Journal of Civil Engineering and Materials Application
Volume:7 Issue: 4, Autumn 2023

The rivers of Bangladesh have a vital role as a valuable natural resource that sustains an economy focused on agriculture. It is very important to make sure the water is safe for many uses, like drinking, cooking, growing, and keeping marine life alive. The rivers Moyur, Bhairab, and Rupsha are very important to the people who live in Khulna City. The water bodies are getting worse because of things like fertilizers, oil, garbage that hasn't been cleaned, and industry waste. For ten months, from August 2022 to May 2023, researchers kept an eye on how polluted the river was. Every month, 18 samples of water were taken from six different places in the three rivers. Each sample was taken one foot below the water's surface. The data showed that during the rainy season, the BOD, TDS, salt, DO, and pH levels in the Bhairab and Rupsha rivers stayed the same. But according to the rules set by ECR (1997) and WHO (2004), the amounts of COD, chloride, total TSS, Total Dissolved Solids (TDS), and turbidity during the dry season were higher than what is good for people and aquatic environments. On the other hand, the Moyur River had chloride, pH, and TDS levels that were within permissible ranges. Problems regarding the water's health arose, however, because DO, BOD, COD, TSS, and turbidity concentrations continuously surpassed clean water limits during the study period.

Usually, after carrying out network design studies, the question arises of how the output variables are affected by the input variables of a model. In other words, how can one use a method to change the inputs of a statistical model in an organized manner so that the effects of these changes can be predicted on the output of the model. This work is usually done by sensitivity analysis, so in this sensitivity analysis study, two parameters, the optimality degree of the objective function of the problem and the time to solve the problem, are examined in relation to the change in the four parameters of the genetic algorithm. In other words, the purpose of this study is to find the best combination of genetic algorithm parameters (survival probability, mutation rate, recombination probability, population) and also to check the changes of the mentioned criteria against the four parameters of the problem, so that the best solution is obtained. The results showed that increasing the initial population will improve the answer. This is because a more accurate search is performed with a larger number of solving factors in the feasible space. The solution time of the model also shows the same 65% as the optimality of the search objective function following the best reproduction probability value. The higher the survival probability, the more chromosomes of the current generation will be transferred to the next generation without any operation, which will naturally reduce the solution time.

In most countries of the world, saving human lives is one of the most important and first factors that have been considered by politicians. Among the causes of death, road accident is known as one of the 10 causes of death and casualties. Therefore, paying attention to reducing the number of accidents and also reducing the severity of accidents, is the goal of the country's officials, and their planning and prioritization is in the same direction. One of the most important parts of traffic accident analysis and prediction is selecting and using appropriate models. In this study, extensive research has been tried to give a good view to researchers to choose a suitable model. Also, the human factor in accidents has been studied and the parameters affecting this factor have been studied and the most important ones have been stated. Therefore, the purpose of this study is to examine in detail the most important factors (appropriate model and appropriate parameter) in the evaluation of accidents. Results are shown that Deep learning approach/Data mining/machine learning models had the highest power with 87.27%, followed by Poisson-lognormal and generalized additive models. It was also concluded that most models were used in suburban accidents, however, there were one model “microscopic simulations” that were used more in urban accidents. Deep learning approach / Data mining / machine learning has allocated the most up-to-date research with an average close to 2016 (2015.82). Random-parameters models are next with an average of 13.4. Duration models with the lowest mean (1998.2) are at the bottom of this classification and have the oldest research. Based on this information, it can be concluded that today researchers are more inclined to new models such as Deep Learning, which may be due to the high accuracy of these models.

The passive control of the structure with bracing system is one of the new methods of structural engineering. Indicators such as the span length and the story height have important effects on the steel structure performance during an earthquake. Determining the relationship between various geometric indicators and the seismic performance of structure leads to better design and reduction of damages during an earthquake. In the current research, a 5-story steel frame with Gate brace was designed with Sap2000 software. The K index was defined as the ratio of the span length to the story height. Three different values of K equal to 1, 2 and 3 were defined in the modelling of the structure. The structures were analyzed by the modal time history analysis method under the accelerometers of Kobe (near-fault) and El Centro (far-fault). The results showed that in the near-fault area, Modal damping energy and base shear force have a direct relationship with the K index. The mentioned 2 seismic parameters, in the far-fault area, do not have a clear relationship with the K index. Also, parameters such as acceleration, roof displacement, brace axial force, story displacement, column shear force and the support reaction force do not have a clear relationship with the K index in the near-fault and far-fault areas.

Roller Compacted Concrete (RCC) has garnered significant attention in the road and infrastructure sectors due to its economic and technical advantages. The integration of specific particles to bolster its strength without compromising workability is garnering increasing focus. This research provides an experimental assessment of the effects of polyethylene terephthalate (PET) particles — derived from recycled beverage bottles and plastic bags — and silica fume on the fresh and hardened properties of RCC. PET particle replacement by coarse aggregate was studied at increments of 0.5 to 2 % by volume or 5, 10, 15, and 20 kg per cubic meter. The fresh properties of RCC were evaluated using the Vebe consistency time test. Hardened characteristics were determined through measures like compressive strength, splitting tensile strength and flexural strength over varied durations. Additionally, water absorption and electrical resistivity tests were conducted to provide insights into durability. Initial results indicate an optimal PET particle concentration of 5 Kg/m^3 for balanced fresh and hardened RCC properties. Nonetheless, it's essential to highlight that a rise in PET particle dosage corresponded with a slight reduction in compressive strength. This investigation furnishes crucial perspectives for industry experts pursuing sustainable and efficacious enhancements for RCC applications.