فهرست مطالب هانیه ملک

The use of wastewater outflowing from the municipal wastewater treatment plants for irrigation of agricultural lands is one of the important and fundamental issues for reuse of the plants’ effluent. In recent years, the artificial neural networks (ANN) have received considerable attention for modeling the sewage treatment process. Accordingly, the ANN models, fuzzy logic (FL) and adaptive neuro fuzzy inference system (ANFIS) have been utilized in this paper to predict quality of the effluent running out of the treatment plant. For this purpose, first, three scenarios were chosen and their inputs were reduced using the principal component analysis (PCA) method. Finally, the process of modeling was conducted with and without this method and then, the results were compared. Evaluating the results obtained from the predictions using the statistical indicators revealed that the ANFIS model with mean error reduction value of 13.92% compared to that of the FL model and a reduction value of 8.22% in contrast to the ANN model, benefits from a higher accuracy and this trend has been held true with and without PCA. Moreover, calculating the percentage of pollutant removal efficiency indicated that the maximum removal efficiency is obtained when total suspended solids (TSS) are removed which is equal to 96.68%. It bears to mention that the rest of the pollutants had values approximately equal to that of TSS. Based on the results, as a simple, accurate, efficient and reliable approach, the ANN models can be applied to predict quality of the effluents.

Many columns that have been built in the past need to be repaired and strengthened for various reasons such as errors during construction, design errors, changes in the use of the building, changes in regulations, strong beam-weak column conditions and also injuries from accidents. Today, various methods for strengthening and improving structures, especially concrete columns, have become popular. One of the most common methods for reinforcing columns is to confine them using polymer fiber composites. The advantages of this method include increasing the axial and lateral bearing capacity of the column, increasing the compressive and tensile strength of the concrete member and increasing the ductility of the member. The mechanical properties of the concrete confined with FRP polymer fibers may be required for the purpose of design and rehabilitation. Artificial intelligence methods are among the modeling methods that have shown great power to coordinate with engineering problems. The aim of this study is to use multivariate adaptive regression spline (MARS) and extreme learning machine (ELM) artificial intelligence models to estimate the compressive strength of concrete in circular concrete columns confined with FRP polymer fibers. In addition, in order to improve the accuracy of these models, the particle swarm Optimization algorithm (PSO) is used in combination with these models and the accuracy of the models is evaluated to estimate the resistance. The results show that in overall, the used artificial intelligence models estimate the compressive strength of FRP- confined columns more accurately than the existing analytical models. In particular, the integrated MARS-PSO model has better performance compared to other models used, so that this model has a correlation coefficient of 0.9972 in the training stage and 0.9961 in the experimental stage. Also, the combination of PSO algorithm with MARS and ELM models improves their accuracy by 6.13 and 4.68 percent, respectively.