An experimental and numerical investigation on strengthening the upright component of thin-walled cold-formed steel rack structures

Cold-formed steel (CFS) racking systems are widely used for storing products in warehouses. However, as commonly used structures in storage systems, thin-walled open sections are subjected to stability loss because of various buckling modes, including flexural, local, torsional and distortional. This research proposes a novel technique to increase the ultimate capacity of uprights, utilising bolts and spacers, under flexural and compressive loads. The proposed components are attached externally to the sections in certain pitches along the length. In this regard, axial tests were performed on 72 upright frames and nine single uprights with various lengths and thicknesses. Also, the impact of using reinforcing elements was evaluated by investigating the failure modes and ultimate load results. It was concluded that the reinforcement technique is able to restrain upright flanges and therefore improve the upright profiles' strength. For testing the flexural behaviour, 18 samples of three types were made, including non-reinforced sections and two types of sections reinforced along the upright length at different pitches. After that, monotonic loading was applied along both the minor and major axes of the samples. The suggested reinforcing method leads to increasing the flexural capacity of the upright sections about both the major and minor axes. Also, by using reinforcing system, the flexural performance was improved, and buckling and deformation were constrained. In addition, the reinforcement technique was evaluated by Finite Element (FE) method. Moreover, Artificial Intelligence (AI) and Machine Learning (ML) algorithms were deployed to predict the normalised ultimate load and deflection of the profiles. Following the empirical tests, the axial and flexural performance of different CFS upright profiles with various lengths, thicknesses and reinforcement spacings were simulated and examined. It was shown that the reinforcing technique improved the capacity of the samples. Consequently, the proposed reinforcements could be considered a highly effective and low-cost technique to strengthen the axial and flexural behaviour of open CFS sections considering a trade-off between performance and cost of utilising the approach

Buckling load estimation of cracked columns using artificial neural network modeling technique

In this paper, buckling analysis of slender prismatic columns with a single non-propagating open edge crack subjected to axial loads has been presented utilizing the transfer matrix method and the artificial neural networks. A multi-layer feedforward neural network learning by backpropagation algorithm has been employed in the study. The main focus of this work is the investigation of feasibility of using an artificial neural network to assess the critical buckling load of axially loaded compression rods. This is explored by comparing the performance of neural network models with the results of the matrix method for all considered support conditions. It can be seen from the results that the critical buckling load values obtained from the neural networks closely follow the values obtained from the matrix method for the whole data sets. The final results show that the proposed methodology may constitute an efficient tool for the estimation of elastic buckling loads of edge-cracked columns. Also, it can be seen from the results that the computational time reduces if the proposed method is used

An innovative metaheuristic strategy for solar energy management through a neural networks framework

Proper management of solar energy as an effective renewable source is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO), is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for nonlinearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development

Review and application of Artificial Neural Networks models in reliability analysis of steel structures

This paper presents a survey on the development and use of Artificial Neural Network (ANN) models in structural reliability analysis. The survey identifies the different types of ANNs, the methods of structural reliability assessment that are typically used, the techniques proposed for ANN training set improvement and also some applications of ANN approximations to structural design and optimization problems. ANN models are then used in the reliability analysis of a ship stiffened panel subjected to uniaxial compression loads induced by hull girder vertical bending moment, for which the collapse strength is obtained by means of nonlinear finite element analysis (FEA). The approaches adopted combine the use of adaptive ANN models to approximate directly the limit state function with Monte Carlo simulation (MCS), first order reliability methods (FORM) and MCS with importance sampling (IS), for reliability assessment. A comprehensive comparison of the predictions of the different reliability methods with ANN based LSFs and classical LSF evaluation linked to the FEA is provided

