نشریه مهندسی عمران فردوسی
سال سی و پنجم شماره 4 (زمستان 1401)

Researchers and engineers are constantly working to improve the performance of asphalt pavements. Pavements, as surfaces that are often loaded by heavy axles, must have sufficient resistance to fatigue, cracking and rutting. In this paper, using the data obtained from the laboratory results of the previous study that warm mix asphalt modified with glass fibers and 0, 20, 40 and 50% recycled asphalt pavement (RAP) were made to evaluate the resistance of the mixture against rutting, rutting depth of the mixtures was determined by multilayer perceptron neural network (MLP) and radial basis function neural network (RBF) and the results were compared with each other. The prediction model of post compaction and rutting depth showed good agreement with the experimental results. To evaluate the generalizability of the neural network using data that were not used during modeling, the multilayer perceptron neural network (MLP) performed better than the radial basis function neural network (RBF).

One of the most critical problems of mechanized tunnelling is the abrasion of cutting tools. Soil abrasivity significantly reduces drilling efficiency and increases the operating costs of urban tunnels. There are extensive studies on abrasivity of rocks. However, limited studies have been performed on the influence of soil particle size distribution on tunnelling machine cutting tools. Despite the wide range of methods and devices for measuring soil abrasivity, so far, no standard and comprehensive method for measuring soil abrasivity have been presented. In this study, considering the effect of some effective parameters on the abrasion of cutting tools, a new laboratory machine to determine soil abrasivity was constructed. Then, using 8 different types of soil granulation, the effect of soil particle size distribution and density on cutting tool abrasion was studied. Also, using the Talbot curve, the abrasion values ​​of cutting tools in different particle sizes were compared. The results showed that the highest values ​​of cutting tools abrasion occur in soils with particle sizes according to the Talbot equation. As the soil granulation curve moves away from the Talbot curve, abrasivity decreases. Also, the maximum abrasion of cutting tools occurs in the amount of fine aggregate of 10% with an average abrasion percentage of 27.3%. By reducing the fine aggregate to values lower than 10%, the soil structure is disturbed and as a result, the average abrasion percentage of cutting tools decreases from 27.3% in soil with 10% fine aggregate to 2.37% in soil without fine aggregate. Also, by increasing soil density from 1.6 to 1.8, the average abrasion percentage of cutting tools increases from 8.1% to 31.4%.

Today, most of the surface water and groundwater resources are exposed to contamination by different materials sewage. because most of the methods have been used to restore the pollution intensity function for groundwater, representing a method to restore pollution intensity function in rivers with more complex flow conditions is considerable. this research aim is to calculate the intensity function of the contamination source, by the numerical solution of group preserving scheme, which has not been observed in other researches done so far. Group preserving scheme is an accurate method to solve ill-posed problems. By implementing this method the one-dimensional advection-dispersion equation with variable coefficients has been solved. The principle of the introduced backward solution method is that by solving the dynamic systems in negative time steps, a general equation will be obtained, which can solve ordinary differential equations. The responses of this equation will lead to convergence of the equation and prevent the divergence of the data. three examples have been presented to show the accuracy of the forward and backward group preserving scheme, Firstly, using direct solutions of the river, the intensity of pollution concentration in the river is calculated, and the implicit finite difference method is applied to verify the accuracy of the direct solutions. In the direct method, the results of the two models were compared with statistical indexes in order to demonstrate the conformity of the two models. In the next step, by solving the backward group preserving scheme in two different examples, the concentration of pollutants upstream is simulated. Following the simulation and verification of the inverse model by direct solution, statistical indexes are used to evaluate the effectiveness of this method.

Tuned mass damper(TMD) is an efficient tool to control wind-induced vibrations of tall buildings. Previous studies on the effect of TMD are generally limited to specific conditions. In the present study, the effect of the mass and installation height of TMD on the wind-induced vibration control of tall buildings are investigated. An example of tall building with the height 400 m and square variable cross section is presented. The analytical model of the building is assumed as a multi-degrees-of-freedom vertical cantilever beam with the masses lumped at the nodes. The wind-induced responses of the structure are computed using the frequency domain analysis and the random vibration method for a wide range of studied parameters. The results indicated that the vibrations of the structure and TMD system decreases with increasing the mass of the TMD. For instance, the 100 and 600-ton TMD installed at top-floor reduced the top-floor crosswind acceleration by 31 and 48 percent, respectively. By increasing the installation height, the control effectiveness of the system increases, while the vibration of the TMD does not change considerably. For a 300-ton TMD installed at 320 and 400 m heights, the crosswind acceleration reduced by 33.72 and 41.28 percent and the RMS displacement of the TMD at these heights were 58.68 and 54.92 cm, respectively.

One of the welding controls in health monitoring of structures is to visually control the appearance of welding defects (cracks, Spatter, Overlap, Lack of Fusion). Currently, according to regulations, the appearance quality of welding is controlled by an inspector visually. The accuracy of work in this method depends on the skill level of the inspector. Non using of equipment and technology leads to a high error in identifying visual defects. In this research, a method is proposed to be able to more accurately identify the appearance of welding defects with the help of imaging using machine vision based on deep learning. Convolutional network is used for deep learning to extract features from the image. The results show that the proposed method can identify welding defects with an acceptable accuracy (over 85%). Also, the results show that by using the proposed method, welding defects are evaluated more quickly compared to the traditional method.

Recycled concrete aggregates are one of the types of recycled aggregates that have the most abundance compared to other recycled aggregates. In this research, by using the replacement of recycled concrete aggregates instead of natural aggregates, the mechanical properties of this type of concrete have been investigated. The replacement of natural aggregates with recycled aggregates was done in four percentages of 0, 15, 30 and 45, and this replacement was done both independently (i.e. sand or gravel separately) and simultaneously (both sand and gravel together). According to the use of 50% sand and 50% sand in the reference mixing plan and by replacing the percentages with coarse and fine recycled concrete aggregates, 21 mixing plans were used. The ratio of water to cement was kept constant in all mixing plans and was considered equal to 0.42. To compensate for the decrease in strength due to the replacement of recycled aggregates, micro silica in different percentages of 7.5 and 15 and super plasticizers were used in the mixing designs. By performing various tests, the mechanical properties of concrete samples were investigated. The results showed that the best mixing design in terms of compressive strength is the design containing 7.5% micro silica and 15% recycled sand, in terms of tensile strength, the design containing 7.5% micro silica and 45% recycled sand, in terms of flexural strength, the design containing 7.5% micro silica and 30% recycled sand, the plan containing 15% micro silica and 15% recycled sand was determined. By examining the mechanical properties of concrete samples, it was determined that the recycled mixing plans contain 15%, 30% recycled gravel and 45% recycled sand with 7.5% microsilica, as well as the mixing plan of 15% recycled sand with 15% microsilica. , among the recycled mixing plans, they had the highest amount of overall utility.