Assessment of structural damage using operational time responses

The problem of vibration induced structural faults has been a real one in engineering over the years. If left unchecked it has led to the unexpected failures of so many structures. Needless to say, this has caused both economic and human life losses. Therefore for over forty years, structural damage identification has been one of the important research areas for engineers. There has been a thrust to develop global structural damage identification techniques to complement and/or supplement the long-practised local experimental techniques. In that respect, studies have shown that vibration-based techniques prove to be more potent. Most of the existing vibration-based techniques monitor changes in modal properties like natural frequencies, damping factors and mode shapes of the structural system to infer the presence of structural damage. Literature also reports other techniques which monitor changes in other vibration quantities like the frequency response functions, transmissibility functions and time-domain responses. However, none of these techniques provide a complete identification of structural damage. This study presents a damage detection technique based on operational response monitoring, which can identify all the four levels of structural damage and be implemented as a continuous structural health monitoring technique. The technique is based on monitoring changes in internal data variability measured by a test statistic c2Ovalue. Structural normality is assumed when the c2Om value calculated from a fresh set of measured data is within the limits prescribed by a threshold c2OTH value . On the other hand, abnormality is assumed when this threshold value has been exceeded. The quantity of damage is determined by matching the c2Om value with the c2Op values predicted using a benchmark finite element model. The use of c2O values is noted to provide better sensitivity to structural damage than the natural frequency shift technique. The analysis carried out on a numerical study showed that the sensitivity of the proposed technique ranged from three to thousand times as much as the sensitivity of the natural frequencies. The results from a laboratory structure showed that accurate estimates of damage quantity and remaining service life could be achieved for crack lengths of less than 0.55 the structural thickness. This was due to the fact that linear elastic fracture mechanics theory was applicable up to this value. Therefore, the study achieved its main objective of identifying all four levels of structural damage using operational response changes.Dissertation (MSc (Mechanics))--University of Pretoria, 2007.Mechanical and Aeronautical Engineeringunrestricte

Application of an ANN-based methodology for road surface condition identification on mining vehicles and roads

An artificial neural networks-based methodology for the identification of road surface condition was applied to two different vehicles in their normal operating environments at two mining sites. An ultra-heavy haul truck used for hauling operations in surface mining and a small utility underground mine vehicle were utilised in the current investigation. Unlike previous studies where numerical models were available and road surfaces were accurately profiled with profilometers, in this study, that was not the case in order to replicate the real mine road management situation. The results show that the methodology performed very well in reconstructing discrete faults such as bumps, depressions or potholes but, owing to the inevitable randomness of the testing conditions, these conditions could not fit the fine undulations present on the arbitrary random rough surface. These are better represented by the spectral displacement densities of the road surfaces. Accordingly, the proposed methodology can be applied to road condition identification in two ways: firstly, by detecting, locating and quantifying any existing discrete road faults/features, and secondly, by identifying the general level of the road’s surface roughness.http://www.elsevier.com/locate/jterrahb201

Stress validation of finite element model of a small-scale wind turbine blade

Wind turbine blades are the first mechanical part of a wind turbine that interacts with the wind and hence play a key role in wind power generation. It is important that the wind turbine blade is tested for structural integrity in accordance to design code IEC 61400-23 such as strain limits, fatigue life, blade tip clearance limit, and surface stress. This paper aims to focus on the calculation and validation of static bending stresses in the blade; it presents the experimental and simulated stress analysis of a small-scale wind turbine blade. The simulation and 3D design software ANSYS, version 19.0 is used in the finite element analysis (FEA). By using FEA, we aim to capture the stress generated on the blade geometry under static loading and unloading conditions. As a first step towards this, the finite element results were validated against experimental results on a kestrel E230i turbine blade. The wind turbine blade was fixed at one end, loaded, and unloaded statically at three selected points. The finite element results are calculated within a 25% error margin of the experimental results. A reverse engineering procedure was used to determine the appropriate ANSYS model blade properties that were used as the exact material properties were not available from the manufacturer

Reconstruction of road defects and road roughness classification using vehicle responses with artificial neural networks simulation

