• Transactions of Materials Processing
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• v.30 no.1
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• pp.35-42
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• 2021

Magnesium alloys are used in electronic devices such as laptops due to their lightweight features as well as vibration absorption and electromagnetic shielding properties. However, the precision of electronics is limited by the large number of small and precise ribs, the cost-effective manufacture of which requires appropriate technology. Plate forging is an efficient manufacturing process that can address these challenges. In this study, plate forging of magnesium alloys was investigated specifically for the fabrication of laptop cover. The plate forging process with back-pressure was used for near-net shape formation. Finite element analysis was used to select appropriate variables for back-pressure formation to generate ribs of various sizes and shapes without defects. The reliability of the analysis was verified to manufacture the prototype. The effect of back-pressure can be verified via fabrication of prototypes as well as structure and forming analysis based on finite element method. The process design factor of back-pressure increases formability without defects of under-filling and flow-through. Moreover, the tensile strength was maintained even after high temperature plate forging at 370 ℃, and the elongation was improved.

• Design & Manufacturing
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• v.15 no.2
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• pp.63-69
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• 2021

Springback is an essential task to be solved in order to make high-precision products in sheet metal forming. In this study, materials with four different elastic regions were used. For the forming analysis, the change of springback caused by the frictional force generated in the flange part during hat shape forming was considered by using the AutoForm analysis program. Factors affecting frictional force were blank holder force, friction coefficient, bead R and bead height. As a result of the forming analysis, the springback increases as the material with a larger elastic region increases. In addition, as the frictional force of the flange part increased, the tensile force in the forming direction increased and the springback decreased. In particular, the blank holder force and friction coefficient had a great effect on springback in mild materials (DC04, Al6016), and the bead effectively affects all materials. Through this study, it was considered that the springback decreased as the material with a smaller elastic region and the tensile force in the forming direction increased.

• Steel and Composite Structures
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• v.38 no.1
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• pp.47-66
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• 2021

This work addresses the free vibration analysis of Functionally Graded Porous (FGP) nanocomposite truncated conical shells with Graphene PLatelet (GPL) reinforcement. In this study, three different distributions for porosity and three different dispersions for graphene platelets have been considered in the direction of the shell thickness. The Halpin-Tsai equations are used to find the effective material properties of the graphene platelet reinforced materials. The equations of motion are derived based on the higher-order shear deformation theory and Sanders's theory. The Fourier Differential Quadrature (FDQ) technique is implemented to solve the governing equations of the problem and to obtain the natural frequencies of the truncated conical shell. The combination of FDQ with higher-order shear deformation theory allows a very accurate prediction of the natural frequencies. The precision and reliability of the proposed method are verified by the results of literature. Moreover, a wide parametric study concerning the effect of some influential parameters, such as the geometrical parameters, porosity distribution, circumferential wave numbers, GPLs dispersion as well as boundary restraint conditions on free vibration response of FGP-GPL truncated conical shell is also carried out and investigated in detail.

• Geomechanics and Engineering
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• v.30 no.2
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• pp.107-121
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• 2022

Coal pillar assessment is of broad importance to underground engineering structure, as the pillar failure can lead to enormous disasters. Because of the highly non-linear correlation between the pillar failure and its influential attributes, conventional forecasting techniques cannot generate accurate outcomes. To approximate the complex behavior of coal pillar, this paper elucidates a new idea to forecast the underground coal pillar stability using combined unsupervised-supervised learning. In order to build a database of the study, a total of 90 patterns of pillar cases were collected from authentic engineering structures. A state-of-the art feature depletion method, t-distribution symmetric neighbor embedding (t-SNE) has been employed to reduce significance of actual data features. Consequently, an unsupervised machine learning technique K-mean clustering was followed to reassign the t-SNE dimensionality reduced data in order to compute the relative class of coal pillar cases. Following that, the reassign dataset was divided into two parts: 70 percent for training dataset and 30 percent for testing dataset, respectively. The accuracy of the predicted data was then examined using support vector classifier (SVC) model performance measures such as precision, recall, and f₁-score. As a result, the proposed model can be employed for properly predicting the pillar failure class in a variety of underground rock engineering projects.

• Structural Engineering and Mechanics
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• v.85 no.2
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• pp.163-177
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• 2023

The design of segmental bridges, a structure that typically employs precast prestressed concrete elements and the balanced cantilever construction method for the deck, may demand a highly complex structural analysis for increased precision of the results. This work presents a comprehensive numerical analysis of a 3D finite element model using the software ANSYS, version 21.2, to simulate the constructive deck stages of the New Guaiba Bridge, a structure located in Porto Alegre city, southern Brazil. The materials concrete and steel were considered viscoelastic. The concrete used a Generalized Kelvin model, with subroutines written in FORTRAN and added to the main model through the customization tool UPF (User Programmable Features). The steel prestressing tendons used a Generalized Maxwell model available in ANSYS. The balanced cantilever constructive steps of a span of the New Guaiba Bridge were then numerically simulated to follow the actual constructive sequence of the bridge. A comparison between the results obtained with the numerical model and the actual vertical displacement data monitored during the bridge's construction was carried out, showing a good correlation.