Design, modeling and implementation of a soft robotic neck for humanoid robots

Mención Internacional en el título de doctorSoft humanoid robotics is an emerging field that combines the flexibility and safety of soft robotics with the form and functionality of humanoid robotics. This thesis explores the potential for collaboration between these two fields with a focus on the development of soft joints for the humanoid robot TEO. The aim is to improve the robot’s adaptability and movement, which are essential for an efficient interaction with its environment. The research described in this thesis involves the development of a simple and easily transportable soft robotic neck for the robot, based on a 2 Degree of Freedom (DOF) Cable Driven Parallel Mechanism (CDPM). For its final integration into TEO, the proposed design is later refined, resulting in an efficiently scaled prototype able to face significant payloads. The nonlinear behaviour of the joints, due mainly to the elastic nature of their soft links, makes their modeling a challenging issue, which is addressed in this thesis from two perspectives: first, the direct and inverse kinematic models of the soft joints are analytically studied, based on CDPM mathematical models; second, a data-driven system identification is performed based on machine learning techniques. Both approaches are deeply studied and compared, both in simulation and experimentally. In addition to the soft neck, this thesis also addresses the design and prototyping of a soft arm capable of handling external loads. The proposed design is also tendon-driven and has a morphology with two main bending configurations, which provides more versatility compared to the soft neck. In summary, this work contributes to the growing field of soft humanoid robotics through the development of soft joints and their application to the humanoid robot TEO, showcasing the potential of soft robotics to improve the adaptability, flexibility, and safety of humanoid robots. The development of these soft joints is a significant achievement and the research presented in this thesis paves the way for further exploration and development in this field.La robótica humanoide blanda es un campo emergente que combina la flexibilidad y seguridad de la robótica blanda con la forma y funcionalidad de la robótica humanoide. Esta tesis explora el potencial de colaboración entre estos dos campos centrándose en el desarrollo de una articulación blanda para el cuello del robot humanoide TEO. El objetivo es mejorar la adaptabilidad y el movimiento del robot, esenciales para una interacción eficaz con su entorno. La investigación descrita en esta tesis consiste en el desarrollo de un prototipo sencillo y fácilmente transportable de cuello blando para el robot, basado en un mecanismo paralelo actuado por cable de 2 grados de libertad. Para su integración final en TEO, el diseño propuesto es posteriormente refinado, resultando en un prototipo eficientemente escalado capaz de manejar cargas significativas. El comportamiemto no lineal de estas articulaciones, debido fundamentalmente a la naturaleza elástica de sus eslabones blandos, hacen de su modelado un gran reto, que en esta tesis se aborda desde dos perspectivas diferentes: primero, los modelos cinemáticos directo e inverso de las articulaciones blandas se estudian analíticamente, basándose en modelos matemáticos de mecanismos paralelos actuados por cable; segundo, se aborda el problema de la identificación del sistema mediante técnicas basadas en machine learning. Ambas propuestas se estudian y comparan en profundidad, tanto en simulación como experimentalmente. Además del cuello blando, esta tesis también aborda el diseño de un brazo robótico blando capaz de manejar cargas externas. El diseño propuesto está igualmente basado en accionamiento por tendones y tiene una morfología con dos configuraciones principales de flexión, lo que proporciona una mayor versatilidad en comparación con el cuello robótico blando. En resumen, este trabajo contribuye al creciente campo de la robótica humanoide blanda mediante el desarrollo de articulaciones blandas y su aplicación al robot humanoide TEO, mostrando el potencial de la robótica blanda para mejorar la adaptabilidad, flexibilidad y seguridad de los robots humanoides. El desarrollo de estas articulaciones es una contribución significativa y la investigación presentada en esta tesis allana el camino hacia nuevos desarrollos y retos en este campo.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidenta: Cecilia Elisabet García Cena.- Secretario: Dorin Sabin Copaci.- Vocal: Martin Fodstad Stole

Vibration Analysis of Cracked Beam

Cracks in vibrating component can initiate catastrophic failures. The presences of cracks change the physical characteristics of a structure which in turn alter its dynamic response characteristics. Therefore there is need to understand dynamics of cracked structures. Crack depth and location are the main parameters for the vibration analysis. So it becomes very important to monitor the changes in the response parameters of the structure to access structural integrity, performance and safety. To examine the effect of the crack to the natural frequency of beams. In the present study, vibration analysis is carried out on a cantilever beam with two open transverse cracks, to study the response characteristics. In first phase local compliance matrices of different degree of freedom have been used model transverse cracks in beam on available expression of stress intensity factors and the associated expressions for strain energy release rates. Suitable boundary condition are used to find out natural frequency and mode shapes. The results obtained numerically are validated with the results obtained from the simulation. The simulations have done with the help of ANSYS software. A neural network for the cracked structure is trained to approximate the response of the structure by the data set prepared for various crack sizes and locations. Feed-forward multilayer neural networks trained by back-propagation are used to learn the input (the location and depth of a crack)-output (the structural eigenfrequencies) relation of the structural system. With this trained neural network minimizing the difference from the measured frequencies. It is verified from both computational and simulation analysis that the presence of crack decreases the natural frequency of vibration. The mode shapes also changes considerably due to the presence of crack