The road damage assessment methodology in this paper utilizes an artificial neural network that reconstructs road surface profiles from measured vehicle accelerations. The paper numerically demonstrates the capabilities of such a methodology in the presence of noise, changing vehicle mass, changing vehicle speeds and road defects. In order to avoid crowding out understanding of the methodology, a simple linear pitch-plane model is employed. Initially, road profiles from known roughness classes were applied to a physical model to calculate vehicle responses. The calculated responses and road profiles were used to train an artificial neural network. In this way, the network renders corresponding road profiles on the availability of fresh data on model responses. The results show that the road profiles and associated defects can be reconstructed to within a 20% error at a minimum correlation value of 94%.The Council for Scientific and Industrial Research (CSIR) and the National Research Foundation under the South African Co-operation Fund for Scientific Research and Technological Developments.http://www.elsevier.com/locate/jterraai201

Determination of Cross-Directional and Cross-Wall Variations of Passive Biaxial Mechanical Properties of Rat Myocardia

Heart myocardia are critical to the facilitation of heart pumping and blood circulating around the body. The biaxial mechanical testing of the left ventricle (LV) has been extensively utilised to build the computational model of the whole heart with little importance given to the unique mechanical properties of the right ventricle (RV) and cardiac septum (SPW). Most of those studies focussed on the LV of the heart and then applied the obtained characteristics with a few modifications to the right side of the heart. However, the assumption that the LV characteristics applies to the RV has been contested over time with the realisation that the right side of the heart possesses its own unique mechanical properties that are widely distinct from that of the left side of the heart. This paper evaluates the passive mechanical property differences in the three main walls of the rat heart based on biaxial tensile test data. Fifteen mature Wistar rats weighing 225 ± 25 g were euthanised by inhalation of 5% halothane. The hearts were excised after which all the top chambers comprising the two atria, pulmonary and vena cava trunks, aorta, and valves were all dissected out. Then, 5 × 5 mm sections from the middle of each wall were carefully dissected with a surgical knife to avoid overly pre-straining the specimens. The specimens were subjected to tensile testing. The elastic moduli, peak stresses in the toe region and stresses at 40% strain, anisotropy indices, as well as the stored strain energy in the toe and linear region of up to 40% strain were used for statistical significance tests. The main findings of this study are: (1) LV and SPW tissues have relatively shorter toe regions of 10–15% strain as compared to RV tissue, whose toe region extends up to twice as much as that; (2) LV tissues have a higher strain energy storage in the linear region despite being lower in stiffness than the RV; and (3) the SPW has the highest strain energy storage along both directions, which might be directly related to its high level of anisotropy. These findings, though for a specific animal species at similar age and around the same body mass, emphasise the importance of the application of wall-specific material parameters to obtain accurate ventricular hyperelastic models. The findings further enhance our understanding of the desired mechanical behaviour of the different ventricle walls

Reconstruction of road defects and road roughness classification using Artificial Neural Networks simulation and vehicle dynamic responses : application to experimental data

This paper reports the performance of an Artificial Neural Network based road condition monitoring methodology on measured data obtained from a Land Rover Defender 110 which was driven over discrete obstacles and Belgian paving. In a previous study it was demonstrated, using data calculated from a numerical model, that the neural network was able to reconstruct road profiles and their associated defects within good levels of fitting accuracy and correlation. A nonlinear autoregressive network with exogenous inputs was trained in a series–parallel framework. When compared to the parallel framework, the series–parallel framework offered the advantage of fast training but had a shortcoming in that it required feed-forward of true road profiles. In this study, the true profiles are not available and the test data are obtained from field measurements. Training data are numerically generated by making minor adjustments to the real measured profiles and applying them to a full vehicle model of the Land Rover. This is done to avoid using the same road profile and acceleration data for training and testing or validating the neural network. A static feed-forward neural network is trained and consequently tested on the real measured data. The results show very good correlations over both the discrete obstacles and the Belgian paving. The random nature of the Belgian paving necessitated correlations to be made using their displacement spectral densities as well as evaluations of RMS error percent values of the raw road profiles. The use of displacement spectral densities is considered to be of much more practical value than the road profiles since they can easily be interpreted into road roughness measures by plotting them over an internationally recognized standard roughness scale.http://www.elsevier.com/locate/jterrahb201

