Optimization of Historic Building Survey Technology under Artificial Intelligence Wireless Network Technology Environment

In order to optimize the technology of the building, the damage identification of the building structure is studied. Firstly, back propagation neural network (BPNN) and information fusion technology are used to build neural network models. Secondly, the established model is trained. Finally, the displacement mode, natural frequency, Modal Assurance Criterion (MAC), and three kinds of information fusion with only one characteristic information are used as input data to analyse the results of BPNN identification damage. The results show that when the natural frequency is used as the sensitive feature of damage, the accuracy is the highest. The difference between the network output value and the expected value is the smallest, the network output is the most stable, and the network recognition effect is the best. The network output of a mixture of two damage depths is compared with the output of a single damage depth. The data of the network training set composed of the feature data with damage depth of 20 mm and 5 mm has higher accuracy and more accurate damage recognition. This research provides a reference for the optimization of building survey technology and has certain practical value.

Research of Vibrations of an Armoured Personnel Carrier Hull with FE Implementation

Modern wheeled armoured vehicles can perform a variety of tasks, making the development of weapon systems that can be safely and effectively integrated with the vehicle structure an area of interest. Due to the cost of implementing new models, it is more economical to test potential configurations using numerical methods, such as the finite element method. The numerical model has been validated to confirm the reliability of the obtained results. Modal tests were also performed using four configurations to identify the frequency and mode shape of natural vibrations occurring within the support structure. In an experimental setting, hull vibrations were forced using the modal hammer testing method. The modal assurance criterion (MAC) and the authors’ procedure were used to confirm the experimental and numerical test results. Additional testing in the form of impact loads was carried out for turret-containing structures. Structural strain at indicated points and forces transmitted by brackets to the bottom of the hull were compared.

A novel identification technique of machine tool support stiffness under the variance of structural weight

The preload-dependent stiffness of machine tool support was investigated in this study. A novel identification method of support stiffness has been proposed through the experimental modal analysis and finite element method. The support stiffness was identified with different machine weight during the assembling process of a machining center. Specifically, the structure weight increase of a machine tool in the assembly process causes its center of gravity to shift. Accordingly, the variance of support reaction affects the support stiffness. To explore the variance of support stiffness, the researchers of this study collaborated with a machine tool manufacturer. Impact testing was performed on each assembly stage. Additionally, finite element analysis was used to establish equations between the reaction force versus stiffness of supports under the structural weight variance. The obtained equations were used to predict the natural frequency and vibration mode of structures in various assembly stages. The maximum error between the experimental and simulated natural frequencies was 7.1%, and the minimum modal assurance criterion was 0.77. Finally, a modal analysis model that updates support stiffness automatically, which could be adopted by machine builders to develop new machine tool, is proposed.

Modeling and analysis of a mistuned fan blisk

Turbomachinery has a vital role in the industrial engineering and the bladed disks such as; compressor, impeller pumps, turbine generator and jet engines are the critical components of turbomachinery. This work is focused on the “mistuning effect” of bladed disks of a turbine, which creates the lack of symmetry and ultimately damages the turbine blade. In order to completely understand the severity of the damage caused by the mistuning effect on the turbine disk, the study and analysis of the model parameters is very important. This work provides an insight to the various effects caused by the presence of crack and mistuning levels, in the mistuned turbine blisk, by using smeared material properties and modal assurance criterion (MAC) techniques. Moreover, a mistuned blisk model with four cracks (at various locations and different depth levels) has been developed and compared with the tuned blisk model, in order to determine the severity of damage occurred. The MAC results indicate that the severity of damage may vary depending on the location and depth of the crack and mistuning may alter the dynamic and vibrational characteristics of the structure.

Similitudes for vibration characteristic of composite laminated plates

Similitude laws can be used to extrapolate the vibration characteristic of a small, inexpensive, and easily tested model into structural behavior for the full-size prototype. In this article, a systematic similitude approach is proposed to predict the natural frequency, mode shape, and vibration response of composite laminated plates. The emphasis of this article is to predict the vibration characteristic of composite laminated plates in an effective and convenient way. Sensitivity analysis (SA) is introduced to improve the prediction accuracy of natural frequency. For distortion similarity, the prediction accuracy is improved close to 5%. Modal assurance criterion (MAC) measures the consistency of mode shapes of the full-size prototype and scaled models. The influence of stacking sequence on mode consistency is investigated. Similitude based on virtual mode and statistical energy (SVMSE) is proposed to extrapolate the transient response of the prototype to simulate the shock environment, such as satellite–rocket separation, etc. In conclusion, the prediction accuracy of natural frequency, mode consistency, and response coincidence are considered comprehensively to extrapolate the vibration characteristic of the full-size laminated plates.