• Structural Engineering and Mechanics
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• v.83 no.5
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• pp.593-607
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• 2022

To solve the problem of detecting structural damage, a two-stage method using the Kalman filter and Particle Swarm Optimization (PSO) is proposed. In this method, the first PSO population is enhanced using the Kalman filter method based on dynamic responses. Due to noise in the sensor responses and errors in the damage detection process, the accuracy of the damage detection process is reduced. This method proposes a novel approach for solve this problem by integrating the Kalman filter and sensitivity analysis. In the Kalman filter, an approximate damage equation is considered as the equation of state and the damage detection equation based on sensitivity analysis is considered as the observation equation. The first population of PSO are the random damage scenarios. These damage scenarios are estimated using a step of the Kalman filter. The results of this stage are then used to detect the exact location of the damage and its severity with the PSO algorithm. The efficiency of the proposed method is investigated using three numerical examples: a 31-element planer truss, a 52-element space dome, and a 56-element space truss. In these examples, damage is detected for several scenarios in two states: using the no noise responses and using the noisy responses. The results show that the precision and efficiency of the proposed method are appropriate in structural damage detection.

• Wind and Structures
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• v.36 no.2
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• pp.105-120
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• 2023

The present study is performed to find the effect of corner recession on a square plan-shaped tall building. A series of numerical simulations have been carried out to find the two orthogonal wind force coefficients on various model configurations using Computational Fluid Dynamics (CFD). Numerical analyses are performed by using ANSYS-CFX (k-ℇ turbulence model) considering the length scale of 1:300. The study is performed for 0° to 360° wind angle of attack. The CFD data thus generated is utilised to fit parametric equations to predict alongwind and crosswind force coefficients, C_fx and C_fy. The precision of the parametric equations is validated by employing a wind tunnel study for the 40% corner recession model, and an excellent match is observed. Upon satisfactory validation, the parametric equations are further used to carry out multiobjective optimization considering two orthogonal force coefficients. Pareto optimal design results are presented to propose suitable percentages of corner recession for the study building. The optimization is based on reducing the alongwind and crosswind forces simultaneously to enhance the aerodynamic performance of the building.

• Computers and Concrete
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• v.32 no.3
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• pp.263-286
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• 2023

Experimenting with concrete to determine its compressive and tensile strengths is a laborious and time-consuming operation that requires a lot of attention to detail. Researchers from all around the world have spent the better part of the last several decades attempting to use machine learning algorithms to make accurate predictions about the technical qualities of various kinds of concrete. The research that is currently available on estimating the strength of concrete draws attention to the applicability and precision of the various machine learning techniques. This article provides a summary of the research that has previously been conducted on estimating the strength of concrete by making use of a variety of different machine learning methods. In this work, a classification of the existing body of research literature is presented, with the classification being based on the machine learning technique used by the researchers. The present review work will open the horizon for the researchers working on the machine learning based prediction of the compressive strength of concrete by providing the recommendations and benefits and drawbacks associated with each model as determining the compressive strength of concrete practically is a laborious and time-consuming task.

• Smart Structures and Systems
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• v.30 no.4
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• pp.385-395
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• 2022

Detecting cracks on a concrete structure is crucial for structural maintenance, a crack being an indicator of possible damage. Conventional crack detection methods which include visual inspection and non-destructive equipment, are typically limited to a small region and require time-consuming processes. Recently, to reduce the human intervention in the inspections, various researchers have sought computer vision-based crack analyses: One class is filter-based methods, which effectively transforms the image to detect crack edges. The other class is using deep-learning algorithms. For example, convolutional neural networks have shown high precision in identifying cracks in an image. However, when the objective is to classify not only the existence of crack but also the types of cracks, only a few studies have been reported, limiting their practical use. Thus, the presented study develops an image processing procedure that detects cracks and classifies crack types; whether the image contains a crazing-type, single crack, or multiple cracks. The properties and steps in the algorithm have been developed using field-obtained images. Subsequently, the algorithm is validated from additional 227 images obtained from an open database. For test datasets, the proposed algorithm showed accuracy of 92.8% in average. In summary, the developed algorithm can precisely classify crazing-type images, while some single crack images may misclassify into multiple cracks, yielding conservative results. As a result, the successful results of the presented study show potentials of using vision-based technologies for providing crack information with reduced human intervention.

• Smart Structures and Systems
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• v.29 no.5
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• pp.729-746
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• 2022

In this paper, a nonlinear structural model updating methodology based on the Deep Belief Network (DBN) is proposed. Firstly, the instantaneous parameters of the vibration responses are obtained by the discrete analytical mode decomposition (DAMD) method and the Hilbert transform (HT). The instantaneous parameters are regarded as the independent variables, and the nonlinear model parameters are considered as the dependent variables. Then the DBN is utilized for approximating the nonlinear mapping relationship between them. At last, the instantaneous parameters of the measured vibration responses are fed into the well-trained DBN. Owing to the strong learning and generalization abilities of the DBN, the updated nonlinear model parameters can be directly estimated. Two nonlinear shear-type structure models under two types of excitation and various noise levels are adopted as numerical simulations to validate the effectiveness of the proposed approach. The nonlinear properties of the structure model are simulated via the hysteretic parameters of a Bouc-Wen model and a Giuffré-Menegotto-Pinto model, respectively. Besides, the proposed approach is verified by a three-story shear-type frame with a piezoelectric friction damper (PFD). Simulated and experimental results suggest that the nonlinear model updating approach has high computational efficiency and precision.