Eco-friendly concrete is produced using the waste of many industries. It reduces the fears concerning energy utilization, raw materials, and mass-produced cost of common concrete. Several stress-strain models documented in the literature can be utilized to estimate the ultimate strength of concrete components reinforced with fibers. Unfortunately, there is a lack of data on how non-metallic fibers, such as polypropylene (PP), affect the properties of concrete, especially eco-friendly concrete. This study presents a novel approach to modeling the stress-strain behavior of eco-friendly polypropylene fiber-reinforced concrete (PFRC) using meta-heuristic particle swarm optimization (PSO) employing 26 PFRC various mixtures. The cement was partially replaced by ground granulated blast furnace slag (GGBFS) with various amounts to make the concrete eco-friendly. The concrete was reinforced with several quantities of PP fiber. Specific cases of beams and cylinders made from PFRC were examined to learn more about their performance. The research contributes valuable insights to eco-friendly concrete design by integrating industrial byproducts (GGBFS) and non-metallic fibers, aligning with sustainable construction trends. The study demonstrates that adding sustainable fibers to concrete improves its structural integrity while lessening its environmental impact. Experimental testing validates the proposed model, showing a significant connection between the expected and actual stress-strain behavior. In terms of absolute relative error (ARE), the dataset proves that the suggested model has both the greatest (ARE 5 %) and worst (ARE > 15 %) frequencies. The proposed model demonstrates promising accuracy (R-value = 0.9975) and highlights the effectiveness of PSO in parameter optimization. Additionally, the usage of GGBFS instead of OPC resulted in CO2 reduction up to 42 %. Comparative analysis of the proposed model against existing models registered an excellent forecasted accuracy.
In this study, a three-dimensional finite element analysis using ANSYS 12.1 program had been employed to simulate simply supported reinforced concrete (RC) T-beams with multiple web circular openings subjected to an impact loading. Three design parameters were considered, including size, location and number of the web openings. Twelve models of simply supported RC T-beams were subjected to one point of transient (impact) loading at mid span. Beams were simulated and analysis results were obtained in terms of mid span deflection-time histories and compared with the results of the solid reference one. The maximum mid span deflection is an important index for evaluating damage levels of the RC beams subjected to impact loading. Three experi
... Show MoreThis paper studied the behaviour of reinforced reactive powder concrete (RPC) two-way slabs under static load. The experimental program included testing three simply supported slabs of 1000 mm length, 1000 mm width, and 70 mm thickness. Tested specimens were of identical properties except their steel fibers volume ratio (0.5 %, 1 %, and 1.5 %). Static test results revealed that, increasing steel fibers volume ratio from 0.5% to 1% and from 1% to 1.5%, led to an increase in: first crack load by (32.2 % and 52.3 %), ultimate load by (36.1 % and 17.0 %), ultimate deflection by (33.6 % and 3.4 %), absorbed energy by (128 % and 20.2 %), and the ultimate strain by (1.1 % and 6.73 %). The stiffness and ductility of the specimens also increased. A
... Show MoreArtificial Neural networks (ANN) are powerful and effective tools in time-series applications. The first aim of this paper is to diagnose better and more efficient ANN models (Back Propagation, Radial Basis Function Neural networks (RBF), and Recurrent neural networks) in solving the linear and nonlinear time-series behavior. The second aim is dealing with finding accurate estimators as the convergence sometimes is stack in the local minima. It is one of the problems that can bias the test of the robustness of the ANN in time series forecasting. To determine the best or the optimal ANN models, forecast Skill (SS) employed to measure the efficiency of the performance of ANN models. The mean square error and
... Show MoreThe change in project cost, or cost growth, occurs from many factors, some of which are related to soil problem conditions that may occurs during construction and/or during site investigation period. This paper described a new soil improvement method with a minimum cost solution by using polymer fiber materials having a length of (3 cm) in both directions and (2.5 mm) in thickness, distributed in uniform medium dense .
sandy soil at different depths (B, 1.5B and 2B) below the footings. Three square footings has been used (5,7.5 and 10 cm) to carry the above investigation by using lever arm loading system design for such purposes.
These fibers were distributed from depth of (0.1B) below the footing base down to the investigated dep
Retinopathy of prematurity (ROP) can cause blindness in premature neonates. It is diagnosed when new blood vessels form abnormally in the retina. However, people at high risk of ROP might benefit significantly from early detection and treatment. Therefore, early diagnosis of ROP is vital in averting visual impairment. However, due to a lack of medical experience in detecting this condition, many people refuse treatment; this is especially troublesome given the rising cases of ROP. To deal with this problem, we trained three transfer learning models (VGG-19, ResNet-50, and EfficientNetB5) and a convolutional neural network (CNN) to identify the zones of ROP in preterm newborns. The dataset to train th