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In the present study, the authors have attempted to present a novel approach for the prediction, analysis, and optimization of the Friction Stir Welding (FSW) process based on the Deep Neural Network (DNN) model. To obtain the DNN structure with high accuracy, the most focus has been on the number of hidden layers and the activation functions. The DNN was developed by a small database containing results of tensile and hardness tests of welded 7075-T6 aluminum alloy. This material and the production method were selected based on the application in the construction of fishing boat flooring, because on the one hand, it faces the corrosion caused by proximity to sea water and on the other hand, due to direct contact with human food, i.e., fish etc., antibacterial issues should be considered. All the major parameters of the FSW process, including axial force, rotational speed, and traverse speed as well as tool diameter and tool hardness, were considered to investigate their correspondence effects on the tensile strength and hardness of welded zone. The most important achievement of this research showed that by using SAE for pre-training of neural networks, higher accuracy can be obtained in predicting responses. Finally, the optimal values for various welding parameters were reported as rotational speed: 1600 rpm, traverse speed: 65 mm/min, axial force: 8 KN, shoulder and pin diameters: 15.5 and 5.75 mm, and tool hardness: 50 HRC. [ABSTRACT FROM AUTHOR]

For the safe and economical construction of embankment dams, the mechanical behaviour of the rockfill materials used in the dam's shell must be analyzed. The characterization of rockfill materials with specified shear strength is difficult and expensive due to the presence of particles greater than 500 mm in diameter. This work investigates the feasibility of using an extreme gradient boosting (XGBoost) computing paradigm to estimate the shear strength of rockfill materials. To train and validate the proposed XGBoost model, a total of 165 databases obtained from the literature are chosen. The XGBoost model was compared against support vector machine (SVM), adaptive boosting (AdaBoost), random forest (RF), and K-nearest neighbor (KNN) models described in the literature. XGBoost beats SVM, RF, AdaBoost, and KNN models in terms of performance evaluation metrics such as coefficient of determination (R2), Nash–Sutcliffe coefficient (NSE), and error in the root mean square ratio (RMSE) to the standard deviation of the measured data (RSR). The results demonstrated that the XGBoost model has the highest prediction performance with (R2 = 0.9707, NSE = 0.9701, and RSR = 0.1729), followed by the SVM model with (R2 = 0.9655, NSE = 0.9639, and RSR = 0.1899), RF (R2 = 0.9545, NSE = 0.9542, and RSR = 0.2140), the AdaBoost model with (R2 = 0.9390, NSE = 0.9388, and RSR = 0.2474) and the KNN model with (R2 = 0.6233, NSE = 0.6180, and RSR = 0.6181). A sensitivity analysis has been conducted to ascertain the impact of each investigated input parameter. This study demonstrates that the established XGBoost model for estimating the shear strength of rockfill materials is reliable. [ABSTRACT FROM AUTHOR]

In the design stage of construction projects, determining the soil permeability coefficient is one of the most important steps in assessing groundwater, infiltration, runoff, and drainage. In this study, various kernel-function-based Gaussian process regression models were developed to estimate the soil permeability coefficient, based on six input parameters such as liquid limit, plastic limit, clay content, void ratio, natural water content, and specific density. In this study, a total of 84 soil samples data reported in the literature from the detailed design-stage investigations of the Da Nang–Quang Ngai national road project in Vietnam were used for developing and validating the models. The models' performance was evaluated and compared using statistical error indicators such as root mean square error and mean absolute error, as well as the determination coefficient and correlation coefficient. The analysis of performance measures demonstrates that the Gaussian process regression model based on Pearson universal kernel achieved comparatively better and reliable results and, thus, should be encouraged in further research. [ABSTRACT FROM AUTHOR]

The flanged, barbell, and rectangular squat reinforced concrete (RC) walls are broadly used in low-rise commercial and highway under and overpasses. The shear strength of squat walls is the major design consideration because of their smaller aspect ratio. Most of the current design codes or available published literature provide separate sets of shear capacity equations for flanged, barbell, and rectangular walls. Also, a substantial scatter exists in the predicted shear capacity due to a large discrepancy in the test data. Thus, this study aims to develop a single gene expression programming (GEP) expression that can be used for predicting the shear strength of these three cross-sectional shapes based on a dataset of 646 experiments. A total of thirteen influencing parameters are identified to contrive this efficient empirical compared to several shear capacity equations. Owing to the larger database, the proposed model shows better performance based on the database analysis results and compared with 9 available empirical models. [ABSTRACT FROM AUTHOR]

