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Author: Fardad Mokhtari Dizaji
Publisher:
ISBN:
Category : Earthquake engineering
Languages : en
Pages : 0
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Book Description
Numerical simulation and hybrid simulation are extensively used in earthquake engineering to evaluate the seismic response of structures under seismic loading. Despite the advances in computing power and the development of efficient integration algorithms in the past, numerical simulation techniques suffer from a high computational cost and the uncertainty associated with the definition of constitutive material models, boundary conditions, and mesh density, in particular in highly nonlinear, large or complex structures. On the other hand, the results of hybrid simulation can become biased when only one or limited number of potential critical components, seismic fuses, are physically tested due to laboratory or cost constraints. The recent progress in machine learning algorithms and applications in engineering has motivated novel and innovative simulation techniques achieved by leveraging data in various fields of engineering including seismic engineering where complexities arising from the stochastic nature of the phenomenon can be tackled by making use of available experimental and numerical data towards the development of more reliable simulation models and dynamic analysis frameworks. Furthermore, to better exploit the potential of data-driven models, such models can efficiently be incorporated into the physics-based and experimental techniques, leading to improved seismic response assessment methods. This M.Sc. thesis proposes two new hybrid analysis frameworks by integrating emerging data-driven techniques into the conventional structural response assessment techniques, namely numerical simulation and hybrid testing, to perform the nonlinear structural analysis under seismic loading. The first framework, referred to as the hybrid data-driven and physics-based simulation (HyDPS) technique, combines the well-understood components of the structure modeled numerically with the critical components of the structure, e.g., seismic fuses, simulated using the proposed data-driven PI-SINDy model. The data-driven model is developed for steel buckling-restrained braces based on experimental data to mathematically estimate the underlying relationship between displacement history and restoring force. The second framework incorporates the data-driven model into the conventional seismic hybrid simulation framework where the experimental test data of one of the critical components (physical twin), e.g., steel buckling-restrained brace, produced during hybrid simulation can be used in real-time to predict the nonlinear cyclic response of the other critical components of the system (digital twins) that are not physically tested. This framework features a novel multi-element seismic hybrid simulation technique achieved by recursively updating the force-deformation response of the digital twin. The performance of the proposed data-driven hybrid analysis frameworks is verified using past experimental test data and nonlinear response history analyses performed under representative earthquake ground motion accelerations. The results reveal that integrating data-driven techniques into conventional seismic analysis methods, namely numerical simulation and hybrid simulation, yields a more efficient seismic simulation tool that can be used to examine the seismic response of structural systems.
Author: Fardad Mokhtari Dizaji
Publisher:
ISBN:
Category : Earthquake engineering
Languages : en
Pages : 0
Get Book Here
Book Description
Numerical simulation and hybrid simulation are extensively used in earthquake engineering to evaluate the seismic response of structures under seismic loading. Despite the advances in computing power and the development of efficient integration algorithms in the past, numerical simulation techniques suffer from a high computational cost and the uncertainty associated with the definition of constitutive material models, boundary conditions, and mesh density, in particular in highly nonlinear, large or complex structures. On the other hand, the results of hybrid simulation can become biased when only one or limited number of potential critical components, seismic fuses, are physically tested due to laboratory or cost constraints. The recent progress in machine learning algorithms and applications in engineering has motivated novel and innovative simulation techniques achieved by leveraging data in various fields of engineering including seismic engineering where complexities arising from the stochastic nature of the phenomenon can be tackled by making use of available experimental and numerical data towards the development of more reliable simulation models and dynamic analysis frameworks. Furthermore, to better exploit the potential of data-driven models, such models can efficiently be incorporated into the physics-based and experimental techniques, leading to improved seismic response assessment methods. This M.Sc. thesis proposes two new hybrid analysis frameworks by integrating emerging data-driven techniques into the conventional structural response assessment techniques, namely numerical simulation and hybrid testing, to perform the nonlinear structural analysis under seismic loading. The first framework, referred to as the hybrid data-driven and physics-based simulation (HyDPS) technique, combines the well-understood components of the structure modeled numerically with the critical components of the structure, e.g., seismic fuses, simulated using the proposed data-driven PI-SINDy model. The data-driven model is developed for steel buckling-restrained braces based on experimental data to mathematically estimate the underlying relationship between displacement history and restoring force. The second framework incorporates the data-driven model into the conventional seismic hybrid simulation framework where the experimental test data of one of the critical components (physical twin), e.g., steel buckling-restrained brace, produced during hybrid simulation can be used in real-time to predict the nonlinear cyclic response of the other critical components of the system (digital twins) that are not physically tested. This framework features a novel multi-element seismic hybrid simulation technique achieved by recursively updating the force-deformation response of the digital twin. The performance of the proposed data-driven hybrid analysis frameworks is verified using past experimental test data and nonlinear response history analyses performed under representative earthquake ground motion accelerations. The results reveal that integrating data-driven techniques into conventional seismic analysis methods, namely numerical simulation and hybrid simulation, yields a more efficient seismic simulation tool that can be used to examine the seismic response of structural systems.
