The global rapid increase in waste tyres accumulation, as well as the looming social and environmental concerns, have become major threats in recent times. The use of Recycled Steel Fiber (RSF) extracted from waste tyres in fiber reinforced concrete can be of great profitable engineering applications however the choice of suitable length and volume fractions of RSF is presently the key challenge that requires research exploration. The present experimental work aims at investigating the influence of varying lengths (7.62 and 10.16 cm) and dosages (1, 1.5, 2, 2.5, 3, 3.5, and 4%) of RSF on the various mechanical properties and durability of concrete. Test results revealed that the varying lengths and dosages of RSF significantly affect the mechanical properties of concrete. The improvements in the compressive strength, splitting tensile strength, and Modulus of Rupture (MOR) of RSF reinforced concrete observed were about 26, 70, and 63%, respectively. Moreover, the RSF reinforced concrete showed an increase of about 20 and 15% in the yield load and ultimate load-carrying capacity, respectively. The durability test results showed a greater loss in compressive strength and modulus of elasticity and a smaller loss in concrete mass of SFRC. Based on the experimental findings of this study, the optimum dosages of RSF as 2.5 and 2% for the lengths 7.62 and 10.16 cm lengths, respectively are recommended for production of structural concrete. Doi: 10.28991/cej-2021-03091750 Full Text: PDF

The mechanical properties of concrete are the important parameters in a design code. The amount of laboratory trial batches and experiments required to produce useful design data can be decreased by using robust prediction models for the mechanical properties of concrete, which can save time and money. Portland cement is frequently substituted with metakaolin (MK) because of its technical and environmental advantages. In this study, three mechanical properties of concrete with MK, i.e., compressive strength (f′c), splitting tensile strength (fst), and flexural strength (FS) were modelled by using four machine learning (ML) techniques: gene expression programming (GEP), artificial neural network (ANN), M5P model tree algorithm, and random forest (RF). For this purpose, a comprehensive database containing detail of concrete mixture proportions and values of f′c, fst, and FS at different ages was gathered from peer-reviewed published documents. Various statistical metrics were used to compare the predictive and generalization capability of the ML techniques. The comparative study of ML techniques revealed that RF has better predictive and generalization capability as compared with GEP, ANN, and M5P model tree algorithm. Moreover, the sensitivity and parametric analysis (PA) was carried out. The PA showed that the most suitable proportions of MK as partial cement replacement were 10% for FS and 15% for both f′c and fst.

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Concrete is the most common and widely used construction material. In the concrete structure, cracks are sometimes produced due to external loads and other reasons. Due to cracks, the concrete begins to take compression loads in the structure; therefore, repairing such cracks is essential. Different methods are used to repair the cracks in concrete, but in this thesis, we are working on bacteria base self-healing of cracks in concrete. For self-healing concrete different types of bacteria are used but we are using the bacteria named Bacillus subtilis in concrete. By adding bacillus subtilis and calcium lactate we 􀅮ind that the concrete becomes more strengthened and self-healed as compared to normal concrete. There are two methods of adding the bacteria to concrete which is direct adding an encapsulation method. We followed the direct application method in this method the bacteria is added directly to concrete. The test results show that the bacterial concrete has higher compression strength and self-healing concrete.

This research study investigates the Fatigue Failure & Permanent Deformation response behaviour of four (04) HMA mixtures. The selected gradations have a Nominal Maximum Aggregate Size (NMAS) of 19.0 mm, and the gradation blends passed Above (ARZ), Below (BRZ), and Through (TRZ), the restricted zone. Along with the Superpave ARZ, BRZ & TRZ conventional NHA, "Class A" gradation was also checked for performance parameters, thus producing results in contrast to the conventional NHA gradation already used by highway industries Pakistan. Three (03) performance tests were carried out in this study that, includes Indirect Tensile Strength Test (IDT), the repeated Indirect Tensile Fatigue Test (ITFT), and the Moisture Susceptibility Test. Statistical analysis was also done based on laboratory-produced results. Two-Level Factorial Design was also carried out using the statistical tool Minitab-16. Statistical analysis shows that OBC, P 0.075 /P be (Dust to Binder Ratio), and the Peak Force signi􀅮icantly affect No of Cycles to Fatigue Failure. A linear Model was developed with an R square of .74 which seems to 􀅮it well. IDT Test evaluated the TRZ mix as having the best laboratory fracture resistance properties of all tested mixes, while ARZ performed best in the Moisture Susceptibility test. Moreover, this study gave us insight into Superpave IDT as a practical and reliable way to measure all the parameters needed in the HMA Fracture Mechanics method.