Empowering Advanced Parametric Modes Clustering from Topological Data Analysis

Modal analysis is widely used for addressing NVH—Noise, Vibration, and Hardness—in automotive engineering. The so-called principal modes constitute an orthogonal basis, obtained from the eigenvectors related to the dynamical problem. When this basis is used for expressing the displacement field of a dynamical problem, the model equations become uncoupled. Moreover, a reduced basis can be defined according to the eigenvalues magnitude, leading to an uncoupled reduced model, especially appealing when solving large dynamical systems. However, engineering looks for optimal designs and therefore it focuses on parametric designs needing the efficient solution of parametric dynamical models. Solving parametrized eigenproblems remains a tricky issue, and, therefore, nonintrusive approaches are privileged. In that framework, a reduced basis consisting of the most significant eigenmodes is retained for each choice of the model parameters under consideration. Then, one is tempted to create a parametric reduced basis, by simply expressing the reduced basis parametrically by using an appropriate regression technique. However, an issue remains that limits the direct application of the just referred approach, the one related to the basis ordering. In order to order the modes before interpolating them, different techniques were proposed in the past, being the Modal Assurance Criterion—MAC—one of the most widely used. In the present paper, we proposed an alternative technique that, instead of operating at the eigenmodes level, classify the modes with respect to the deformed structure shapes that the eigenmodes induce, by invoking the so-called Topological Data Analysis—TDA—that ensures the invariance properties that topology ensure.

Experimental Validation of Finite Element Models for Reinforced Concrete Beams with Discontinuities That Form Dowel-Type Joints

Earthquakes have the highest rate of mortality among the natural disasters and regularly lead to collapsed structures with people trapped inside them. When a reinforced concrete building collapses due to an earthquake, many of the concrete elements (i.e., beams and columns) are damaged and there are large sections where the concrete is missing and the steel reinforcement is exposed (i.e., concrete discontinuities). The prediction of vibration transmission in collapsed and severely damaged reinforced-concrete buildings could help decisions when trying to detect trapped survivors; hence there is need for experimentally validated finite element models of damaged concrete elements. This paper investigates the dynamic behaviour of damaged reinforced concrete beams using Experimental Modal Analysis (EMA) and Finite Element Methods (FEM). FEM models are assessed using two beams with one or more concrete discontinuities that form dowel-type joints. These models used either beam or spring elements for the exposed steel bars and were experimentally validated against EMA in terms of eigenfrequencies and mode shapes. Improved agreement was achieved when using springs instead of beam elements in the FEM model. The comparison of mode shapes used the Partial Modal Vector Ratio (PMVR) as a supplement to the Modal Assurance Criterion (MAC) to confirm that spring elements provide a more accurate representation of the response on all concrete parts of the beams.

Gaussian Stochastic Process Modeling of Blend Repaired Airfoil Modal Response Using Reduced Basis Mode Shape Approach

A machine learning (ML) approach is developed to predict the effect of blend repairs on airfoil frequency, modal assurance criterion (MAC), and modal displacement vectors. The method is demonstrated on a transonic research rig compressor rotor airfoil. A parametric definition of blend geometry is developed and shown to be capable of encompassing a large range of blend geometry. This blend repair geometry is used to modify the airfoil surface definition and a mesh morphing process transforms the nominal finite element model (FEM) to the repaired configuration. A multi-level full factorial sampling of the blend repair design space provides training data to a Guassian stochastic process (GSP) regressor. The frequency and MAC results create a vector of training data for GSP calibration, but the airfoil mode shapes require further mathematical manipulation to avoid creating GSP models for each nodal displacement. This paper develops a method to significantly reduce blended airfoil mode shape emulation cost by transforming the mode shape training data into a reduced basis space using principal component analysis (PCA). The coefficients of this reduced basis are used to train a GSP that can then predict the values for new blended airfoils. The emulated coefficients are used with the reduced basis vectors in a reconstruction of blended airfoil mode shape. Validation data is computed at a full-factorial design that maximizes the distance from training points. It is found that large variations in modal properties from large blend repairs can be accurately emulated with a reasonable number of training points. The reduced basis approach of mode shape variation is shown to more accurately predict MAC variation when compared to direct MAC emulation. The added benefit of having the full modal displacement field also allows determination of other influences such as tip-timing limits and modal force values.