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.
Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematicall
... Show MoreOptical fiber chemical sensor based surface Plasmon resonance for sensing and measuring the refractive index and concentration for Acetic acid is designed and implemented during this work. Optical grade plastic optical fibers with a diameter of 1000μm were used with a diameter core of 980μm and a cladding of 20μm, where the sensor is fabricated by a small part (10mm) of optical fiber in the middle is embedded in a resin block and then the polishing process is done, after that it is deposited with about (40nm) thickness of gold metal and the Acetic acid is placed on the sensing probe.
Rapid worldwide urbanization and drastic population growth have increased the demand for new road construction, which will cause a substantial amount of natural resources such as aggregates to be consumed. The use of recycled concrete aggregate could be one of the possible ways to offset the aggregate shortage problem and reduce environmental pollution. This paper reports an experimental study of unbound granular material using recycled concrete aggregate for pavement subbase construction. Five percentages of recycled concrete aggregate obtained from two different sources with an originally designed compressive strength of 20–30 MPa as well as 31–40 MPa at three particle size levels, i.e., coarse, fine, and extra fine, were test
... Show MoreThe presented study investigated the scheduling regarding jobs on a single machine. Each job will be processed with no interruptions and becomes available for the processing at time 0. The aim is finding a processing order with regard to jobs, minimizing total completion time , total late work , and maximal tardiness which is an NP-hard problem. In the theoretical part of the present work, the mathematical formula for the examined problem will be presented, and a sub-problem of the original problem of minimizing the multi-objective functions is introduced. Also, then the importance regarding the dominance rule (DR) that could be applied to the problem to improve good solutions will be shown. While in the practical part, two
... Show MoreThe uptake of Cd(II) ions from simulated wastewater onto olive pips was modeled using artificial neural network (ANN) which consisted of three layers. Based on 112 batch experiments, the effect of contact time (10-240 min), initial pH (2-6), initial concentration (25-250 mg/l), biosorbent dosage (0.05-2 g/100 ml), agitation speed (0-250 rpm) and temperature (20-60ºC) were studied. The maximum uptake (=92 %) of Cd(II) was achieved at optimum parameters of 60 min, 6, 50 mg/l, 1 g/100 ml, 250 rpm and 25ºC respectively.
Tangent sigmoid and linear transfer functions of ANN for hidden and output layers respectively with 7 neurons were sufficient to present good predictions for cadmium removal efficiency with coefficient of correlatio
... Show MoreThis study measured and analyzed job satisfaction among faculty members at the university of Irbid College in relation to gender social status, years of experience, academic rank , qualification, the population consisted of 110 full-time Jordanian factually members. A total of 72. (61%) factually, members participated in this study. The research instrument which was developed by the researcher consisted of tow parts: personal information and the job Satisfaction Questionnaire Frequencies, percentages, means and one-way analyses of variance were employed to analyze the data. The level of significance was set at 0.05. A Secheffe method of multiple comparisons was used for follow-up investigation.
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