• Journal of Korean Society of Environmental Engineers
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• v.30 no.5
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• pp.544-551
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• 2008

QUAL2K enhanced QUAL2E and applied in real fields efficiently incorporates denitrification process, sediment-water interaction process, bottom algae and detritus. Also, the CBOD of QUAL2K is divided into two real parts, one is slow CBOD(sCBOD) and another is fast CBOD(fCBOD). The simulation results of QUAL2E and QUAL2K models in Anyang Stream were compared and analyzed in water quality constituents of DO, BOD, Org-N, NH$_3$-N, NO$_3$-N, Org-P, Dis-P and Chl-a respectively. The similar results were shown in Org-N, NH$_3$-N, Org-P and Chl-a both QUAL2K and QUAL2E models. But the different results were revealed in DO, BOD, Dis-P and NO$_3$-N by the influence of new incorporating processes. DO was shown relatively low values in the effect of bottom algae. BOD which is influenced by particulate organic matter was revealed high values. NO$_3$-N was closed to the real values by the two processes of denitrification and sediment-water interaction. To evaluate the running results of QUAL2K and QUAL2E models, a simple statistical analysis was conducted. According to the statistical analysis, QUAL2K represented less relative error and coefficient of variation than QUAL2E in almost all of constituents. It was found that QUAL2K, which simulates the water quality more realistically, can be applied to control and manage the water problems of river or river-run reservoir effectively.

• Steel and Composite Structures
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• v.34 no.5
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• pp.743-767
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• 2020

Due to the impressive flexural performance, enhanced compressive strength and more constrained crack propagation, Fibre-reinforced concrete (FRC) have been widely employed in the construction application. Majority of experimental studies have focused on the seismic behavior of FRC columns. Based on the valid experimental data obtained from the previous studies, the current study has evaluated the seismic response and compressive strength of FRC rectangular columns while following hybrid metaheuristic techniques. Due to the non-linearity of seismic data, Adaptive neuro-fuzzy inference system (ANFIS) has been incorporated with metaheuristic algorithms. 317 different datasets from FRC column tests has been applied as one database in order to determine the most influential factor on the ultimate strengths of FRC rectangular columns subjected to the simulated seismic loading. ANFIS has been used with the incorporation of Particle Swarm Optimization (PSO) and Genetic algorithm (GA). For the analysis of the attained results, Extreme learning machine (ELM) as an authentic prediction method has been concurrently used. The variable selection procedure is to choose the most dominant parameters affecting the ultimate strengths of FRC rectangular columns subjected to simulated seismic loading. Accordingly, the results have shown that ANFIS-PSO has successfully predicted the seismic lateral load with R² = 0.857 and 0.902 for the test and train phase, respectively, nominated as the lateral load prediction estimator. On the other hand, in case of compressive strength prediction, ELM is to predict the compressive strength with R² = 0.657 and 0.862 for test and train phase, respectively. The results have shown that the seismic lateral force trend is more predictable than the compressive strength of FRC rectangular columns, in which the best results belong to the lateral force prediction. Compressive strength prediction has illustrated a significant deviation above 40 Mpa which could be related to the considerable non-linearity and possible empirical shortcomings. Finally, employing ANFIS-GA and ANFIS-PSO techniques to evaluate the seismic response of FRC are a promising reliable approach to be replaced for high cost and time-consuming experimental tests.