The current research is conducted to investigate the experimental and numerical study of crushing behavior and buckling modes of thin-walled truncated conical shells with or without cutouts and discontinuities under axial loading. In this regard, Instron 8802 servohydraulic machine is used to perform the experiments. Additionally, the buckling modes, derived from the axial collapse phenomenon, are simulated with Finite Element (FE) software. The force-displacement diagrams extracted numerically are compared with experimental results. Various factors, including maximum force, energy absorption, specific energy, and failure modes of each case, are also discussed. The results indicate that the increasing cutout cause a decrease in the maximum force and energy absorption. Moreover, with cutouts reduction, the failure modes of the samples changed from the diamond asymmetric mode and single-lobe mode to multi-lobes, and with removing cutouts, the failure mode is observed to be completely symmetric. [ABSTRACT FROM AUTHOR]

Increasing the lifetime and improving the performance of structures through redesign and optimization are important, especially in marine structures. In general, there are two main groups of marine structures: onshore and offshore structures. Most marine structures are offshore, and these are divided into two categories: floating or sunken. One of the important parameters in the design of sunken structures is the critical load resulting from the buckling of walls, which can cause damage to the structure. In the present paper, three rectangular aluminum and steel compartments of different conditions and sizes were modeled using design analysis methods. Then, different finite element analyses were performed, and the compartments were optimized to reduce the weight of the structure. Finally, the buckling results of three types of rectangular reinforced compartments were calculated and were compared with each other. The results show that the stresses calculated using the analytical method are in good agreement with the results of the finite element analyses. In addition, the weight of the compartment is reduced by utilizing the reinforced conductors in accordance with the design principles and considering the minimum thickness. [ABSTRACT FROM AUTHOR]

Welcome to the world of marine structures, as this fascinating compilation of cutting-edge research unfolds before your eyes. Embrace the revolution brought about by the marriage of research and cutting-edge technology, and witness the power of machine learning techniques such as neural networks and deep learning being harnessed to unveil precise and astute analysis [[4], [9]]. This remarkable edition delves into a myriad of industrial conundrums, from awe-inspiring bridges that stand proud upon the water's surface [[2]], to the intricate network of oil and gas transmission pipelines [[4]]. [Extracted from the article]

Hardened nickel coating is widely used in many industrial applications and manufacturing processes because of its benefits in improving the corrosion fatigue life. It is clear that increasing the coating thickness provides good protection against corrosion. However, it reduces the fatigue life. Thus, applying a thin layer of coated nickel might give an acceptable corrosion protection with minimum loss of the fatigue life. In the present study, the effects of hardened nickel coating with different thicknesses on the fatigue behavior of CK45 mild steel were experimentally investigated. After conducting the experimental tests, we carried out two different modeling approaches of finite element method (FEM) and artificial neural network (ANN). In the FEM modeling, an attempt was made to analyze the fatigue of the components by modeling the interface phase between the base metal and coating more accurately and using the spring elements; ANNs were developed based on the back propagation (BP) error algorithm. The comparison of the obtained results from FEM and ANN modeling with the experimental values indicates that both of the modeling approaches were tuned finely. [ABSTRACT FROM AUTHOR]

The main purpose of this research is to design a high-fatigue performance hoop wrapped compressed natural gas (CNG) composite cylinder. To this end, an optimization algorithm was presented as a combination of finite element simulation (FES) and response surface analysis (RSA). The geometrical model was prepared as a variable wall-thickness following the experimental measurements. Next, transient dynamic analysis was performed subjected to the refueling process, including the minimum and maximum internal pressures of 20 and 200 bar, respectively. The time histories of stress tensor components were extracted in the critical region. Furthermore, RSA was utilized to investigate the interaction effects of various polymer composite shell manufacturing process parameters (thickness and fiber angle) on the fatigue life of polymer composite CNG pressure tank (type-4). In the optimization procedure, four parameters including wall-thickness of the composite shell in three different sections of the CNG tank and fiber angle were considered as input variables. In addition, the maximum principal stress of the component was considered as the objective function. Eventually, the fatigue life of the polymer composite tank was calculated using stress-based failure criterion. The results indicated that the proposed new design (applying optimal parameters) leads to improve the fatigue life of the polymer composite tank with polyethylene liner about 2.4 times in comparison with the initial design. [ABSTRACT FROM AUTHOR]