Author: HAN SUN
Publisher:
ISBN:
Category :
Languages : en
Pages : 211
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Book Description
Structural seismic resilience society has been grown rapidly in the past three decades. Extensive probabilistic techniques have been developed to address uncertainties from ground motions and building systems to reduce structural damage, economic loss and social impact of buildings subjected to seismic hazards where seismic structural responses are essential and often are retrieved through Nonlinear Response History Analysis. This process is largely limited by accuracy of model and computational effort. An alternative data-driven framework is proposed to reconstruct structure responses through machine learning techniques from limited available sources which may potentially benefit for "real-time" interpolating monitoring data to enable rapid damage assessment and reducing computational effort for regional seismic hazard assessment. It also provides statistical insight to understand uncertainties of seismic building responses from both structural and earthquake engineering perspective.
Author: Plevris, Vagelis
Publisher: IGI Global
ISBN: 1466616415
Category : Technology & Engineering
Languages : en
Pages : 456
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Book Description
Throughout the past few years, there has been extensive research done on structural design in terms of optimization methods or problem formulation. But, much of this attention has been on the linear elastic structural behavior, under static loading condition. Such a focus has left researchers scratching their heads as it has led to vulnerable structural configurations. What researchers have left out of the equation is the element of seismic loading. It is essential for researchers to take this into account in order to develop earthquake resistant real-world structures. Structural Seismic Design Optimization and Earthquake Engineering: Formulations and Applications focuses on the research around earthquake engineering, in particular, the field of implementation of optimization algorithms in earthquake engineering problems. Topics discussed within this book include, but are not limited to, simulation issues for the accurate prediction of the seismic response of structures, design optimization procedures, soft computing applications, and other important advancements in seismic analysis and design where optimization algorithms can be implemented. Readers will discover that this book provides relevant theoretical frameworks in order to enhance their learning on earthquake engineering as it deals with the latest research findings and their practical implementations, as well as new formulations and solutions.
Author: Wei Song
Publisher: Frontiers Media SA
ISBN: 2889663809
Category : Technology & Engineering
Languages : en
Pages : 213
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Book Description
Author: Peng-Yu Chen
Publisher:
ISBN:
Category :
Languages : en
Pages : 154
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Book Description
The urban region's seismic resilience is being actively studied in recent years as a measure for risk mitigation, where the identification of seismically vulnerable buildings and the assessment of their performance play indispensable roles. However, it is a labor-intensive and computationally expensive task to evaluate tens of thousands of buildings in a region because the identification requires professional judgment at a site and the seismic assessment demands comprehensive modeling depending on structure-specific data. Nevertheless, it is feasible with the aid of advanced development of the Internet of Things (IoT) and computer technology. In this study, a data-driven framework including two pipelines that focus on soft-story buildings and non-ductile reinforced concrete frames is proposed. The first pipeline focuses on identifying soft-story buildings in the city of Santa Monica (California) through 3D point clouds and convolutional neural networks (CNNs). Although prior studies showed promising results in detecting soft-story buildings based on well-selected street-view images, false predictions are common when it is applied to real-world data. To address this issue, the pipeline implements point-cloud data where spatial information is available to segment building points and extract density features for training deep learning models and identifying soft-story buildings. The transfer learning (TL) technique is adopted to avoid overfitting in deep neural networks, and the parameters within the pipeline are investigated for optimal performance. The results illustrate the potential applicability of the pipeline for developing pre-and post-event countermeasures. The second pipeline focuses on another seismically vulnerable building, namely, the non-ductile reinforced concrete building (NDRCB). Prior studies indicated around 1,500 NDRCBs in Los Angeles that are urgently waiting for detailed assessment and mandatory retrofit or demolition if necessary. Because the fulfillment of these ordinances will last for decades, the potential risk of major losses will persist. To this end, an automatic method that harvests building information from IoT and imagery data generates archetypal models, conducts probabilistic seismic assessment, and estimates the losses for NDRCB frames is hence developed. The accuracy of the data harvesting module using deep CNNs is validated with the existing inventory data. The archetypal frames are developed based on the era-specific representative code and are validated through nonlinear static and nonlinear dynamic analyses of previously investigated NDRCBs. State-of-the-practice loss estimation methodologies including HAZUS and FEMA P-58 are adopted in the pipeline for constructing damage fragility functions and corresponding losses. The regional application focuses on intensity-based assessment for thousands of individual buildings instead of a scenario-based assessment. The outcomes of expected losses and repair/reconstruction time emphasize the vulnerability of NDRCBs in Los Angeles, and the presented pipeline is believed to bridge the gaps between property owners, engineers, and decision-makers. This research demonstrates how advanced data mining techniques and data-driven approaches can aid to solve civil engineering problems. While the framework currently focuses on soft-story and non-ductile frame buildings, it is expected to be extended in-depth and breadth in the future. That is, more detailed models and other seismically vulnerable infrastructures can be included.
