756 research outputs found

    Automated damage diagnosis of concrete jack arch beam using optimized deep stacked autoencoders and multi-sensor fusion

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    A novel hybrid framework of optimized deep learning models combined with multi-sensor fusion is developed for condition diagnosis of concrete arch beam. The vibration responses of structure are first processed by principal component analysis for dimensionality reduction and noise elimination. Then, the deep network based on stacked autoencoders (SAE) is established at each sensor for initial condition diagnosis, where extracted principal components and corresponding condition categories are inputs and output, respectively. To enhance diagnostic accuracy of proposed deep SAE, an enhanced whale optimization algorithm is proposed to optimize network meta-parameters. Eventually, Dempster-Shafer fusion algorithm is employed to combine initial diagnosis results from each sensor to make a final diagnosis. A miniature structural component of Sydney Harbour Bridge with artificial multiple progressive damages is tested in laboratory. The results demonstrate that the proposed method can detect structural damage accurately, even under the condition of limited sensors and high levels of uncertainties

    What's cracking? A review and analysis of deep learning methods for structural crack segmentation, detection and quantification

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    Surface cracks are a very common indicator of potential structural faults. Their early detection and monitoring is an important factor in structural health monitoring. Left untreated, they can grow in size over time and require expensive repairs or maintenance. With recent advances in computer vision and deep learning algorithms, the automatic detection and segmentation of cracks for this monitoring process have become a major topic of interest. This review aims to give researchers an overview of the published work within the field of crack analysis algorithms that make use of deep learning. It outlines the various tasks that are solved through applying computer vision algorithms to surface cracks in a structural health monitoring setting and also provides in-depth reviews of recent fully, semi and unsupervised approaches that perform crack classification, detection, segmentation and quantification. Additionally, this review also highlights popular datasets used for cracks and the metrics that are used to evaluate the performance of those algorithms. Finally, potential research gaps are outlined and further research directions are provided

    Mathematical Problems in Rock Mechanics and Rock Engineering

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    With increasing requirements for energy, resources and space, rock engineering projects are being constructed more often and are operated in large-scale environments with complex geology. Meanwhile, rock failures and rock instabilities occur more frequently, and severely threaten the safety and stability of rock engineering projects. It is well-recognized that rock has multi-scale structures and involves multi-scale fracture processes. Meanwhile, rocks are commonly subjected simultaneously to complex static stress and strong dynamic disturbance, providing a hotbed for the occurrence of rock failures. In addition, there are many multi-physics coupling processes in a rock mass. It is still difficult to understand these rock mechanics and characterize rock behavior during complex stress conditions, multi-physics processes, and multi-scale changes. Therefore, our understanding of rock mechanics and the prevention and control of failure and instability in rock engineering needs to be furthered. The primary aim of this Special Issue ā€œMathematical Problems in Rock Mechanics and Rock Engineeringā€ is to bring together original research discussing innovative efforts regarding in situ observations, laboratory experiments and theoretical, numerical, and big-data-based methods to overcome the mathematical problems related to rock mechanics and rock engineering. It includes 12 manuscripts that illustrate the valuable efforts for addressing mathematical problems in rock mechanics and rock engineering

    Investigation of wireless power transfer-based eddy current non-destructive testing and evaluation

