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.

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.

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.

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.

An experimental and numerical investigation on fatigue of composite and metal aircraft structures

Siddharth Pitta,Jose I. Rojas-Mendez,Francesc Rour,Daniel Crespo,Magd Abdel Wahab 국제구조공학회 2022 Steel and Composite Structures, An International J Vol.43 No.1

The static strength and fatigue crack resistance of the aircraft skin structures depend on the materials used and joint type. Most of the commercial aircraft’s skin panel structures are made from aluminium alloy and carbon fibre reinforced epoxy. In this study, the fatigue resistance of four joint configurations (metal/metal, metal/composite, composite/composite and composite/metal) with riveted, adhesive bonded, and hybrid joining techniques are investigated with experiments and finite element analysis. The fatigue tests were tension-tension because of the typical nature of the loads on aircraft skin panels susceptible of experimenting fatigue. Experiment results suggest that the fatigue life of hybrid joints is superior to adhesive bonded joints, and these in turn much better than conventional riveted joints. Thanks to the fact that, for hybrid joints, the adhesive bond provides better load distribution and ensures load-carrying capacity in the event of premature adhesive failure while rivets induce compressive residual stresses in the joint. Results from FE tool ABAQUS analysis for adhesive bonded and hybrid joints agrees with the experiments. From the analysis, the energy release rate for adhesive bonded joints is higher than that of hybrid joints in both opening (mode I) and shear direction (mode II). Most joints show higher energy release rate in mode II. This indicates that the joints experience fatigue crack in the shear direction, which is responsible for crack opening

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.

Finite element model updating of a cable-stayed bridge using metaheuristic algorithms combined with Morris method for sensitivity analysis

Long V. Ho,Samir Khatir,Guido D. Roeck,Thanh Bui-Tien,Magd Abdel Wahab 국제구조공학회 2020 Smart Structures and Systems, An International Jou Vol.26 No.4

Although model updating has been widely applied using a specific optimization algorithm with a single objective function using frequencies, mode shapes or frequency response functions, there are few studies that investigate hybrid optimization algorithms for real structures. Many of them did not take into account the sensitivity of the updating parameters to the model outputs. Therefore, in this paper, optimization algorithms and sensitivity analysis are applied for model updating of a real cable-stayed bridge, i.e., the Kien bridge in Vietnam, based on experimental data. First, a global sensitivity analysis using Morris method is employed to find out the most sensitive parameters among twenty surveyed parameters based on the outputs of a Finite Element (FE) model. Then, an objective function related to the differences between frequencies, and mode shapes by means of MAC, COMAC and eCOMAC indices, is introduced. Three metaheuristic algorithms, namely Gravitational Search Algorithm (GSA), Particle Swarm Optimization algorithm (PSO) and hybrid PSOGSA algorithm, are applied to minimize the difference between simulation and experimental results. A laboratory pipe and Kien bridge are used to validate the proposed approach. Efficiency and reliability of the proposed algorithms are investigated by comparing their convergence rate, computational time, errors in frequencies and mode shapes with experimental data. From the results, PSO and PSOGSA show good performance and are suitable for complex and time-consuming analysis such as model updating of a real cable-stayed bridge. Meanwhile, GSA shows a slow convergence for the same number of population and iterations as PSO and PSOGSA.

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.

Numerical modelling of bottom-hole rock in underbalanced drilling using thermo-poroelastoplasticity model

Weiji Liu,Yun-Lai Zhou,Xiaohua Zhu,Xiannan Meng,Mei Liu,Magd Abdel Wahab 국제구조공학회 2019 Structural Engineering and Mechanics, An Int'l Jou Vol.69 No.5

Stress analysis of bottom-hole rock has to be considered with much care to further understand rock fragmentation mechanism and high penetration rate. This original study establishes a fully coupled simulation model and explores the effects of overburden pressure, horizontal in-situ stresses, drilling mud pressure, pore pressure and temperature on the stress distribution in bottom-hole rock. The research finds that in air drilling, as the well depth increases, the more easily the bottom-hole rock is to be broken. Moreover, the mud pressure has a great effect on the bottom-hole rock. The bigger the mud pressure is, the more difficult to break the bottom-hole rock is. Furthermore, the maximum principal stress of the bottom-hole increases as the mud pressure, well depth and temperature difference increase. The bottom-hole rock can be divided into three main regions according to the stress state, namely a) three directions tensile area, b) two directions compression areas and c) three directions compression area, which are classified as a) easy, b) normal and c) hard, respectively, for the corresponding fragmentation degree of difficulty. The main contribution of this paper is that it presents for the first time a thorough study of the effect of related factors, including stress distribution and temperature, on the bottom-hole rock fracture rather than the well wall, using a thermo-poroelastoplasticity model.

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.