Author: Roland Platz
Publisher: Springer Nature
ISBN: 3031370031
Category : Technology & Engineering
Languages : en
Pages : 208
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Book Description
Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics, 2023, the third volume of ten from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Model Validation and Uncertainty Quantification, including papers on: Introduction of Uncertainty Quantification Uncertainty Quantification in Dynamics Model Form Uncertainty and Selection incl. Round Robin Challenge Sensor and Information Fusion Virtual Sensing, Certification, and Real-Time Monitoring Surrogate Modeling
Author: Gebrail Bekdaş
Publisher: Springer Nature
ISBN: 3031347285
Category : Technology & Engineering
Languages : en
Pages : 306
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Book Description
From the start of life, people used their brains to make something better in design in ordinary works. Due to that, metaheuristics are essential to living things, and several inspirations from life have been used in the generation of new algorithms. These algorithms have unique features, but the usage of different features of different algorithms may give more effective optimum results in means of precision in optimum results, computational effort, and convergence. This book is a timely book to summarize the latest developments in the optimization of structural engineering systems covering all classical approaches and new trends including hybrids metaheuristic algorithms. Also, artificial intelligence and machine learning methods are included to predict optimum results by skipping long optimization processes. The main objective of this book is to introduce the fundamentals and current development of methods and their applications in structural engineering.
Author: Tarek Elkhoraibi
Publisher:
ISBN:
Category : Earthquake resistant construction
Languages : en
Pages : 201
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Book Description
Author: Zhu Mao
Publisher: Springer Nature
ISBN: 3030476383
Category : Technology & Engineering
Languages : en
Pages : 426
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Book Description
Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 38th IMAC, A Conference and Exposition on Structural Dynamics, 2020, the third volume of nine from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Model Validation and Uncertainty Quantification, including papers on: Uncertainty Quantification in Material Models Uncertainty Propagation in Structural Dynamics Practical Applications of MVUQ Advances in Model Validation & Uncertainty Quantification: Model Updating Model Validation & Uncertainty Quantification: Industrial Applications Controlling Uncertainty Uncertainty in Early Stage Design Modeling of Musical Instruments Overview of Model Validation and Uncertainty
Author: Yannis Tsompanakis
Publisher: Taylor & Francis
ISBN: 1134055064
Category : Technology & Engineering
Languages : en
Pages : 669
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Book Description
Uncertainties play a dominant role in the design and optimization of structures and infrastructures. In optimum design of structural systems due to variations of the material, manufacturing variations, variations of the external loads and modelling uncertainty, the parameters of a structure, a structural system and its environment are not given, fixed coefficients, but random variables with a certain probability distribution. The increasing necessity to solve complex problems in Structural Optimization, Structural Reliability and Probabilistic Mechanics, requires the development of new ideas, innovative methods and numerical tools for providing accurate numerical solutions in affordable computing times. This book presents the latest findings on structural optimization considering uncertainties. It contains selected contributions dealing with the use of probabilistic methods for the optimal design of different types of structures and various considerations of uncertainties. The first part is focused on reliability-based design optimization and the second part on robust design optimization. Comprising twenty-one, self-contained chapters by prominent authors in the field, it forms a complete collection of state-of-the-art theoretical advances and applications in the fields of structural optimization, structural reliability, and probabilistic computational mechanics. It is recommended to researchers, engineers, and students in civil, mechanical, naval and aerospace engineering and to professionals working on complicated costs-effective design problems.
