Free vibration and harmonic response of cracked frames using a single variable shear deformation theory

Baran Bozyigit,Yusuf Yesilce,Magd Abdel Wahab 국제구조공학회 2020 Structural Engineering and Mechanics, An Int'l Jou Vol.74 No.1

The aim of this study is to calculate natural frequencies and harmonic responses of cracked frames with general boundary conditions by using transfer matrix method (TMM). The TMM is a straightforward technique to obtain harmonic responses and natural frequencies of frame structures as the method is based on constructing a relationship between state vectors of two ends of structure by a chain multiplication procedure. A single variable shear deformation theory (SVSDT) is applied, as well as, Timoshenko beam theory (TBT) and Euler-Bernoulli beam theory (EBT) for comparison purposes. Firstly, free vibration analysis of intact and cracked frames are performed for different crack ratios using TMM. The crack is modelled by means of a linear rotational spring that divides frame members into segments. The results are verified by experimental data and finite element method (FEM) solutions. The harmonic response curves that represent resonant and anti-resonant frequencies directly are plotted for various crack lengths. It is seen that the TMM can be used effectively for harmonic response analysis of cracked frames as well as natural frequencies calculation. The results imply that the SVSDT is an efficient alternative for investigation of cracked frame vibrations especially with thick frame members. Moreover, EBT results can easily be obtained by ignoring shear deformation related terms from governing equation of motion of SVSDT.

Transfer matrix formulations and single variable shear deformation theory for crack detection in beam-like structures

Baran Bozyigit,Yusuf Yesilce,Magd Abdel Wahab 국제구조공학회 2020 Structural Engineering and Mechanics, An Int'l Jou Vol.73 No.2

This study aims to estimate crack location and crack length in damaged beam structures using transfer matrix formulations, which are based on analytical solutions of governing equations of motion. A single variable shear deformation theory (SVSDT) that considers parabolic shear stress distribution along beam cross-section is used, as well as, Timoshenko beam theory (TBT). The cracks are modelled using massless rotational springs that divide beams into segments. In the forward problem, natural frequencies of intact and cracked beam models are calculated for different crack length and location combinations. In the inverse approach, which is the main concern of this paper, the natural frequency values obtained from experimental studies, finite element simulations and analytical solutions are used for crack identification via plots of rotational spring flexibilities against crack location. The estimated crack length and crack location values are tabulated with actual data. Three different beam models that have free-free, fixed-free and simple-simple boundary conditions are considered in the numerical analyses.

Management of Tomato Root-knot Nematode Meloidogyne incognita by Plant Extracts and Essential Oils

Kamal A. M. Abo-Elyousr,Magd El-Morsi Awad,M. A. Abdel Gaid 한국식물병리학회 2009 Plant Pathology Journal Vol.25 No.2

The effect of plant extracts of eucalyptus (Eucalyptus chamadulonsis), garlic (Allium sativium), marigold (Tagetes erecta) and neem (Azadirachta indica) and essential oils were tested on the suppression of root-knot nematode Meloidogyne incognita under greenhouse and field conditions. In vitro study, all tested treatments had nematicidal effect on nematode juveniles after 24 and 48 hours from exposures. The highest percentage of nematode mortality was achieved by application of neem extract (65.4%), essential oils (64.4%) and marigold extract (60.5%), followed by garlic and eucalyptus extracts (38.7-39.5%). Under greenhouse and field conditions, neem extract and essential oils treatments were more effective in reducing population numbers of the M. incognita in soil and root gall index compared to other treatments. In field experiments, the maximum protection of tomato plant against root-knot nematode was obtained by application of neem and essential oil treatments, 44.2 and 32.6%, respectively.

