• Wind and Structures
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• v.32 no.5
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• pp.501-508
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• 2021

The turbine industry demands a reliable design with affordable cost. As technological advances begin to support turbines of huge sizes, and the increasing importance of wind turbines from day to day make design safety conditions more important. Wind turbines are exposed to environmental conditions that can affect their installation, durability, and operation. International Electrotechnical Commission (IEC) 61400-1 design load cases consist of analyses involving wind turbine operating conditions. This design load cases (DLC) is important for determining fatigue loads (i.e., forces and moments) that occur as a result of expected conditions throughout the life of the machine. With the help of FAST (Fatigue, Aerodynamics, Structures, and Turbulence), an open source software, the NREL 5MW land base wind turbine model was used. IEC 61400-1 wind turbine design standard procedures assessed turbine behavior and fatigue damage to the tower base of dynamic loads in different design conditions. Real characteristic wind speed distribution and multi-directional effect specific to the site were taken into consideration. The effect of these conditions on the economic service life of the turbine has been studied.

• Advances in Computational Design
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• v.6 no.1
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• pp.43-53
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• 2021

Multi-stage pumps are the most popular pumps among various kinds of centrifugal pumps. Athorough review of different kinds of literature has led to the conclusion that there is a desperate need to increase the performance of the multi-stage centrifugal pump. Many investigators have put their efforts to increase the pump performance and also the work is being projected on various aspects of pump performance variables. To improve the multistage centrifugal pump performance by investigation, modification, and analysis many works of literature are available. For analysis, many researchers used the Navier-Stokes solver to create the three-dimensional unsteady turbulent flow numerical model with the standard k-ε turbulent equation. This paper mainly focuses on research related to the multi-stage centrifugal pump.

• Wind and Structures
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• v.32 no.3
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• pp.249-265
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• 2021

As high-rise buildings become more and more slender and flexible, the wind effect has become a major concern to modern buildings. At present, wind engineering for high-rise buildings mainly focuses on the following four issues: wind excitation and response, aerodynamic damping, aerodynamic modifications and proximity effect. Taking these four issues of concern in high-rise buildings as the mainline, this paper summarizes the development history and current research progress of wind engineering for high-rise buildings. Some critical previous work and remarks are listed at the end of each chapter. From the future perspective, the CFD is still the most promising technique for structural wind engineering. The wind load inversion and the introduction of machine learning are two research directions worth exploring.

• Computers and Concrete
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• v.29 no.6
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• pp.419-432
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• 2022

This article deals with the vibrational response of a nanobeam made of bi-directional FG materials which is modeled via nonlocal strain gradient theory along with HSDT. Also, the nanobeam is placed on a Winkler-Pasternak foundation and is under axial mechanical loading. By using the variational energy method, the formulation and end conditions are obtained. Then, DSC-IM, as the numerical solution procedure is employed to extract the results. The material properties of the nanobeam are FG which varies in two directions with in exponential manner. The results from DDN are verified by using other papers. Lastly, a thorough parametric investigation is presented to investigated the effect of different parameters.

• Smart Structures and Systems
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• v.28 no.6
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• pp.779-789
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• 2021

Cracks in railway sleeper are an inevitable condition and has a significant influence on the safety of railway system. Although the technology of railway sleeper condition monitoring using machine learning (ML) models has been widely applied, the crack recognition accuracy is still in need of improvement. In this paper, a two-stage method using edge detection and convolutional neural network (CNN) is proposed to reduce the burden of computing for detecting cracks in railway sleepers with high accuracy. In the first stage, the edge detection is carried out by using the 3×3 neighborhood range algorithm to find out the possible crack areas, and a series of mathematical morphology operations are further used to eliminate the influence of noise targets to the edge detection results. In the second stage, a CNN model is employed to classify the results of edge detection. Through the analysis of abundant images of sleepers with cracks, it is proved that the cracks detected by the neighborhood range algorithm are superior to those detected by Sobel and Canny algorithms, which can be classified by proposed CNN model with high accuracy.

