Forecasting the Mechanical Properties of Plastic Concrete Employing Experimental Data Using Machine Learning Algorithms: DT, MLPNN, SVM, and RF.

Increased population necessitates an expansion of infrastructure and urbanization, resulting in growth in the construction industry. A rise in population also results in an increased plastic waste, globally. Recycling plastic waste is a global concern. Utilization of plastic waste in concrete can be an optimal solution from recycling perspective in construction industry. As environmental issues continue to grow, the development of predictive machine learning models is critical. Thus, this study aims to create modelling tools for estimating the compressive and tensile strengths of plastic concrete. For predicting the strength of concrete produced with plastic waste, this research integrates machine learning algorithms (individual and ensemble techniques), including bagging and adaptive boosting by including weak learners. For predicting the mechanical properties, 80 cylinders for compressive strength and 80 cylinders for split tensile strength were casted and tested with varying percentages of irradiated plastic waste, either as of cement or fine aggregate replacement. In addition, a thorough and reliable database, including 320 compressive strength tests and 320 split tensile strength tests, was generated from existing literature. Individual, bagging and adaptive boosting models of decision tree, multilayer perceptron neural network, and support vector machines were developed and compared with modified learner model of random forest. The results implied that individual model response was enriched by utilizing bagging and boosting learners. A random forest with a modified learner algorithm provided the robust performance of the models with coefficient correlation of 0.932 for compressive strength and 0.86 for split tensile strength with the least errors. Sensitivity analyses showed that tensile strength models were least sensitive to water and coarse aggregates, while cement, silica fume, coarse aggregate, and age have a substantial effect on compressive strength models. To minimize overfitting errors and corroborate the generalized modelling result, a cross-validation K-Fold technique was used. Machine learning algorithms are used to predict mechanical properties of plastic concrete to promote sustainability in construction industry.

Predictive Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Artificial Intelligence Approaches: MLPNN, ANFIS, and GEP.

Silica fume (SF) is a mineral additive that is widely used in the construction industry when producing sustainable concrete. The integration of SF in concrete as a partial replacement for cement has several evident benefits, including reduced CO2 emissions, cost-effective concrete, increased durability, and mechanical qualities. As environmental issues continue to grow, the development of predictive machine learning models is critical. Thus, this study aims to create modelling tools for estimating the compressive and cracking tensile strengths of silica fume concrete. Multilayer perceptron neural networks (MLPNN), adaptive neural fuzzy detection systems (ANFIS), and genetic programming are all used (GEP). From accessible literature data, a broad and accurate database of 283 compressive strengths and 149 split tensile strengths was created. The six most significant input parameters were cement, fine aggregate, coarse aggregate, water, superplasticizer, and silica fume. Different statistical measures were used to evaluate models, including mean absolute error, root mean square error, root mean squared log error and the coefficient of determination. Both machine learning models, MLPNN and ANFIS, produced acceptable results with high prediction accuracy. Statistical analysis revealed that the ANFIS model outperformed the MLPNN model in terms of compressive and tensile strength prediction. The GEP models outperformed all other models. The predicted values for compressive strength and splitting tensile strength for GEP models were consistent with experimental values, with an R2 value of 0.97 for compressive strength and 0.93 for splitting tensile strength. Furthermore, sensitivity tests revealed that cement and water are the determining parameters in the growth of compressive strength but have the least effect on splitting tensile strength. Cross-validation was used to avoid overfitting and to confirm the output of the generalized modelling technique. GEP develops an empirical expression for each outcome to forecast future databases' features to promote the usage of green concrete.

Randomized controlled trial of standard versus double dose cotrimoxazole for childhood pneumonia in Pakistan.

OBJECTIVE: Increasing concern over bacterial resistance to cotrimoxazole, which is recommended by WHO as a first-line drug for treating non-severe pneumonia, led to the suggestion that this might not be optimal therapy. However, changing to alternative antimicrobial agents, such as amoxicillin, is costly. We compared the clinical efficacy of twice-daily cotrimoxazole in standard versus double dosage for treating non-severe pneumonia in children. METHODS: A randomized controlled multicentre trial was implemented in seven hospital outpatient departments and two community health programmes. A total of 1143 children aged 2-59 months with non-severe pneumonia were randomly allocated to receive 4 mg trimethoprim plus 20 mg sulfamethoxazole/kg of body weight or 8 mg trimethoprim plus 40 mg sulfamethoxazole/kg of body weight orally twice-daily for 5 days Treatment failure occurred when a child required a change of therapy, died or was lost to follow-up. Children required a change of therapy if their condition worsened (they developed chest indrawing or danger signs) or if at 48 hours after enrollment, their clinical condition was the same (defined as having a respiratory rate that was 5 breaths/minute higher or lower than at the time of enrollment). FINDINGS: The results of 1134 children were analysed: 578 were assigned to the standard dose of cotrimoxazole and 556 to the double dose. Treatment failed in 112 children (19.4%) in the standard group and 118 (21.2%) in the double-dose group (relative risk 1.10; 95% confidence interval = 0.87-1.37). Using multivariate analysis we found that treatment was more likely to fail in children who were not given the medicine correctly (P = 0.001), in those younger than 12 months (P = 0.004), those who had used antibiotics previously (P = 0.002), those whose respiratory rate was > or =20 breaths/minute above the age-specific cut-off point (P = 0.006), and those from urban areas (P = 0.042). CONCLUSION: Both standard and double strength cotrimoxazole were equally effective in treating non-severe pneumonia. Close follow-up of patients is essential to prevent worsening of disease. Definitions of clinical failure need to be more specific. Surveillance in both rural and urban areas is essential in the development of treatment policies that are based on clinical outcomes.