Simulation of sea-state sequences

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The present PhD study, in its first part, uses artificial neural networks (ANNs), an optimization technique called simulated annealing, and statistics to simulate the significant wave height (Hs) and mean zero-up-crossing period ( ) of 3-hourly sea-states of a location in the North East Pacific using a proposed distribution called hepta-parameter spline distribution for the conditional distribution of Hs or given some inputs. Two different seven- network sets of ANNs for the simulation and prediction of Hs and were trained using 20-year observed Hs’s and ’s. The preceding Hs’s and ’s were the most important inputs given to the networks, but the starting day of the simulated period was also necessary. However, the code replaced the day with the corresponding time and the season. The networks were trained by a simulated annealing algorithm and the outputs of the two sets of networks were used for calculating the parameters of the probability density function (pdf) of the proposed hepta-parameter distribution. After the calculation of the seven parameters of the pdf from the network outputs, the Hs and of the future sea-state is predicted by generating random numbers from the corresponding pdf. In another part of the thesis, vertical piles have been studied with the goal of identifying the range of sea-states suitable for the safe pile driving operation. Pile configuration including the non-linear foundation and the gap between the pile and the pile sleeve shims were modeled using the finite elements analysis facilities within ABAQUS. Dynamic analyses of the system for a sea-state characterized by Hs and and modeled as a combination of several wave components were performed. A table of safe and unsafe sea-states was generated by repeating the analysis for various sea-states. If the prediction for a particular sea-state is repeated N times of which n times prove to be safe, then it could be said that the predicted sea-state is safe with the probability of 100(n/N). The last part of the thesis deals with the Hs return values. The return value is a widely used measure of wave extremes having an important role in determining the design wave used in the design of maritime structures. In this part, Hs return value was calculated demonstrating another application of the above simulation of future 3-hourly Hs’s. The maxima method for calculating return values was applied in such a way that avoids the conventional need for unrealistic assumptions. The significant wave height return value has also been calculated using the convolution concept from a model presented by Anderson et al. (2001)

Reliability-Calibrated ANN-Based Load and Resistance Factor Load Rating for Steel Girder Bridges

This research aimed to develop a supplemental ANN-based tool to support the Nebraska Department of Transportation (NDOT) in optimizing bridge management investments when choosing between refined modeling, field testing, retrofitting, or bridge replacement. ANNs require an initial investment to collect data and train a network, but offer future benefits of speed and accessibility to engineers utilizing the trained ANN in the future. As the population of rural bridges in the Midwest approaching the end of their design service lives increases, Departments of Transportation are under mounting pressure to balance safety of the traveling public with fiscal constraints. While it is well-documented that standard code-based evaluation methods tend to conservatively overestimate live load distributions, alternate methods of capturing more accurate live load distributions, such as finite element modeling and diagnostic field testing, are not fiscally justified for broad implementation across bridge inventories. Meanwhile, ANNs trained using comprehensive, representative data are broadly applicable across the bridge population represented by the training data. The ANN tool developed in this research will allow NDOT engineers to predict critical girder distribution factors (GDFs), removing unnecessary conservativism from approximate AASHTO GDFs, potentially justifying load posting removal for existing bridges, and enabling more optimized design for new construction, using ten readily available parameters, such as bridge span, girder spacing, and deck thickness. A key drawback obstructing implementation of ANNs in bridge rating and design is the potential for unconservative ANN predictions. This research provides a framework to account for increased live load effect uncertainty incurred from neural network prediction errors by performing a reliability calibration philosophically consistent with AASHTO Load and Resistance Factor Rating. The study included detailed FEA for 174 simple span, steel girder bridges with concrete decks. Subsets of 163 and 161 bridges within these available cases comprised the ANN design and training datasets for critical moment and shear live load effects, respectively. The reliability calibration found that the ANN live load effect prediction error with mean absolute independent testing error of 3.65% could be safely accommodated by increasing the live load factor by less than 0.05. The study also demonstrates application of the neural network model validated with a diagnostic field test, including discussion of potential adjustments to account for noncomposite bridge capacity and Load Factor Rating instead of Load and Resistance Factor Rating. Advisor: Joshua S. Steelma

Optimization of an Intelligent Autonomous Drilling Rig: Testing and Implementation of Machine Learning and Control Algorithms for Formation Classification, Downhole Vibrations Management and Directional Drilling

Master's thesis in Petroleum EngineeringIn recent years, considerable resources have been invested to explore applications for- and to exploit the vast amount of data that gets collected during exploration, drilling and production of oil and gas. Such data will potentially become a game changer for the industry in terms of reduced costs through improved operational efficiency and fewer accidents, improved HSE through strengthened situational awareness, ensured optimal placement of wells, less wear on equipment and so on. While machine learning algorithms have been around for decades, it is only in the last five to ten years that increased computational power along with heavily digitalized control- and monitoring systems have been made available. Considering the state of art technology that exists today and the significant resources that are being invested into the technology of tomorrow, the idea of intelligent and fully automated machinery on the drill floor that is capable of consistently selecting the best decisions or predictions based on the information available and providing the driller and operator with such recommendations, becomes closer to a reality every day. This thesis is the result of research carried out on the topic of drilling automation. Its basis has been improvements and upgrades conducted on a laboratory-scale drilling rig developed at the University of Stavanger, as part of the multi-disciplinary project; UiS Drillbotics. Main contribution of the thesis is a study on how machine learning can be used to develop models that are capable of accurately predicting what rock formation is being drilled using an autonomous control system, along with detecting some common drilling incidents in real-time on the laboratory rig. Methodology is also applied to field data from the Volve field. Furthermore, research and implementation of search algorithms to ensure optimal drilling speed (ROP), safety to personnel and environment (HSE), and efficiency along with a digitalized drilling program for directional drilling, gets presented. Finally, rig upgrades for directional drilling and research into downhole sensors that get used in a closed-loop steering model is elaborated on.submittedVersio