• Smart Structures and Systems
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• v.30 no.6
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• pp.649-659
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• 2022

Structural Health Monitoring (SHM) researchers have identified Augmented Reality (AR) as a new technology that can assist inspections. Post-seismic structural inspections are conducted to evaluate the safety level of the damaged structures. Quantification of nearby structural changes over short-term and long-term periods can provide building inspectors with information to improve their safety. This paper proposes a Time Machine Measure (TMM) application based on an Augmented Reality (AR) Head-Mounted-Device (HMD) platform. The primary function of TMM is to restore the saved meshes of a past environment and overlay them onto the real environment so that inspectors can intuitively measure dynamic structural deformation and other environmental movements. The proposed TMM application was verified by demo experiments simulating a real inspection environment.

• Structural Engineering and Mechanics
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• v.81 no.5
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• pp.647-664
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• 2022

The powerful data mapping capability of computational deep learning methods has been recently explored in academic works to develop strategies for structural health monitoring through appropriate characterization of dynamic responses. In many cases, these studies concern laboratory prototypes and finite element models to validate the proposed methodologies. Therefore, the present work aims to investigate the capability of a deep learning algorithm called Sparse Autoencoder (SAE) specifically focused on detecting structural alterations in real-case studies. The idea is to characterize the dynamic responses via SAE models and, subsequently, to detect the onset of abnormal behavior through the Shewhart T control chart, calculated with SAE extracted features. The anomaly detection approach is exemplified using data from the Z24 bridge, a classical benchmark, and data from the continuous monitoring of the San Vittore bell-tower, Italy. In both cases, the influence of temperature is also evaluated. The proposed approach achieved good performance, detecting structural changes even under temperature variations.

• Advances in Energy Research
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• v.8 no.3
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• pp.175-184
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• 2022

The dual-fuel technology, which uses gaseous fuel as the main fuel and liquid as the pilot fuel, is an appealing technology for reducing the exhaust emissions. The current study proposes emission models based on ANFIS for a dual-fuel using producer gas (PG)-diesel engine. Emissions measurements were taken at different engine load levels and fuel injection timings. The proposed model predictions were examined using statistical methods. With R2 values in the range of 0.9903 to 0.9951, the established ANFIS model was found to be consistently robust in predicting emission characteristics. The mean absolute percentage deviate in range 1.9 to 4.6%, and mean squared error varies in range 0.0018 to 13.9%. The evaluation of the ANFIS model developed shows a reliable claim of intrinsic sensitivity, strength, and outstanding generalization. The presented meta-model can be used to simulate the engine's operation in order to create an efficient control tool.

• Geomechanics and Engineering
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• v.35 no.1
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• pp.67-80
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• 2023

Due to the nature of the conjunctive Cone Penetration Test(CPT), which does not verify the actual sample directly, geotechnical engineers commonly classify the underground geomaterials using CPT results with the classification diagrams proposed by various researchers. However, such classification diagrams may fail to reflect local geotechnical characteristics, potentially resulting in misclassification that does not align with the actual stratification in regions with strong local features. To address this, this paper presents an objective method for more accurate local CPT soil classification criteria, which utilizes C4.5 decision tree models trained with the CPT results from the clay-dominant southern coast of Korea and the sand-dominant region in South Carolina, USA. The results and analyses demonstrate that the C4.5 algorithm, in conjunction with oversampling, outlier removal, and pruning methods, can enhance and optimize the decision tree-based CPT soil classification model.

• Earthquakes and Structures
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• v.24 no.2
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• pp.111-126
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• 2023

Highly reliable and versatile methods artificial intelligence (AI) have found multiple application in the different fields of science, engineering and health care system. In the present study, we aim to utilize AI method to investigated vibrations in the human leg bone. In this regard, the bone geometry is simplified as a thick cylindrical shell structure. The deep neural network (DNN) is selected for prediction of natural frequency and critical buckling load of the bone cylindrical model. Training of the network is conducted with results of the numerical solution of the governing equations of the bone structure. A suitable optimization algorithm is selected for minimizing the loss function of the DNN. Generalized differential quadrature method (GDQM), and Hamilton's principle are used for solving and obtaining the governing equations of the system. As well as this, in the results section, with the aid of AI some predictions for improving the behaviors of the various sport systems will be given in detail.