Road surface profile monitoring based on vehicle response and artificial neural network simulation

Road damage identification is still largely based on visual inspection methods and profilometer data. Visual inspection methods heavily rely on expert knowledge which is often very subjective. They also result in traffic flow interference due to the need for redirection of traffic to alternative routes during inspection. In addition to this, accurate high-speed profilometers, such as scanning vehicles, are extremely expensive often requiring strong economic justifications for their acquisition. The low-cost profilometers are very slow, typically operating at or less than walking speeds, causing their use to be labour-intensive if applied to large networks.This study aims at developing a road damage identification methodology for both paved and unpaved roads based on modelling the road-vehicle interaction system with an artificial neural network. The artificial neural network is created and trained with vehicle acceleration data as inputs and road profiles as targets. Then the trained neural network is consequently used for reconstruction of road profiles upon simulating it with vertical vehicle accelerations. The simulation process is very fast and can often be completed in a very short time thus making it possible to implement the methodology in real-time. Three case studies were used to demonstrate the feasibility of the methodology and the results on field tests carried out on mine vehicles with crudely measured road profiles showed a majority of the tested roads were reconstructed to within a fitting accuracy of less than 40% at a correlation level of greater than 55% which in this study was found to be practically acceptable considering the limitations imposed by the sizes of the haul trucks and their tyres as well as the quality of the road profiles and lack of control in the vehicle operation.Thesis (PhD)--University of Pretoria, 2015.Mechanical and Aeronautical EngineeringUnrestricte

Evaluation of 3D Printing Orientation on Volume Parameters and Mechanical Properties of As-Build TI64ELI

The discovery of the utility of various titanium alloys as implant biomaterials has resulted in these materials becoming far more popular than other metals in the medical world. However, the production of these materials using additive manufacturing has its own challenges some of those being the surface finish that can be used as an implantology material. As such, the purpose of this study is to evaluate the influence of 3D-printed Ti64ELI on the as-built samples printed at 60°, 90°, and 180° orientations. Such studies are very limited, specifically in the development of the laser shock peening surface modification of dental implants. The study showed that each mechanical test that was performed contributes differently to the printing orientation, e.g., some tests yielded better properties when 180° printing orientation was used, and others had poorer properties when a 180° printing orientation was used. It was observed that 60° testing yielded a micro-hardness value of 349.6, and this value was increased by 0.37% when 90° orientation was measured. The lowest HV value was observed under a 180° orientation with 342.2 HV. The core material volume (Vmc) was 0.05266 mm3/mm2 at a 60° orientation, which increased by 11.48% for the 90° orientation. Furthermore, it was observed that the surface roughness (Sa) at 60° orientation was 43.68 μm. This was further increased by 6% when using the 90° orientation

Dataset from the uniaxial tensile testing of human curly hair fibers under different conditions

Individual human hair fibers exhibiting a curly morphology were procured from a female donor within her early thirties (30s). The selected hair fibers donor had refrained from undergoing any form of chemical treatment, including dyeing, relaxing, and bleaching, for a minimum period of six (6) months prior to specimen collection. The isolated single fibers were subjected to uniaxial tensile testing at various strain rates (100.s−1,10−2. s−1 10−3. & 10−4.s−1). Furthermore, the specimens underwent testing under dry conditions at a temperature of 25°C, as well as full immersion in a saline solution at both 25°C and 35°C. The ensuing mechanical attributes, encompassing engineering was analyzed following the tensile testing

An overview of the neural network based technique for monitoring of road condition via reconstructe4d road profiles

Paper presented at the 27th Annual Southern African Transport Conference 7 - 11 July 2008 "Partnership for research and progress in transportation", CSIR International Convention Centre, Pretoria, South Africa.This paper was transferred from the original CD ROM created for this conference. The material on the CD ROM was published using Adobe Acrobat technology. The original CD ROM was produced by Document Transformation Technologies Postal Address: PO Box 560 Irene 0062 South Africa. Tel.: +27 12 667 2074 Fax: +27 12 667 2766 E-mail: [email protected] URL: http://www.doctech.co.z