• Structural Engineering and Mechanics
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• v.45 no.3
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• pp.337-354
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• 2013

For shield TBM (Tunnel Boring Machine) tunnel lining, the segment joint is the most critical component for determining the mechanical response of the complete lining ring. To investigate the mechanical behavior of the segment joint in a water conveyance tunnel, which is different from the vehicle tunnel because of the external loads and the high internal water pressure during the tunnel's service life, full-scale joint tests were conducted. The main advantage of the joint tests over previous ones was the definiteness of the loads applied to the joints using a unique testing facility and the acquisition of the mechanical behavior of actual joints. Furthermore, based on the test results and the theoretical analysis, a mechanical model of segment joints has been proposed, which consists of all important influencing factors, including the elastic-plastic behavior of concrete, the pre-tightening force of the bolts and the deformations of all joint components, i.e., concrete blocks, bolts and cast iron panels. Finally, the proposed mechanical model of segment joints has been verified by the aforementioned full-scale joint tests.

• Journal of the Korean Society of Clothing and Textiles
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• v.23 no.1
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• pp.66-77
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• 1999

For this study It was measured the seam puckering based on the mechanical properties of tencel under the proper condition of needlework and machine sewing analyzed the mechanical properties which re influenced to the seam puckering and estimated the seam puckering based on the mechanical properties. The results of this study are as follows : There are three types of the seam puckering for each step which is caused by repeated washing and press. Concerning the seam puckering with the number of washing the more the number of washing is increased the less the seam puckering is decreased. Concerning the mecanical properties of the sample with the seam puckering. The seam puckering is related to LT positively B, 2HB, T, W negatively Among the mechanical properties LT, B, 2HB, T, W are most influenced to the seam puckering. Judging from the result of estimating seam puckering based on mechanical properties the estimate-formula is satisfied in this study.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.5
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• pp.1183-1192
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• 1993

Since the punching time is strongly related to the productivity in sheet metal stamping, there have been a lot of efforts to obtain the optimal tool path. However, most of the conventional efforts have the basic limitations to provide the global optimal solution because of the inherent difficulties of the NP hard combinatorial optimization problem. The existing methods search the optimal tool path with limiting tool changes to the minimal number, which proves not to be a global optimal solution. In this work, the turret rotation time is also considered in addition to the bed translation time of the NCT machine, and the total punching time is minimized by the simulated annealing algorithm. Some manufacturing constraints in punching sequences such as punching priority constraint and punching accuracy constraint are incorporated automatically in optimization, while several user-interactions to edit the final tool path are usually required in commercial systems.

• Structural Engineering and Mechanics
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• v.84 no.1
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• pp.101-111
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• 2022

It is widely known that axially loaded fiber-reinforced polymer (FRP) confined concrete presents significant and enhanced mechanical properties with reference to the unconfined concrete. Therefore, to predict the mechanical behavior of FRP-confined concrete two quantities-peak strength and ultimate strain are required. Despite the significant advances, the determination of the ultimate strain of FRP-confined concrete is one of the most challenging problems to be resolved. This is often attributed to our persistence in desiring the conventional methods as the sole technique to examine this phenomenon and the complex nature of the ultimate strain of FRP-confined concrete. To bridge the research gap, this study adopted two machine learning (ML) techniques-artificial neural network (ANN) and Gaussian process regression (GPR)-to analyze observations obtained from 627 datasets of FRP-confined concrete circular and non-circular sections under axial loading test. Besides, the techniques are also used to predict the ultimate strain of FRP-confined concrete. Seven parameters namely width/diameter of the specimens, corner radius ratio, the strength of concrete, FRP elastic modulus, FRP thickness, FRP tensile rupture strain, and the axial strain of unconfined concrete-are the input parameters used to predict the ultimate strain of FRP-confined concrete. The results of the current study highlight the merit of using AI techniques in structural engineering applications given their extraordinary ability to comprehend multidimensional phenomena of FRP-confined concrete structures with ease, low computational cost, and high performance over the existing empirical models.

• Smart Structures and Systems
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• v.26 no.1
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• pp.35-47
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• 2020

This paper proposes the use of transmissibility functions combined with a machine learning algorithm, Artificial Neural Networks (ANNs), to assess damage in a truss bridge. A new approach method, which makes use of the input parameters calculated from the transmissibility function, is proposed. The network not only can predict the existence of damage, but also can classify the damage types and identity the location of the damage. Sensors are installed in the truss joints in order to measure the bridge vibration responses under train and ambient excitations. A finite element (FE) model is constructed for the bridge and updated using FE software and experimental data. Both single damage and multiple damage cases are simulated in the bridge model with different scenarios. In each scenario, the vibration responses at the considered nodes are recorded and then used to calculate the transmissibility functions. The transmissibility damage indicators are calculated and stored as ANNs inputs. The outputs of the ANNs are the damage type, location and severity. Two machine learning algorithms are used; one for classifying the type and location of damage, whereas the other for finding the severity of damage. The measurements of the Nam O bridge, a truss railway bridge in Vietnam, is used to illustrate the method. The proposed method not only can distinguish the damage type, but also it can accurately identify damage level.

