نشریه مهندسی عمران امیرکبیر
سال پنجاه و ششم شماره 12 (اسفند 1403)

This study assessed the effectiveness of an innovative nano-geopolymer (SNZBG) enhanced by fiber in improving the geo-mechanical performance and durability of highly expansive clays. The experimental results showed that the addition low contents of lime alone may have a relatively favorable influence in controlling the swelling potential of soil; however, the improvement of mechanical parameters requires considerable amounts of additives and long times of curing. It was also appeared that harsh conditions (including frequent periods of freezing and thawing, F-T) will lead to the deterioration of engineering properties. On the contrast, the application of proposed geopolymer not only could diminish the required lime and time of curing for successful modification of soil but also may significantly increase the durability of composites. The better performance upon the SNZBG application can be ascribed to increasing the solidification processes and reducing the tendency of clay surfaces to absorb water due to the more formation of geopolymeric nanostructures as well as the generation of physical clogged fabric. In this system, there was a dramatic reduction in the strength against the increase in external loadings. Inclusion of fibers plays a significant role in enhancing the ductility of soil-geopolymer matrix. The combined effect of SNZBG and fibers leads to an increase in the tensile capacity as well as a strong reduction in the degree of damage upon the F-T action. In general, it can be concluded that the treatment of swelling clayey soil with SNZBG/fiber is an effective approach compared to the traditional stabilization method.

Conventional column bases are fundamental components of the steel frame. Their behavior has been proved to have a significant effect on the overall building seismic response. Base connections, flexibility largely affect the period of the building and the lateral displacement of the stories. In this research, a new type of connection at the steel column bases has been proposed. the main purpose of proposing this connection is to decrease residual displacement and minimize repair time and disruption of the building serviceability after a strong earthquake. Therefore, to be able to evaluate and compare the seismic performance of this connection with seismic lateral force resisting systems which have developed recently, a self-centering concentrically braced frame (SC-CBF) has been selected from Hasan’s research and analyzed and validated. Also, the performance of the system with the proposed column base is evaluated using numerical analysis. The results show that in the proposed system, the amount of residual drift in the structure is equal to 2.2×10-14%. Therefore, it can be concluded that the proposed connection satisfies the main purpose of the research well, which is to eliminate residual drifts in the system. they also have higher energy dissipation and stiffness than the SC-CBF system that has been developed so far. While the post-tensioned bars are not used.

One of the techniques of stabilization soil is the electrical method. In this method, stabilization of fine-grained soils, especially saturated clay soils, is done by reducing the thickness of the double water layer by establishing an electric current. Soft clay is one of the problematic soils, because it is affected by weather conditions, and as a result, building structures on them is always at risk. In this study, to improve and stabilize soft clay soils, the electro-kinetic injection method, which is one of the limited stabilization methods in situ and is very economical in terms of time and cost, was used. For this purpose, a layer of soil with water content and specific gravity in situ is compacted in five layers in a chamber with dimensions of 50*30*20 cm and direct current (DC) with voltages of 1/5 and 1 v/cm of soil mass has been applied for 48 hours. The used electrodes were made of two types of aluminum and graphite and were used in two shapes, rod and plate. The index results of the studies showed that the use of this method increases the unconfined compressive strength from 98 kPa to 223 kPa, decreases the water content from 24 to 15.2 and the plasticity index from 24 to 10. According to the water content, Atterberg limits, unconfined compressive strength and consolidation tests, it was determined that the best performance is related to the use of plate aluminum electrode with a voltage of 1/5.

Considering the characteristic of ordinary concrete, which is a composite material with characteristics such as resistance and low tensile strain range. Basically, the two main weaknesses of concrete, which include the brittle behavior of concrete and the weakness in its elasticity, have made the use of concrete structures made of ordinary concrete face major problems. In general, by adding fibers to the concrete mixture, it is possible to improve the mechanical properties of concrete. In this research, an innovative approach using the water cycle algorithm was used to combine with the adaptive multivariate regression spline method (MARS) to model and predict the compressive strength and tensile strength of concrete containing hemp bio composites. For the development of each of the proposed models, 153 mixing designs along with their compressive strength results were collected from authoritative articles. After analyzing and evaluating the influencing parameters by Mallow coefficient, the input parameters to the smart models include the ratio of water to cement base materials, the ratio of hemp seeds to cement base materials, the weight percentage of hemp seeds, cement base materials, seeds hemp, density of cement base material, density of dry material and strength of cement base material were selected. The results showed that the correlation coefficient for the compressive strength model for Mars optimized with the algorithm and MARS is 0.991 and 0.971, respectively, and the tensile strength is 0.928 and 0.911, respectively, in the training and testing phase.

Accurate prediction of the carbonation depth of concrete is very important to protect against harmful consequences such as cracking and corrosion. Nevertheless, due to the complexities of the process and the multitude of available variables, identifying the parameters that are most important in modeling the carbonate depth of concrete is considered a big challenge. This paper deals with the development of a new feature selection method called MOEA/D-ANN. The purpose of this method is to identify the most important variables that help to achieve the highest forecasting accuracy. This proposed method combines the separation-based multi-objective optimization evolutionary algorithm with artificial neural networks to effectively solve the feature selection problem by using the power of optimization methods and machine learning. To evaluate the performance of the introduced method, the algorithm (RReliefF), which is a feature ranking algorithm, has also been used. ANN method has been used to predict concrete carbonate depth and combined MOEA/D-ANN and RReliefF methods have been used to find the influencing variables. The obtained results have shown that the model created using the MOEA/D-ANN approach, by combining the variables determined by it, has a significant reduction in the percentage of errors and an increase in accuracy. In addition, this model reaches the significant value of the coefficient of determination R2 = 0.99, which emphasizes its exceptional accuracy in predicting the depth of concrete carbonate and confirming the accurate selection of influential variables.

Removing heavy metals from wastewater has been one of the main challenges in recent decades, and the ion flotation process has been one of the most effective methods for heavy metals removing. The advantages of ion flotation are its simplicity, requiring less energy, having high recovery and selectivity, and by a low concentration of residual metal in the solution. One drawback of ion flotation is the collector's high consumption. The purpose of this study is to enhance the recovery of manganese ions while minimizing the consumption of collectors. In this study, graphene oxide prepared as a nanocollector was identified by XRD, DLS, FTIR, and SEM analyses. The effective parameters included pH, graphene oxide concentration, air flow rate, impeller speed, and SDS concentration as an auxiliary collector. The results indicated that a pH of approximately 10 is the most effective for removing Mn ions from synthetic wastewater using ion flotation. Other optimal parameters of nanocollector concentration, air flow rate, impeller speed, and SDS concentration are equal to 25 ppm, 800 rpm, 2 L/min, and 43 ppm, respectively. Under these conditions, the recovery of Mn ions from synthetic wastewater and water recovery were 82.6% and 45%, respectively. The results of this study demonstrated that utilizing graphene oxide as a nanocollector in the ion flotation method for wastewater treatment has significant advantages such as high removal recovery, reduction of collector consumption, reusability of graphene oxide.