Model Updating in Complex Bridge Structures using Kriging Model Ensemble with Genetic Algorithm

Shiqiang Qin,Yun-Lai Zhou,Hongyou Cao,Magd Abdel Wahab 대한토목학회 2018 KSCE Journal of Civil Engineering Vol.22 No.9

Computational cost reduction and the best solution seeking are frequently encountered during model updating for complex structures. In this study, a hybrid algorithm using kriging model and genetic algorithms (GAs) is proposed for updating the Finite Element (FE) model of complex bridge structures employing both static and dynamic experimental measurements. The kriging model is first established to approximate the implicit relationship between structural parameters and responses, serving as a surrogate model for complex FE model when deriving analytical responses. An objective function is later defined based on the residual between analytical response values and experimental measured ones. GAs are finally employed to find the best solution by searching on the whole design space of updating parameters selected based on a sensitivity analysis. To verify the proposed algorithm, Caiyuanba Yangtze River Bridge, a double decked of roadway and light railway bridge with a main span of 420 m is used. Both frequencies and displacements predicted by the updated model are more close to experimental measured ones. The results show that the kriging surrogate model has good accuracy in predicting response and can be used as a surrogate model to reduce computational cost, and GAs provide a higher chance to obtain global best solution.

Management of Tomato Root-knot Nematode Meloidogyne incognita by Plant Extracts and Essential Oils

Abo-Elyousr, Kamal A.M.,Awad, Magd El-Morsi,Gaid, M.A. Abdel The Korean Society of Plant Pathology 2009 Plant Pathology Journal Vol.25 No.2

The effect of plant extracts of eucalyptus (Eucalyptus chamadulonsis), garlic (Allium sativium), marigold (Tagetes erecta) and neem (Azadirachta indica) and essential oils were tested on the suppression of root-knot nematode Meloidogyne incognita under greenhouse and field conditions. In vitro study, all tested treatments had nematicidal effect on nematode juveniles after 24 and 48 hours from exposures. The highest percentage of nematode mortality was achieved by application of neem extract (65.4%), essential oils (64.4%) and marigold extract (60.5%), followed by garlic and eucalyptus extracts (38.7-39.5%). Under greenhouse and field conditions, neem extract and essential oils treatments were more effective in reducing population numbers of the M. incognita in soil and root gall index compared to other treatments. In field experiments, the maximum protection of tomato plant against root-knot nematode was obtained by application of neem and essential oil treatments, 44.2 and 32.6%, respectively.

Form- finding analysis of suspension bridges using an explicit Iterative approach

Hongyou Cao,Yun-Lai Zhou,Zhijun Chen,Magd Abdel Wahab 국제구조공학회 2017 Structural Engineering and Mechanics, An Int'l Jou Vol.62 No.1

This paper presents an explicit analytical iteration method for form-finding analysis of suspension bridges. By extending the conventional analytical form-finding method predicated on the elastic catenary theory, two nonlinear governing equations are derived for calculating the accurate unstrained lengths of the entire cable systems both the main cable and the hangers. And for the gradient-based iteration method, the derivation of explicit calculation for the Jacobian matrix while solving the nonlinear governing equation enhances the computational efficiency. The results from sensitivity analysis show well performance of the explicit Jacobian matrix compared with the traditional finite difference method. According to two numerical examples of long span suspension bridges studied, the proposed method is also compared with those reported approaches or the fundamental criterions in suspension bridge structural analysis, which eventually confirms the accuracy and efficiency of the proposed approach.

Green tea ameliorates the side effects of the silver nanoparticles treatment of Ehrlich ascites tumor in mice

Ahmed Magdy,Emad Sadaka,Nemany Hanafy,Mohammed A. El‑Magd,Nasr Allahloubi,Maged El Kemary 대한독성 유전단백체 학회 2020 Molecular & cellular toxicology Vol.16 No.3

Background Silver nanoparticles (AgNPs) have anti-cancer effects with fewer side effects than standard chemotherapeutic agents, however, they exert oxidative stress-based adverse effects on normal cells and so their applications have raised concern about possible health and environmental risks. Objective We evaluated whether green tea extract (GTE), which contains potent antioxidants, could ameliorate AgNPs geno-, cyto-, and histotoxicities without decreasing their therapeutic potential against Ehrlich ascetic carcinoma (EAC). Results GTE enhanced the anti-cancer effect of AgNPs against EAC cells and ameliorated the genotoxic effect of AgNPs as indicated by lowering chromosomal aberrations and micronucleus frequencies. Additionally, GTE relieved most of degenerative histological changes induced by AgNPs. GTE restored the increased MDA and the decreased SOD, GPx and CAT serum levels induced by AgNPs to levels comparable to normal. The pre-treatment with GTE and AgNPs showed better improvement than the post-treatment strategy. Conclusions GTE can not only ameliorate AgNPs-induced adverse effects but also improve their anti-cancer effect against EAC. So, it could be useful in the treatment of liver dysfunction associated with AgNPs.

