This paper aimed to show possible applicability of artificial neural networks (ANN) to predict the performance of the bond between reinforcement and concrete. An ANN model is constructed, trained and tested using the available test data of 117 different pull-out cylindrical concrete specimens with an embedded reinforcing bar. The data used in ANN model are arranged in a format of four input parameters that cover the concrete compressive strength, cover thickness, embedment length and related rib area. The ANN model, which performs in Matlab software, predicts the bond strength of anchoring capacity of the reinforcement in the concrete. The results showed that ANNs have strong potential as a feasible tool for predicting bond strength. Comparisons with empirical formula and experimental results of several different researchers show an acceptable accuracy of the proposed ANN model.

The accumulation of end-of-life tires and the rapid increase in demolition activities pose significant environmental and waste-management challenges. The redevelopment of construction materials incorporating this waste is a potentially promising strategy for minimizing environmental impact while promoting the principles of a circular economy. This study investigates the performance of self-compacting concrete (SCC) incorporating up to 20% rubber aggregates (sand and gravel) and 40% recycled concrete aggregate (RCA) for non-structural applications. A series of tests was conducted to assess fresh and hardened properties, including flowability, compressive strength, tensile strength, flexural strength, water absorption, and density. The results indicated that increasing RCA content reduced density and compressive strength, while tensile and flexural strengths were only moderately affected. Response surface methodology (RSM), utilizing a Box–Behnken design, was employed to optimize compressive, tensile, and flexural strength responses. Statistical analysis was used to identify the optimal mix proportions, which balance the mechanical performance and sustainability of SCC with recycled components. Mixtures incorporating moderate rubber content—specifically, 5–5.5% sand rubber and 0–6% coarse rubber—and 40% recycled-concrete aggregate (RCA) achieved the highest predicted performance, with compressive strength ranging from 20.00 to 28.26 MPa, tensile strength from 2.16 to 2.85 MPa, and flexural strength reaching 5.81 MPa, making them suitable for sidewalks and walkways. Conversely, mixtures containing higher rubber proportions (5.5–20% sand rubber and 20% coarse rubber) combined with the same RCA level (40%) showed the lowest mechanical performance, with compressive strength between 5.2 and 10.08 MPa, tensile strength of 1.05–1.41 MPa, and flexural strength from 2.18 to 3.54 MPa. These findings underscore the broad performance range achievable through targeted optimization. They confirm the viability of recycled materials for producing environmentally friendly SCC in non-structural applications.

’This study investigates the mechanical performance of concrete reinforced with recycled polyethylene terephthalate (PET) fibers obtained from discarded plastic bottles, aiming to promote sustainable waste reuse in construction materials. Previous studies on PET fiber reinforced concrete have mainly examined the influence of fiber length and content separately, without considering their combined effects on mechanical properties. In this work, the interactions between fiber length, volume fraction, and mechanical behavior were systematically analyzed using a Central Composite Design (CCD) within the framework of Response Surface Methodology (RSM). Concrete incorporating recycled PET fibers was evaluated at three volume fractions (0.3%, 0.8%, and 1.3%) and three lengths (20 mm, 40 mm, and 60 mm), while maintaining a constant water-to-cement ratio. Sixty specimens were tested to assess both fresh and hardened properties. The greatest loss of workability occurred for the mix containing 1.3% fibers with a length of 60 mm, corresponding to about a 25% reduction compared with the control. Response Surface Methodology (RSM) based on a Central Composite Design (CCD) identified 0.3% fiber content and 40 mm length as the optimal combination, representing the mix that simultaneously maximized both compressive (26 MPa) and tensile strengths (3 MPa) according to the predictive model.