Modelling the mechanical properties of concrete produced with polycarbonate waste ash by machine learning.

India's cement industry is the second largest in the world, generating 6.9% of the global cement output. Polycarbonate waste ash is a major problem in India and around the globe. Approximately 370,000 tons of scientific waste are generated annually from fitness care facilities in India. Polycarbonate waste helps reduce the environmental burden associated with disposal and decreases the need for new raw materials. The primary variable in this study is the quantity of polycarbonate waste ash (5, 10, 15, 20 and 25% of the weight of cement), partial replacement of cement, water-cement ratio and aggregates. The mechanical properties, such as compressive strength, split tensile strength and flexural test results, of the mixtures with the polycarbonate waste ash were superior at 7, 14 and 28 days compared to those of the control mix. The water absorption rate is less than that of standard concrete. Compared with those of conventional concrete, polycarbonate waste concrete mixtures undergo minimal weight loss under acid curing conditions. Polycarbonate waste is utilized in the construction industry to reduce pollution and improve the economy. This study further simulated the strength characteristics of concrete made with waste polycarbonate ash using least absolute shrinkage and selection operator regression and decision trees. Cement, polycarbonate waste, slump, water absorption, and the ratio of water to cement were the main components that were considered input variables. The suggested decision tree model was successful with unparalleled predictive accuracy across important metrics. Its outstanding predictive ability for split tensile strength (R2 = 0.879403), flexural strength (R2 = 0.91197), and compressive strength (R2 = 0.853683) confirmed that this method was the preferred choice for these strength predictions.

Modeling the influence of bacteria concentration on the mechanical properties of self-healing concrete (SHC) for sustainable bio-concrete structures.

In this research paper, the intelligent learning abilities of the gray wolf optimization (GWO), multi-verse optimization (MVO), moth fly optimization, particle swarm optimization (PSO), and whale optimization algorithm (WOA) metaheuristic techniques and the response surface methodology (RSM) has been studied in the prediction of the mechanical properties of self-healing concrete. Bio-concrete technology stimulated by the concentration of bacteria has been utilized as a sustainable structural concrete for the future of the built environment. This is due to the recovery tendency of the concrete structures after noticeable structural failures. However, it requires a somewhat expensive exercise and technology to create the medium for the growth of the bacteria needed for this self-healing ability. The method of data gathering, analysis and intelligent prediction has been adopted to propose parametric relationships between the bacteria usage and the concrete performance in terms of strength and durability. This makes is cheaper to design self-healing concrete structures based on the optimized mathematical relationships and models proposed from this exercise. The performance of the models was tested by using the coefficient of determination (R2), root mean squared errors, mean absolute errors, mean squared errors, variance accounted for and the coefficient of error. At the end of the prediction protocol and model performance evaluation, it was found that the classified metaheuristic techniques outclassed the RSM due their ability to mimic human and animal genetics of mutation. Furthermore, it can be finally remarked that the GWO outclassed the other methods in predicting the concrete slump (Sl) with R2 of 0.998 and 0.989 for the train and test, respectively, the PSO outclassed the rest in predicting the flexural strength with R2 of 0.989 and 0.937 for train and test, respectively and the MVO outclassed the others in predicting the compressive strength with R2 of 0.998 and 0.958 for train and test, respectively.

Preparation, characterization, and evaluation of compressive strength of polypropylene fiber reinforced geopolymer mortars.

The study of the fiber-matrix interface represents a crucial topic to determine the mechanical performance of geopolymer-based materials reinforced with polypropylene fibers (PPF). This research proposes the use of natural zeolite in the preparation geopolymers mortars through alkaline activation with NaOH, Ca(OH)2 and Na2SiO3, and with river sand as a fine aggregate. PPF were incorporated into the geopolymer-based mortar matrix in different proportions like 0, 0.5, and 1 wt.%. The mortars were cured for 24 h at 60 °C and then aged for six days more at room temperature. All samples analyzed through compressive strength were also characterized by X-ray diffraction, thermal analysis, Infrared Spectroscopy, and scanning electron microscopy techniques. The results indicated that the best mix design among the ones used: NaOH (10 M), Na2SiO3/NaOH = 3, Ca(OH)2 = 1.5 wt.% and PPF = 0.5 wt.%. The optimum mix design showed a compressive strength of 4.63 MPa on average. Besides, the fibers enhanced the compressive strength of those samples which the PP fibers probably have better dispersion inside the matrix of the geopolymer mortar.