-box model that is highly sophisticated for humans to comprehend directly. For this reason, Shapley additive explanations (SHAP), one of the methods used to open a black-box machine learning model, is combined with highly accurate machine learning techniques to build an explainable ML model to predict the shear strength of SFRC slender beams. For this, a database of 330 beams with varying design attributes and geometries was developed. The new gradient boosting regression tree (GBRT) machine learning model was compared statistically to experimental data and current shear design models to evaluate its performance. The proposed GBRT model gives predictions that are very similar to the experimentally observed shear strength and has a better and unbiased predictive performance in comparison to other existing developed models. The SHAP approach shows that the beam width and effective depth are the most important factors, followed by the concrete strength and the longitudinal reinforcement ratio. In addition, the outputs are also affected by the steel fiber factor and the shear-span to effective depth ratio. The fiber tensile strength and the aggregate size have the lowest effect, with only about 1% on average to change the predicted value of the shear strength. By building an accurate ML model and by opening its black-box, future researchers can focus on some attributes rather than ot