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    PhD ThesisEddy current testing (ECT) is a non-contact inspection widely used as non-destructive testing and evaluation (NDT&E) of pipeline and rail lines due to its high sensitivity to surface and subsurface defects, cheap operating cost, tolerance to harsh environments, and capability of a customisable probe for complex geometric surfaces. However, the remote field of transmitter-receiver (Tx-Rx) ECT depends on the Tx-Rx coils gap, orientation, and lift-off distance, despite each coil responding to the effect of sample parameters according to its liftoff distance. They bring challenges to accurate defect detection and characterisation by weakening the ECT probeā€™s transfer response, affecting sensitivity to the defect, distorting the amplitude of the extracted features, and responding with fewer feature points at non-efficient energy transfer. Therefore, this study proposed a magnetically-coupled resonant wireless power transfer (WPT)-based ECT (WPTECT) concept to build the relationship between Tx-Rx coil at maximum energy transfer response, including shifting and splitting (resonance) frequency behaviour. The proposed WPTECT system was investigated in three different studies viz., (1) investigated the multiple resonance point features for detection and characterisation of slots on two different aluminium samples using a series-series (SS) topology of WPTECT; (2) mapped and scanned pipeline with a natural dent defect using a flexible printed coil (FPC) array probe based on the parallel-parallel (PP) topology of WPTECT; and (3) evaluated five different WPTECT topologies for optimal response and extracted features and characterised entire parameters of inclined angular Rolling Contact Fatigue (RCF) cracks in a rail-line material via an optimised topology. Multiple feature extraction, selection, and fusion were evaluated for the defect profile and compared in the study, unattainable by other ECT methods. The first study's contribution investigated multiple resonances and principal component analysis (PCA) features of the transfer response from scanning (eight) slots on two aluminium samples. The results have shown the potential of the multiple features for slot depth and width characterisation and demonstrated that the eddy-current density is highest at two points proportionate to the slot width. The second study's contribution provided a larger area scanning capability in a single probe amenable to complex geometrical structures like curvature surfaces. Among the extracted individual and fused features for defect reconstruction, the multi-layer feed-forward Deep learning-based multiple feature fusion has better 3D defect reconstruction, whilst the second resonances feature provided better local information than the first one for investigating pipeline dent area. The third study's contribution optimised WPTECT topology for multiple feature points capability and its optimal features extraction at the desired lift-off conditions. The PP and combined PP and SS (PS-PS) WPTECT topologies responded with multiple resonances compared to the other three topologies, with single resonance, under the same experimental situation. However, the extracted features from PS-PS topology provided the lowest sensitivity to lift-off distances and reconstructed depth, width, and inclined angle of RCF cracks with a maximum correlation, R2 -value of 96.4%, 93.1%, and 79.1%, respectively, and root-mean-square-error of 0.05mm, 0.08mm, and 6.60 , respectively. The demonstrated magnetically-coupled resonant WPTECT Tx-Rx probe characterised defects in oil and gas pipelines and rail lines through multiple features for multiple parameters information. Further work can investigate the phase of the transfer response as expected to offer robust features for material characterisation. The WPTECT system can be miniaturised using WPT IC chips as portable systems to characterise multiple layers parameters. It can further evaluate the thickness and gap between two concentric conductive tubes; pressure tube encircled by calandria tube in nuclear reactor fuel channels.PTDF Nigeri

    EG-ICE 2021 Workshop on Intelligent Computing in Engineering

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    The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

    Vision-based Monitoring System for High Quality TIG Welding

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    The current study evaluates an automatic system for real-time arc welding quality assessment and defect detection. The system research focuses on the identification of defects that may arise during the welding process by analysing the occurrence of any changes in the visible spectrum of the weld pool and the surrounding area. Currently, the state-of-the-art is very simplistic, involving an operator observing the process continuously. The operator assessment is subjective, and the criteria of acceptance based solely on operator observations can change over time due to the fatigue leading to incorrect classification. Variations in the weld pool are the initial result of the chosen welding parameters and torch position and at the same time the very first indication of the resulting weld quality. The system investigated in this research study consists of a camera used to record the welding process and a processing unit which analyse the frames giving an indication of the quality expected. The categorisation is achieved by employing artificial neural networks and correlating the weld pool appearance with the resulting quality. Six categories denote the resulting quality of a weld for stainless steel and aluminium. The models use images to learn the correlation between the aspect of the weld pool and the surrounding area and the state of the weld as denoted by the six categories, similar to a welder categorisation. Therefore the models learn the probability distribution of imagesā€™ aspect over the categories considered

    Applied Metaheuristic Computing

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    For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

    Applications of Finite Element Modeling for Mechanical and Mechatronic Systems

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    Modern engineering practice requires advanced numerical modeling because, among other things, it reduces the costs associated with prototyping or predicting the occurrence of potentially dangerous situations during operation in certain defined conditions. Thus far, different methods have been used to implement the real structure into the numerical version. The most popular uses have been variations of the finite element method (FEM). The aim of this Special Issue has been to familiarize the reader with the latest applications of the FEM for the modeling and analysis of diverse mechanical problems. Authors are encouraged to provide a concise description of the specific application or a potential application of the Special Issue

    EG-ICE 2021 Workshop on Intelligent Computing in Engineering

    Get PDF
    The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways
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