Damage detection in structures using modal curvatures gapped smoothing method and deep learning

Duong Huong Nguyen,T. Bui-Tien,Guido De Roeck,Magd Abdel Wahab 국제구조공학회 2021 Structural Engineering and Mechanics, An Int'l Jou Vol.77 No.1

This paper deals with damage detection using a Gapped Smoothing Method (GSM) combined with deep learning. Convolutional Neural Network (CNN) is a model of deep learning. CNN has an input layer, an output layer, and a number of hidden layers that consist of convolutional layers. The input layer is a tensor with shape (number of images) × (image width) × (image height) × (image depth). An activation function is applied each time to this tensor passing through a hidden layer and the last layer is the fully connected layer. After the fully connected layer, the output layer, which is the final layer, is predicted by CNN. In this paper, a complete machine learning system is introduced. The training data was taken from a Finite Element (FE) model. The input images are the contour plots of curvature gapped smooth damage index. A free-free beam is used as a case study. In the first step, the FE model of the beam was used to generate data. The collected data were then divided into two parts, i.e. 70% for training and 30% for validation. In the second step, the proposed CNN was trained using training data and then validated using available data. Furthermore, a vibration experiment on steel damaged beam in free-free support condition was carried out in the laboratory to test the method. A total number of 15 accelerometers were set up to measure the mode shapes and calculate the curvature gapped smooth of the damaged beam. Two scenarios were introduced with different severities of the damage. The results showed that the trained CNN was successful in detecting the location as well as the severity of the damage in the experimental damaged beam.

Crack prediction in pipeline using ANN-PSO based on numerical and experimental modal analysis

Meriem Seguini,Samir Khatir,Djilali Boutchicha,Djamel Nedjar,Magd Abdel Wahab 국제구조공학회 2021 Smart Structures and Systems, An International Jou Vol.27 No.3

In this paper, a crack identification using Artificial Neural Network (ANN) is investigated to predict the crack depth in pipeline structure based on modal analysis technique using Finite Element Method (FEM). In various fields, ANN has become one of the most effective instruments using computational intelligence techniques to solve complex problems. This paper uses Particle Swarm Optimization (PSO) to enhance ANN training parameters (bias and weight) by minimizing the difference between actual and desired outputs and then using these parameters to generate the network. The convergence study during the process proves the advantage of using PSO based on two selected parameters. The data are collected from FEM based on different crack depths and locations. The provided technique is validated after collecting the data from experimental modal analysis. To study the effectiveness of ANN-PSO, different hidden layers values are considered to study the sensitivity of the predicted crack depth. The results demonstrate that ANN combined with PSO (ANN-PSO) is accurate and requires a lower computational time in terms of crack identification based on inverse problem.

Damage detection in Ca-Non Bridge using transmissibility and artificial neural networks

Duong H. Nguyen,Thanh T. Bui,Guido De Roeck,Magd Abdel Wahab 국제구조공학회 2019 Structural Engineering and Mechanics, An Int'l Jou Vol.71 No.2

This paper deals with damage detection in a girder bridge using transmissibility functions as input data to ArtificialNeural Networks (ANNs). The original contribution in this work is that these two novel methods are combined to detect damage ina bridge. The damage was simulated in a real bridge in Vietnam, i.e. Ca-Non Bridge. Finite Element Method (FEM) of this bridgewas used to show the reliability of the proposed technique. The vibration responses at some points of the bridge under a movingtruck are simulated and used to calculate the transmissibility functions. These functions are then used as input data to train theANNs, in which the target is the location and the severity of the damage in the bridge. After training successfully, the network canbe used to assess the damage. Although simulated responses data are used in this paper, the practical application of the technique toreal bridge data is potentially high.