• Steel and Composite Structures
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• v.25 no.6
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• pp.663-670
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• 2017

The problem of layup optimization of the composite laminates involves a very complex multidimensional solution space which is usually non-exhaustively explored using different heuristic computational methods such as genetic algorithms (GA). To ensure the convergence to the global optimum of the applied heuristic during the optimization process it is necessary to evaluate a lot of layup configurations. As a consequence the analysis of an individual layup configuration should be fast enough to maintain the convergence time range to an acceptable level. On the other hand the mechanical behavior analysis of composite laminates for any geometry and boundary condition is very convoluted and is performed by computational expensive numerical tools such as finite element analysis (FEA). In this respect some studies propose very fast FEA models used in layup optimization. However, the lower bound of the execution time of FEA models is determined by the global linear system solving which in some complex applications can be unacceptable. Moreover, in some situation it may be highly preferred to decrease the optimization time with the cost of a small reduction in the analysis accuracy. In this paper we explore some machine learning techniques in order to estimate the failure of a layup configuration. The estimated response can be qualitative (the configuration fails or not) or quantitative (the value of the failure factor). The procedure consists of generating a population of random observations (configurations) spread across solution space and evaluating using a FEA model. The machine learning method is then trained using this population and the trained model is then used to estimate failure in the optimization process. The results obtained are very promising as illustrated with an example where the misclassification rate of the qualitative response is smaller than 2%.

• Geomechanics and Engineering
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• v.34 no.6
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• pp.697-726
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• 2023

Brittleness as an important property of rock plays a crucial role both in the failure process of intact rock and rock mass response to excavation in engineering geological and geotechnical projects. Generally, rock brittleness indices are calculated from the mechanical properties of rocks such as uniaxial compressive strength, tensile strength and modulus of elasticity. These properties are generally determined from complicated, expensive and time-consuming tests in laboratory. For this reason, in the present research, an attempt has been made to predict the rock brittleness indices from simple, inexpensive, and quick laboratory test results namely dry unit weight, porosity, slake-durability index, P-wave velocity, Schmidt rebound hardness, and point load strength index using multiple linear regression, exponential regression, support vector machine (SVM) with various kernels, generating fuzzy inference system, and regression tree ensemble (RTE) with boosting framework. So, this could be considered as an innovation for the present research. For this purpose, the number of 39 rock samples including five igneous, twenty-six sedimentary, and eight metamorphic were collected from different regions of Iran. Mineralogical, physical and mechanical properties as well as five well known rock brittleness indices (i.e., B₁, B₂, B₃, B₄, and B₅) were measured for the selected rock samples before application of the above-mentioned machine learning techniques. The performance of the developed models was evaluated based on several statistical metrics such as mean square error, relative absolute error, root relative absolute error, determination coefficients, variance account for, mean absolute percentage error and standard deviation of the error. The comparison of the obtained results revealed that among the studied methods, SVM is the most suitable one for predicting B₁, B₂ and B₅, while RTE predicts B₃ and B₄ better than other methods.

• Journal of the Korean Society of Clothing and Textiles
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• v.21 no.6
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• pp.1010-1020
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• 1997

For this study, we did needlework of the sample considering practical aspects of production and consumption of silk, high-quality material for women's clothes, and then analyzed the state of the seam puckering after press and dry cleaning, estimated the Seam Puckering based on the mechanical properties of silk related to machine sewing, and examined the effects which Mechanical Properties have on Seam Puckering closely. Through this, we reach the following conclusion. 1. There are three types of seam puckering for each stage, which are caused bathe smoothness of the surface by press, and the difference between the shrinkage rates of fabric and sewing thread by Dry Cleaning. 2. In analyzing seam puckering classified by each step, seam puckering after sewing the fabric is related to WT negatively, while to RT and W positively. Seam puckering after sewing and pressing the fabric is related to WT, RC, MMD negatively and seam puckering after sewing, pressing anddry cleaning the fabric isrelated to WT negatively, too. 3. Concerning the mechanical properties of the sample with a little seam puckering, WT, LC, WC, RC, MMD, SMD is relatively large while RT, B, 2HB is small. 4. Judging from the result of estimating seam puckering based on mechanical properties, the estimate-formula is satisfied in this study.

• Advances in materials Research
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• v.5 no.2
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• pp.121-130
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• 2016

In an attempt to reach a balance of performances in homo-polypropylene based system, the effects of single and hybrid reinforcements inclusions comprising calcium carbonate nanoparticles (2, 4 and 6 phc) and glass fibers (10 wt.%) on the mechanical and thermal properties were investigated. Different samples were prepared by employing twin-screw extruder and injection molding machine. In morphological studies, the uniform distribution of glass fibers in PP matrix, relative adhesion between glass fibers and polymer, and existence of nanoparticles in polymer matrix were observed. $PP/CaCO_3$ (6 phc) as compared to pure PP and PP/GF had superior tensile and flexural strengths, impact resistance and deformation temperature under load (DTUL). $PP/GF/CaCO_3$ (6 phc) composite displayed comparable tensile and flexural strengths and impact resistance to neat PP, while its tensile and flexural moduli and deformation temperature under load (DTUL) were 436%, 99% and $26^{\circ}C$greater respectively. The maximum impact resistance was observed in $PP/CaCO_3$(6 phc). The highest DTUL was perceived in PP hybrid nanocomposite containing 10 wt.% glass fiber and 4 phc $CaCO_3$ nanoparticle.

• Wind and Structures
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• v.29 no.6
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• pp.431-440
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• 2019

The aim of this study was to explore the influence of structure coupling effect on structural damping of blade based on the blade vibration characteristic. For this purpose, the scaled blade model of NREL 5 MW offshore wind turbine was processed and employed in the wind tunnel test to validate the reliability of theoretical and numerical models. The attenuation curves of maximum displacement and the varying curves of equivalent damping coefficient of the blade under the rated condition were respectively compared and analyzed by constructing single blade model and whole machine model. The attenuation law of blade dynamic response was obtained and the structure coupling effect was proved to exert a significant influence on the equivalent damping coefficient. The results indicate that the attenuation trend of the maximum displacement response curve of the single blade varies more obviously with the increase of elastic modulus as compared to that under the structure coupling effect. In contrast to the single blade model, the varying curve of equivalent damping coefficient with the period is relatively steep for the whole machine model. The findings are of great significance to guide the structure design and material selection for wind turbine blades.